diff --git a/README.md b/README.md index 36c4644e..40eac2b8 100644 --- a/README.md +++ b/README.md @@ -2,9 +2,10 @@ Python wrapper for MOA to allow efficient use of existing algorithms with a more modern API > [!IMPORTANT] -> * **[How to Install CapyMOA](docs/installation.md)** -> * **[How to Contribute Tests](docs/testing.md)** -> * **[How to Contribute Documentation](docs/README.md)** +> * **[How to install CapyMOA](docs/installation.md)** +> * **[How to add documentation](docs/contributing/docs.md)** +> * **[How to add tests](docs/contributing/tests.md)** +> * **[How to add new algorithms or methods](docs/contributing/learners.md)** # Functionality diff --git a/docs/api/classifiers.rst b/docs/api/classifiers.rst index d58707eb..cefeff58 100644 --- a/docs/api/classifiers.rst +++ b/docs/api/classifiers.rst @@ -1,9 +1,8 @@ Classifiers =========== -Classifiers implement the :class:`capymoa.learner.learners.Classifier` interface. +Classifiers implement the :class:`capymoa.base.Classifier` interface. -.. automodule:: capymoa.learner.classifier +.. automodule:: capymoa.classifier :members: :undoc-members: :show-inheritance: - :inherited-members: diff --git a/docs/api/datasets.rst b/docs/api/datasets.rst index b8efe4ec..6e213949 100644 --- a/docs/api/datasets.rst +++ b/docs/api/datasets.rst @@ -8,3 +8,9 @@ and used being downloaded the first time you use them. :undoc-members: :show-inheritance: :inherited-members: + +.. automodule:: capymoa.datasets.downloader + :members: + :undoc-members: + :show-inheritance: + :inherited-members: \ No newline at end of file diff --git a/docs/api/api.rst b/docs/api/index.rst similarity index 74% rename from docs/api/api.rst rename to docs/api/index.rst index a025fb2f..818a4962 100644 --- a/docs/api/api.rst +++ b/docs/api/index.rst @@ -16,12 +16,18 @@ with the :ref:`tutorials`. datasets instance +.. toctree:: + :maxdepth: 1 + :caption: Interfaces + + learner + moa_learner + .. toctree:: :maxdepth: 1 :caption: Learners regressor - learners ssl classifiers @@ -30,3 +36,10 @@ with the :ref:`tutorials`. :caption: Evaluation evaluation + + +.. toctree:: + :maxdepth: 1 + :caption: Other + + splitcriteria diff --git a/docs/api/instance.rst b/docs/api/instance.rst index 7e93f96f..26a113f6 100644 --- a/docs/api/instance.rst +++ b/docs/api/instance.rst @@ -2,7 +2,7 @@ Instance ======== Instances are the basic unit of data in CapyMOA. -.. automodule:: capymoa.stream.instance +.. automodule:: capymoa.instance :members: :undoc-members: :show-inheritance: diff --git a/docs/api/learners.rst b/docs/api/learner.rst similarity index 64% rename from docs/api/learners.rst rename to docs/api/learner.rst index 643dd941..6472ad92 100644 --- a/docs/api/learners.rst +++ b/docs/api/learner.rst @@ -3,17 +3,17 @@ Learners CapyMOA defines different interfaces for learners performing different machine learning tasks. -.. autoclass:: capymoa.learner.learners.Classifier +.. autoclass:: capymoa.base.Classifier :members: :undoc-members: :inherited-members: -.. autoclass:: capymoa.learner.learners.Regressor +.. autoclass:: capymoa.base.Regressor :members: :undoc-members: :inherited-members: -.. autoclass:: capymoa.learner.learners.ClassifierSSL +.. autoclass:: capymoa.base.ClassifierSSL :members: :undoc-members: :inherited-members: \ No newline at end of file diff --git a/docs/api/moa_learner.rst b/docs/api/moa_learner.rst new file mode 100644 index 00000000..e10e9965 --- /dev/null +++ b/docs/api/moa_learner.rst @@ -0,0 +1,14 @@ +MOA Learners +============ +Interfaces for objects that wrap MOA functionality. + +.. autoclass:: capymoa.base.MOAClassifier + :members: + :undoc-members: + :show-inheritance: + +.. autoclass:: capymoa.base.MOARegressor + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/api/regressor.rst b/docs/api/regressor.rst index 79a64286..93c01821 100644 --- a/docs/api/regressor.rst +++ b/docs/api/regressor.rst @@ -1,10 +1,9 @@ Regressors ========== -Regressors implement the :class:`capymoa.learner.learners.Regressor` interface. +Regressors implement the :class:`capymoa.base.Regressor` interface. -.. automodule:: capymoa.learner.regressor +.. automodule:: capymoa.regressor :members: :undoc-members: :show-inheritance: - :inherited-members: diff --git a/docs/api/splitcriteria.rst b/docs/api/splitcriteria.rst new file mode 100644 index 00000000..9416fd37 --- /dev/null +++ b/docs/api/splitcriteria.rst @@ -0,0 +1,10 @@ +Split Criterions +================ +Decision trees are built by splitting the data into groups based on a split +criterion. The split criterion is a function that measures the quality of a +split. + +.. automodule:: capymoa.splitcriteria + :members: + :undoc-members: + :inherited-members: \ No newline at end of file diff --git a/docs/api/ssl.rst b/docs/api/ssl.rst index 752d76f6..2719af93 100644 --- a/docs/api/ssl.rst +++ b/docs/api/ssl.rst @@ -1,8 +1,8 @@ -Semi-Supervised Classifiers -=========================== -Semi-Supervised classifiers implement the :class:`capymoa.learner.learners.ClassifierSSL` interface. +Semi-Supervised Learners (SSL) +============================== +Semi-Supervised classifiers implement the :class:`capymoa.base.ClassifierSSL` interface. -.. automodule:: capymoa.learner.ssl.classifier +.. automodule:: capymoa.ssl.classifier :members: :undoc-members: :show-inheritance: diff --git a/docs/conf.py b/docs/conf.py index 30d2a854..51933aa2 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -25,6 +25,18 @@ "myst_parser", ] +nitpick_ignore_regex = [ + ('py:class', r'sklearn\..*'), + ('py:class', r'numpy\..*'), + ('py:class', r'pathlib\..*'), + ('py:class', r'abc\..*'), + ('py:class', r'moa\..*'), + ('py:class', r'com\..*'), + ('py:class', r'java\..*'), + ('py:class', r'org\..*'), + ('py:class', r'torch\..*'), + +] bibtex_bibfiles = ['references.bib'] autoclass_content = 'class' @@ -45,11 +57,13 @@ # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output -html_theme = "sphinx_book_theme" +html_theme = "pydata_sphinx_theme" html_static_path = ['_static'] # Setup symbolic links for notebooks +python_maximum_signature_line_length = 88 + notebooks = Path("../notebooks") notebook_doc_source = Path("notebooks") if not notebook_doc_source.exists(): diff --git a/docs/README.md b/docs/contributing/docs.md similarity index 100% rename from docs/README.md rename to docs/contributing/docs.md diff --git a/docs/contributing/index.rst b/docs/contributing/index.rst new file mode 100644 index 00000000..8b552ca7 --- /dev/null +++ b/docs/contributing/index.rst @@ -0,0 +1,10 @@ +Contributing +============ +This part of the documentation is for developers and contributors. + +.. toctree:: + :maxdepth: 2 + + learners + tests + docs diff --git a/docs/contributing/learners.md b/docs/contributing/learners.md new file mode 100644 index 00000000..29cc5628 --- /dev/null +++ b/docs/contributing/learners.md @@ -0,0 +1,76 @@ +# Adding Learners +This document describes adding a new classifier, regressor, or +another learner to CapyMOA. Before doing this, you should have read the +[installation guide](../installation.md) to set up your development environment. + +## Where does my new learner go? +You should add your new learner to the appropriate directory: +- Classifiers go in `src/capymoa/classifier`. +- Regressors go in `src/capymoa/regressor`. +- Semi-supervised classifiers go in `src/capymoa/ssl/classifier`. + +Each standalone learner should be in its own file, prefixed with `_` to indicate that they are not meant to be imported directly. Instead, they are imported by an `__init__.py` file. The `__init__.py` file is a special file that tells Python to treat the directory as a package. + +For example, to add a new classifier class called `MyNewLearner`, you should implement it in `src/capymoa/classifier/_my_new_learner.py` and add it to the `src/capymoa/classifier/__init__.py` file. The `__init__.py` will look like this: +```python +from ._my_new_learner import MyNewLearner +... +__all__ = [ + 'MyNewLearner', + ... +] +``` + +The prefix and init files allow users to import all classifiers, regressors, +or semi-supervised from one package while splitting the code into multiple files. You can, for example, import your new learner with the following: +```python +from capymoa.classifier import MyNewLearner +``` + +## What does a learner implement? + +A learner should implement the appropriate interface: +* `capymoa.base.Classifier` for classifiers. +* `capymoa.base.Regressor` for regressors. +* `capymoa.base.ClassifierSSL` for semi-supervised classifiers. + +If your method is a wrapper around a MOA learner, you should use the appropriate +base class: +* `capymoa.base.MOAClassifier` for classifiers. +* `capymoa.base.MOARegressor` for regressors. + +## How do I test my new learner? +You should add a test to ensure your learner achieves and continues to achieves +the expected performance in future versions. CapyMOA provides parametrized +tests for classifiers, regressors, and semi-supervised classifiers. You should +not need to write any new test code. Instead, you should add your test's +parameters to the appropriate test file: +- `tests/test_classifiers.py` for classifiers. +- `tests/test_regressors.py` for regressors. +- `tests/test_ssl_classifiers.py` for semi-supervised classifiers. + +To run your tests, use the following command: +```bash +python -m pytest -k MyNewLearner +``` +The `-k MyNewLearner` flag tells PyTest to run tests containing `MyNewLearner` in the test ID. + +* If you want to add documented exemplar usage of your learner, you can add doctests. +See the [testing guide](tests.md) for more information. + +* If you need custom test code for your learner, you can add a new test file in +`tests`. + +## How do I document my new learner? +You should add a docstring to your learner that describes the learner and its +parameters. The docstring should be in the Sphinx format. Check the +[documenation guide](docs.md) for more information and an example. + +## How to debug failed GitHub Actions? +Before submitting your pull request, you may wish to run all tests to +ensure your changes will succeed in GitHub Actions. You can run all tests with: +```bash +invoke test +``` +If you run into issues with GitHub actions failing to build documentation. Follow +the instructions in the [documentation guide](docs.md) to build the documentation locally. The documentation build settings are intentionally strict to ensure the documentation builds correctly. diff --git a/docs/testing.md b/docs/contributing/tests.md similarity index 95% rename from docs/testing.md rename to docs/contributing/tests.md index add6b1fd..1eacf348 100644 --- a/docs/testing.md +++ b/docs/contributing/tests.md @@ -1,6 +1,6 @@ # Adding Tests Ensure you have installed the development dependencies by following the instructions -in the [installation guide](installation.md). To run all tests, use the following command: +in the [installation guide](../installation.md). To run all tests, use the following command: ```bash invoke test ``` diff --git a/docs/index.rst b/docs/index.rst index a1b6f0a5..2a40b3f1 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -33,7 +33,7 @@ and modules. .. toctree:: :maxdepth: 2 - api/api + api/index Contributing ------------ @@ -41,10 +41,8 @@ This part of the documentation is for developers and contributors. .. toctree:: :maxdepth: 2 - :caption: Contributing - testing - README + contributing/index Indices and tables ================== diff --git a/invoke.yml b/invoke.yml index 7421975d..913af84c 100644 --- a/invoke.yml +++ b/invoke.yml @@ -5,8 +5,14 @@ moa_url: "https://homepages.ecs.vuw.ac.nz/~antonlee/capymoa/versions/240412_moa. # What notebooks to skip when running them as tests. test_skip_notebooks: - - notebooks/04_drift_streams.ipynb - - notebooks/02_learners_api_examples.ipynb - - notebooks/Basic_Classification_Examples.ipynb - notebooks/00_getting_started.ipynb + - notebooks/01_evaluation_and_data_reading.ipynb + - notebooks/02_learners_api_examples.ipynb - notebooks/03_using_sklearn_pytorch.ipynb + - notebooks/04_drift_streams.ipynb + - notebooks/Basic_Classification_Examples.ipynb + - notebooks/Creating_new_classifier.ipynb + - notebooks/Data_Reading.ipynb + - notebooks/Preprocessing.ipynb + - notebooks/SSL_example.ipynb + diff --git a/notebooks/00_getting_started.ipynb b/notebooks/00_getting_started.ipynb index 69f4d7f0..c54b6fb2 100644 --- a/notebooks/00_getting_started.ipynb +++ b/notebooks/00_getting_started.ipynb @@ -1,491 +1,157 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", - "id": "b773bf8e-c420-44e1-80a6-99f75dd12268", + "id": "5dbd70f8-ca5f-455b-bc3b-94c909431b60", "metadata": {}, "source": [ "# Getting started\n", "\n", - "* Examples on how to use this library for classification and regression, reading files from CSVs and using synthetic generators from MOA.\n", + "* This notebook shows some basic usage of CapyMOA for supervised learning (classification and regression)\n", + "* There are more detailed notebooks and documentation available (see link below), our goal here is just present some high-level functions and demonstrate a subset of CapyMOA's functionalities. \n", + "* For simplicity, we simulate data streams in the following examples by reading data from files and employing synthetic generators.\n", "\n", - "**notebook last updated on 15/02/2024**" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "deff0053-1130-43f1-bf69-02bd34bc8e03", - "metadata": {}, - "source": [ - "## Classification " + "CapyMOA complete documentation is available on: LINK_CAPYMOA_ORG\n", + "\n", + "**last update on 08/04/2024**" ] }, { "cell_type": "code", "execution_count": 1, "id": "b02aaf7b-11b5-4512-8a3b-5c26d8235b3a", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:38:40.294089Z", - "iopub.status.busy": "2024-03-21T04:38:40.293476Z", - "iopub.status.idle": "2024-03-21T04:38:43.152528Z", - "shell.execute_reply": "2024-03-21T04:38:43.151901Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "capymoa_root: /home/antonlee/github.com/tachyonicClock/MOABridge/src/capymoa\n", - "MOA jar path location (config.ini): /home/antonlee/github.com/tachyonicClock/MOABridge/src/capymoa/jar/moa.jar\n", + "capymoa_root: /Users/gomeshe/Dropbox/ciencia_computacao/dev/main-projects/CapyMOA/src/capymoa\n", + "MOA jar path location (config.ini): /Users/gomeshe/Dropbox/ciencia_computacao/dev/main-projects/CapyMOA/src/capymoa/jar/moa.jar\n", "JVM Location (system): \n", - "JAVA_HOME: /usr/lib/jvm/java-17-openjdk\n", - "JVM args: ['-Xmx8g', '-Xss10M']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "JAVA_HOME: /Users/gomeshe/Library/Java/JavaVirtualMachines/openjdk-20.0.1/Contents/Home\n", + "JVM args: ['-Xmx8g', '-Xss10M']\n", "Sucessfully started the JVM and added MOA jar to the class path\n" ] } ], "source": [ - "from capymoa.stream.stream import stream_from_file\n", + "# importing the file reader functionality and setting a path for the data used in the examples. \n", + "from capymoa.stream import stream_from_file\n", + "\n", + "DATA_PATH = \"../data/\"\n", + "\n", + "## Creating the stream used in all Classification examples. \n", + "elec_stream = stream_from_file(path_to_csv_or_arff=DATA_PATH+\"electricity.csv\")\n", + "\n", + "## Creating the stream used in all Regression examples. Note that we are using an arff file instead of a CSV. \n", + "fried_stream = stream_from_file(path_to_csv_or_arff=DATA_PATH+\"fried.arff\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "deff0053-1130-43f1-bf69-02bd34bc8e03", + "metadata": {}, + "source": [ + "## 1. Classification\n", "\n", - "DATA_PATH = \"../data/\"" + "* Classification for data streams tradicionally assumes instances are available to the classifier in an incremental fashion and labels become available before a new instance becomes available\n", + "* It is common to simulate this behavior using a **while loop**, often referred to as **test-then-train loop** which contains 4 distinct steps:\n", + " 1. Fetches the next instance from the stream\n", + " 2. Makes a prediction\n", + " 3. Train the model with the instance\n", + " 4. Update a mechanism to keep track of metrics\n", + "\n", + "\n", + "**Some remarks about test-then-train loop**:\n", + "* We must not train before testing, meaning that steps 2 and 3 should not be interchanged, as this would invalidate our interpretation concerning how the model performs on unseen data, leading to unreliable evaluations of its efficacy. \n", + "* Steps 3 and 4 can be completed in any order without altering the result. \n", + "* What if labels are not immediately available? Then you might want to read about delayed labeling and partially labeled data, see [A Survey on Semi-supervised Learning for Delayed Partially Labelled Data Streams](https://dl.acm.org/doi/full/10.1145/3523055)\n", + "* More information on classification for data streams is available on section *2.2 Classification* from [Machine Learning for Data Streams](https://moa.cms.waikato.ac.nz/book-html/) book\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "79a66284-4998-4423-bdde-d5229b56061d", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:38:43.155169Z", - "iopub.status.busy": "2024-03-21T04:38:43.154895Z", - "iopub.status.idle": "2024-03-21T04:38:50.107519Z", - "shell.execute_reply": "2024-03-21T04:38:50.106829Z" - } - }, + "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "79.05190677966102" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "79.05190677966102\n" + ] } ], "source": [ - "## Test-then-train loop\n", + "## Test-then-train loop example\n", "from capymoa.evaluation import ClassificationEvaluator\n", - "from capymoa.learner.classifier.classifiers import OnlineBagging\n", - "\n", - "## Opening a file as a stream\n", - "elec_stream = stream_from_file(path_to_csv_or_arff=DATA_PATH+\"electricity.csv\")\n", + "from capymoa.classifier import OnlineBagging\n", "\n", "# Creating a learner\n", "ob_learner = OnlineBagging(schema=elec_stream.get_schema(), ensemble_size=5)\n", "\n", - "# Creating the evaluator\n", + "# Creating the evaluator, i.e. object that keeps track of metrics\n", "ob_evaluator = ClassificationEvaluator(schema=elec_stream.get_schema())\n", "\n", "while elec_stream.has_more_instances():\n", + " # Step 1: fetch a new instance from the stream\n", " instance = elec_stream.next_instance()\n", - " \n", + "\n", + " # Step 2: make a prediction\n", " prediction = ob_learner.predict(instance)\n", + " \n", + " # Step 3: train the model using the instance\n", + " # Assumes the label became available for this instance. Note that this is a simulation, \n", + " # this step can be a bit more complex in a real setting or when labels are delayed. \n", + " ob_learner.train(instance)\n", "\n", + " # Step 4: uses the prediction made earlier to update metrics. \n", + " # The evaluator object records the correct and incorrect predictions and updates several metrics. \n", " ob_evaluator.update(instance.y_index, prediction)\n", - " ob_learner.train(instance)\n", "\n", - "ob_evaluator.accuracy()" + "# Prints the accuracy of the learner through the evaluator object. \n", + "print(ob_evaluator.accuracy())" ] }, { - "cell_type": "code", - "execution_count": 3, - "id": "1ac9ffd4-6dd0-436b-8c35-eb61393f985d", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:38:50.113821Z", - "iopub.status.busy": "2024-03-21T04:38:50.109978Z", - "iopub.status.idle": "2024-03-21T04:38:53.600363Z", - "shell.execute_reply": "2024-03-21T04:38:53.596728Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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classified instancesclassifications correct (percent)Kappa Statistic (percent)Kappa Temporal Statistic (percent)Kappa M Statistic (percent)F1 Score (percent)F1 Score for class 0 (percent)F1 Score for class 1 (percent)Precision (percent)Precision for class 0 (percent)Precision for class 1 (percent)Recall (percent)Recall for class 0 (percent)Recall for class 1 (percent)
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" - ], - "text/plain": [ - " classified instances classifications correct (percent) \\\n", - "0 4500.0 82.800000 \n", - "1 9000.0 83.888889 \n", - "2 13500.0 83.888889 \n", - "3 18000.0 78.866667 \n", - "4 22500.0 77.600000 \n", - "5 27000.0 72.911111 \n", - "6 31500.0 71.800000 \n", - "7 36000.0 73.022222 \n", - "8 40500.0 73.111111 \n", - "9 45000.0 83.200000 \n", - "10 45312.0 82.533333 \n", - "\n", - " Kappa Statistic (percent) Kappa Temporal Statistic (percent) \\\n", - "0 63.678342 -6.464924 \n", - "1 67.451307 3.973510 \n", - "2 67.844978 -9.682300 \n", - "3 54.893623 -46.759259 \n", - "4 51.771432 -40.389972 \n", - "5 39.373878 -113.111888 \n", - "6 35.217702 -131.569343 \n", - "7 43.702270 -104.033613 \n", - "8 48.118952 -81.409295 \n", - "9 66.153645 -8.000000 \n", - "10 64.753869 -14.078374 \n", - "\n", - " Kappa M Statistic (percent) F1 Score (percent) \\\n", - "0 56.172140 81.860773 \n", - "1 64.941973 83.757743 \n", - "2 66.279070 84.066362 \n", - "3 49.602544 78.323085 \n", - "4 42.824731 76.080437 \n", - "5 34.812834 73.219063 \n", - "6 27.609812 69.540773 \n", - "7 32.518066 71.851746 \n", - "8 37.980523 76.175950 \n", - "9 66.007194 83.077035 \n", - "10 64.337568 82.377143 \n", - "\n", - " F1 Score for class 0 (percent) F1 Score for class 1 (percent) \\\n", - "0 86.023835 77.642981 \n", - "1 85.386011 82.050012 \n", - "2 83.806120 83.970816 \n", - "3 83.434942 70.819270 \n", - "4 82.396088 69.211973 \n", - "5 80.641575 54.901961 \n", - "6 79.904988 52.737430 \n", - "7 77.593208 66.108319 \n", - "8 71.052632 74.896266 \n", - "9 84.520885 81.632653 \n", - "10 84.037368 80.716389 \n", - "\n", - " Precision (percent) Precision for class 0 (percent) \\\n", - "0 82.130842 84.829060 \n", - "1 83.908759 83.748517 \n", - "2 84.082418 87.868852 \n", - "3 80.338642 76.566496 \n", - "4 76.953086 78.922717 \n", - "5 79.157395 69.239160 \n", - "6 73.338774 70.711883 \n", - "7 71.884838 77.364741 \n", - "8 76.981172 91.048437 \n", - "9 83.066927 84.659557 \n", - "10 82.366201 84.174125 \n", - "\n", - " Precision for class 1 (percent) Recall (percent) \\\n", - "0 79.432624 81.592475 \n", - "1 84.069001 83.607271 \n", - "2 80.295983 84.050311 \n", - "3 84.110787 76.406186 \n", - "4 74.983455 75.227357 \n", - "5 89.075630 68.109534 \n", - "6 75.965665 66.116778 \n", - "7 66.404936 71.818685 \n", - "8 62.913907 75.387399 \n", - "9 81.474297 83.087146 \n", - "10 80.558276 82.388089 \n", - "\n", - " Recall for class 0 (percent) Recall for class 1 (percent) \n", - "0 87.252747 75.932203 \n", - "1 87.088816 80.125725 \n", - "2 80.102477 87.998146 \n", - "3 91.657099 61.155273 \n", - "4 86.189258 64.265457 \n", - "5 96.539924 39.679144 \n", - "6 91.845650 40.387906 \n", - "7 77.823029 65.814341 \n", - "8 58.258140 92.516658 \n", - "9 84.382666 81.791626 \n", - "10 83.901054 80.875123 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TTT accuracy = 78.12279307909604\n" - ] - } - ], + "cell_type": "markdown", + "id": "5934db15-b6e8-4084-978f-4af71b34df46", + "metadata": {}, "source": [ - "## Test-then-train loop\n", - "from capymoa.evaluation import prequential_evaluation\n", - "from capymoa.learner.learners import MOAClassifier\n", - "from moa.classifiers.trees import EFDT\n", + "### 1.1 High-level evaluation functions\n", "\n", - "## Opening a file as a stream\n", - "elec_stream = stream_from_file(path_to_csv_or_arff=\"../data/electricity.csv\")\n", + "* If our goal is just to evaluate learners it would be tedious to keep writing the **test-then-train loop**. \n", + "Thus, it makes sense to encapsulate that loop inside **high-level evaluation functions**. \n", "\n", - "# Creating a learner\n", - "EFDT_MOA = MOAClassifier(schema=elec_stream.get_schema(), moa_learner=EFDT)\n", + "* Furthermore, sometimes we are interested in **cumulative metrics** and sometimes we care about metrics **windowed metrics**. For example, if we want to know how accurate our model is so far, considering all the instances it has seen, then we would look at its cumulative metrics. However, we might also be interested in how well the model is every **n** number of instances, so that we can, for example, identify periods in which our model was really struggling to produce correct predictions. \n", + "\n", + "* In this example, we use the ```prequential_evaluation``` function, which provides us with both the cumulative and the windowed metrics! If you are only interested in the test-then-train evaluation or the windowed evaluation, there are high-level functions for those as well (see the remarks below). \n", "\n", - "results_EFDT = prequential_evaluation(stream=elec_stream, learner=EFDT_MOA, window_size=4500)\n", + "* Some remarks:\n", + " * If you want to know more about other **high-level evaluation functions**, **evaluators**, or which **metrics** are available, check the complete **Evaluation documentation** in LINK_CAPYMOA_ORG\n", + " * The **results** from evaluation functions such as **prequential_evaluation** follow a standard, also discussed thoroughly in the **Evaluation documentation** in LINK_CAPYMOA_ORG\n", + " * Sometimes authors refer to the **cumulative** metrics as **test-then-train** metrics, such as **test-then-train accuracy** (or TTT accuracy for short). They all refer to the same concept.\n", + " * Shouldn't we recreate the stream object ```elec_stream```? No, high-level evaluators automatically ```restart()``` streams when they are reused.\n", "\n", - "display(results_EFDT['windowed'].metrics_per_window())\n", - "print(f\"TTT accuracy = {results_EFDT['cumulative'].accuracy()}\")" + "In the below example ```prequential_evaluation``` is used with ```HoeffdingTree``` classifier on ```elec_stream``` data stream." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "9a382df9-5790-4414-a088-9ebfe50a6446", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:38:53.605366Z", - "iopub.status.busy": "2024-03-21T04:38:53.605122Z", - "iopub.status.idle": "2024-03-21T04:38:56.936819Z", - "shell.execute_reply": "2024-03-21T04:38:56.936333Z" - } - }, + "execution_count": 3, + "id": "1ac9ffd4-6dd0-436b-8c35-eb61393f985d", + "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cumulative accuracy = 75.46566031073446, wall-clock time: 1.1470880508422852\n" + ] + }, { "data": { "text/html": [ @@ -527,189 +193,189 @@ " \n", " 0\n", " 4500.0\n", - 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"0 67.338484 3.851444 \n", - "1 65.431704 -1.721854 \n", - "2 68.456750 -7.564297 \n", - "3 53.581883 -49.074074 \n", - "4 58.335220 -19.637883 \n", - "5 40.992724 -109.965035 \n", - "6 44.775669 -103.102190 \n", - "7 45.151302 -96.638655 \n", - "8 49.995390 -73.463268 \n", - "9 64.986886 -11.714286 \n", - "10 63.596765 -17.706821 \n", + "0 53.207943 -30.949106 \n", + "1 65.427882 -2.251656 \n", + "2 65.537501 -18.003026 \n", + "3 51.373406 -56.481481 \n", + "4 46.519471 -45.682451 \n", + "5 34.745533 -128.321678 \n", + "6 37.076969 -125.912409 \n", + "7 32.423123 -142.521008 \n", + "8 37.187118 -123.388306 \n", + "9 55.517607 -43.571429 \n", + "10 55.172316 -46.589260 \n", "\n", " Kappa M Statistic (percent) F1 Score (percent) \\\n", - "0 60.419026 83.673788 \n", - "1 62.862669 82.810065 \n", - "2 66.930233 84.351925 \n", - "3 48.807631 78.413236 \n", - "4 51.276234 79.696597 \n", - "5 35.775401 72.930982 \n", - "6 36.508842 73.366319 \n", - "7 34.963869 72.619864 \n", - "8 40.697078 76.548381 \n", - "9 64.838129 82.493561 \n", - "10 63.203267 81.799105 \n", + "0 46.092865 77.587084 \n", + "1 62.669246 82.716517 \n", + "2 63.720930 83.342622 \n", + "3 46.263911 77.111614 \n", + "4 40.669314 76.153770 \n", + "5 30.160428 71.029935 \n", + "6 29.378209 70.326696 \n", + "7 19.788772 66.238340 \n", + "8 23.628908 71.697034 \n", + "9 54.811151 78.011882 \n", + "10 54.174229 77.754888 \n", "\n", " F1 Score for class 0 (percent) F1 Score for class 1 (percent) \\\n", - "0 87.279345 80.057061 \n", - "1 84.737679 80.645161 \n", - "2 84.175384 84.224540 \n", - "3 83.654822 68.737864 \n", - "4 85.333789 72.669424 \n", - "5 80.474720 57.844858 \n", - "6 81.630632 62.155729 \n", - "7 78.857969 66.243508 \n", - "8 73.211392 75.283059 \n", - "9 83.995088 80.991736 \n", - "10 83.606226 79.990131 \n", + "0 84.106845 68.372093 \n", + "1 84.219133 81.207400 \n", + "2 81.715893 83.523447 \n", + "3 82.766825 67.458280 \n", + "4 83.517176 60.587792 \n", + "5 79.439547 50.679758 \n", + "6 80.274060 54.552129 \n", + "7 73.872895 58.498706 \n", + "8 61.597938 70.898438 \n", + "9 78.099804 77.216051 \n", + "10 78.437233 76.598703 \n", "\n", " Precision (percent) Precision for class 0 (percent) \\\n", - "0 83.795749 86.726944 \n", - "1 83.105263 82.000000 \n", - "2 84.356470 87.912599 \n", - "3 81.628277 74.977252 \n", - "4 81.424889 80.096154 \n", - "5 77.230024 70.292531 \n", - "6 75.801951 74.667874 \n", - "7 72.943526 77.020826 \n", - "8 76.932079 89.322034 \n", - "9 82.486074 84.098361 \n", - "10 81.820379 83.353486 \n", + "0 80.126905 77.269939 \n", + "1 82.752272 83.739837 \n", + "2 83.669705 90.592516 \n", + "3 79.979373 74.442067 \n", + "4 81.831616 73.427542 \n", + "5 77.016462 67.786136 \n", + "6 73.949523 71.379994 \n", + "7 66.448512 72.289157 \n", + "8 73.527853 89.782119 \n", + "9 77.936978 83.621092 \n", + "10 77.629411 82.822362 \n", "\n", " Precision for class 1 (percent) Recall (percent) \\\n", - "0 80.864553 83.552182 \n", - "1 84.210526 82.516956 \n", - "2 80.800341 84.347381 \n", - "3 88.279302 75.441856 \n", - "4 82.753623 78.040149 \n", - "5 84.167518 69.085318 \n", - "6 76.936027 71.082336 \n", - "7 68.866227 72.299062 \n", - "8 64.542125 76.168492 \n", - "9 80.873786 82.501049 \n", - "10 80.287271 81.777842 \n", + "0 82.983871 75.203328 \n", + "1 81.764706 82.680794 \n", + "2 76.746894 83.018087 \n", + "3 85.516680 74.442390 \n", + "4 90.235690 71.212711 \n", + "5 86.246787 65.906956 \n", + "6 76.519053 67.042260 \n", + "7 60.607867 66.029494 \n", + "8 57.273588 69.955173 \n", + "9 72.252864 78.086931 \n", + "10 72.436459 77.880771 \n", "\n", " Recall for class 0 (percent) Recall for class 1 (percent) \n", - "0 87.838828 79.265537 \n", - "1 87.664474 77.369439 \n", - "2 80.742955 87.951807 \n", - "3 94.603904 56.279809 \n", - "4 91.304348 64.775950 \n", - "5 94.106464 44.064171 \n", - "6 90.025482 52.139190 \n", - "7 80.784894 63.813230 \n", - "8 62.024323 90.312660 \n", - "9 83.892069 81.110029 \n", - "10 83.860503 79.695182 " + "0 92.271062 58.135593 \n", + "1 84.703947 80.657640 \n", + "2 74.423570 91.612604 \n", + "3 93.187907 55.696873 \n", + "4 96.821337 45.604084 \n", + "5 95.931559 35.882353 \n", + "6 91.700036 42.384484 \n", + "7 75.527582 56.531406 \n", + "8 46.881130 93.029216 \n", + "9 73.262469 82.911392 \n", + "10 74.493106 81.268437 " ] }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TTT accuracy = 79.05190677966102\n" - ] } ], "source": [ "## Test-then-train and windowed evaluation wrapped in prequential evaluation\n", "from capymoa.evaluation import prequential_evaluation\n", + "from capymoa.classifier import HoeffdingTree\n", "\n", - "ob_learner = OnlineBagging(schema=elec_stream.get_schema(), ensemble_size=5)\n", + "# Create a HoeffdingTree classifier\n", + "ht = HoeffdingTree(schema=elec_stream.get_schema(), grace_period=50)\n", + "\n", + "# Obtain the results from the high-level function. \n", + " # Note that we need to specify a window_size as we obtain both windowed and cumulative results. \n", + " # The results from a high-level evaluation function are represented as a dictionary\n", + "results_ht = prequential_evaluation(stream=elec_stream, learner=ht, window_size=4500)\n", "\n", - "results_OB = prequential_evaluation(stream=elec_stream, learner=ob_learner, window_size=4500)\n", + "print(f\"Cumulative accuracy = {results_ht['cumulative'].accuracy()}, wall-clock time: {results_ht['wallclock']}\")\n", "\n", - "display(results_OB['windowed'].metrics_per_window())\n", - "print(f\"TTT accuracy = {results_OB['cumulative'].accuracy()}\")" + "# The windowed results are conveniently stored in a pandas DataFrame. \n", + "display(results_ht['windowed'].metrics_per_window())" ] }, { - "cell_type": "code", - "execution_count": 5, - "id": "63a4f3fc-bc48-4849-9040-682672a2a46e", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:38:56.938937Z", - "iopub.status.busy": "2024-03-21T04:38:56.938740Z", - "iopub.status.idle": "2024-03-21T04:38:56.945749Z", - "shell.execute_reply": "2024-03-21T04:38:56.945140Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'learner': 'OnlineBagging',\n", - " 'cumulative': ,\n", - " 'windowed': ,\n", - " 'wallclock': 3.283531665802002,\n", - " 'cpu_time': 2.6955994280000013,\n", - " 'max_instances': None,\n", - " 'stream': }" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "markdown", + "id": "23f0220e-6b79-4cef-a6d9-abaabdb66e94", + "metadata": {}, "source": [ - "## The results dictionary\n", - "results_OB" + "### 1.2 Using any MOA learner\n", + "\n", + "* **CapyMOA gives you access to any MOA classifier or regressor**\n", + "\n", + "* For some of the MOA learners there are corresponding Python objects (such as the HoeffdingTree or Adaptive Random Forest Classifier). However, MOA has over a hundred learners, and more are added constantly.\n", + "\n", + "* To allow advanced users to access **any** MOA learner from CapyMOA, we included the ```MOAClassifier``` and ```MOARegressor``` generic wrappers.\n", + "\n", + "In the below example we use ```kNN``` classifier directly from MOA." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "c6c1e7f8-2e6a-4ad9-9ace-ce73ff6224d2", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:38:56.953001Z", - "iopub.status.busy": "2024-03-21T04:38:56.952781Z", - "iopub.status.idle": "2024-03-21T04:38:59.620381Z", - "shell.execute_reply": "2024-03-21T04:38:59.619891Z" - } - }, + "execution_count": 4, + "id": "88210e9b-ec64-4d92-b875-96f8aa915cba", + "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cumulative accuracy = 80.32971398305084, wall-clock time: 3.057931900024414\n" + ] + }, { "data": { "text/html": [ @@ -920,189 +567,189 @@ " \n", " 0\n", " 4500.0\n", - 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" 84.288889\n", - " 67.991902\n", - " -2.612482\n", - " 67.921960\n", - " 84.155747\n", - " 86.258503\n", - " 81.660182\n", - " 84.632719\n", - " 82.829414\n", - " 86.436024\n", - " 83.684122\n", - " 89.983779\n", - " 77.384464\n", + " 83.777778\n", + " 67.000817\n", + " -5.950653\n", + " 66.878403\n", + " 83.609780\n", + " 85.714286\n", + " 81.233933\n", + " 83.979179\n", + " 82.829047\n", + " 85.129310\n", + " 83.243618\n", + " 88.807786\n", + " 77.679449\n", " \n", " \n", "\n", @@ -1110,299 +757,194 @@ ], "text/plain": [ " classified instances classifications correct (percent) \\\n", - "0 4500.0 85.711111 \n", - "1 9000.0 85.888889 \n", - "2 13500.0 84.888889 \n", - "3 18000.0 85.866667 \n", - "4 22500.0 80.888889 \n", - "5 27000.0 77.333333 \n", - "6 31500.0 79.288889 \n", - "7 36000.0 80.955556 \n", - "8 40500.0 77.911111 \n", - "9 45000.0 85.044444 \n", - "10 45312.0 84.288889 \n", + "0 4500.0 82.377778 \n", + "1 9000.0 82.466667 \n", + "2 13500.0 83.777778 \n", + "3 18000.0 82.044444 \n", + "4 22500.0 80.622222 \n", + "5 27000.0 76.644444 \n", + "6 31500.0 76.888889 \n", + "7 36000.0 76.133333 \n", + "8 40500.0 78.333333 \n", + "9 45000.0 83.644444 \n", + "10 45312.0 83.777778 \n", "\n", " Kappa Statistic (percent) Kappa Temporal Statistic (percent) \\\n", - "0 70.014923 11.554333 \n", - "1 71.541441 15.894040 \n", - "2 69.730471 -2.874433 \n", - "3 70.736645 1.851852 \n", - "4 57.741025 -19.777159 \n", - "5 52.288688 -78.321678 \n", - "6 55.299674 -70.072993 \n", - "7 60.836519 -44.033613 \n", - "8 53.925519 -49.025487 \n", - "9 69.595669 3.857143 \n", - "10 67.991902 -2.612482 \n", + "0 62.345679 -9.078404 \n", + "1 64.685767 -4.503311 \n", + "2 67.478725 -10.438729 \n", + "3 62.324843 -24.691358 \n", + "4 57.332883 -21.448468 \n", + "5 49.730664 -83.741259 \n", + "6 49.650404 -89.781022 \n", + "7 49.096856 -80.504202 \n", + "8 55.052628 -46.176912 \n", + "9 66.782180 -5.142857 \n", + "10 67.000817 -5.950653 \n", "\n", " Kappa M Statistic (percent) F1 Score (percent) \\\n", - "0 63.590034 85.008325 \n", - "1 69.294004 85.779361 \n", - "2 68.372093 84.865360 \n", - "3 66.295707 85.415658 \n", - "4 51.219512 79.833354 \n", - "5 45.454545 76.382074 \n", - "6 46.833999 77.855182 \n", - "7 52.362424 80.483869 \n", - "8 49.051768 77.440535 \n", - "9 69.739209 84.976375 \n", - "10 67.921960 84.155747 \n", + "0 55.096263 81.309383 \n", + "1 61.847195 82.343432 \n", + "2 66.046512 83.744200 \n", + "3 57.180710 81.454682 \n", + "4 50.538854 79.463353 \n", + "5 43.796791 75.846804 \n", + "6 40.673132 75.174411 \n", + "7 40.300167 74.723612 \n", + "8 50.025628 77.813249 \n", + "9 66.906475 83.525678 \n", + "10 66.878403 83.609780 \n", "\n", " F1 Score for class 0 (percent) F1 Score for class 1 (percent) \\\n", - "0 88.255708 81.758865 \n", - "1 87.075107 84.462931 \n", - "2 85.463873 84.266543 \n", - "3 88.085425 82.632441 \n", - "4 85.618729 71.523179 \n", - "5 81.601732 70.486111 \n", - "6 83.774373 71.375921 \n", - "7 83.704126 77.091687 \n", - "8 81.914119 71.632420 \n", - "9 86.842620 82.676963 \n", - "10 86.258503 81.660182 \n", + "0 85.977011 76.292975 \n", + "1 83.822022 80.863449 \n", + "2 84.540449 82.935951 \n", + "3 85.351704 76.808266 \n", + "4 85.319865 71.503268 \n", + "5 81.950884 66.918477 \n", + "6 82.167353 67.171717 \n", + "7 80.991150 67.940299 \n", + "8 81.967819 72.863902 \n", + "9 85.540275 81.176471 \n", + "10 85.714286 81.233933 \n", "\n", " Precision (percent) Precision for class 0 (percent) \\\n", - "0 85.061417 88.014572 \n", - "1 85.851551 86.215236 \n", - "2 84.861676 85.573630 \n", - "3 85.757333 86.275229 \n", - "4 82.429054 78.939254 \n", - "5 77.211115 77.625257 \n", - "6 78.796161 80.280280 \n", - "7 80.148566 86.043784 \n", - "8 78.597048 76.382762 \n", - "9 85.463160 83.214687 \n", - "10 84.632719 82.829414 \n", + "0 82.063492 83.111111 \n", + "1 82.359202 83.599182 \n", + "2 83.772396 83.865546 \n", + "3 82.435310 81.088529 \n", + "4 81.730914 79.113331 \n", + "5 78.028628 74.725963 \n", + "6 76.430236 77.666126 \n", + "7 75.478820 77.585622 \n", + "8 78.628296 77.536739 \n", + "9 83.921917 82.337368 \n", + "10 83.979179 82.829047 \n", "\n", " Precision for class 1 (percent) Recall (percent) \\\n", - "0 82.108262 84.955299 \n", - "1 85.487865 85.707293 \n", - "2 84.149723 84.869043 \n", - "3 85.239437 85.076696 \n", - "4 85.918854 77.396141 \n", - "5 76.796974 75.570647 \n", - "6 77.312043 76.936412 \n", - "7 74.253347 80.821990 \n", - "8 80.811333 76.317564 \n", - "9 87.711633 84.495104 \n", - "10 86.436024 83.684122 \n", + "0 81.015873 80.569007 \n", + "1 81.119221 82.327668 \n", + "2 83.679245 83.716024 \n", + "3 83.782091 80.497111 \n", + "4 84.348497 77.318219 \n", + "5 81.331293 73.783677 \n", + "6 75.194346 73.959187 \n", + "7 73.372018 73.983366 \n", + "8 79.719854 77.014926 \n", + "9 85.506466 83.133164 \n", + "10 85.129310 83.243618 \n", "\n", " Recall for class 0 (percent) Recall for class 1 (percent) \n", - "0 88.498168 81.412429 \n", - "1 87.952303 83.462282 \n", - "2 85.354398 84.383689 \n", - "3 89.973211 80.180180 \n", - "4 93.533065 61.259217 \n", - "5 86.007605 65.133690 \n", - "6 87.586458 66.286366 \n", - "7 81.488338 80.155642 \n", - "8 88.309141 64.325987 \n", - "9 90.801308 78.188900 \n", - "10 89.983779 77.384464 " + "0 89.047619 72.090395 \n", + "1 84.046053 80.609284 \n", + "2 85.226302 82.205746 \n", + "3 90.088021 70.906200 \n", + "4 92.583120 62.053318 \n", + "5 90.722433 56.844920 \n", + "6 87.222424 60.695950 \n", + "7 84.709367 63.257365 \n", + "8 86.936053 67.093798 \n", + "9 89.002453 77.263875 \n", + "10 88.807786 77.679449 " ] }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TTT accuracy = 82.36228813559322\n" - ] } ], "source": [ - "# What if we want to use any MOA Learner? \n", - "from capymoa.learner.learners import MOAClassifier\n", - "from moa.classifiers.trees import HoeffdingAdaptiveTree\n", + "from capymoa.evaluation import prequential_evaluation\n", + "from capymoa.base import MOAClassifier\n", + "from moa.classifiers.lazy import kNN\n", "\n", - "# Restart the stream\n", - "elec_stream.restart()\n", + "# Creating a learner\n", + "knn = MOAClassifier(schema=elec_stream.get_schema(), moa_learner=kNN)\n", "\n", - "# Create the wrapper\n", - "moa_HAT = MOAClassifier(schema=elec_stream.get_schema(), moa_learner=HoeffdingAdaptiveTree, CLI=\"-g 50\")\n", + "results_kNN = prequential_evaluation(stream=elec_stream, learner=knn, window_size=4500)\n", "\n", - "hat_evaluator = ClassificationEvaluator(schema=elec_stream.get_schema())\n", + "print(f\"Cumulative accuracy = {results_kNN['cumulative'].accuracy()}, wall-clock time: {results_kNN['wallclock']}\")\n", + "display(results_kNN['windowed'].metrics_per_window())" + ] + }, + { + "cell_type": "markdown", + "id": "cae0ca48-fb54-4934-9852-15d967c30fc4", + "metadata": {}, + "source": [ + "#### 1.2.1 Parameters for generic wrappers\n", "\n", - "results_HAT = prequential_evaluation(stream=elec_stream, learner=moa_HAT, window_size=4500)\n", + "* MOA objects can be parametrized using the MOA CLI (Command Line Interface)\n", + "* Sometimes you may not know the relevent parameters for ```moa_learner```, ```moa_learner.CLI_help()``` presents all the hyperparameters available for the ```moa_learner``` object.\n", "\n", - "display(results_HAT['windowed'].metrics_per_window())\n", - "print(f\"TTT accuracy = {results_HAT['cumulative'].accuracy()}\")" + "In the code below, we show all hyperparameters for kNN (in MOA) and then create a knn object with ```k=1``` and a window of ```5000``` instances (hyperparameter ```limit```). " ] }, { "cell_type": "code", - "execution_count": 7, - "id": "b170a6fc-d7be-455e-91f5-183255643807", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:38:59.622954Z", - "iopub.status.busy": "2024-03-21T04:38:59.622717Z", - "iopub.status.idle": "2024-03-21T04:38:59.627440Z", - "shell.execute_reply": "2024-03-21T04:38:59.626790Z" - }, - "scrolled": true - }, + "execution_count": 5, + "id": "0e1275a1-7ced-47d7-8dea-29037497ba41", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "-m maxByteSize (default: 33554432)\n", - "Maximum memory consumed by the tree.\n", - "-n numericEstimator (default: GaussianNumericAttributeClassObserver)\n", - "Numeric estimator to use.\n", - "-d nominalEstimator (default: NominalAttributeClassObserver)\n", - "Nominal estimator to use.\n", - "-e memoryEstimatePeriod (default: 1000000)\n", - "How many instances between memory consumption checks.\n", - "-g gracePeriod (default: 200)\n", - "The number of instances a leaf should observe between split attempts.\n", - "-s splitCriterion (default: InfoGainSplitCriterion)\n", - "Split criterion to use.\n", - "-c splitConfidence (default: 1.0E-7)\n", - "The allowable error in split decision, values closer to 0 will take longer to decide.\n", - "-t tieThreshold (default: 0.05)\n", - "Threshold below which a split will be forced to break ties.\n", - "-b binarySplits\n", - "Only allow binary splits.\n", - "-z stopMemManagement\n", - "Stop growing as soon as memory limit is hit.\n", - "-r removePoorAtts\n", - "Disable poor attributes.\n", - "-p noPrePrune\n", - "Disable pre-pruning.\n", - "-l leafprediction (default: NBAdaptive)\n", - "Leaf prediction to use.\n", - "-q nbThreshold (default: 0)\n", - "The number of instances a leaf should observe before permitting Naive Bayes.\n", - "\n" + "-k k (default: 10)\n", + "The number of neighbors\n", + "-m median\n", + "median or mean\n", + "-w limit (default: 1000)\n", + "The maximum number of instances to store\n", + "-n nearestNeighbourSearch (default: LinearNN)\n", + "Nearest Neighbour Search to use\n", + "\n", + "Cumulative accuracy = 78.06320621468926, wall-clock time: 7.857517957687378\n" ] } ], "source": [ - "# Shows the command line (CLI) parameters available for the MOA learner\n", - "print(moa_HAT.CLI_help())" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "ac835936-fc44-4f7b-acae-fe629b8a4ec6", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:38:59.630563Z", - "iopub.status.busy": "2024-03-21T04:38:59.630299Z", - "iopub.status.idle": "2024-03-21T04:39:00.162440Z", - "shell.execute_reply": "2024-03-21T04:39:00.161987Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## What about comparing the results of both algorithms in a plot? \n", - "from capymoa.evaluation.visualization import plot_windowed_results\n", + "knn = MOAClassifier(schema=elec_stream.get_schema(), moa_learner=kNN)\n", "\n", - "plot_windowed_results(results_OB, results_HAT)" + "print(knn.CLI_help())\n", + "\n", + "# Creating the knn classifier with different parameters\n", + "knn_k1_w5000 = MOAClassifier(schema=elec_stream.get_schema(), moa_learner=kNN, CLI='-k 1 -w 5000')\n", + "\n", + "results_kNN_k1_w5000 = prequential_evaluation(stream=elec_stream, learner=knn_k1_w5000, window_size=4500)\n", + "\n", + "print(f\"Cumulative accuracy = {results_kNN_k1_w5000['cumulative'].accuracy()}, wall-clock time: {results_kNN_k1_w5000['wallclock']}\")" ] }, { - "cell_type": "code", - "execution_count": 9, - "id": "61e684d8-2992-4ca9-bada-4660ff5fd9b4", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:39:00.164535Z", - "iopub.status.busy": "2024-03-21T04:39:00.164113Z", - "iopub.status.idle": "2024-03-21T04:39:27.023872Z", - "shell.execute_reply": "2024-03-21T04:39:27.023477Z" - } - }, - "outputs": [], + "cell_type": "markdown", + "id": "54fd75ab-1671-4d9e-a1ea-a3f0f07b8b63", + "metadata": {}, "source": [ - "from moa.classifiers.meta import StreamingRandomPatches\n", - "\n", - "elec_stream.restart()\n", - "\n", - "srp = MOAClassifier(schema=elec_stream.get_schema(), moa_learner=StreamingRandomPatches(), CLI=\"-s 10 -m 80\")\n", + "### 1.3 Comparing results among classifiers\n", "\n", - "results_srp = prequential_evaluation(stream=elec_stream, learner=srp, window_size=4500)" + "* CapyMOA provides ```plot_windowed_results``` as an easy visualization function for quickly comparing **windowed metrics**\n", + "* In the example below, we create three classifiers: HoeffdingAdaptiveTree, HoeffdingTree and AdaptiveRandomForest, and plot the results using ```plot_windowed_results```\n", + "* More details about ```plot_windowed_results``` options are described in the documentation: LINK_CAPYMOA_ORG" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "b2cca100-0a41-4abd-a700-f4c640871be7", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:39:27.031785Z", - "iopub.status.busy": "2024-03-21T04:39:27.031547Z", - "iopub.status.idle": "2024-03-21T04:39:27.038152Z", - "shell.execute_reply": "2024-03-21T04:39:27.037834Z" - } - }, + "execution_count": 6, + "id": "c6c1e7f8-2e6a-4ad9-9ace-ce73ff6224d2", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "-l baseLearner (default: trees.HoeffdingTree -g 50 -c 0.01)\n", - "Classifier to train on instances.\n", - "-s ensembleSize (default: 100)\n", - "The number of models.\n", - "-o subspaceMode (default: Percentage (M * (m / 100)))\n", - "Defines how m, defined by mFeaturesPerTreeSize, is interpreted. M represents the total number of features.\n", - "-m subspaceSize (default: 60)\n", - "# attributes per subset for each classifier. Negative values = totalAttributes - #attributes\n", - "-t trainingMethod (default: Random Patches)\n", - "The training method to use: Random Patches, Random Subspaces or Bagging.\n", - "-a lambda (default: 6.0)\n", - "The lambda parameter for bagging.\n", - "-x driftDetectionMethod (default: ADWINChangeDetector -a 1.0E-5)\n", - "Change detector for drifts and its parameters\n", - "-p warningDetectionMethod (default: ADWINChangeDetector -a 1.0E-4)\n", - "Change detector for warnings (start training bkg learner)\n", - "-w disableWeightedVote\n", - "Should use weighted voting?\n", - "-u disableDriftDetection\n", - "Should use drift detection? If disabled, then the bkg learner is also disabled.\n", - "-q disableBackgroundLearner\n", - "Should use bkg learner? If disabled, then trees are reset immediately.\n", - "\n" + "HAT accuracy = 82.36228813559322\n", + "HT accuracy = 75.46566031073446\n", + "ARF accuracy = 87.5684145480226\n" ] - } - ], - "source": [ - "print(srp.CLI_help())" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "1d23ca82-4967-4a99-90c8-34841cf58f66", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:39:27.041930Z", - "iopub.status.busy": "2024-03-21T04:39:27.041723Z", - "iopub.status.idle": "2024-03-21T04:39:27.453854Z", - "shell.execute_reply": "2024-03-21T04:39:27.453471Z" - } - }, - "outputs": [ + }, { "data": { - "image/png": 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", + "image/png": 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8VjfzGvTt2xdLS0s++eSTUq/DV199RUZGBvfdd98tx1vCw8ODu+66iwULFnDu3Lky6y9cuGD+fcSIERw6dIh169aV2a4krpILGddOx1aRrl27ApR7joNpjP3VY8B3795NeHg4AwcOvOX4K9K/f38cHBx49913y/xNlDyvDh06EBQUxP/+9z8uXbp0W49THjs7u+u+Vvv3779ulXohhBB3TlrIhRBClOudd97h119/pXfv3kydOpXQ0FDOnTvHqlWr2L59u7mQ2NVCQkIICgri3//+NwkJCTg6OrJmzZoy41ZPnDhBnz59GD16NM2bN8fCwoJ169aRnJzMmDFjAFi8eDFz585l2LBhBAUFkZWVxRdffIGjo+NtJa3XatKkCa+++ipvvfUWPXv2ZPjw4VhZWbFnzx68vb1599136datGy4uLkycOJHnnnsORVFYsmRJuRckOnTowIoVK3j++ecJCwvD3t6e+++/v9zHnjNnDgMHDqRr165MmTLFPO2Zk5OTeU71W/Hmm2+ybds27rvvPvz9/Tl//jxz586lUaNG9OjR445egwYNGvDyyy8za9Ys7r33Xh544AGioqKYO3cuYWFh5vH0t+v//u//6NGjB61ateKxxx6jcePGJCcns2vXLs6ePcuhQ4cAeOGFF1i9ejWjRo1i8uTJdOjQgbS0NDZu3Mj8+fNp06YNQUFBODs7M3/+fBwcHLCzs6Nz585lxm2XaNy4MS1btuS3334rM799yevTo0cPnnzySfLz8/noo49wc3PjxRdfvOX4K+Lo6MiHH37Io48+SlhYGOPGjcPFxYVDhw6Rk5PD4sWL0el0fPnllwwcOJAWLVowadIkfHx8SEhIYMuWLTg6OvL999/f8mvfoUMHfvvtNz744AO8vb0JDAykc+fOAOzbt4+0tDSGDBlyy8cVQghxC6q9rrsQQohaIy4uTp0wYYLaoEED1crKSm3cuLH69NNPq/n5+aqqlj/tWWRkpNq3b1/V3t5edXd3Vx977DH10KFDpaajSklJUZ9++mk1JCREtbOzU52cnNTOnTurK1euNB9n//796tixY1U/Pz/VyspK9fDwUAcPHqzu3bu3VIzc5rRnJb7++mu1Xbt2qpWVleri4qL27t1b3bx5s3n9jh071C5duqg2Njaqt7e3+uKLL6q//PJLmed96dIlddy4caqzs7MKmKdAK2/aM1VV1d9++03t3r27amNjozo6Oqr333+/GhkZWW7M105nVjKFVmxsrKqqqvr777+rQ4YMUb29vVVLS0vV29tbHTt2rHrixIkyz7c8N3oNVNU0zVlISIhqMBjUhg0bqk8++aR68eLFUtv07t1bbdGiRZnjl7wGFU1zFxMTo06YMEH19PRUDQaD6uPjow4ePFhdvXp1qe1SU1PVZ555RvXx8VEtLS3VRo0aqRMnTlRTUlLM22zYsEFt3ry5amFhcVNToH3wwQeqvb19qentro73/fffV319fVUrKyu1Z8+e6qFDh24r/pJ/s4qm8du4caParVs38/nQqVMnddmyZaW2OXDggDp8+HDVzc1NtbKyUv39/dXRo0erv//+u3mbmz1nVFVVjx8/rvbq1Uu1sbFRgVJToE2fPl318/MrNc2aEEKIyqeo6i1U9xBCCCGEqEMyMjJo3Lgx7733HlOmTAHg9OnTBAYGMmfOHP79739rHGH1y8/PJyAggJdeeol//OMfWocjhBB1mowhF0IIIUS95eTkxIsvvsicOXOqrWp+Tbdw4UIMBkOZucyFEEJUPmkhF0IIIYS4Sn1vIRdCCFF9pIVcCCGEEEIIIYTQgLSQCyGEEEIIIYQQGpAWciGEEEIIIYQQQgOSkAshhBBCCCGEEBqw0DqAqmY0GklMTMTBwQFFUbQORwghhBBCCCFEHaeqKllZWXh7e6PTVdwOXucT8sTERHx9fbUOQwghhBBCCCFEPRMfH0+jRo0qXF/nE3IHBwfA9EI4OjpqHE3FCgsL+fXXX+nfvz8Gg0HrcIQoQ85RUdPJOSpqOjlHRW0g56mo6WrLOZqZmYmvr685H62Ipgl5VlYWr7/+OuvWreP8+fO0a9eOjz/+mLCwMMDUzP/GG2/wxRdfkJ6eTvfu3Zk3bx7BwcE3/Rgl3dQdHR1rfEJua2uLo6NjjT6xRP0l56io6eQcFTWdnKOiNpDzVNR0te0cvdGwaU2Luj366KNs3ryZJUuWcPjwYfr370/fvn1JSEgA4L333uOTTz5h/vz5hIeHY2dnx4ABA8jLy9MybCGEEEIIIYQQ4o5plpDn5uayZs0a3nvvPXr16kWTJk2YOXMmTZo0Yd68eaiqykcffcRrr73GkCFDaN26Nd988w2JiYmsX79eq7CFEEIIIYQQQohKoVmX9aKiIoqLi7G2ti613MbGhu3btxMbG0tSUhJ9+/Y1r3NycqJz587s2rWLMWPGlHvc/Px88vPzzfczMzMBU9eGwsLCKngmlaMktpoco6jf5BwVNZ2co6Kmk3NU1AZynoqarracozcbn6KqqlrFsVSoW7duWFpasnTpUho2bMiyZcuYOHEiTZo0YeHChXTv3p3ExES8vLzM+4wePRpFUVixYkW5x5w5cyazZs0qs3zp0qXY2tpW2XMRQgghhBBCCCEAcnJyGDduHBkZGdetZaZpUbclS5YwefJkfHx80Ov1tG/fnrFjx7Jv377bPubLL7/M888/b75fUt2uf//+Nb6o2+bNm+nXr1+tKE4g6h85R0VNJ+eoqOnkHBW1gZynoqarLedoSU/tG9E0IQ8KCuLPP/8kOzubzMxMvLy8ePDBB2ncuDGenp4AJCcnl2ohT05Opm3bthUe08rKCisrqzLLDQZDjf4HK1Fb4hT1l5yjoqaTc1TUdHKOitpAzlNR09X0c/RmY9O0ynoJOzs7vLy8uHjxIr/88gtDhgwhMDAQT09Pfv/9d/N2mZmZhIeH07VrVw2jFUIIIYQQQggh7pymLeS//PILqqrSrFkzTp48yQsvvEBISAiTJk1CURSmTZvGf/7zH4KDgwkMDOT111/H29uboUOHahm2EEIIIYQQQghxxzRNyDMyMnj55Zc5e/Ysrq6ujBgxgrffftvcvP/iiy+SnZ3N1KlTSU9Pp0ePHvz8889lKrMLIYQQQgghhBC1jaYJ+ejRoxk9enSF6xVF4c033+TNN9+sxqiEEEIIIYQQQoiqp2lCLoSoHeIy41gTtYa92XuJORjDiGYj8Hf01zosIYQQQgghajVJyEWdFJcZx7rodSReSsTb3pthwcMkgbxN66LXMXPXTBQUjKqRyGORLD62mFndZjG0yVCtwxNCCCGEEKLWkoS8BpDWx8p1dQKpoqKgsPDoQkkgb0NcZhwzd83EqBrNy4rVYgDe2PkG7T3a4+fop1V4QgghhBBC1GqSkGusLrQ+qqpKkVqEUTVSbCw23apXbstbZlSNFBlN+1xv3bXLi43FZZepxRiNRorUIlJyU/jq8FeoqGXinLFjBsdTj+No5YiKiqqq5lugzLJStzfYruR1KG9bo2q87nYqKqb/r3PMCuIp+c+8f3nLKniuRtVY4b4l2yXnJJdKxq+moLA2ei3TOkyrpDNJCCGEEEKI+kUScg1dr/Vxxo4ZHE87jqOl442T0XIS2Ku3Ly+pNSfL1yS1FS0rs/1Vxygv+a2JVFS+O/6d1mHUGcVqMWuj12Kpt6STZydaN2iNpd5S67CEEEIIIYSoNSQh19C66HUoKOWuU1H57ljdSB4tFAt0ig69To9O0Zl+V/TmH53OdP/q5dcuM6/TlV2mU3RY6EyPEXEhgnPZ5yqMxdfBl27e3QBTC6+iKKVugSvLUNApOlAw3y93u6u2L29bRVHKPl45j3297a6N63pxlCzTobuyrLy4biLeDTEb2By3ucJW8ov5F5l3aB7zDs3DWm9N+4bt6eTZic5enQl1DUWv09/RuSOEEEIIIURdJgm5hhIvJV63dbkkebxRolqSjJbcv946861OX27Ce73jlbuP7ppE+pplOkVXja8ofLTvIxYdXWTuaXA1vaKnv39/6WJ9C/wc/dgct7ncdQoKz7R9hpPpJwlPCictL42diTvZmbgTAAeDAx09O9LZqzOdPDvRxLmJOdkXQgghhBBCSEKuKW977wpbyCV5vD3Dgoex8OjCctepqAwPHl7NEdVu/o7+zOo2izd2vmGuc6BTdKiopeocqKpKTHoM4UnhhJ8LZ2/SXrIKs9gSv4Ut8VsAcLV2Nbeed/bsTCOHRpKgCyGEEEKIek0Scg1J8lj5rk0gS6qslySQUhH81g1tMpT2Hu1ZHbWavdF76RjckZHNRpZ6LRVFoYlLE5q4NGF86HiKjcUcTzvO3+f+ZnfSbvYn7yctL42fT//Mz6d/BsDLzsucoHfy7ERDu4ZaPUUhhBBCCCE0IQm5hm7U+ijJ4+0pSSDXRq81z0M+PHi4vJ53wM/Rj2fbPsumxE0MajsIg8Fw3e31Oj0t3FvQwr0FU1pNoaC4gMMphwk/Z2pBj0gxjfXfELOBDTEbAAhwDDAn5508O+Fs7VwNz0wIIYQQQgjtSEKusZtpfRS3zs/RT7r71yCWeks6NOxAh4YdeKrtU+QU5nDw/EFzF/djacc4nXma05mnWRG1AoAQ1xBzC3qHhh2wM9hp/CyEEEIIIW5eXGYc66LXmRuIhgUPw9/RX+uwRA0jCXkNcKutj0LUdrYGW7r5dKObj6nifUZ+BvuS97E7aTfh58I5mX6S42nHOZ52nG8iv0Gv6Gnp3tKcoLf1aIuV3krjZyGEEEIIUb510euYuWtmqSGUC48uLFWDRwiQhFwIUQM4WTlxj9893ON3DwApuSnsSdpj7uJ+9tJZDl04xKELh/ji8BdY6ixp59GOTl6m7u0t3Ftg0MmFLCGEEEJoLy4zjpm7ZpY7bewbO9+gvUd76Q0rzCQhF0LUOO427gwMHMjAwIEAJFxKYPe53eYW9Au5F0zd3ZPCAbAz2NGhYQdzC3pTl6bVPuWeEEIIIQSYWscrmklJVVVe+uslejXqha2FLXYGO+wMdtgabM33bQ2Xby1ssbGwkVlp6jhJyIUQNZ6PvQ/DgocxLHgYqqoSmxlrTtB3J+0mIz+DbWe3se3sNgCcrZwJ8wyjs2dnOnl1IsAxQD7MhBBCCFFlio3FRKZGsj1hO6tPrKZYLS53OxWVwymHOZxy+KaOq6CUSdbNv1vYXkneDbbYWVxef9Xv5e2j1+kr86lXq7jMONZErWFv9l5iDsYwotmIWj8uXxJyIUStoigKjZ0a09ipMWNCxmBUjUSlRZlbz/cl7yM9P53NcZvZHLcZAA9bD3Ny3tmzM172Xho/CyGEEELUdqm5qexM3Mn2hO3sTNxJen76DfdRUGjh1oJmrs3IKcohpzCH7MJs8+85hTlkF2WTU5iDevm/7MJssguzuZB7oVLittZbl0rS7Qx22BhsSifxVyf75VwMuHq5QV89wwavHpdvVI1EHotk8bHFtX5cviTkQohaTafoCHULJdQtlIktJlJoLORoylHCz4WzO2k3B88f5HzOeb4/9T3fn/oeAF8HXzp5dqKLVxfCPMNws3HT+FkIIYQQoqYrMhZxOOUw2xO2sz1hO5GpkaXW2xvs6erdlVDXUD498CkqapljKIrC7F6zbziG3KgaySvKK5W0X524l/xeZtlVCf21vxepRQDkFeeRV5xHGmmV8rpY6CxMSXoFLfQ2FjZlkviSCwDlte5b663L9Gwsb1x+SS+E2j4uXxJyIUSdYtAZaOvRlrYebXm8zePkFeVx8MJBdp/bTXhSOEdTjhKfFU98VjxrotcA0MS5CZ29OtPZszMdPDvgaOmo8bMQQgghRE1wIecC2xO2syNxBzsTd5JVkFVqfahrKN19utPDpwetG7Q2F5ltYNuAN3a+UarKuorKrG6zbipx1Ck6c4KKzZ0/D1VVKTAWlEruc4tySyX2pZYVlk32r07uc4pyyC/OB0wXKjLyM8jIz7jzQLn83K9poU/LSyu3SB6Yeh2sjV5ba6c8loRcCFGnWVtY08WrC128ugBwqeAS+8/v5+9zf7P73G6iLkZxMv0kJ9NP8t2x79ApOpq7Njd1b/fqTDuPdthYVMInoRBCCCFqvEJjIQfPH2RHwg62J2wn6mJUqfWOlo508+5GD58edPPuRgPbBuUeZ2iTobT3aM/a6LXmeciHBw/XrBVXURSs9FZY6a1wsXaplGMWGgvJKcwxJ/GlWu2vaaEvr3X/2pb+3KJcwNQ74FLhJS4VXrqpOFRUEi8lVspz0oIk5EKIesXe0p5ejXrRq1EvAC7mXTRPsbY7aTenM09zJPUIR1KP8PWRr7HQWdCmQRvzGPTW7q2rbayUEEIIIapeUnaSuRv63+f+Jrsw27yuZMx3j0Y96O7dnVburW66KJqfo1+tbbW9GQadAScrJ5ysnCrleEbVWLqF/qoW+dUnVvPn2T/LHwaAgre9d6XEoAVJyIUQ9ZqLtQv9A/rTP6A/YPpQ3pO0h7/P/U34uXCSc5LZl7yPfcn7mHtoLjYWNrT3aG8uEBfiGlKrq5UKIYQQ9U1BcQH7z+9n+1lTV/ST6SdLrXexcqGbz5VWcFdrV40irV90is481vxaAU4BbEvYhqqWTchVVIYHD6+OEKuEJORCCHEVTztP7g+6n/uD7kdVVeKz4glPCjdPs5aWl8aOxB3sSNwBgIOlA2ENw8wJepBzkEyxJoQQQtQwZ7POmruhhyeFm7tHgykRbOXeih4+Pejh04Pmbs3RKToNoxXX8nf0Z1a3WeZx+UbViE7R3dK4/JpKEnIhhKiAoij4Ofrh5+jHqKajMKpGTqafNBeI25u0l6yCLP6I/4M/4v8AwM3azZycd/LqhK+Db7nHjsuMY130OvO4smHBw2r9PJpCCCFETZFXlMe+5H3mruinM0+XWu9u4053b1Mxtq7eXSut27WoOiXj8ldHrWZv9F46BndkZLORtToZB0nIhRDipukUHU1dmtLUpSkPNX+IImMRx1KPmVvQD5w/QGpeKj/F/sRPsT8B4GPvQyfPTnTy6kQnz0542HqUmkezpPLqwqMLa/08mkIIIYSW4jLjzAn43qS95BXnmdfpFT1tGrShZ6OedPfuTjPXZtIKXgv5OfrxbNtn2ZS4iUFtB2Ew1P66PpKQCyHEbbLQWdCqQStaNWjFo60epaC4gEMXDrE7aTe7z+0m4kIECZcSWHdyHetOrgOgkX0jzl46W+7xavs8mkIIIUR1yinMYW/yXv46+xc7EncQnxVfar2HrQc9fXrS3ac7Xby64GDpoFGkQlRMEnIhhKgklnpLwjzDCPMM4+m2T5NTmMP+8/vNXdyPpR6rMBkHU3XRCT9NwN/RH0u9JdZ6a9Othem2ZLqSq3+uXl+yvZXe6sq+Vy2zsjDtUxdbBOIy41gTtYa92XuJORjDiGYjZAiAEELUMaqqEpsRy18Jf7EjYQf7kvdRYCwwr7fQWdDBo4N5XvAmzk2krouo8SQhF0KIKmJrsDUXiAHIyM/g2T+e5cD5AxXuk5qXSmpeapXGZdAZbiqpryj5v5ULA+YfCyssFIsq+WJ09RAAo2ok8lgki48tliEAQghRB2QXZvP3ub/ZkbCDHQk7SMwuPd+0t503PXx60N2nO529OpdboVuImkwSciGEqCZOVk6092hPxIUIitXiMut16Ojr35d7A+8lvzif/KJ80+3ln4LiAvKK80y3RXml7l+9XantL29XpBaZH6fQWEihsZBLhZeq8+mjU3SlE/gKEvfyEv6r1129b2Z+Jm/9/VapeUlLXlsZAiCEELWPqqpEp0ezPWE7OxJ2sP/8foqMVz7DLHWWdPTsaCrI1qgHgY6B0gouajVJyIUQohoNCx7GwqMLy1+pwD/a/6NKEsgiY1GZBP7apL5Ucn+9iwHXXBQo72JAyTGu7kpoVI3kFuWWmmqmKikorI1ey7QO06rl8YQQQtyezIJM/k78mx2JpmnJzuecL7Xe18HX3OOsY8OO2BpsNYpUiMonCbkQQlSja+fRLKmyXtXzaFroLLDQWVT7lxijaiyTtN90S39RBYn+Veti0mPIKMgo97GL1WIiLkSgqqq0ngghRA1iVI0cTztunhf80IVDpXqOWeutCfMMo7tPd3r69JSeTqJOk4RcCCGqWck8mmuj15rnIR8ePLxOfuHQKTqsLayxtrCukuN/tO8jFh1dVO4QAIA9yXt46KeHeLz14/T06SmJuRBCaCQjP4OdiTvNXdGvrZcS4BhAD58e9PTpSfuG7avsc0OImkYSciGE0ICfo590pa4E1x0CABgUAxEXInj696dp7tacx1s/zt2+d0tiLoQQVcyoGjmacpTtiaZ5wY+kHMGoGs3rbSxs6OzVmR7epoJsjRwaaRitENqRhFwIIUStde0QAKNqRKfozEMAunt3Z9HRRaw6sYrI1Ej+seUfNHVpytTWU+nn369OTgEnhBBaSc1NNbeC70rcxcX8i6XWN3FuYh4L3s6jHZZ6S40iFaLmkIRcCCFErVYyBGB11Gr2Ru+lY3BHRjYbaR4C8ELYC0xpNYVvjn7DsuPLOHHxBP/+8980dmrMY60f496Ae7HQycehEELcqiJjEUdSjpjnBY9MjSw164W9wZ4uXl3M84J72nlqGK0QNZN8AxFCCFHr+Tn68WzbZ9mUuIlBbQdhMBhKrXe1dmVah2lMajmJb499y3eR33Eq4xQv//Uy8w7O49FWjzI4aDAGnaGCRxBCiLovLjOONVFr2Ju9l5iDMYxoNgJ/R/9S21zIuWCuhr4rcReZBZml1oe4hpimJPPpQRuPNvK+KsQNSEIuhBCi3nCycuLptk8zofkElh1fxjeR33Am6wwzds5gQcQCJreczNAmQ6UbpRCi3lkXvY6Zu2aah/9EHotk8bHFzOg6A38Hf1MxtsQdHE87Xmo/B0sHunl3o4dPD7p7d6eBbQONnoEQtZMk5EIIIeodB0sHpraeykOhD7EiagWLji4i4VICb/39Fp9HfM7klpMZ0XQEVnorrUMVQogqF5cZx8xdM0sVXSuZvWLmzplltm/h1sI8JVlL95Yy7EeIOyB/PUIIIeotW4Mtk1pOYkzIGFafWM3CIwtJzknm3d3v8sXhL3ikxSOMajqq2udvF0KI6lBsLObspbN8tO8jVFWtcDsrvRV9/PrQw6cH3by74WbjVo1RClG3SUIuhBCi3rOxsOHh5g8zutlo1kWv46sjX5GUncT/9v6Pr498zcPNH2ZsyFjsDHZahyqEELes0FhIfGY8MRkxxKTHcCr9FDEZMZzOOE2BseC6+yoo3O17N7N7za6maIWoXyQhF0IIIS6z0lsxJmQMI4JHsDFmI18e/pKzl87y8f6PWXhkIQ81f4jxoeNxtHTUOlQhhCijoLiA05mnzQl3SfIdlxlHkVpU7j7WemvsDHak5aWVqpBeQqfo8LH3qerQhai3JCEXQgghrmHQGxjRdARDmgxhU+wmvoj4gtOZp5l7cC7fHP2GcaHjeDj0YZytnbUOVQhRD+UV5XE68zQx6ZeT7oxTxKTHEJ8Vbx77fS0bCxuCnIJo7NyYIOcgmjg3obFTY7ztvYnPiueB9Q+U221dRWV48PCqfkpC1FuSkAshhBAVsNBZ8EDQA9wXeB+/xv3K5xGfczL9JJ9HfM63kd/yYMiDTGw+UcZTCiGqRE5hDrEZsWW6mp/NOltuazaAg8HBnHQ3djLdBjkF4WnniaIo5e7j7+jPrG6zeGPnG+Yq6zpFh4rKrG6z8HP0q8qnKUS9Jgm5EEIIcQN6nZ6BgQMZEDCA38/8zoJDC4i6GMXCIwtZdmwZI5uOZFLLSXjYemgdqhCiFsoqyOJUxilTwp0eQ0yGKflOzE6scB8nKyeCnIJMCfdVyXcDmwYVJt7XM7TJUNp7tGd11Gr2Ru+lY3BHRjYbKcm4EFVMEnIhhBDiJukUHf38+9HXry9/nv2TBYcWcCT1CN8e+5aVUSsZFjyMKS2n4GXvpXWoQogaKCM/w9y9/Oqu5sk5yRXu42rtWqa1u7FzY9ys3W4r8b4eP0c/nm37LJsSNzGo7SAMBkOlHl8IUZYk5EIIIcQtUhSFu3zvonej3uxM3MmCiAUcOH+AFVErWBO9hiFBQ5jSagq+Dr5ahyqE0EBaXlqpLuYltym5KRXu42HjUaareWOnxrhYu1Rj5EKI6iYJuRBCCHGbFEWhu093unl3Y0/SHuZHzGdP0h7WRK9h/cn13Nf4Ph5r9RgBTgFahyqEqGSqqpKSm1JmfPep9FNczL9Y4X5edl6mxNvpSlfzxs6NZfYGIeopSciFEEKIO6QoCp28OtHJqxP7k/ezIGIBOxN3sjFmIz+c+oEBAQOY2moqTVyaaB2qEOIWqapKck5y6anELnc1zyzIrHA/H3ufUl3MS27tDHbVGL0QoqaThFwIIYSoRO0btmdBvwUcvnCYBREL+PPsn/wU+xM/x/5MX/++PN76cZq5NtM6TCHENYyqkXPZ58rtap5dmF3uPjpFh6+Db6ku5kHOQQQ4BmBrsK3mZyCEqI0kIRdCCCGqQKsGrfisz2ccSz3G5xGf89uZ39gct5nNcZu5y/cunmj9BC3cW2gdphC1VlxmHOui15F4KRFve2+GBQ/D39H/hvsVG4tJuJRQqpp5TEYMsRmx5BbllruPhWKBn6NfmfHdAU4BWOmtKvupCSHqEUnIhRBCiCoU6hbKh3d/SPTFaL6I+IKfT//M1vitbI3fSnef7jzR+gnaerTVOEohapd10euYuWsmCgoqKgoKC48uZFa3WQxtMhSAImMR8VnxZbqax2bEkl+cX+5xLXQWBDgGlOlq7u/oj0EvFceFEJVPEnIhhBCiGgS7BPNe7/d4ou0TfBnxJZtiN7EjYQc7EnbQ2bMzj7d5nDDPMK3DFKLGi8uMY+aumRhVY5l1M3bM4JfTv5CUncTpzNMUGYvKPYaV3opAp8AyU4n5OvhioZOvx0KI6iPvOEIIIUQ1auzUmHd6vsOTbZ7kyyNfsvHkRsKTwglPCqe9R3seb/M4Xb26Vvr8wkLUBblFucw/NB9VVctdr6KyPWG7+b6NhU2Z8d1BTkF423uj1+mrK2whhKiQJORCCCGEBnwdfZnVbRaPt36cr498zdrotew/v5/HNz9Oa/fWPN7mcXr69JTEXNRbeUV5RF2MIjI1kqMpR4lMiyQmPabclvGrNXNpxnPtnyPIOQgvOy90iq6aIhZCiFsnCbmok2JTslm5N56zF3Np5GLD6I6+BLrLNCNCiJrH296b17q8xmOtHmPR0UWsOrGKiJQInv79aUJdQ3m89ePc7Xe3JBWiTssvzudE2gmOph41JeCpR4lJj6FYLS6zrbXemrzivHKPo1f09PDpQa9Gvao6ZCGEqBSSkIs6Z+XeeF5aE4GiKKiqiqIoLPgzhtkjWjOqo6/W4QkByEUjUVZDu4ZM7zSdKa2m8M3Rb1getZxjaceYtnUaTZyb8Hjrx+nn30+62Ypar6C4gBMXT5gT78jUSE5ePEmRWna8t6u1Ky3cWtDcrbn5NrcolyEbhpTbUq6iMjx4eHU8DSGEqBSSkIs6JTYlm5fWRGBUgZLxZZdvp6+JICzAlQBJeoTG5KKRuB53G3ee7/g8k1pOYknkEpYeX8rJ9JO8sO0FAp0CeazVYwwMHCiFp0StUFhcyIn0E6Yu56mRRKZGEp0eXW6xNRcrF5q7Ny+VgDe0bVjusI1Z3Wbxxs43SlVZV1GZ1W0Wfo5+1fHUhBCiUsineQ0Qm5LN8vA49pzQEWkRzZjO/vWupUxVVfKLjOQXGskrKi51m19UTN41t/lFRvIKS9/mFxr5+1QqFdR5QUFhxd54pt8bUr1PToiryEUjcbNcrF14rv1zTGwxkaXHlrLk2BJiM2J5ZfsrzDs0j8daPcbgoMEYdDIVk6gZCosLOZl+slS38xMXT5SbfDtbOZdp+fa087zpmglDmwylvUd71kavNc9DPjx4uCTjQohaRxJyjZlbylAwqgqHtp/mi+2xmrWUFRUbySsykl9YQdJbbiJ85bZk3+smzuUsKyi6foGWylCsqizZFUdiei6hXo6EejnS3MuRBg5WVf7Yov7KyivkRHIWkeeyOH4uk9+PnTcl4+VQFLloJMpysnLiybZP8nDzh1ketZxvjn5DfFY8M3bOYP6h+UxpNYWhTYZiqbfUOlRRjxQaC4lJj7lScC01kqiLURQaC8ts62jpeCX5djfdett533HBQj9HP6Z1mHZHxxBCCK1JQq6hUi1lqIBC8VUtZSFejjR0sLqcxJbTSnx1a7I5wb25RLjcJLrISHFFmUI10ilgbdBjbdBjZaHDykJ35XfzMj3WhtK3VgYde0+nsTfuYoWt5Jfyi9hwMJENBxPNy9ztrQj1cqC5tylBD/VypLG7HRZ6KaAkbp7RqBKXlsPxc5kcS8ri2LlMjidlEp+We9PHUFWVsxdvfntRv9hb2vNoq0cZFzKOVSdWsfDIQhKzE3nr77dYELGAyS0nMyJ4BNYW1lqHKuqYImPRleT7cut3VFoUBcaCMts6WDqUavVu4dYCH3sfmS1ACCEqIAm5hlbujTd9QJWTPRpVuP/T7eXsVX0s9TqsSiW9FSfC1iW3lxPmq5NpqzLLrjnGNesNd5AIx6Zk0+f9rZSXj+sUeGdYK1KzCzh2LpPIc5nEpmSTcimfv6Lz+Ss65cpzt9DRrKEDoV4O5pb0EC9HnGyka6iAjNxCoq5Kuo+dyyIqKYvcwrLVgAEaOloR6uVIiKcjJ89n8cfxilvJG7nYVGHkoi6wNdgyscVEHmz2IGui1/D1ka85n3Oe/+7+L19EfMEjLR5hdLPR2BpstQ5V1EJFxiJOZZwqNdVYVFoU+cX5ZbZ1MJiS7+ZuzU1jv11b0MihkSTfQghxCyQh19DZi7moFTXlXqYoXEl2r7ktr+W4wqTXovR6a0PZY1x9a6nXodPVvg/UQHc7Zo9ozfRrCmapqlruMIDcgmKikrOITMzk2LkrP9kFxRxOyOBwQkap7X2cbWjuXdLd3YHmXk40crGpla+VuLFio0psSjbHkzI5fq4kAc8iIb38VmwrCx1NGzoQ4mm6kBPi5UCIpyOudle6EsemZPPH8fPl7m9UTccQ4mZYW1gzPnQ8o5qOYv3J9Xx1+CsSsxN5f9/7fHXkKya2mMiYZmOwt7TXOlRRQxUbi4nNiC015jsqLarcKcXsDfaEuoWWavlu5NBIpuMTQog7JAm5hhq52FTYQq5XYErPxrw8MESuNN+iUR19CQtwZcVVU0o92NG33EJZNpZ62vo609bX2bzMaFSJv5hjakVPzCTyciKWkJ5r/tkcmWze3t7KwpyAlSTrzRo6YGMpUxPVJhezCzh+Vav38SRTq3d+BfUNfJxtCPF0MCfdoV6OBLjZ3nCoQ7kXja4arvLRb9HoFYVn7mkif/vipljqLRndbDTDgofxQ8wPfHH4C+Kz4vl4/8csPLKQh0IfYlzoOJysnLQOVWio2FhMXGZcqeT7eNpxcovKXmC0tbAtk3z7OfpJ8i2EEFVAEnINje7oy4I/Y8pdpwLjOvnJF/LbFOBud9uFsXQ6BX83O/zd7Li3pZd5eUZOIceSMs2J+rGkTE4kX+JSfhF74y6yN+7ilWMopsTLXDzu8vh0Dwcr+TfVWFGxkVMp2ebW7mPnTK3fSZllW4QAbAx6mno60Pxy4h3iabp1sr394QvlXTQa3aER6w4m8snv0by/+QSp2QXMGNxcel+Im2bQGRgWPIz7g+7np9if+OLwF8RmxDL30Fy+ifyGsSFjebj5w7hYu2gdqqhiRtXI6czTpQquHUs7Vm7ybWNhQ6hraKmCawGOAZJ8CyFENZGEXEPXtpQZjUZ0ioIKzB7RWqY+qmGcbA10aexGl8Zu5mWFxUZiU7LNXd4jL3d5T7lUQMyFbGIuZPNDxDnz9q52lpcLx11pUQ9qYH9H4+ZFxVIv5ZuT7mPnsjielEl08iUKistv9fZ1tTG1dpu7nDvi52qLvgqS4vIuGj3frymutgZmfh/Jop2nSc8pYM6oNnJ+iFtiobPg/qD7GRQ4iM1nNrPg0AJOpp/ki8Nf8O2xb3mw2YNMbDERdxt3rUMVlcCoGjmTeaZMy3d2YXaZbW0sbAhxDSnV8u3v6I9eJz26hBBCK5KQa6ykpWxZeBx7ImMIa96YsZ39JRmvJQx605jhpg0dGNrOx7z8fFYex86VHpt+KiWbtOwCtp9MYfvJqwrI6XU08bC/qsu7A829HHG2lSmMblZBkZGYC5fMY70jL7d+X8gqW4QIwM5ST4jX5dZuL1MC3szTAQdr7Yv2PdI9EGdbS/696hDrDyaSkVvI3PEdZAiEuGV6nZ57A+6lv39/tpzZwoKIBRxLO8aio4tYdnwZI5uOZFKLSTS0a6h1qOImGVUj8VnxpQquHUs9xqXCS2W2tdZbE+IacqXl27U5gU6BknwLIUQNIwl5DRDgbse/+wezqSiaQf2DMRi0TwrEnfFwsMbDwZreTRuYl+UVFnMi+UprbUmynpVfROTl1vU1+68cw9vJulSX91AvR/xdbet1F2ZVVblwKd/U2n1Vl/OYC5coLC5bi0FRwN/V1jzGO8TLgVBPxxpfiG9oOx+cbA08+e0+tkRd4KGvwvl6YtgddZMX9ZdO0dHHvw/3+N3DXwl/seDQAiJSIvju2HesjFrJ8ODhTG45GW97b61DrdPiMuNYE7WGvdl7iTkYw4hmI/B39K9we1VVOZt1lqNpR4lMMbV8H0s9RlZhVpltrfRWNHNtVqrlO9ApEAudfM0TQoiaTt6phagm1gY9rRs507qRs3lZybzTkVdVeI88Z5q7OjEjj8SMPH6/qiK3raXeXECuJFEP8XTA1rLu/SnnFRZz8vwljidlXZ7b29T6nZpddt5bAAdrC0I9Ha8qsmbquWBnVTtfm7ubefDdo52ZtHAP++IuMnrBLr6Z0omGjjLHtLg9iqLQq1Evevr0ZNe5XSw4tID95/ezImoFa06s4YEmD/Boy0fxdTTNRhGXGce66HUkXkrE296bYcHDrptAioqti17HzF0zUVAwqkYij0Wy+NhiZnWbxdAmQ1FVlYRLCaW6nUemRpJVUDb5ttRZ0sy1Wam5voOcgyT5FkKIWkrTd+/i4mJmzpzJt99+S1JSEt7e3jzyyCO89tpr5sJXjzzyCIsXLy6134ABA/j555+1CFmISqUoCr6utvi62jKghad5eVZeIceTSnd5P56URU5BMfvPpLP/TPpVx4AANztzV/eSZN3LybpWFJBTVZXkzHzT87ycdB9PyiTmQjbF5UzWrVNMvUpCL3c1D7mchPs429SK53srOvi7svKJrkz4ajdRyVmMnL+TJZM7y5AWcUcURaGbdze6eXdjT9IeFkQsIPxcOGuj17Lh5AYGBQ4i0CmQzw5+hoKCimkmgIVHF5oTSHHz4jLjmLlrJkb1Su2KYrUYgBk7ZrDmxBpiM2PJyM8os69BZ6CZS7NSBdeCnIMw6KS3jBC1QWxKNiuvLuDa0ZdA+QwX19A0IZ89ezbz5s1j8eLFtGjRgr179zJp0iScnJx47rnnzNvde++9LFy40HzfyspKi3CFqDYO1gbCAlwJC3A1LysqNnI6NZvIa8amn8/KJzYlm9iUbDYdTjJv72xrINTTsdTY9GAPByw1nOe6pNv+lXHepgsN6TmF5W7vZGMg9HKLd/PLXc6DPerXlHIhno6sebIbD30VTlxqDiPn72Lx5DBaeMsUVuLOhXmGEeYZxsHzB5kfMZ8dCTv4/tT3FW7/xs43aO/RHj9Hvwq3MapGitVi062x2Hy/vGVG1UiRWoTReOV+qXXGotLHu+oYZZZVsM8Nj2e8Jr5bjf2q5eU9r5TclFLJ+NVUVA5eOAiYivE1dWlaqtt5E+cmGPSSfAtRG63cG89LV09xqigs+DOG2SNaM6qjr9bhiRpE04R8586dDBkyhPvuuw+AgIAAli1bxu7du0ttZ2VlhaenZ3mHEKLesNDraOLhQBMPBx5oc2WsZ8qlfHNyXjI2/eSFS6TnFLLrVCq7TqVeOYZOoYmHfamW9FAvB9zsr3+RKzYlm+Xhcew5oSPSIpoxnf2ve4VXVVUSM/I4lmhKuo9d7nYem5JNOY3e6HUKQQ3szK3dJRcSGjrKNHEAvq62rH6iGxO/3k3kuUzGLPibLyd2pPNVFf+FuBNtPdoyv+98jqQcYfq26ZzJOlPudkbVyIiNI7C2sC6d8F5OkEtafsXNa+HWgte7vk6wczCWeinmKURdEJuSzUtrIkzfedTLX3wu305fE0FYgKv0dhNmmibk3bp14/PPP+fEiRM0bdqUQ4cOsX37dj744INS223duhUPDw9cXFy45557+M9//oObW/lfRPPz88nPv1JZOTMzE4DCwkIKC8tvhasJSmKryTGKmsnJSkeXAGe6BDibl+UXGTl5/pIpEb78c+xcFpl5Reb7HEgwb9/Qwery2HSHy/NsOxDgZprua/X+BF5dfxQFMKoKh7bH8sX2WN4Z2oIR7X3IKSjiRPIlopIvmY8dlXyJrLyicuN1sTWYq5qHeNrTrKEDTRrYYWUo2+pdVFT+MeojZ2sd307uwOPfHWTP6YtM+Ho3H49uTZ9QD61DqzHkffTONXNqRqhrKPFZ8aiUc/UMyCvOI68477aOr6CgV/ToFB06RWf+Xa/TX/n9mvV6XenlV6+3UCzKPZZ53/L20ZWzj3LNPrpy4ihn24pivPr3Dac28EvcL+W2kusVPZ0adqKpY1MwQqFRzl2hPXkvvXPLw+NQUKCc91GjCoM++YsW3qZivX6uNvi72uLvZvq9Jsz4UtPVlnP0ZuNTVFUt/xO3GhiNRl555RXee+899Ho9xcXFvP3227z88svmbZYvX46trS2BgYHExMTwyiuvYG9vz65du9Dry36BnzlzJrNmzSqzfOnSpdja2lbp8xGiJlNVuFgAidkKCTmQkK2QkKOQkld+C7RBp9LAGhJzAK7dxvS24WppOqZaZj3oFZWGNuBtq5p+7MDHVsXBYBr3Lm5PQTEsjtZx5KIOHSpjg4x08tDsbVzUQb/m/spf+X+Vm5ArKLSzbEcPqx4oKOjQmW4VHSX/mZcrSun7l7erb1KKU/g46+MKX89pDtNw00tvFyHqksUndBxIVcr9fnQjdham71/u1urlH2hw+dbOQr5D1SY5OTmMGzeOjIwMHB0dK9xO04R8+fLlvPDCC8yZM4cWLVpw8OBBpk2bxgcffMDEiRPL3efUqVMEBQXx22+/0adPnzLry2sh9/X1JSUl5bovhNYKCwvZvHkz/fr1k2nPRLW6lG9q4T52VUv6ieQscgvLH/NYngb2lpdbvB0IaWhPM08HGrvbaTpevS4rKjby6oZI1h5IBOCle5sypXuAtkHVAPI+WjnOZJ5h+A/DMVL2PUCHjrX3r8XPoeIx5KKsjac28ubfb6IopirrOkWHqqrM6DKDBxo/oHV4QpQi76V3bs4vJ/h8++ly1+kVeKCNFz2D3TmTlktcWg5nUnOIS8sh5VL5M8mUcLC2uNKifrkosL+b6b6HQ/0Z5ldbztHMzEzc3d1vmJBr2mX9hRde4KWXXmLMmDEAtGrViri4ON59990KE/LGjRvj7u7OyZMny03Iraysyi36ZjAYavQ/WInaEqeoO1wMBjrb29A56Mqc6cVGlbjUbF5YfYj9cenldlxVgC6N3fh0XDvcbzAGXVQugwHeH90WdwdrPt92iv/+fIKMvGJeHNCs3nwYX4+8j96ZILcgZnWfxRs73yhVZV1FZVa3WQS5BmkdYq0zotkIwrzCWB21mr3Re+kY3JGRzUZetzieEFqT99LbZ7zOZ7EK/KNvs3LHkF/KLzIl56nZnL58G3f5NjEjj6y8Io4mZnE0seyUiDYGPX6Xu74HuNvh72aLv6vp1tvZBr2u7n0/qOnn6M3GpmlCnpOTg05XugVNr9djNFbcMnf27FlSU1Px8vKq6vCEqLf0OoXGDezpFOjGwfiM8qcf0ym09XOWZFwjiqLwyqBQXO0s+e9Px5m3NYaL2QW8PaxVnfzQFdVraJOhtPdoz9roteZ5yIcHD5cE8g74OfrxbNtn2ZS4iUFtB9XoL5FCiNv396lUvt4eC5gaL3S6K1XWVVVl9ojWFRZ0s7eyoLm3aXaca+UVFhOflkNcag6nLyfqp1OzOZOWw9mLueQWFhOVnEVUctlk3aA3TbNrGqtuR4CbLf7udgS42eHjbCM9GjWmaUJ+//338/bbb+Pn50eLFi04cOAAH3zwAZMnTwbg0qVLzJo1ixEjRuDp6UlMTAwvvvgiTZo0YcCAAVqGLkS9MLqjLwv+jCl3naqqPCjTdmjuid5BONsYeGXdYZbviSc9p5CPxrTFupwieULcCj9HP6Z1mKZ1GEIIUWtcyMrnuWUHMKowvL0Pz97dhJX7zprnIX+wo+9tV1e3NugJbuhAcEOHMusKi40kXMw1J+inU0pa2bOJT8uloNjIqQvZnLqQDVwota9OAR8XGwLc7PBztSXAzc7cyu7naivfJ6qBpgn5p59+yuuvv85TTz3F+fPn8fb25vHHH2fGjBmAqbU8IiKCxYsXk56ejre3N/379+ett96SuciFqAaB7nbMHtGa6Zfn0TQajegUBRWue4VXVK8xnfxwtjXw3LKD/Hw0icmL9vD5hI7YW2n6Fi+EEELUG8VGlWkrDnA+K59gD3v+M7QltpYWTL83pMof26DXEeBuV+73smKjSlJmHnEpl7vBp2UTl3KllT23sJj4tFzi03LLPbano7UpQXezw8/tSsLu72YrFeEriabf1hwcHPjoo4/46KOPyl1vY2PDL7/8Ur1BCSFKGdXRl7AAV5aFx7EnMoaw5o0Z29lfkvEa5t6WXiyabGDqN/vYGZPK2M//ZtGksBvOMS+EEEKIO/fx79HsOJmKjUHPvIfaY2tZMy6K63UKPs42+Djb0K1J6XWqqnLhUr6p+3vK5fHqaabW9diUbLLyikjKzCMpM4/w2LQyx3azszQn6/5XJeoBbnY42xqkrs1NqhlnihCiRgtwt+Pf/YPZVBTNoP7BMvaxhuoW5M6yx7owceFuDidkMGr+LpY82hkfZxutQxNCCCHqrG0nLvDpH9EAvDu8FU08ynYrr4kURcHDwRoPB2vCAlxLrVNVlfScQnOCXtINvuR+yqUCUrNNP/vPpJc5tqO1hTlJv9Kqbhq/3uAOKsLHpmSzPDyOPSd0RFpEM6azP4G1vJFIEnIhhKhDWjVyYvUTXXn4q92cSslmxNydLJnSqdwxZ0IIIYS4M0kZeUxbcRBVhXGd/RjazkfrkCqFoii42FniYmdJW1/nMuuz8gqJS80xjVlPvdIN/kxaDucy8sjMK+JwQgaHEzLK7Gtj0JdqTS9J1P3cbPFyqrgi/Mq98by0JgIFBaOqcGj7ab7YHsvsEa0ZVYvrGklCLoQQdUzjBvasfrIrE77aTfT5S4xasIuFj4TRzs9F69CEEEKIOqOw2Mizy/aTll1Acy9HZgxurnVI1cbB2kBLHyda+jiVWZdXWHy5uFz2lYT9clX4hMsV4Y8nZXE8qWxFeEu9Dl9XmzJj1i10Ci+ticA08Y8KKBSrplmApq+JICzAtdYOp5SEXAgh6iAvJxtWPt6VSYv2cDA+nfFfhjP/oQ70atrgxjsLIYQQ4ob+92sUe05fxMHKgrnj20tF8susDXqaNnSgaTm98wqKjCSk515uVS/pAp9zuSJ8DgXFRmIuZBNzIfumH09RFFbsja+WAnpVQRJyIYSoo1zsLPnu0c488e0+/opOYcriPXz4YFsGt/bWOjQhhBCiVtscmcyCP08B8N5ImXnmZlla6Ah0tzON+25Wel2xUeVcRm6pudbjLt+eSM663DpelqqqnL1YfpX42kASciGEqMPsrCz4amIYz688yA8R53h22QEu5hTycBd/rUMTQgghaqX4tBz+tfIgAJO6BzCwlZe2AdURep1CIxdbGrnY0r2Je6l1s386xufbYs3d1K+mKAqNXGpvAVud1gEIIYSoWpYWOj4e046Hu/ijqvD6+iN88ns0ajkfakIIIYSoWH5RMc8s3U9mXhFtfZ15eWCo1iHVC6PD/FAp/3uLqqo8WIuLuklCLoQQ9YBep/DmkBY81ycYgA82n2DW95EYK+r/JYQQQogy3t10nENnM3C2NfB/49tjaSHpVHUIdLdj9ojW6BTTdxoFFb0COgVmj6jdQwaky7oQQtQTiqLwfL+muNoamPl9JIt2niY9p4A5o9pg0MsXCiGEEOJ6fow4x6KdpwH4YHQbfJxrbzfp2mhUR1/CAlxZFh7HnsgYwpo3Zmxn/1qdjIMk5EIIUe880j0QFztL/rXyEOsPJpKeW8i88R2wsZTqsEIIIUR5Tl24xPQ1EQA8eVcQ94Q01Dii+inA3Y5/9w9mU1E0g/oHYzAYtA7pjkmTiBBC1END2vrwxcSOWBt0bI26wENfhZORU6h1WEIIIUSNk1dYzFPf7edSfhGdAl35V7+mWock6hBJyIUQop66u5kH3z3aGUdrC/bFXWT0gl0kZ+ZpHZYQQghRo8zceJTjSVm421vy6dh2WMgwL1GJ5GwSQoh6rIO/Kyuf6IqHgxVRyVmMmLeT0ynZWoclhBBC1Ahr9p1l+Z54FAU+HtOOho7WWock6hhJyIUQop4L8XRkzZPdCHCz5ezFXEbO38mRhAytwxJCCCE0dSI5i9fWHwFgWp+mZebGFqIySEIuhBACX1dbVj3RjeZejqRcKmDs538TfipV67CEEEIITWTnF/HUd/vJLSymZ7A7z9zTROuQRB0lCbkQQggAGjhYsfzxLnQKdCUrv4gJX+9mc2Sy1mEJIYQQ1UpVVV5dd5iT5y/R0NGKDx9si16naB2WqKMkIRdCCGHmaG3gm8md6BvakPwiI098u49Ve+O1DksIIYSoNst2x7P+YCJ6ncJn49rjbm+ldUiiDpOEXAghRCnWBj3zH2rPyA6NKDaqvLA6gi+2ndI6LCGEEKLKHUnIYOb3RwF4cUAzwgJcNY5I1HWSkAshhCjDQq9jzsjWTO3VGIC3Nx1j9s/HUVVV48iEEEKIqpGZV8jTS/dTUGSkb6gHj/VsrHVIoh6QhFwIIUS5FEXhlUGhvDQwBIB5W2N4ee1hioqNGkcmhBBCVC5VVZm+OoK41Bx8nG3436g26GTcuKgGkpALIYS4rid6BzF7RCt0CizfE8/TS/eTV1isdVhCCCFEpVm44zQ/HUnCoFf4v/Htcba11DokUU9IQi6EEOKGHgzzY+74DljqdfxyNJlJC/eQlVeodVhCCCHEHdt/5iLvbDoGwKuDQmnr66xtQKJekYRcCCHETbm3pSeLJodhb2XBrlOpjPsinJRL+VqHJYQQQty2i9kFPLv0AEVGlftaeTGxW4DWIYl6RhJyUTelxsBvM2H1ZNNtaozWEQlRJ3QLcmfZY11ws7PkcEIGo+fv4uzFHK3DEkIIIW6Z0ajy/MqDJKTnEuBmy39HtEJRZNy4qF6SkIu658C38FlH2PEJHF1nuv2sIxz4TuvIhKgTWjVyYtUTXfFxtuFUSjYj5+0iOjlL67CEEEKIW7Jg2ym2RF3A0kLH3PEdcLA2aB2SqIckIRd1S2oMbHwWVCOoxaVvNz4jLeVCVJLGDexZ/WRXgj3sScrMY9SCXRw4c1HrsIQQQoibEn4qlf/9GgXAmw+0oLm3o8YRifrKQusAhKhUB5YAFXQ1Uo2mlnJLB7CwAgtrMFhf/t3mmmVX/ZjvX7WdwaaCZVbl76uv5VdcU2PQ7VtMh9hwdFv2QYeJ4BakdVRCY15ONqx8vCuTF+/hwJl0xn8ZzvyHOtCraQOtQxNCCCEqdCErn2eXHaDYqDK8nQ8PhvlqHZKoxyQhF3XLhROmFvGKqEbIz4DqrkOl6K8k8ObE/ToXAW52uwovFlx1YeBOx0Id+BY2PosOBW/ViLJrD+z6FB74DNqNr5zXR9RaLnaWfPdoZ574dj/bTlxgyuI9fDC6Lfe38dY6NCGEEKKMYqPKtBUHOJ+VT7CHPf8Z1lLGjQtNSUIu6o6YP+DUHxWvV/TQcRJ0fhKK8qAoH4pyTb8X5l2zLB8Kc0vfL7XdVdtfu13JNsVXZf1qMRRmm35yq/6lKEVvdXOJe3kJf8El+HseoKJwue9ByQWPjc+AXxdpKRfYWlrw5YSOPL/yID9EnOO55QdIzy3k4S7+WocmhBBClPLJ79HsOJmKjUHPvIfaY2sp6ZDQlpyBovYrLoQt78D2DwH1Ohuq0OWp6ksgjUZTUl7ZCX952117fNV4JY7i/MsXBzIq+QkqpiECfWdW8nFFbWRpoePjMe1wsbVkyd9xvL7+CGmXCniuTxNpeRBCCFEj/BV9gU/+iAbgneEtaeLhoHFEQkhCLmq79HhYMwXiw033O04Gz9bw4/OY2nPVK7cPfFa9rbk6HehsTC3RNtX3sKgqGIuuSuTLS9yvTfgruAgQ/Sukx1X0QJB+phqfmKjp9DqFN4e0wNXOko9/j+bD305wMaeAGYObo9NJUi6EEEI7SRl5TFt+EFWFsZ38GNaukdYhCQFIQi5qs+M/wvqnIC8drBzhgU+gxTDTusBeptbb9DPg7AftHq4/XasVxVRErjIKyf020zRtXHnj8lUVHOXDTJSmKAr/7NcUVztL3th4lEU7T3Mxp4D/jWqDQS8TewghhKh+hcVGnl22n9TsApp7OfLG/c21DkkIM0nIRe1TlA+bZ0D4fNN97/Yw8mtwDbyyjVuQdKWuDO0ehh0fV7BShfNHoKgALCyrNSxR803sFoCzrYF/rTzEhoOJZOQWMm98B2ws9VqHJoQQop75369R7Dl9EQcrC+aOb4+1QT6LRM0hzRWidkmNga/6XUnGuz4Dk38pnYyLyuMWZOrqr+hQFT1GFFRFDyimInknf4eVD5u6wgtxjSFtffhiYkesDTq2Rl3goa/CSc8p0DosIYQQ9chvkcks+PMUAO+NbE2Au53GEQlRmiTkovaIWAULesG5Q2DjCuNWwoC3pXW2qrUbD8/sxdj1GRKdO2Ps+gw8uw/GrzRVYj/xMyx7EAqytY5U1EB3N/Pgu0c742htwb64izy44G+SM+UCjhBCiKoXn5bDv1YdAmBS9wAGtvLSOCIhypKEXNR8BTmw4RlY+6hpGi7/7vDEdmg6QOvI6g+3IIx3v86+wKcw3v26qeW8SV8YvxoMdnBqK3w7EvIytY5U1EAd/F1Z9UQ3PBysiErOYsS8nZxOkQs4Qgghqk5BkZFnlu4nI7eQNr7OvDwwVOuQhCiXJOSiZkuOhC/uNhVoQ4He02HCRnDy0ToyARDYEyasBysnOLMTlgyF3ItaRyVqoGaeDqx5shsBbracvZjLyPk7OZJQ2VPxCSGEECbvbDrGobMZONkY+L9x7bC0kLRH1ExyZoqaSVVh3yJTMn7hONg3hAkb4O5XQC+1CGsU304wcaNpGEHCPlh8P2SnaB2VqIF8XW1Z9UQ3Wng7knKpgLGf/83fp1K1DksIIUQd82PEORbtPA3Ahw+2oZGLrbYBCXEdkpCLmicv0zS3+Pf/MM2LHdQHntgBjXtrHZmoiHdbeORHsPOApMOw6D7IStI6KlEDNXCwYtnULnQOdCUrv4gJX+9mc2Sy1mEJIYSoI2JTspm+JgKAJ+8K4p6QhhpHJMT1SUIuapbEA6bCbUfWmKp4951lGqds30DryMSNNGwOkzaBg7epV8PCgZAer3VUogZytDaweHIn+jVvSEGRkSe+3ceqvXKuCCGEuDN5hcU89d1+LuUX0SnAlX/1a6p1SELckCTkomZQVdg1F77sBxdjwckXJv8MPaaBTk7TWsM9GCb/BM5+kHbKlJSnndI6KlEDWRv0zBvfnpEdGlFsVHlhdQSfb4vROiwhhBC12Kzvj3LsXCZudpZ8Oq4dFnr5DilqPjlLhfZy0mD5OPjlZTAWQshgeOIv09hkUfu4BMCkn8GtCWTEw9cD4UKU1lGJGshCr2POyNZM7dUYgHc2Hee/Px1HVVWNIxNCCFHbrN1/lmW741EU+HhMOxo6WmsdkhA3RRJyoa24XTC/B0RtAr0lDJwDD34LNi5aRybuhJMPTPoJPJrDpSRYOMg0tlyIayiKwiuDQnlpYAgA8/+M4aU1hykqNmocmRBCiNriRHIWr647AsA/+gTTI9hd44iEuHmSkAttGIth2xxT8a/MBHANgkd/g85TQVG0jk5UBnsPmPgDeLWBnBRYNNhUhV2IcjzRO4jZI1qhU2DF3nieXrqfvMJircMSQghRw2XnF/HUd/vJLSymZ7A7z94TrHVIQtwSSchF9ctKhm+Hwx//AbUYWo2Gx/80JW6ibrFzM80b36gT5KXD4iGmXhFClOPBMD/mju+ApYWOX44mM2nhHrLyCrUOSwghRA2lqiqvrjvMyfOXaOhoxYcPtkWvk4YdUbtIQi6qV8wfML87nNoKBlsY8n8w/HOwctA6MlFVbJzh4XUQ0BMKskwXY05t1ToqUUPd29KTRZPCsLeyYNepVMZ+8Tcpl/K1DksIIUQNtHxPPOsPJqLXKXw6tj3u9lZahyTELZOEXFSP4iL4/U1YMhyyL5jGFk/dCu0eki7q9YGVPYxbaZpTvjAHvhsNJ37VOipRQ3ULcmf51C642VlyJCGT0fN3cfZijtZhCSGEqEGOJmbwxsajALwwoBmdAl01jkiI2yMJuah66fGmseJ/vQ+o0GESPPYHNGimdWSiOlnawthl0Ow+KM43VdaP3Kh1VKKGaunjxKonuuLjbMOplGxGztvFieQsrcMSQghRA2TmFfLUd/spKDLSJ8SDqT0bax2SELdNEnJRtY5vMlVRj/8brBxh5EK4/yMw2GgdmdCChRWMXgwthpumuFv1CESs0joqUUM1bmDPmie7EexhT1JmHqPm72L/mYtahyWEEEJDqqoyfXUEcak5+Djb8P7oNuhk3LioxSQhF1WjKB9+egmWjzUV8/JuB49vg5bDtY5MaE1vgBFfQtvxpqJ+ax+D/d9oHZWooTydrFn1RFfa+TmTkVvI+C/C+fPEBa3DEkIIoZFFO0/z05EkDHqF/xvfHmdbS61DEuKOSEIuKl9qDHzVD8Lnme53fQYm/wqugdrGJWoOnR4e+Aw6TgFU2PgshH+udVSihnK2teS7RzvTq2kDcguLeXTxHr4/lKh1WEIIIarZgTMXeWfTMQBeHRRKW19nbQMSohJIQi4q1+HVsKA3nDsENi4wdgUMeBss5OqluIZOB/e9b7pgA/DTC7D9I01DEjWXraUFX07oyP1tvCksVnlu+QGW7DqtdVhCCCGqSXpOAc8sPUBhscqgVp5M7BagdUhCVAoLrQMQdURBDvw8/UrXY79upm7JTj7axiVqNkWB/v8xTYG37T347Q0ozIW7XpLq+6IMSwsdHz/YFhdbA9/siuP1DUdJyy7kuT5NUOR8EaLeiU3JZuXeeM5ezKWRiw2jO/oS6G6ndViiChiNKv9aeYiE9FwC3Gz574jW8r4v6gxJyMWdO38MVk2CC8cABXq9AL2ng15OL3ETFAXueRUM1qap8f78r2lqtH5vSlIuytDpFGY90AIXW0s+/j2aD387wcWcAh7u4s/KPWfYc0JHpEU0Yzr7yxdzIeqwlXvjeWlNBIqioKoqiqKw4M8YZo9ozaiOvlqHJyrZgm2n+P34eSwtdMwd3wFHa4PWIQlRaSRjErdPVU0t4j9Nh6JcsG8Iwz+HxndpHZmojXr+y9RS/vNLsPMTU0v5wPdMXduFuIqiKPyzX1Nc7Sx5Y+NRFu08zaKdp9ErCkZV4dD203yxPVa+mAtRR8WmZPPSmgiMKqbvIly5nb4mgrAAVwLkglydEX4qlf/9GgXAmw+0oLm3o8YRCVG55JuuuD15mbBmCnz/nCkZD7oHntguybi4M12ehMEfAQrs+cJ0fhmLtY5K1FATuwXw6n2h5vvFqoqKQrGqYlRNX8xPp2RrGKEQoiqs3BtfYXdlRVFYsTe+miMSVSXlUj7PLjtAsVFleDsfHgyTi6yi7pGEXNy6xAOwoBccWQOKHvrOhPFrwN5D68hEXdBxEgybD4oODiyBdY9DcaHWUYkaKi27gIqmn5Uv5kLULflFxWyNOs+mw+coNqrlbqOqKmcv5lZzZKIqFBtVpi0/yPmsfII97PnPsJYyblzUSdJlXdw8VYXw+fDr62AsBCdfGPEV+HXWOjJR17QZAxZWsOZROLzK1H195EKp1i/KuN4Xb/liLkTtl5FTyJao82yOTObPExe4lF903e2NKlhIc1Od8Okf0Ww/mYKNQc/c8e2xtZS0RdRNcmaLm5OTBhuehqhNpvshg+GBT8HWVdu4RN3VYhhY2MDKh+H4D7B8HDy4BAw2WkcmapBGLjamFhO1bGuZUQVnGyn8I0RtE5+Ww+bIZH47lkx4bFqp1vAGDlZ0DnTlx8PnyvuzB2DdgUQKilSm3xuCn5ttNUUtKtNf0Rf4+PdoAN4Z3pLghg4aRyRE1ZGEXNzYmb9h9RTIPAt6S+j/NnR6TCpgi6rX7F4YtwKWjYOTm+G7UTB2OVjZax2ZqCFGd/RlwZ8xFa5fs/8szb0dGRPmK10dhaihVFXlSEImmyOT+DUymeNJWaXWN21oT7/mDenX3JPWPk7odAq998Yz/Zoq66qqEhbgyp7Tafx4+BybI5OZ1D2Ap+5ugpNcnKs1kjLymLb8IKoKYzv5MaxdI61DEqJKSUIuKmY0wo4P4Y+3QS0G18YwahF4tdE6MlGfBN0DD681JeOn/4JvR8D4lWDtpHVkogYIdLdj9ojW5i/mRqMRnaKgYmo9P5OWy8trD/NDRCL/Hd4aX1dpLROiJigoMrLrVCq/XW4JP5eRZ16nUyAswPVyEt4Qf7eyFdNHdfQlLMCVFVfNQ/5gR18C3O04di6Tt388xvaTKSzYdopV+87yz77BjO3kh4Ve+rPXZEXFRp5dtp/U7AKaeznyxv3NtQ5JiConCbko36XzsHYqnNpiut9qNAz+AKyky5DQgH83mLABvh0O8X/DN0PgobUyZEIAV76YLwuPY09kDGHNGzO2sz++rrYs3BHL/36NYsfJVPp/uI3p9zZjQtcAdBVVghNCVJmM3EK2Rp3n18hk/owqPR7c1lJPr+AG9GvekHtCPHCxu3HNkAB3O6bfG1JmeaiXI0umdGJr1AX+82MkMReyeX3DURbviuPVQaHc1ayB9Jipof736wn2nL6IvZUFc8e3x9qg1zokIaqcJOSirJgtpmQ8+7xpXuhBc6DteOmiLrTVqCNM/AGWDDVV+l802JSk2zfQOjJRAwS42/Hv/sFsKopmUP9gDAZT99RHezamb2hDpq+JIDw2jZnfR/Lj4XPMHtGaxg1k6IMQVe3sRdN48M2RyeyOTaPomvHgfUM96Ne8Id2C3Cs1+VIUhbtDPOgR7M7y3Wf48LdoTp6/xKRFe+gZ7M6r94US4inzWdckvx9LZv7lIUhzRraWueRFvSEJubiiuAi2vgt/vQ+o4NHcVNnao+zVZyE04dUaHtkE3zwA54/CokGmpNzRW+vIRA0W4G7Hsse68F14HO/+dJw9py8y8OO/+Ff/pkzp0Ri9tJYLUWlUVeVoYia/Xk7Cj53LLLU+2MPe3BW9TSPnKu+tYtDreLhrAA+09WHulpMs3HGav6JTGPTxXzwY5ss/+zXFw8G6SmMQNxaflsPzKw8BMKl7AANbeWkckRDVRxJyYZJx1lS4Lf5v0/0Oj8C9/5WK1qLm8QiBST/B4gcg5QQsHAgTNoKLv9aRiRpMp1N4uGsAdzXz4JV1h/krOoV3Nh3nx8NJzBnZmqZSwVeI21ZQZOTvU6n8diyZ3yKTSbxmPHhH/yvjwbVq9XSyMfDyoFDGd/Zn9s/H+fHwOZbtjmfjwUSeursJU3oESvdojRQUGXlm6X4ycgtp4+vMywNDtQ5JiGolCbmA45tgw1OQexEsHeCBj6HlCK2jEqJibkEw+SdYfD9cPA0LB8HEjablQlyHr6st30zuxKq9Z3nrx0gOxacz+JPtPNenCY/3DsIgBZ+EuCkl48E3Xx4PnnXVeHAbg55eTd3p19yTe0I8cL2J8eDVxc/Nlv8b355Jp9N464dIDp3NYM4vUXz3dxzTB4Zwf2tvqTFRzd7ZdIxDZzNwsjHwf+PaYSkTyYt6RhLy+qwoHza/AeHzTPe928HIr03V1IWo6Zz9TC3l3wy5qqV8A3jIlXVxfYqiMDrMl15NG/DqusP8fvw8//v1BJsOJzFnVGtaeEsFfyHKk5Cey2+Xu6L/fSq11Hhwd/sr48G7N6nc8eBVoWOAK+ue6s73EYnM/uk4iRl5/GP5Qb7ecZrX7wulY4AUDa0Omw6fY9HO0wB8MLoNjVxkJgxR/2h6Caq4uJjXX3+dwMBAbGxsCAoK4q233kJVr7zBq6rKjBkz8PLywsbGhr59+xIdHa1h1HVE2in4qv+VZLzL0zD5V0nGRe3i6G0aU96wJVxKhkX3wblDWkclaglPJ2u+nNiRjx5si7OtgchzmQz5bAcf/BpFflGx1uEJoTnT/OAZfLj5BIM+/ovu//2DNzYeZfvJFIqMKk087HnyriDWPNmN3a/04b8jWtMntGGNT8ZL6HQKQ9r68Me/7+KFAc2ws9RzKD6dkfN38fR3+zmTmqN1iHXa6ZRsXlwdAcATvYPoE9pQ44iE0IamLeSzZ89m3rx5LF68mBYtWrB3714mTZqEk5MTzz33HADvvfcen3zyCYsXLyYwMJDXX3+dAQMGEBkZibW1FOG4LYdXw/fToCALbFxg6DxoNlDrqIS4PfYNYOL3pinREg+YurE/tNZUlV2IG1AUhaHtfOjexJ0ZG47w05EkPvnjJD8fTeK9kW1o6+usdYhCVKuCIiO7Y9PYHJnEb8fOk5Cea16nU6CDv8vl8eCeBNaRKtjWBj1P392EUR0b8eHmE6zYE8+Ph8+xOTKZSd0DePqeJjhaG7QOs07JKyzmqe/2cym/iE4Brvy7f1OtQxJCM5om5Dt37mTIkCHcd999AAQEBLBs2TJ2794NmK7MfvTRR7z22msMGTIEgG+++YaGDRuyfv16xowZo1nstVJBDvw8HfZ/Y7rv1xVGfAlOjbSNS4g7Zetq6q7+3egr85SPWwkB3bWOTNQSDRysmPdQBzYdPseMDUc4kXyJ4XN38FjPxvyzX9Na0+InxO3IzCtka9QFNkcmszXqPFl5V8aDWxt0peYHd7O30jDSquXhYM27w1szoWsAb/94jO0nU1iw7RSr9p3ln32DGdvJDwupM1EpZn1/lMhzmbjZWfLpuHbyuop6TdOEvFu3bnz++eecOHGCpk2bcujQIbZv384HH3wAQGxsLElJSfTt29e8j5OTE507d2bXrl3lJuT5+fnk5+eb72dmmqbbKCwspLCwsIqf0e0ria3KYrwQhcW6KSgXjqOiYOz+PMZeL4DOAmrw6yJqjio/R++U3hbGrEC/6iF0p/9C/XYExaOWoDa+S+vIRDWpjHO0X4g7HXy78famKDZGnGPBtlP8cjSJd4e1oKO/S2WFKuqpmvQ+ei4jj9+Pn+e3YxfYfTqNwuIrwwXd7Cy5J6QBfUIa0D3IrdQFqZoQe1Vr4m7D1xPa8Wd0Cu/+dIJTKdm8vuEoi3ae5qV7m9I72B1FqbuF36r6PF1/MJFlu+NRFHh/VCtcbfT14rwSlacmvZdez83Gp6hXD9iuZkajkVdeeYX33nsPvV5PcXExb7/9Ni+//DJgakHv3r07iYmJeHldmY9w9OjRKIrCihUryhxz5syZzJo1q8zypUuXYmtbDwtFqCp+adtoFb8EC7WAPAsn9gU8QYpDC60jE6JK6IwFhMV+imfmIYoVC/YEPkuyUzutwxK10JE0hZWndGQUKiio9PRUGexnxEoay0UtpKqQkAOH0xSOXNRxNrt0QtnQRqWli0orVyP+9qbu6QKKjbDzvMJP8Tqyi0wvSjMnI0P9jXjXjR771SopB94/rKfAqHBvo2IG+mqWhghR5XJychg3bhwZGRk4OjpWuJ2mCfny5ct54YUXmDNnDi1atODgwYNMmzaNDz74gIkTJ95WQl5eC7mvry8pKSnXfSG0VlhYyObNm+nXrx8GQyWNU8rPQv/Tv9AdXQuAsfHdFN//f2DvUTnHF/VKlZyjVaW4AP26qeiifkDVWVA8dAFq6BCtoxJVrCrO0czcQv77ywlW7UsAoJGLDe8MbU7Xxm6VcnxRv1T3+2hhsZE9py/y2/EL/HH8PAnpV+YHVxRo7+tMn9AG9A3xqDPjwatKZm4h87bFsnhXHIXFKjoFRnXw4R/3NKGBQ93qxl9V52lOQRHD54cTcyGbbkGufD2hA3q58iNuQ235TpqZmYm7u/sNE/Jb7rIeEBDA5MmTeeSRR/Dz87ujIF944QVeeuklc9fzVq1aERcXx7vvvsvEiRPx9PQEIDk5uVRCnpycTNu2bcs9ppWVFVZWZd8YDQZDjf4HK1FpcSYehNWTTNXUFT3c8xq67tPQ6WSMjrgzteJvyWCA0Yth/RMoh1dhse4xUIugjdSdqA8q8xx1MxiYM6ot97fx4eW1hzl7MZcJC/cxtpMfrwwKwUEKPYnbUJXvo1mXx4P/diyZLcfPk3nNePCewQ3oF9qQe0I9cK/D48Erm5vBwGuDWzChayCzfz7Oj4fPsWJvAj9EJPHU3U2Y0iOwztWaqMzzVFVVZq09SsyFbBo6WvHJ2PZYW9Wc+elF7VTTv5PebGy3nJ1NmzaNtWvX0rhxY/r168fy5ctLtUjfipycnDIJol6vx2g0AhAYGIinpye///67eX1mZibh4eF07dr1th6zzlNV+Hs+fNXPlIw7+Zrmau75PEgyLuoTvQUMWwDtHgbVCOuegL0LtY5K1FK9mjbgl3/24uEu/gAs232G/h9uY0vUeY0jEwLOZeSyZNdpHv4qnPZvbebZZQfYcDCRzLwi3OwsGd2xEV9M6MiB1/vzxYSOjA7zlWT8Nvm52fJ/49uz+omutGnkRHZBMXN+iaLP+3+y4WACGnY8rdGW74ln7YEE9DqFT8e2l/NPiKvccgv5tGnTmDZtGvv372fRokU8++yzPPXUU4wbN47JkyfTvn37mz7W/fffz9tvv42fnx8tWrTgwIEDfPDBB0yePBkwTUczbdo0/vOf/xAcHGye9szb25uhQ4feauh1X04abHgGon403W92Hwz5zFSBWoj6SKeH+z8Bgy3sXgA/TIPCXOj6lNaRiVrI3sqCt4a2ZFArL6avieBMWg6TFu5hRPtGvD44FGdbae0R1UNVVY6dy2JzZDKbjyVxJCGz1PrGDexMU5OFNqSdn4t0C64CHQNcWfdUd76PSGT2T8dJSM/lH8sPsnDHaV4fHEoHf/nuVeJoYgZvbDwKwAsDmtEpUF4bIa5221XW27dvT/v27Xn//feZO3cu06dPZ968ebRq1YrnnnuOSZMm3bAC5aeffsrrr7/OU089xfnz5/H29ubxxx9nxowZ5m1efPFFsrOzmTp1Kunp6fTo0YOff/5Z5iC/1plwWD0ZMs+C3hL6/wc6TTUNEhOiPtPpYOBsMFjDjo/hl5ehKBd6/kvryEQt1TXIjZ+n9eT9X0/w9Y5Y1uw/y7boC/xnaEsGtPDUOjxRRxUWG9kTm8avkcn8diyZsxevzA+uKNDer2R+8IYENbDXMNL6Q6dTGNLWhwEtPPlqeyxzt5zkYHw6I+bt4r7WXrx0bwi+rvWwoPBVMvMKefq7/RQUGekT4sHUno21DkmIGue2E/LCwkLWrVvHwoUL2bx5M126dGHKlCmcPXuWV155hd9++42lS5de9xgODg589NFHfPTRRxVuoygKb775Jm+++ebthlq3GY2w40P4421Qi8G1MYxcCN5ttY5MiJpDUaDvLDDYwdZ34Pc3TS3ld78qF63EbbG1tOD1wc0Z1MqLF1cfIuZCNo8v2cf9bbyZeX/zOj1Xs6g+WXmF/HnCND/4tePBrSwujwdv7sE9IQ3rXGGx2sTaoOfpu5swqmMjPtx8ghV74vkx4hybjyYzqUcAT9/dBMd6WG9CVVVeWhPB6dQcfJxteH90G3TSW0OIMm45Id+/fz8LFy5k2bJl6HQ6JkyYwIcffkhISIh5m2HDhhEWFlapgYpyXDoPa6fCqS2m+61GweAPwcpB27iEqIkUBe6abmop3zwDts0xJeX9/yNJubhtHfxd+PG5nnz8ezSfbzvF94cS2XEyhVkPtGBwa686PVexuDWxKdksD49jzwkdkRbRjOnsX25l86SMPDYfS2ZzZDJ/x6RSUGw0r3O1s6RPiAf9mjekZ3ADbCzrVhGx2s7DwZp3h7dmQtcA3v7xGNtPprDgz1Os2nuWf/ZrytgwXyz09aeez+Kdp9l0OAmDXuH/xreXYT1CVOCWE/KwsDD69evHvHnzGDp0aLnV4wIDA82V00UVidliSsazz4OFDQyaA+0eksRCiBvp/g/T38xPL8Cuz6AwBwa9L0UPxW2zNuiZfm8IA1t68uLqCI4nZfHssgP8EJHIW0Na4uEoQ6zqu5V743lpTQQKCkZV4dD203yxPZbZI1ozskMjjidl8VtkMpuPJRNxNqPUvoHuduau6O1lPHitEOrlyJIpndgadYH//BhJzIVsXl9/hMU7T/PqfaHc1bRBnb9Yd+DMRd7edAyAVweF0tbXWduAhKjBbjkhP3XqFP7+/tfdxs7OjoULpZpxlSgugq3vwl/vAyp4NDd1UfcIueGuQojLOk8Fgw1sfBb2fg2FeaYCiDppbRK3r3UjZzY+04O5W0/y2R8n+eVoMn+fSmPG4OYMb+9T57+Ai/LFpmTz0poIjCqACigUX67E/eLqCN7fHEVSxpXZahQF2vk606+5J/2aN6SJh4wHr40UReHuEA96BLuzfPcZPth8gpPnLzFp4R56Brvz6n2hhHhWPC9xbZaeU8AzSw9QWKwyqJUnE7sFaB2SEDXaLSfk58+fJykpic6dO5daHh4ejl6vp2PHjpUWnLhGxllY8yic2WW6334i3PtfsKzfBUOEuC3tHzYl5WunwqGlUJQHwz8Hff0b5ycqj6WFjml9mzKghScvrD7EkYRM/rXqEN9HJPLOsFZ4O9toHaKoZiv3xpsuxpQzHZYKJGXkY2Who0cTd/o1b0ifUBkPXpcY9Doe7hrAA219mLvlJAt3nOav6BQGffwXD4b58Xy/pnXq39toVPnXykMkpOcS4GbLf0e0louRQtzALffRfPrpp4mPjy+zPCEhgaeffrpSghLliPoJ5vcwJeOWDjDiK3jgE0nGhbgTrUbCqEWgM8DRtbByIhTl33A3IW4k1MuR9U9158V7m2FpoWNr1AX6f7iNpeFnZJ7iekRVVY4kZGA0VvxvHhbgwoEZ/fjqkTDGdPKrU8mZuMLJxsDLg0LZ/HwvBrXyxKjCst1nuGvOFv5vy0nyCou1DrFSfP7XKX4/fh5LCx3/N759vSxmJ8StuuWEPDIysty5xtu1a0dkZGSlBCWuUlQAP78My8ZA7kXwaguP/2lKJIQQd675AzBmKVhYQ9SPpr+1ghytoxJ1gIVex1N3NWHTcz1o5+fMpfwiXll3mPFfhhOfJudYXXYmNYdPfo+mz/t/8ld0ChWl43qdQscAV2wtb3vSG1HL+LvZMXd8B1Y90ZU2jZzILihmzi9R9Hn/TzYcTKjVF+x2x6Yx55coAGY90IIW3k4aRyRE7XDLCbmVlRXJyclllp87dw4LC/lAqVRpp+Dr/vD3XNP9Lk/BlF/BLUjbuISoa5r2h3ErwWALMX/A0tGQn6V1VKKOaOLhwOonuvH64OZYG3TsjEml/4fbWLQj9rotp6J2Sc8p4Nu/4xg5bye95mzhg80nOJWSjaVFxd11VVXlwY6+1RilqCnCAlxZ91R3PnqwLV5O1iSk5/KP5QcZNncn++LStA7vlqVcyufZZfspNqoMb+fDmDA5r4W4WbeckPfv35+XX36ZjIwrVUDT09N55ZVX6NevX6UGV2+kxqDb8hYdYuei2/IWpMbAkTUwvxckHgAbFxi7HO59FyykK5sQVaJxb3h4HVg5wum/YMkwyE3XOipRR+h1ClN6BPLzP3rROdCV3MJiZn4fyYOf7+LUhUtahyduU35RMT8fOcfUb/YS9vZvvLb+CHvjLqJToGewO++PasP+1/szZ2RrdIrpPFBQ0SugU2D2iNYElDP1magfdDqFoe18+ONfd/Hv/k2xtdRzMD6dEfN28fTS/bWmJ02xUWXa8oMkZ+YT7GHPf4a1lHHjQtyCW27S/t///kevXr3w9/enXbt2ABw8eJCGDRuyZMmSSg+wzjvwLWx8Fh0K3qoRZdce2PkJlHRw8+sKI74Ep0aahilEveDXBSZsMCXjZ/fA4vvh4fVg56Z1ZKKOCHC3Y9ljXfhu9xn+u+kYe05fZODHf/Gv/k2Z0qOxTGlVCxiNKvvOXGTt/gR+jEgkM6/IvC7Uy5Hh7Xx4oK03Da+a7m5UR1/CAlxZFh7HnsgYwpo3Zmxnf0nGBQA2lnqeuSeY0WG+fPDrCVbsjefHiHNsPprMpB4BPH13kxo9FvvTP6LZfjIFG4OeuePbyxAMIW7RLf/F+Pj4EBERwXfffcehQ4ewsbFh0qRJjB07ttw5ycV1pMaYpl1SjSiAAqBeVdQj7FG4dzbo5Y1NiGrj0x4e+RG+GQJJEbDoPlOS7tBQ68hEHaHTKTzcxZ+7mzXg5bWH+Ss6hXc2HefHw0nMGdmapg0dtA5RlCPmwiXWH0hg3YEEzl7MNS/3dLRmSDtvhrXzue40VgHudvy7fzCbiqIZ1D9YvjOJMjwcrPnviNZM6BrA25si2XEylQV/nmLV3rP8s19Txob5YqG/5c6tVWp7dAof/x4NwDvDWxIs719C3LLbyvTs7OyYOnVqZcdS/xxYwuU0vCxFB1YOkowLoQXPljDpJ/jmAbhwDBYOhIkbpaeKqFSNXGz5ZnInVu09y1s/RnIoPp37PvmL5+4J5om7gjDUsC/e9VHKpXy+P5TIugMJRJy9MlTP3sqCe1t6MrydD50bu0nPBlGpmns78u2UzmyJOs/bPx4j5kI2r68/wjc7T/PKfaHc3cxD6xABSM7M4x/LD6CqMLaTL8PayWekELfjtrO9yMhIzpw5Q0FBQanlDzzwwB0HVW+kn4EKa6+WrBdCaKJBU5i0CRYPgbSYy0n59+ASoHVkog5RFIXRYb70atqAV9cd5vfj53l/8wl+OpLEnFGtpUqxBnILitl8LJl1+8+yLTqF4suF9/Q6hd5NGzC0nQ/9QhtiY6nXOFJRlymKwj0hDekZ3IBlu8/w4eYTRJ+/xKSFe0zvF4NCaeapXWt0UbGRZ5ceIDW7gOZejrxxfwvNYhGitrvlhPzUqVMMGzaMw4cPoyiKeXqGkuINxcV1Yx7FauHsR4Ut5CiX1wshNOPa2JSUf/PA5VkPLreUuwdrHZmoYzydrPlyYkc2HExk5vdHiTyXyZDPdvDkXUE8c08TrCwk+atKxUaVv0+lsu5AAj8fSeJS/pVx4W0aOTGsnQ+D23jjbi+FVUX1Muh1TOgawJC2PvzflpMs3BHLthMX2B59gTGd/Phn36aazF3/v19PsPt0GvZWFswd3x5rg7xHCXG7brk/3D/+8Q8CAwM5f/48tra2HD16lG3bttGxY0e2bt1aBSHWYe0epuIWcvXyeiGEppx9Td3XG4RAVqKppTz5qNZRiTpIUUwVlzf/szeDWnlSZFT59I+T3P/pdg7Gp2sdXp10PCmTdzcdo/t//2D8l+Gs3neWS/lFNHKx4dl7mvD7v3qz4ZkePNI9UJJxoSknGwOvDArlt+dN7w9GFZaGn+Hu/21l7taT5BVWX4PY78eSmf9nDADvjZSZAoS4U7fcQr5r1y7++OMP3N3d0el06HQ6evTowbvvvstzzz3HgQMHqiLOusktCB74DDY+g4qCqhpRFB0Kqmm5zDcuRM3g4Gkq9LZkKCQdNhV6e3g9eLfVODBRFzVwsGLu+A5sOnyOGRuOcCL5EsPn7uCxno35Z7+m0hJ1h5Iy8th4KIG1+xM4npRlXu5obcHgNqbibB39XWTaJlEj+bvZMXd8B/acTuOtHyKJOJvBez9H8d3fZ5g+MIT7W3tV6bl79mIOz688BMAj3QIY1Mqryh5LiPrilhPy4uJiHBxMY1bc3d1JTEykWbNm+Pv7ExUVVekB1nntxoNfF4z7FnMuMhyv5p3Rd5goybgQNY2du2kM+bcjIWEvLH4AHloNvp20jkzUUYNaedG1sRuzvj/K+oOJLNh2il8jk3lvZGvCAly1Dq9WuZRfxM9Hklh/IIEdMSlcHm2HQa9wT4gHw9o14u6QBjI0QNQaYQGurH+qOxsPJTL75+MkpOfy3LIDLNwRy2v3NaeDv0ulP2ZBkZGnlx4gI7eQNr7OvDIotNIfQ4j66JYT8pYtW3Lo0CECAwPp3Lkz7733HpaWlnz++ec0bty4KmKs+9yCMN79OvtyNzHo7kHoZSoUIWomGxeYsB6WPghxO+CboTBuBQT21DoyUUe52Fny0Zh2DG7tzavrDxObks3oBbuY2DWAFwY0w85KZuKoSFGxkb9OprBufwK/RiaRV2g0rwsLcGFoOx/ua+WFs62lhlEKcft0OtMwlwEtPPlq+ynmbo3hwJl0RszbyeDWXky/NwRfV9tKe7x3fzrGofh0nGwM/N+4dlhayEwQQlSGW/4kf+2118jOzgbgzTffZPDgwfTs2RM3NzdWrFhR6QEKIUSNYuUA41fD8nFwagt8NxIe/A6C+2odmajD+jZvSFigK+/8eIwVe+NZtPM0vx9PZvbw1nRr4q51eDWGqqocSchk7YGzfH8okZRLV2aCaexux9B2Pgxt64OfW+UlKUJozcZSzzP3BDO6oy8fbD7Bir3x/BBxjl8jk5ncPZCn7g7C0frOGnt+OnyOhTtOA/DB6DY0cpG/ISEqyy0n5AMGDDD/3qRJE44fP05aWhouLjLeSghRT1jawtjlsOoROPETLBsDoxZB6GCtIxN1mJONgdkjW3Nfay9eXnuY+LRcxn0ZzthOfrw8KOSOv3DXZvFpOWw8lMja/WeJuZBtXu5mZ8n9bbwZ2s6HNo2c5HuKqNM8HK3574jWTOgawNubItlxMpX5f8awam88/+zXlDFhvljob71V+3RKNi+ujgDgid5B9AltWNmhC1Gv3dJfZWFhIRYWFhw5cqTUcldXV/mQE0LULwZrGP0NNB8KxkJYOQGOrNE6KlEP9GragF/+2YuHu/gDsGz3GQZ8uI0tUec1jqx6ZeQWsmz3GUbP30XP97Yw55coYi5kY2WhY3BrL75+pCN/v9KHmQ+0oK2vs3xPEfVGc29Hvp3Sma8f6UjjBnakZhfw2vojDPz4L7be4vtEXmExT323n6z8IjoFuPLv/k2rKGoh6q9baiE3GAz4+fnJXONCCAFgYQkjvgILa4hYDmsehcI8U7FGIaqQvZUFbw1tyX2tvZi+JoK41BwmLdzD8PY+zBjcvM6Oiy4oMrI16jzrDiTw+7HzFBSbxoUrCnRt7MbQdj4MbOmJQz3uLSAEmKZRvCekIT2DG7A0/Awf/XaC6POXeGThHno1bcCrg0Jp5ulww+PM+j6SyHOZuNlZ8snYdrfVwi6EuL5b7rL+6quv8sorr7BkyRJcXaXKqxCintNbwNB5YLCBfQthw1NQmAOdHtM6MlEPdGnsxs//6MX7v0bx1Y5Y1u5P4K/oFP4ztCUDWnhqHV6lUFWV/Wcusu5AAj9EnCM9p9C8rllDB4a19+GBNt54O9toGKUQNZNBr2NitwCGtvXhsy3RLNp5mm0nLrA9+gJjOvnxz75NaeBgVe6+Gw4msmz3GRQFPh7TDk8n62qOXoj64ZYT8s8++4yTJ0/i7e2Nv78/dnZ2pdbv37+/0oITQohaQaeDwR+aWsrD58Gmf0NRHnR7VuvIRD1gY6nntcHNGdjKixdXHyLmQjaPL9nH4NZezHqgBW725X/ZruliU7JZdyCB9QcSOJOWY17u4WDFkLbeDGvXiFAvB+mKLsRNcLI18Op9zXmoiz///ek4Px1JYmn4GTYeTOSpu4OY3D0Qa4Oe2JRslofHse2Yjuhw0xDVf/QJpkewFI8UoqrcckI+dOjQKghDCCFqOUWBe981FXz763349TUozIVeL5jWCVHFOvi78ONzPfnk92gWbDvFDxHn2BmTyqwHWjC4tVetSFzTsgv4ISKRtfsTOBifbl5ua6nn3paeDGvnQ7cgd/S6mv9chKiJ/N3smPdQB3bHpvGfHyOJOJvBez9H8d3fZ7irWQNTizhQrF75G/Nykt4nQlSlW07I33jjjaqIQwghaj9FgT4zTN3X//gPbHnblJT3mSFJuagW1gY9L94bwsCWXryw+hDHk7J4dtkBvj+UyH+GtsTDseZ1Oc0rLOa3Y8msP5DA1qgLFBlVAHQK9AxuwLB2PvRv0RBbS5lzXYjK0inQlfVPdWfDoQTe+zmKhPRcvgs/c9UWVz6zXl4bQedAVwLc7coeSAhxx+TTTQghKluvF8BgC7+8Ats/MI0pv/e/kpSLatOqkRMbn+nB3K0n+eyPk/wamUx4bBozBjdneHsfzVvLjUaV8Ng01h04y0+Hk8jKLzKva+XjxNB2PtzfxgsPh5p3AUGIukKnUxjWrhH3tvBi4tfh7D59sdztFEVhxd54pt8bUs0RClE/3HJCrtPprvtBLhXYhRAC6Pq0aUz5j89D+HxTS/ngj0zjzYWoBpYWOqb1bcqAFp68uDqCwwkZ/GvVIb6PSOSdYa00KYIWnZzF2gMJbDiQQGJGnnm5j7PN5XHhPgQ3vHHlZyFE5bGx1NPQyQadcpHLHVRKUVWVsxdzqz+wuiA1Bg4sgfQz4OwH7R4GtyCtoxI1zC0n5OvWrSt1v7CwkAMHDrB48WJmzZpVaYEJIUStFzbF1H19w9OwfzEU5cOQ/zNVZheimoR6ObLuqW58/tcpPvotmq1RF+j/4TZeGRTK2E6+Vd5afj4zj42HEll3IIGjiZnm5Q7WFtzXyouh7XzoFOCKTsaFC6GZRi42pvcCtWxGrigKjVxkHPktO/AtbHwWU/d/1XS742N44DOZHlWUcsvfCocMGVJm2ciRI2nRogUrVqxgypQplRKYEELUCW3HgYUVrJ1qmqu8KBeGf2maw1yIamKh1/HUXU3o37whL66OYP+ZdF5Zd5gfIhL57/DW+LnZVurj5RQU8cvRJNYdSGR79AVzq5uFTuGuZh4Mb+/DPSEeWBv0lfq4QojbM7qjLwv+jCl3naqqPNjRt5ojquVSY0zJuGosu27jM+DkA65BoLcEvQF0Flf9Lu+L15Uag27fYjrEhqPbsg86TKz1vQ4qrZmmS5cuTJ06tbIOJ4QQdUfLEabu66segcgNppbyPm/A4ZXSjU1UqyYeDqx6ohuLdp5mzi/H2RmTyoCPtjH93mZM6BpwR63UxUaVHSdTWHcggV+OJpFTcGUIW3s/Z4a18+G+1t642snFKCFqmkB3O2aPaM30NREoioLRaESnKKjA7BGtpaDbrTqwhKsL45WiGuGbsg2cVyimxFxveTlRN4DOcHlZye+XE/iS5aUSeotb3//qbSo81uX9dJfvm3+/dn9D1Q3Pu9zrQIeCt2pE2bUHdn1a63sdVEpCnpubyyeffIKPj09lHE4IIeqekPtg7DJYPh5O/Gz6UfRINzZR3fQ6hSk9AukT4sH0NRGEx6Yx8/tIfjx8jtkjWtO4gf1NH0tVVY4mZrL+QAIbDiVyISvfvM7fzZZh7XwY2tZHvswLUQuM6uhLWIAry8Lj2BMZQ1jzxozt7C9/v7cj/Qymz/cKKHpTS3hxYTnbqVBcYPqprRR9Bcl/eQn9DZL7kmX52XBoKaCicPlyh3r5wu/GZ8CvS61t2LjlhNzFxaXUeDNVVcnKysLW1pZvv/22UoMTQog6pUlfeOATU/d1uPJBUqKWf6CI2iXA3Y5lj3Xhu91n+O+mY+w5fZGBH//F8/2aMqVHIPEXc1m5N56z/9/efcfndL9/HH/d2ZEhg0hCiBV7xKjas2bVqlWEGv32Vx2KVhelWnRoaXWoKi1a1apRVTUqdtWuVauItogdMbLu+/fHkZtIQpA4Ge/n43EeOfc55z7nuu+cjOu+PuPcFYr4utOlRgjFr/1j/t/5K8zf/i/zt/3L/pOx9nP65nPm4crBdKhWmPAQH9NHcxeROxNawIOhzUuzOPEArZuXxtnZ2eyQciafoqRbIbc4Qt1nodlIo8++NQmsCUZybk28lownXNt27XHyujXh2v7E689Jir/2vIQbtqX1/JvPn3iHz0/n+taE1K/RlgSJScDV1PuyhMVoldBs5H26Xua644T8gw8+SPEH1sHBgYIFC1KrVi18fX0zNTgRkVwnei9YHNLuV2azGQPAVX8cfIuBbyh4FtJ0aZJlHBws9HqwGI3LFOTlH3ey5sBpxv7yFzN/P8o/568YTVZtNiwWC5NXHeLRakWIOneZjYfP2sd+cnFy4KFyhWgfXpiGYQVxcdJMAiKSx4X3grUT0tlpM/aD8ffd0clYnHPowHk22y0S+pvWM/SBwM0fOFzbvmcBnD5A2i0PbNdaJeRMd5yQ9+nTJwvCEBHJI275B8MGURuMJZmTu/FJu2/otaXY9XWfYuCa8ebFIukp4puPr/s+wPdb/mHUwt0cuzbFUVJy1n3t65wt/9ifU6u4Hx3CC9OqUhD53VVFExGx8w4GVy+IizE+hAfso60/Mil3tYSzWK43K89K1kRY92Hq1oVGENdaJeRMd5yQT5s2DU9PTzp37pxi+/fff8/ly5fp3bt3pgUnIpLr3KoZGxYoVB7c/eD8UbjwjzEq++l9xpKWfAVSJuo+NyTs3oU1xZpkmMVioUuNEHb9e4EZG46m2/uxVnE/xnepQhHfzB2ZXUQk19gy3UjGPYOgcmeI+VcDuN6r8F7GeDtpuqHVQQ50x/+pjR07lsmTJ6faHhAQwBNPPKGEXETkVm71B8VigS4zrv+xTkqAC8fg3FE4d8RYzievH4UrZ+HyaWP5d3Pq8zk4Qf4iqRN132LgWxzcfdUcXlI5dzmBdKYjxsECAd5uSsZFRNKTcAXWfmCsN34JqvcxNZxcw7+k0bpg4dPYsGCzWbFYHLDkglYHd5yQR0VFUbx48VTbixUrRlRUzm27LyJyX9zwB8XefC29ZmyOzuBXwljScvWCkZifvyFhT07ezx81+mElb0+Lq/e1RP3GZD3U2OZTFJzdMuc1S45SxNfdGCsmjYzcYrFQxDeH9nMUEbkfNk+D2JOQvyhUeczsaHKX8B5Q9EGsW77i+J6NBJWvhWNenIc8ICCAP//8k9DQ0BTbd+zYgb+/f2bFJSKSe137g8K2Gfc2D7lbfgiqbCw3s1oh9kTaifq5I3DxuNGc7uROY0mLV3DqPuvJ656Fsm6eUTFVlxohTF51KM19NpuNrjVC7nNEIiI5RPzl69XxBkPBycXceHIj/5JYGw9ny5XFtG7cGsdcMBPAHSfk3bt359lnn8XLy4sGDRoAsGrVKp577jm6deuW6QGKiORK/iWzdnoOBwdjUBnvYChWJ/X+hCtw/lgaTeGvLfGxcPE/Y7lxkLlkjq5Gsp6qKXyosc3NO+tem2Sp4gU8eLtTZYbN/RPLDaOs22w23u5UWXMSi4ikZ/OXcCna+KC9qqrjkjF3nJCPHj2aI0eO0LRpU5ycjKdbrVYiIiIYM2ZMpgcoIiJZwNkdCoYZy81sNrh89lqifiRlhf3cEWOwuaQ4OL3fWNLi7pf+yPD5i2T9aKxyTzrXCKFmqB/f3TAPedcaIUrGRUTSE38Z1k0w1hu8oL9zkmF3nJC7uLjw3Xff8eabb7J9+3bc3d2pVKkSxYoVy4r4RETkfrNYwMPfWIpUT70/KRFi/kmdqCdX2S+fMQacu3IW/tuaxvkdIX/hNJrCFzeS93z+dz7Y3JlDOGz5iuqHN+Kwcgvkgj5lZgst4MGwlmXNDkNEJGfYPBUunTL+plXpbnY0koPc9Xw4pUuXpnTp0pkZi4iI5ASOTteT6bTEXUzdZz05eT9/FBKvGn3nz0fB4dWpn+/imc7I8KHXBpu7aVCxbTNh4TM4YCHYZsWyYRNs+MgYJC+8R6a9bBERkTTFX7o+g4qq43KH7jgh79SpEw888ADDhg1Lsf2dd95h06ZNfP/995kWnIiI5ECuXhBY0VhuZrUao8+mNTJ88mBz8bFwcpexpMUz8HqS7uoNm74AbFi4NsO7Lck4buHTxuB5qpSLiEhW2nStOu4bClU0ppbcmTtOyFevXs3IkSNTbW/VqhXjx4/PjJhERCS3cnAA7yBjKfpg6v0JV6/NvX4k7bnX42KM0eNjT8Cx329zMYsxkn1WDp4nIiJ5m6rjco/uOCGPjY3FxSX1EP7Ozs7ExMRkSlAiIpJHObtBgdLGcjObDa6cS5mob/kKzh1O52Q2o1m8iIhIVtn0BVw+bVTHK3c1OxrJge54EtlKlSrx3Xffpdo+e/ZsypcvnylBiYiIpGKxQD4/KFwNKnaEes9DhfbGIHHp8Sl638ITEZE8JkV1/EVVx+Wu3HGFfPjw4XTs2JFDhw7RpEkTAFasWME333zDDz/8kOkBioiIpCu81/V/hm5ms0Kx+vc3HhERyTv+mGLMLOJbXNVxuWt3XCFv27Yt8+fP5+DBgzz11FMMGTKEf//9l99++41SpUplRYwiIiJp8y9pjKZuccBmccSKBduNFfOFTxt9z0VERDJTXCys/9BYb/iiMQOJyF2444QcoE2bNqxbt45Lly7x999/06VLF4YOHUqVKlUyOz4REZFbC+8BT2/GWvtp/vOphbX209D/NyhQBi7+BzPaw8WTZkcpIiK5yaZr1XG/ElCpi9nRSA52Vwk5GKOt9+7dm+DgYMaPH0+TJk34/ffbjXgrIiKSBfxLYm08nC3Fn8LaeDgUqQ4R840+5Gf/hhkdjAHhRERE7lVcLKy7Vh1voOq43Js7SshPnDjBuHHjKF26NJ07d8bb25u4uDjmz5/PuHHjqFmzZlbFKSIicme8gyFiAXgWgujdMKuL8U+UiIjIvfjjc7hyFvxKQqXOZkcjOVyGE/K2bdtSpkwZ/vzzTyZMmMB///3HRx99lJWxiYiI3Bu/EtBrPrj5wD9/wHc9IDHO7KhERCSnirsI66/lQOo7Lpkgwwn5L7/8Qr9+/Rg1ahRt2rTB0fEW08yIiIhkF4XKQ8+54OwBf0fCD30hKdHsqEREJCdKro77l4KKj5odjeQCGU7I165dy8WLF6levTq1atVi0qRJnD59OitjExERyRxFakD3b8HRFf5aBAufAavV7KhERCQnubE6rr7jkkkynJA/+OCDTJkyhePHj/O///2P2bNnExwcjNVqZdmyZVy8eDEr4xQREbk3JRpC52lgcYQd38CvL4PNZnZUIiKSU2ycbAwQ6l8KKqk6LpnjjkdZ9/DwoG/fvqxdu5adO3cyZMgQxo0bR0BAAI888khWxCgiIpI5yraB9p8Y6xs/g8hx5sYjIiI5w9UY2DDJWG84DBzUfVcyx11PewZQpkwZ3nnnHf755x++/fbbzIpJREQk61TpBq3eNdZXjYMNH5sbj4iIZH9/JFfHS0PFTmZHI7nIPSXkyRwdHWnfvj0LFy7MjNOJiIhkrVpPQJPXjPVfX4GtM8yNR0REsq+rMbBe1XHJGpmSkIuIiOQ49YdC7aeN9Z+ehT0LzI1HRESyp42T4ep5KBAGFTuaHY3kMkrIRUQkb7JYoPmbEN4LbFb4oR8cXG52VCIikp1cvaC+45KllJCLiEjeZbFA24lQvj1YE+C7XhC10eyoREQku7BXx8tAhQ5mRyO5kBJyERHJ2xwcoeMUKNUMEi7DrM5wYqfZUYmIiNmunL+hOv6iquOSJZSQi4iIOLlAlxlQtDbEXYAZHeD0QbOjEhERM22cbDRZL1hW1XHJMkrIRUREAFzywWPfQWBluHQKvm4H54+ZHZWIiJjhyvnr02KqOi5ZSAm5iIhIMrf80PNHY57ZmH9gRnuIPWV2VCIicr9t/MxoMVWwLJRXdVyyjhJyERGRG3kWhIj5kD8EzhyEmR2MSomIiOQNV87Dhk+M9YbDwEEpk2Qd3V0iIiI3y18EIhaAR0FjgLdvukL8ZbOjEhGR++H3T69Vx8sZs3CIZCEl5CIiImnxLwm95oFrfjj2O3zXExLjzY5KRESy0pVz8Pu16ngjVccl65l6h4WGhmKxWFItAwcOBKBRo0ap9j355JNmhiwiInlJYCXo8T0454NDK+DHAWBNMjsqERHJKr9/CnExEFAeyrUzOxrJA0xNyDdt2sTx48fty7JlywDo3Lmz/ZgBAwakOOadd94xK1wREcmLitaCbrPA0QX2zIefngObzeyoREQks105ZyTkoL7jct84mXnxggULpng8btw4SpYsScOGDe3b8uXLR2BgYIbPGRcXR1xcnP1xTEwMAAkJCSQkJNxjxFknObbsHKPkbbpHJbvL0nu0aH0s7T/H8ce+WLbNIMnFE2vTN8BiyfxrSa6l36OSE+Tl+9Rh3Uc4xsVgCyhPYulWkAffg5wgp9yjGY3PYrNlj4/54+PjCQ4OZvDgwbzyyiuA0WR99+7d2Gw2AgMDadu2LcOHDydfvnzpnmfkyJGMGjUq1fZvvvnmls8TERG5naJnVhMe9QUAe4M6sT9QzRlFRHID58RYHto9GGfrVf4o/gzHfWqaHZLkcJcvX+axxx7jwoULeHt7p3tctknI58yZw2OPPUZUVBTBwcEAfP755xQrVozg4GD+/PNPhg0bxgMPPMCPP/6Y7nnSqpCHhIRw+vTpW74RZktISGDZsmU89NBDODs7mx2OSCq6RyW7u1/3qMMfk3Fc9ioASc3HYq05IMuuJbmLfo9KTpBX71OHyLE4rhuPLaACif1XgkXN1bOrnHKPxsTEUKBAgdsm5KY2Wb/R1KlTadWqlT0ZB3jiiSfs65UqVSIoKIimTZty6NAhSpYsmeZ5XF1dcXV1TbXd2dk5W3/DkuWUOCXv0j0q2V2W36N1n4b4i7BqHI5LX8Yxny9U7Z5115NcR79HJSfIU/fp5bOw6XMALI1ewtkldS4h2U92v0czGlu2+Ojn6NGjLF++nP79+9/yuFq1agFw8ODB+xGWiIhI2hq9BLX+z1hfMBD2LjI3HhERuXsbPjY+aC1UCco+bHY0ksdki4R82rRpBAQE0KZNm1set337dgCCgoLuQ1QiIiLpsFigxRio2gNsSfDD4/B3pNlRiYjInbp8FjZ+Zqw3ekkjq8t9Z/odZ7VamTZtGr1798bJ6XoL+kOHDjF69Gi2bNnCkSNHWLhwIRERETRo0IDKlSubGLGIiAjGP21tP4RybSEpHr59DI5tMjsqERG5ExsmQXwsBFaCsrcuDopkBdMT8uXLlxMVFUXfvn1TbHdxcWH58uU0b96csmXLMmTIEDp16sRPP/1kUqQiIiI3cXSCTlOhRGNIuASzHoWTu82OSkREMuLSGdg42Vhv9LKmshRTmD6oW/PmzUlroPeQkBBWrVplQkQiIiJ3wMkVus2Cr9vDP3/AjA7w+C/gn/bgoyIikk3Yq+OVoUxrs6ORPMr0CrmIiEiO5+IBPeZAoYoQe9JIzi/8a3ZUIiKSnktn4A9jZHVVx8VMSshFREQyg7sv9JoHfiXgQpRRKb90xuyoREQkLRs+MqrjQVWgTCuzo5E8TAm5iIhIZvEMgIgF4F0YTu+DmR3haozZUYmIyI0unYaNqo5L9qCEXEREJDP5FIVe8yGfPxzfDt92g4QrZkclIiLJ1n9kDMQZVBXCWpodjeRxSshFREQyW8Ew6PkjuHrD0XUwJwIS482OSkRELp2GP6YY66qOSzaghFxERCQrBFeFx74DJ3c4sBTmPwnWJLOjEhHJ29ZNNKrjweEQ1sLsaESUkIuIiGSZYnWg6wxwcIJdc+HnIZDGVJ8iInIfxJ6CTV8Y66qOSzahhFxERCQrlX4IOk4BLLBlGiwfaXZEIiJ50/qJkHAZgqtB6eZmRyMCKCEXERHJehU7QtuJxvq6CbDmfVPDERHJc2JPwR+qjkv2o4RcRETkfqjeGx4abayvGHW92aSIiGS9dRMg8QoUrm60XBLJJpSQi4iI3C91n4X6Q431n4fCn9+bG4+ISF4QGw2bphrrqo5LNqOEXERE5H5q8hrUHADYYN7/YN8vZkckIpK7rZt4rTpeA0o1MzsakRSUkIuIiNxPFgu0egcqdwVbEszpDYfXmB2ViEjudPGkquOSrSkhFxERud8cHKDdx1CmNSTFwbfd4N8tZkclIpL7pKiONzU7GpFUlJCLiIiYwdEZHp0GofUhPhZmdoLovWZHJSKSe1w8CZuvVccbqzou2ZMSchEREbM4u0H3b41Rf6+cg6/bw9nDZkclIpI7rJsAiVehSE0oqeq4ZE9KyEVERMzk6gU9foCA8hB7Ama0h5jjZkclIpKzXTwBm7801tV3XLIxJeQiIiJmy+cHveaBbyicOwIzOsDls2ZHJSKSc62dcK06/gCUbGJ2NCLpUkIuIiKSHXgFQsQC8AqCU3th1qMQd9HsqEREcp6LJ2DLNGNdfcclm1NCLiIikl34hkKv+eDuZ4y6/m13SLhqdlQiIjnL2g+M6nhILSjR2OxoRG5JCbmIiEh2ElAWes4FFy84sgZ+eBySEsyOSkQkZ4g5DpuvVcfVd1xyACXkIiIi2U3hasbo646usG8xLBgIVqvZUYmIZH9rP4CkOChaG0o0MjsakdtSQi4iIpIdFa8PXb4GByf48zv45UWw2cyOSkQk+4r5D7ZMN9YbvaTquOQISshFRESyqzItocNkwAKbpsBvb5odkYhI9mWvjteB4g3NjkYkQ5SQi4iIZGeVHoU24431Ne/BuonmxiMikh2pOi45lBJyERGR7K5mP2j6urG+bMT1fzpFRMSw5n1IiodidaF4A7OjEckwJeQiIiI5Qf3BUHeQsf7TINg118xoRESyjwv/wtavjHVVxyWHUUIuIiKSUzQbCdUfB2zw4xOwf6nZEYmImG9tcnW8nqrjkuMoIRcREckpLBajP3nFTmBNhDm94Oh6s6OSjDhzCIeVo6l++BMcVo6GM4fMjkgkd7jwD2z92lhv9JK5sYjcBSXkIiIiOYmDozHyeukWkHgVvukK/203Oyq5lW0zYVINHDZMIvj8Rhw2TIJJNWDbLLMjE8n5kvuOh9Y3posUyWGUkIuIiOQ0js7Q5Stj8KK4GJjZEU7tNzsqScuZQ7DwGbBZsdiScMCGxZYENissfFqVcpF7cf6YquOS4ykhFxERyYmc3aH7bAiqCpfPwNft4NxRs6OSGyXGwcq3wGZL5wALbJtxX0MSyVXWvg/WBKM6HlrP7GhE7ooSchERkZzKzRt6/ggFysDF/2BGe7h40uyo8i6bzah4b/wcZnWBt0OvjYafXkJug/NR9zFAkVzkfBRsvfaBVqOXzY1F5B44mR2AiIiI3AMPf4iYD1+2gLN/G83X+ywCd1+zI8sb4i7C4TVwcDkcWgHnjqTc75wPEq6QZlJuswKanknkrqy5Vh0v3gBC65odjchdU0IuIiKS03kHQ6/5MK0VnNxlVGd7zQNXT7Mjy31sNjix81oC/htE/W4kBckcnKHog1CqKZRsCk7u8MkD6Tdb3z0PSjSEahH3J36R3OB8lDFYIqg6LjmeEnIREZHcwL+kkYRPaw3//AHf9YDH5oCTq9mR5XyXTsOhlUYF/OAKuBSdcr9vKJRqZiyh9cDVK+X+RybBwqexYcFms2KxOGDBBsHh8O8WY9C30/uh2ShjFH0RubU1469VxxtCsTpmRyNyT5SQi4iI5BaFKkDPufDVI/B3JPzQFzp/BY76c39HkhLhn03XEvDl16aVu6HC7exhTK9UsqlRCfcveevzhfeAog9i3fIVx/dsJKh8LRyr9wa/ErDqbYgcC+s/Mvqfd5yilg0it3LuqKrjkqvoL7SIiEhuUqQGdP8GZnWGvxbBT88aFVoHjeN6S+ePXU/A/14NcRdS7i9U8Xoz9KIP3nnLA/+SWBsPZ8uVxbRu3BpHZ2dje6OXwL8UzH8K9i2GL1tC92/BJyRzXpdIbrNmPFgToUQjKFbb7GhE7pkSchERkdymRCN4dBrMiYDts4wm1C3HgUUDiNklXIGj64wm6AdXwOl9Kfe7+0LJJkYCXrIJeAdlXSyVHgWfYjD7MTi5E6Y0MZLyIjWy7poiOdG5I8bvNFB1XHINJeQiIiK5UbmHof0nMO9/sPEzcPOBxnn4H1ibzeinfXC5kYAfXQeJV6/vtzhAkZrXmqE3g+Cq97c/d0hNGPAbfNvNGJhvehvj+1ex0/2LQSS7s1fHGxstVURyASXkIiIiuVWVbnA1Bn55AVaNA7f8UPsps6O6f66ch8OrrlfBY/5Jud+7sFH9LtXMGOnc7KnifEKg7xKY2x/2LzHGADh9EBq+qNYNIueOwPZvjHVVxyUXUUIuIiKSm9V6Aq5egJVvwq8vg5s3hPc0O6qsYbXC8W1w8DejEv7PJrAlXd/v6GqMyFyqmdEfvGDZ7JfounpBt29g2QjYMAkixxiV/XYfg7Ob2dGJmGf1e0Z1vGQTKFrL7GhEMo0SchERkdyuwVC4et5I8BY+YyR95duZHVXmuHjSmA/84HL4eyVcPpNyv3/p6wl4sbrgks+cOO+EgyO0eAsKlIafh8CuH+D8USNR9wwwOzqR++/sYVXHJddSQi4iIpLbWSzQ/E2jUr5tBvzQDx7zMBLVnCYxHo5tNBLwQyvgxM6U+128jObnySOi+xYzJ87MUL2PMTXad72Mav+UJvDYd8b0diJ5yZr3jNYuJZtCyANmRyOSqZSQi4iI5AUWC7SdCHEXYc98I8nrNT9nNP08+7fRB/zQb3B4NcTHptwfVNVIwEs1MwZmc3Q2JcwsUbwB9F8B33SBs4dganN49EsIa2F2ZCL3x9m/Yfu3xrqq45ILKSEXERHJKxwcoeMUI6E9uNyYq/zxnyGwktmRpRQXC0fWXp8X/OzfKfd7FLxhMLbG4FnQnDjvlwKloP9y+L638YHEt92MFg8PPpX9+sCLZLbV443qeKlmxmwEIrmMEnIREZG8xMkFusyAmR0hagPM6ACPLzGSPrPYbHBy9/UEPOp3SIq/vt/BCUIehFLXkvBClcDBwbx4zZDPD3r+CIuHwpbp8OsrxmBvrd/LXS0CRG505hDsUHVccjcl5CIiInmNSz7oPhu+etjog/11O2O6LZ+Q+xfD5bNGE/RDvxnN0WNPpNzvU/TaYGzNILS+MTp8XufoDA9PgAJh8OurRmJ+9m/o8rX5U7aJZIU1ydXxh6BIDbOjEckSSshFRETyIncf6DkPprWEMwdhRnujUp5Vzb+TEuG/rUYF/OBy+HcrYLu+38kditc3EvCSTcG/pJpjp8VigdoDwa8kzO1nNGH/ohk8Nsd4z0RyizOHYMdsY13VccnFlJCLiIjkVZ4FIWIBfHktKZ/ZAXovMpL1zHDh3+vN0P+ONEZ5v1FA+eujoRetrXm270SZltD3V6M/+ZmDxgjsXWcYg8CJ5Aarr42sXro5FKludjQiWUYJuYiISF6Wv4gx2vq0lkbz9W+6Qq95dzdfd8JViFpvNEE/uAJO7U25380HSjY2EvCSTSB/4cx4BXlXYEUY8BvMfsyYFm1GB2jzPlTvbXZkIvfmzCH4M7k6/pK5sYhkMSXkIiIieV2BUkYSPq0NHPsdvusJLd6CP7+D81FGf+7wXqmbRNtsRnX24HIjAT+yFhKvXN9vcYDC1Y0EvFQzKFzNGOldMo9ngNGqYcFA2PUD/PSsMdjbQ2/ovZaca9U7YLNC6RbG7xCRXEwJuYiIiBhTn/WYY1RZD62AT1aAxRGjn7cF1k2ERyZBubZweNX1KviFqJTn8Qq6loA3hRKNjNHBJWs5u0GnL4zB3iLHwIZJxgclnb4AVy+zoxO5M6cPws45xrqq45IHKCEXERERQ9EHodU7sPBp47EtKeX+BU/BwmdSbnd0Mfp/l2pmJOEB5TUYmxksFmg0zGjtMP8p2L/EGBug++z7O3q+yL1afa06HtbSaFUjksspIRcREZHrzh4ymprbrGnvtyUZI3wnJ+Ch9cDF4/7GKOmr2Al8isG33eHkLmOwt+7fasooyRlOH4Cd3xvrqo5LHuFgdgAiIiKSjZyPusVOi1G1enYrtH4HwlooGc+OitQwBnsrVBEuRcO01rDzB7OjErm95L7jYa0gONzsaETuCyXkIiIicp1PUSCdJucWBwgod1/DkbvkEwJ9lxiJTVKcMWd55DhjID6R7OjUfmNgQlB1XPIUJeQiIiJyXXgvjIHc0mK7tl9yBFcv6DYLal8bEyByLMztDwlXbv08ETMk9x0v0xqCq5odjch9o4RcRERErvMvaYymbnEwRlm/8esjk1JPfSbZm4OjMYXdIx+Bg5NRgZz+MFw8aXZkIted2n+9W4Wq45LHaFA3ERERSSm8hzHi+rYZt56HXHKOahHgWxzm9IJ/N8MXTY0R2AMrmh2ZCKx6G7BBmTYQVMXsaETuKyXkIiIikpp/SWg20uwoJDMVrw/9V8A3XYx5yr9sYcxVXqaV2ZFJXnZqH+yaa6w3GmZuLCImUJN1ERERkbzCvyT0Xw7FG0B8rDE92vpJGuxNzJNcHS/7sKrjkiepQn5NUlISCQkJpl0/ISEBJycnrl69SlJSkmlxiKTnVveos7Mzjo6OJkUmIiJ3xN0Xev4Ii4fClumw9FU4vQ9ajwcnF7Ojk7wk+i/Y9aOx3lDVccmb8nxCbrPZOHHiBOfPnzc9jsDAQI4dO4bFks50MyImut096uPjQ2BgoO5fEZGcwNEZHp4ABcoYCfnWr+HsYejyNeTzMzs6yStSVMcrmx2NiCnyfEKenIwHBASQL18+05IJq9VKbGwsnp6eODioJ4FkP+ndozabjcuXLxMdHQ1AUFCQWSGKiMidsFig9lNGM/Yf+sKRNfBFM3hsDhQoZXZ0kttF74Xd84x1jawueVieTsiTkpLsybi/v7+psVitVuLj43Fzc1NCLtnSre5Rd3d3AKKjowkICFDzdRGRnCSsBfRbCt90g7OHjBHYu3wNJRqaHZnkZsnV8XJtIbCS2dGImCZPZ37Jfcbz5ctnciQiOV/yz5GZYzGIiMhdKlQBBqyAIjXh6nmY2dHoXy6SFU7ugd3zjfWGqo5L3mZqQh4aGorFYkm1DBw4EICrV68ycOBA/P398fT0pFOnTpw8eTLT41CfV5F7p58jEZEczjMAei+Cio+CNRF+eg5+fRWsGmxWMllydbx8OwisaHY0IqYyNSHftGkTx48fty/Lli0DoHPnzgA8//zz/PTTT3z//fesWrWK//77j44dO5oZsoiIiEju5exmzE3e+FXj8YZJMPsxiLtoblySe5zcA3vmG+saWV3E3IS8YMGCBAYG2pdFixZRsmRJGjZsyIULF5g6dSrvv/8+TZo0oXr16kybNo3169fz+++/mxl2njN//nxKlSqFo6MjgwYNSnfbzaZPn46Pj4/98ciRI6latWqWx5tRjRo1Sjf2zBYaGsqECRPuy7VERETuicUCDV+ER6eBkxvsXwJTW8D5KLMjk9xg1Tjja/n2RlcJkTwu2wzqFh8fz8yZMxk8eDAWi4UtW7aQkJBAs2bN7MeULVuWokWLsmHDBh588ME0zxMXF0dcXJz9cUxMDGD0a725b2tCQgI2mw2r1YrVar3r2A+fvsT3W/7hn3NXKOLrTufqRShewOOOzmGz2exfMxLL448/zvnz55k3b16K7ZGRkTRt2pQzZ86kSIbvxf/+9z/69OnDM888g5eXF1arNc1tN0velvx18ODBDBw48J7e61sZN24cw4cPZ+zYsQwdOjRDz8no+51R06dPZ/DgwZw9ezbF9o0bN+Lh4ZEp1xo1ahRvvPHGLY/Jirnsb3ePWq1WbDYbCQkJGtRNTJH8O17jGEh2lePu0TJtsfQsjOP3PbFE78Y2pQlJnWdgK1zD7MgkC2XpfRq9B+c9C7BhIbHuEMgpPwuSreSU36UZjS/bJOTz58/n/Pnz9OnTBzCmI3NxcUmVVBYqVIgTJ06ke56xY8cyatSoVNuXLl2aavA2JycnAgMDiY2NJT4+/u7i/vMkb/xyEAtgAyzA56v/5vVWpWhXudAdn+/ixYw1CUtISCAxMdH+gUOyy5cv28+TGaO1x8bGEh0dTb169fD09MRms/Hff/+l2nZzHGCMAXDzPmdn5zSPzQxTp07l2WefZerUqTzxxBO3PT4xMZH4+PhMjSet1wzg6uqa5vfrbgwYMIDHHnvM/rhJkyb06dOHiIgI+7YbrxMfH4+Li8s9XzdZevdofHw8V65cYfXq1SQmJmba9UTuVHL3J5HsKqfdo26hL/Pg3x+Q/1IUlq/asr3YAP71TbswIrlHVtynNQ9/RDDwn09NNm8+DBzO9GtI3pHdf5cm52W3k20S8qlTp9KqVSuCg4Pv6Twvv/wygwcPtj+OiYkhJCSE5s2b4+3tneLYq1evcuzYMTw9PXFzc8Nms3ElIeOVxaNnLvHGLwex2lLvG/XLQWqWLERR/4yN4O7m5EBsbCxeXl4ZGhzL2dkZJyenVK8p+UMHLy8vvL29mTt3LiNHjuTgwYMEBQXx9NNPp3h/4uLieO2115g9ezbnz5+nYsWKjB07lkaNGtmr7QCPPPIIACtWrEhzW6NGjZg+fTojR47k9OnTNG/enHr16mGxWOwxjho1igULFrB161bgepW/Xr16vP/++8THx9O1a1c++OADnJ2dATh+/DgDBgxg5cqVBAYGMnr0aF577TWee+45nnvuOfvrWLVqFXFxcYwbN445c+awa9cu6tSpY99/6dIlnnrqKebNm4eXlxdDhgzByckJFxcXe3wzZszgo48+Yt++fXh4eNC4cWM++OADAgICgOutDxYuXMirr77K/v37qVq1Kp9//jkVK1YkMjLSPiChr68vACNGjOD111+nRIkS9ph79OhBUlISs2fPtseXkJBA4cKFee+994iIiMBqtfLOO+8wZcoUTpw4QVhYGK+++iqPPvpoqu+5s7MzBQoUoHTp0oCRoFeoUAEnJydmzZpFpUqVWLFiBbt27eLFF19k7dq1eHh48NBDD/H+++9ToEABgFteE4zK+MWLF9O9R69evYq7uzsNGjTAzc0trdtWJEslJCSwbNkyHnroIfvvEJHsJEffo/EdsM5/EscDS6hx5BPCi+TD2mCY0bxdcpUsu09P7sZ52yZsWAjo8j6tC5bNvHNLnpJTfpdmtBCXLRLyo0ePsnz5cn788Uf7tsDAQOLj4zl//nyKKvnJkycJDAxM91yurq64urqm2u7s7JzqG5aUlITFYsHBwQEHBwcuxydScWTmfNJitUHrj9Zl+PhdIx8CsMdzO8kj0t98bPJjBwcHtm3bRrdu3Rg5ciRdu3Zl/fr1PPXUUxQoUMDeEuHZZ59lz549zJ49m+DgYObNm0fr1q3ZuXMn9erVY9++fZQpU4a5c+dSp04d/Pz80ty2adMmBgwYwNixY2nfvj1Llizh9ddfTxFTchJ34+PIyEiCg4NZuXIlBw8epGvXroSHhzNgwAAA+vTpw+nTp4mMjMTZ2ZnBgwcTHR2d6rVPmzaN7t274+rqSvfu3Zk2bRr16tWz7x82bBirV69mwYIFBAQE8Morr7B161aqVq1qP09SUhKjR4+mTJkyREdHM3jwYPr27cvixYtTxD1s2DAmTpxIYGAgr7zyCu3atWP//v3Uq1ePCRMmMGLECPbt2weAp6dnitfr4OBAz5496dy5M5cvX8bT0xMwPuG7fPkynTp1wsHBgbFjxzJz5kw+++wzSpcuzerVq4mIiKBQoUI0bJh6Xtib34+vv/6a//u//2PdOuMejImJoVmzZvTv358JEyZw5coVhg0bRrdu3fjtt98AbnvN5Gbq6d2jDg4OWCyWNH/WRO4n3YOS3eXIe9TZF7p/A8tHwvoPcVz7Ho7n/ob2n4Czu9nRSRbI9Pt03XsAWCp0wDlY847Lvcvuv0szGlu2SMinTZtGQEAAbdq0sW+rXr06zs7OrFixgk6dOgGwb98+oqKiqF27tlmhZiuLFi2yJ3TJbuw7/P7779O0aVOGDx8OQFhYGHv27OHdd9+lT58+REVFMW3aNKKiouwtE4YOHcqSJUuYNm0aY8aMsVeH/fz87B+EpLVt4sSJtGzZkhdffNF+rfXr17NkyZJbvgZfX18mTZqEo6MjZcuWpU2bNqxYsYIBAwbw119/sXz5cjZt2kSNGkZ/tS+++MJeCU4WExPDDz/8wIYNGwDo2bMn9evXZ+LEiXh6ehIbG8vUqVOZOXOmvbr/1VdfUaRIkRTn6du3r329RIkSfPjhh9SsWZPY2NgU7/Prr7/OQw89lOI88+bNo0uXLuTPnx+LxXLLD41atGiBh4cH8+bNo1evXgB88803PPLII3h5eREXF8eYMWNYvny5/V4vUaIEa9euZfLkyWkm5DcrXbo077zzjv3xm2++SXh4OGPGjLFv+/LLLwkJCWH//v0UK1bsnq8pIiK5nIMjNB8NBcJg0SDY/SOcPwrdvgWvO++mJ3nI8T9h70+ARSOri9zE9ITcarUybdo0evfujZPT9XDy589Pv379GDx4MH5+fnh7e/PMM89Qu3btdAd0u1fuzo7seaNFho9/f9l+pq09QpItdZt1R4uFx+uFMvihsAydy9XRwsWrGb40AI0bN+bTTz9NsW3jxo307NkTgL1799KuXbsU++vWrcuECRNISkpi586dJCUlERaWMsa4uDj8/f3vKJa9e/fSoUOHFNtq165924S8QoUKKQYACwoKYufOnYDxAYyTkxPVqlWz7y9VqpS9OXiyb7/9lpIlS1KlShUAqlatSrFixfjuu+/o168fhw4dIj4+nlq1atmf4+fnR5kyZVKcZ8uWLYwcOZIdO3Zw7tw5e0U4KiqK8uXLp3hdN59n7969t3ydN3JycqJLly7MmjWLXr16cenSJRYsWGBvwn7w4EEuX75sT/qTxcfHEx4enqFrVK9ePcXjHTt2sHLlylQf4AAcOnSIhISEe76miIjkEdV6gW8ozOkF/26BKU3gsdkQqKqnpGPV28bXih0hQE3VRW5kekK+fPlyoqKiUlQnk33wwQc4ODjQqVMn4uLiaNGiBZ988kmWxWKxWMjnkvG3pEetYny5Nu3BKGzY6FmrWIbPdzejb3t4eFCqVKkU2/75558MPz82NhZHR0e2bNmSalTstBK3rHBzUw6LxXLH78XUqVPZvXt3ig90rFYrX375Jf369cvQOS5dukSLFi1o0aIFs2bNomDBgkRFRdGiRYu7HvDvVnr06EHDhg2Jjo5m2bJluLu707JlS8D4vgD8/PPPFC5cOMXz0uqOkRYPj5Sj/MfGxtK2bVvefvvtVMcGBQWxa9eue76miIjkIcXrQ/8V8E1XOHPAmBbt0alQppXZkUl2c/xP+GsRYIEGL5odjUi2Y3pC3rx5c/t0Sjdzc3Pj448/5uOPP77PUWVM8QIevN2pMsPm/onFYsFms9m/vt2pMqF3OPVZZitXrpy9D3GydevWERYWhqOjI+Hh4SQlJREdHU39+vXv+VobN25Mse1e54svU6YMiYmJbNu2zV7xPXjwIOfOnbMfs3PnTjZv3kxkZCR+fn727WfPnqVRo0b89ddflCxZEmdnZzZu3EjRokUBOHfuHPv377c3xf7rr784c+YM48aNIyQkBIDNmzenGdfvv/+e6jzlypUDwMXFJUNTjtWpU4eQkBC+++47fvnlFzp37mz/cKJ8+fK4uroSFRWVaU3Fq1Wrxty5cwkNDU3xwUWyrLimiIjkcv4lof8ymNMbDq+Cb7sbTdprP63B3uQ6e3W8k6rjImkwPSHP6TrXCKFmqB/fbT5mn4e8a40Q05NxgCFDhlCzZk1Gjx5N165d2bBhA5MmTbK3MggLC6NHjx5EREQwfvx4wsPDOXXqFCtWrKBy5cop+vTfzrPPPkvdunV57733aNeuHb/++uttm6vfTtmyZWnWrBlPPPEEn376Kc7OzgwZMgR3d3f7AHFTp07lgQceoEGDBqmeX7NmTaZOncq7775Lv379eOGFF/D39ycgIIBXX301xcBkRYsWxcXFhY8++ognn3ySXbt2MXr06DTjeuONN/D396dQoUK8+uqrFChQgPbt2wMQGhpKbGwsK1asoEqVKuTLly/VdHvJHnvsMT777DP279/PypUr7du9vLwYOnQozz//PFarlXr16nHhwgXWrVuHt7c3vXv3vuP3cuDAgUyZMoXu3bvz4osv4ufnx8GDB5k9ezZffPFFllxTRETyAHdf6DkXFr8AW6bB0tfg1D5o8z44Zd6Um5JDHd9xvTreUNVxkbTc+0TVQmgBD4a1LMtH3cMZ1rJstkjGwaiKzpkzh9mzZ1OxYkVGjBjBG2+8YR9hHYwB9SIiIhgyZAhlypShffv2bNq0yV4BzqgHH3yQKVOmMHHiRKpUqcLSpUt57bXX7vk1fP311xQqVIgGDRrQoUMHBgwYgJeXF25ubsTHxzNz5kz7oH8369SpE19//TUJCQm8++671K9fn7Zt29KsWTPq1auXop91wYIFmT59Ot9//z3ly5dn3LhxvPfee2med9y4cTz33HNUr16dEydO8NNPP9nn+a5Tpw5PPvkkXbt2pWDBgikGVrtZjx492LNnD4ULF6Zu3bop9o0ePZrhw4czduxYypUrR8uWLfn5558pXrz4nb6FAAQHB7Nu3TqSkpJo3rw5lSpVYtCgQfj4+Ng/mMjsa4qISB7h6AwPfwAtx4HFAbbNgJkd4fJZsyMTs0Veq45XehQKlrn1sSJ5lMWWXnvxXCImJob8+fNz4cKFNOchP3z4MMWLFzd93mSr1UpMTAze3t4ZmvYsr/rnn38ICQlh+fLl9hHT75fIyEgaN27MuXPnUkzFl1fc7h7NTj9PkjclJCSwePFiWrduna2nQZG8K0/co/uXwg99If4i+JWEx+ZAgVK3f55kG5l2n/63HT5vaHxI89RGKJixgY5Fbien/C69VR56I2V+kq399ttvLFy4kMOHD7N+/Xq6detGaGhomk3URURExGRhzaHfr5C/KJw9BF80gb8jzY5KzGDvO/6oknGRW1BCLtlaQkICr7zyChUqVKBDhw4ULFiQyMjIbP1pmIiISJ5WqAIM+A2KPABXL8DMTrB5mtlRyf303zbYt9iojqvvuMgtaVA3ydaSpyLLDho1apTujAAiIiJyA8+C0PsnWPg07PweFg2C0weMUdgdHG/7dMnh7H3HO0OB0ubGIpLNqUIuIiIiIpnP2Q06ToHG1wZ5/f1jY2q0uIvmxiVZ69+tsP8XozquecdFbksJuYiIiIhkDYsFGr4AnaeDkxsc+BWmtoDzUWZHJlklue94pS4a0E8kA5SQi4iIiEjWqtABHl8MnoUgejdMaQLH/jA7Ksls/26B/UuuVcdfMDsakRxBCbmIiIiIZL3C1Y3B3gIrwaVTMP1h+PN7s6OSzJTcd7xyV1XHRTJICbmIiIiI3B/5i8DjS6BMG0iKgx/7w29vgdVqdmRyr/7ZYnRJsDiqOi5yB5SQi4iIiMj94+oJXWdC3eeMx6vfgbl9IeGKuXHJvVk1zvhauSv4lzQ3FpEcRAm53Nb8+fMpVaoUjo6ODBo0KN1tN5s+fTo+Pj72xyNHjqRq1apZHq+IiIhkcw4O8NAb0O5jcHCG3fNgehu4eMLsyORu/LMZDiy9Vh0fanY0IjmKEvLMcOYQLB8JP/Q1vp45lOWX7NOnD+3bt0+1PTIyEovFwvnz5zPtWv/73/949NFHOXbsGKNHj0532+0MHTqUFStWZFpcya/1VktkZGSmXU9EREQyWXhPiJgP7r7GgGBTmsDxP82OSu5U5LXqeJVuqo6L3CEnswPI8bbNhIXPABbAZnxdNxEemQThPUwO7t7FxsYSHR1NixYtCA4OTndbRnh6euLp6ZlpsdWpU4fjx4/bHz/33HPExMQwbdo0+zY/Pz/7enx8PC4uLpl2fREREckEofWg/wr4piucOQBftoROX0DZ1mZHJhlxbBMcXKbquMhdUoX8RjYbxF/K+HJip5GM26xgS0r5deHTcGJXxs9ls2XJS5o7dy4VKlTA1dWV0NBQxo8fn2J/XFwcQ4cOpXDhwnh4eFCrVi17VTkyMhIvLy8AmjRpYq84p7UNjCbqRYsWJV++fHTo0IEzZ86kuNbNTdaTq/zvvfceQUFB+Pv7M3DgQBISEuzHHD9+nDZt2uDu7k7x4sX55ptvCA0NZcKECbi4uBAYGGhf3N3dcXV1tT/+7LPPeOCBB/jiiy8oXrw4bm5uAJw/f57+/ftTsGBBvL29adKkCTt27EgR64IFC6hWrRpubm6UKFGCUaNGkZiYeM/fDxEREUmDf0novwxKNIKESzD7MVj3YZb9fySZKLnveJXu4FfC3FhEciBVyG+UcBnGZLzie0s2K3xWN+PHv/RP5lz3Blu2bKFLly6MHDmSrl27sn79ep566in8/f3p06cPAE8//TR79uxh9uzZBAcHM2/ePFq2bMnOnTupU6cO+/bto0yZMsydO5c6derg5+eX5raNGzfSr18/xo4dS/v27VmyZAmvv/76bWNcuXIlQUFBrFy5koMHD9K1a1eqVq3KgAEDAIiIiOD06dNERkbi7OzM4MGDiY6OzvB7cPDgQebOncuPP/6Io6MjAJ07d8bd3Z1ffvmF/PnzM3nyZJo2bcr+/fvx8/NjzZo1RERE8OGHH1K/fn0OHTrEE088AZCh1yQiIiJ3wd0XevwAv7wIm7+EZcPh9H5o8z44qYVbtnTsDzi4XNVxkXughDwHW7RoUaom4ElJSfb1999/n6ZNmzJ8+HAAwsLC2LNnD++++y59+vQhKiqKadOmERUVZW96PnToUJYsWcK0adMYM2YMAQEBgNH0OzAwECDNbRMnTqRly5a8+OKL9mutX7+eJUuW3PI1+Pr6MmnSJBwdHSlbtixt2rRhxYoVDBgwgL/++ovly5ezadMmatSoAcAXX3xB6dKlM/wexcfH8/XXX1OwYEEA1q5dyx9//EF0dDSurq4AvPfee8yfP58ffviBJ554glGjRvHSSy/Ru3dvAEqUKMHo0aN58cUXlZCLiIhkJUdnIwEvUAZ+fRm2zYCzh6HrDMjnd/vny/2V3He8anfwK25uLCI5lBLyGznng1f+y/jxK8fA758azdRvZnGEB/8PGr+SsXM5usHVixm/NtC4cWM+/fTTFNs2btxIz549Adi7dy/t2rVLsb9u3bpMmDCBpKQkdu7cSVJSEmFhYSmOiYuLw9/f/45i2bt3Lx06dEixrXbt2rdNyCtUqGCvXAMEBQWxc+dOAPbt24eTkxPVqlWz7y9VqhS+vr4ZjqtYsWL2ZBxgx44dxMbGpnp9V65c4dChQ/Zj1q1bx1tvvWXfn5SUxNWrV7l8+TL58uXL8PVFRETkDlks8OCTRjP27x+Ho2vhi6bw2BwokPEP5SWLHfsDDq0AByeor+q4yN1SQn4jiwVcPDJ+fI2+8Psn6ey0Gfszej6rNePXvcbDw4NSpUql2PbPPxlv+h4bG4ujoyNbtmxJkRQDmTr42q04OzuneGyxWLDexXuRHg+PlO9/bGwsQUFBaY6+njxFW2xsLKNGjaJjx46pjknuhy4iIiJZrPRD0G+pMdjb2b+NpLzzV1CysdmRCRiFKbjWd1zVcZG7pYT8XviXNEZTX/g0KUZZx2ZsN3nah3LlyrFu3boU29atW0dYWBiOjo6Eh4eTlJREdHQ09evXv+drbdy4McW233///Z7OWaZMGRITE9m2bRvVq1cHjD7h586du+tzVqtWjRMnTuDk5ERoaGi6x+zbty/Vhx0iIiJynxUqDwN+g+96wLGNMLMTtH4XavYzO7K8Lep3+HulUR1X33GRe6KE/F6F94CiDxp9nM5HgU9RCO9lejIOMGTIEGrWrMno0aPp2rUrGzZsYNKkSXzyiVHVDwsLo0ePHkRERDB+/HjCw8M5deoUK1asoHLlyrRp0ybD13r22WepW7cu7733Hu3atePXX3+9bXP12ylbtizNmjXjiSee4NNPP8XZ2ZkhQ4bg7u6OxWK5q3M2a9aM2rVr0759e9555x3CwsL477//+Pnnn+nQoQM1atRgxIgRPPzwwxQtWpRHH30UBwcHduzYwa5du3jzzTfv6TWJiIjIHfIsCBEL4adn4c/v4OfBcPqA0RJxxzfZ7v+vPCFyrPG16mPgG2pqKCI5naY9ywz+JaHZSHj0S+NrNvljUK1aNebMmcPs2bOpWLEiI0aM4I033rCPsA4wbdo0IiIiGDJkCGXKlKF9+/Zs2rSJokWL3tG1HnzwQaZMmcLEiROpUqUKS5cu5bXXXrvn1/D1119TqFAhGjRoQIcOHRgwYABeXl533XTcYrGwePFiGjRowOOPP05YWBjdunXj6NGjFCpUCIAWLVqwaNEili5dSs2aNXnwwQf54IMPKFas2D2/HhEREbkLzm7QYTI0ufa/xcZP4eOaxtRou+cZXyfVgG2zzI0zLzi6Af6OVN9xkUxisdly9wSPMTEx5M+fnwsXLuDt7Z1i39WrVzl8+HCKOarNYrVaiYmJwdvbGwcHfU6Snn/++YeQkBCWL19O06ZNzQ4nT7ndPZqdfp4kb0pISGDx4sW0bt061fgUItmB7tFM8vtnsGRY2vssDvD05mxTHMmJbnuffvUIHF4F1XrDIx/e/wAlz8spv0tvlYfeSJmfZGu//fYbCxcu5PDhw6xfv55u3boRGhpKgwYNzA5NREREzBB7wki802KzwYo34OKJ+xtTXnF0vZGMq++4SKZRH3LJ1hISEnjllVf4+++/8fLyok6dOsyaNStbfxomIiIiWeh81C122mDPfGPxCICgyhBUBQKvffUNNWbVkbuT3Hc8vKfRb19E7pkScsnWWrRoQYsWLcwOQ0RERLILn6IYs9qkxQLuvnD1PFyKhoPLjSWZa34IrGQk58nJun9pcNS/xLd1ZB0cXg0OzlB/iNnRiOQa+u0jIiIiIjlHeC9YNzHtfRYL9F8OXkFwcjcc3w4n/oTjf0L0Hoi7AEfXGksyJzcoVNFI0JMr6QHljYHk5DpVx0WyhBJyEREREck5/EvCI5Ng4dMYlXLb9a+PTLo+oFtITWNJlhgPp/fB8R1Ggn58B5zYCQmX4N/NxpLMwQkKlr2eoAdVNirrrl7373VmJ0fWwpE1qo6LZAEl5CIiIiKSs4T3gKIPwrYZGZ+H3MnFSKoDK0H4tW1WK5z9+4ZK+rVk/cpZOLnLWHZ8c/0cfiVT90v3KJCVrzR7iBxnfK3WC3xCzI1FJJdRQi4iIiIiOY9/SWg28t7O4eAABUoZS6VHjW02G1z453pT9+M7jPWYf+HsIWPZPe/6ObwLp6ykB1UxtuWWweMOr1F1XCQLKSEXEREREUlmsRhVYJ8QKNvm+vZLp68n58mV9LOHjEQ95l/Y/8v1Y939bqqkVwW/EsYHADmNvToeAfmLmBuLSC6khFxERERE5HY8CkCppsaS7GqM0az9xkp69F6jyfvfkcaSzMXzWpP5yteT9YJlwTEbT+V6eI0xAJ6jC9QfbHY0IrmSEvI8YuTIkcyfP5/t27dn+bX69OnD+fPnmT9/fpZf636IjIykcePGnDt3Dh8fH7PDERERkezCzRuK1TGWZAlXjRHd7dX0P42kPT4WojYYSzJHF2NEd3s1vQoUqgAu+e7/a7mZzXZ9ZHVVx0WyjBLyTHA05ijzDszjv9j/CPYMpkPpDhTzLpbl192wYQP16tWjZcuW/Pzzz1l+vZsdOXKE4sWLs23bNqpWrWrfPnHiRGw2W6ZeKzQ0lKNHjwLg7u5OyZIlee655+jfv3+mXsdMyYn/zV599VXefPNNEyJK+WGEt7e3KTGIiIjkKM5uULiasSRLSoQzB643dU9O1OMuGAPKHd9+/ViLAxQIu2mE98rg7nNfX4bl6Fo4us740KCequMiWUUJ+T2ad2AeIzeMxIIFGzYsWJi2exqj6oyifan2WXrtqVOn8swzzzB16lT+++8/goODs/R6GZU/f/4sOe8bb7zBgAEDuHz5Mt9//z0DBgygcOHCtGrVKkuuZ5Z9+/alSH49PT3v6jxJSUlYLBYccmJ/NRERkdzE0QkCyhlLlW7GNpsNzh1J3S/9UjSc+stYds65fg6fYikr6UFVwKtQ1sRrs+Gw+m1jvXofyF84a64jIug/9RvYbDYuJ1zO8PLXmb8YuX4kVpuVJFtSiq+vr3udfWf3Zfhcd1pRjo2N5bvvvuP//u//aNOmDdOnT0+xf9y4cRQqVAgvLy/69evH1atXU+zftGkTDz30EAUKFCB//vw0bNiQrVu3pjjGYrHw6aef0qpVK9zd3SlRogQ//PCDfX/x4sUBCA8Px2Kx0KhRI8Bost6+fXsAPv/8c4KDg7FarSnO3a5dO/r27Wt/vGDBAqpVq4abmxslSpRg1KhRJCYmpniOl5cXgYGBlChRgmHDhuHn58eyZcvu+DV98cUXdOjQgXz58lG6dGkWLlyY4pjFixcTFhaGu7s7jRs35siRI6ne/7lz51KhQgVcXV0JDQ1l/PjxKfaHhoby5ptvEhERgaenJ8WKFWPhwoWcOnWKdu3a4enpSeXKldm8eXOqcwcEBBAYGGhfkhPyc+fOERERga+vL/ny5aNVq1YcOHDA/rzp06fj4+PDwoULKV++PK6urkRFRREXF8fQoUMpXLgwHh4e1KpVi8jISPvzjh49Stu2bfH19cXDw4MKFSqwePFijhw5Yq/Y+/r64ujoyFNPPZUqXhEREbkLFgv4FYcK7aHpCOg5F144AEP2wWNzoPFrUPZhY0o3gPNHYe9P8Nub8E1nGB8G74XBrM6wYjTsWWAk+JnQSrFA7F4cjv0Ojq5Q7/l7Pp+IpE8V8htcSbxCrW9qZcq5rFh59KdHM3z8hm4bbn/QDebMmUPZsmUpU6YMPXv2ZNCgQbz88stYLBbmzJnDyJEj+fjjj6lXrx4zZszgww8/pESJEvbnX7x4kd69e/PRRx9hs9kYP348rVu35sCBA3h5edmPGz58OOPGjWPixInMmDGDbt26sXPnTsqVK8cff/zBAw88wPLly6lQoQIuLi6p4uzcuTPPPPMMK1eupGlTYxCUs2fPsmTJEhYvXgzAmjVriIiI4MMPP6R+/focOnSIJ554AoDXX3891TmtVivz5s3j3LlzKa6Z0dc0atQo3nnnHd59910++ugjevTowdGjR/Hz8+PYsWN07NiRgQMH8sQTT7B582aGDEk5xceWLVvo0qULI0eOpGvXrqxfv56nnnoKf39/+vTpYz/ugw8+YMyYMQwfPpwPPviAXr16UadOHfr27cu7777LsGHDiIiIYPfu3VgyMDVKnz59OHDgAAsXLsTb25thw4bRunVr9uzZg7OzMSDM5cuXefvtt/niiy/w9/cnICCAp59+mj179jB79myCg4OZN28eLVu2ZOfOnZQuXZqBAwcSHx/P6tWr8fDwYM+ePXh6ehISEsLcuXPp1KkT+/btw9PTk4SEhNvGKSIiIvfAK9BYwlpc33b5LJzYmbKSfno/xJ6EA0uNJZlb/uvN3ZO/FigNDo63v/aZQzhsnk6Nw9OMxxU6gHf2aIEpkltZbJnd2TebiYmJIX/+/Fy4cCFVH9irV69y+PBhihcvjpubG5cTLmdaQn6nNnTbQOKVRLy9vTPUxLhu3bp06dKF5557jsTERIKCgvj+++9p1KgRderUITw8nI8//th+/IMPPsjVq1fTHdTNarXi4+PDN998w8MPPwwY1eQnn3ySTz/9NMV5qlWrxieffJJuH/KbB3Vr3749/v7+TJ06FTCq5qNGjeLYsWM4ODjQrFkzmjZtyssvv2w/x8yZM3nxxRf577//AKPifPz4cZydnYmLiyMxMRE/Pz82btxIqVKl7ug1vfbaa4wePRqAS5cu4enpyS+//ELLli155ZVXWLBgAbt377af56WXXuLtt9+2D+rWo0cPTp06xdKl1//4vfjii/z888/254WGhlK/fn1mzJgBwIkTJwgKCmL48OG88cYbAPz+++/Url2b48ePExgYaO+v7eHhkeJ1HD16lLNnzxIWFsa6deuoU8cYOObMmTOEhITw1Vdf0blzZ6ZPn87jjz/O9u3bqVKlCgBRUVGUKFGCqKioFF0amjVrxgMPPMCYMWOoXLkynTp1SvPDj5v7kMfExKR7j9788yRyvyUkJLB48WJat25t/5BKJDvRPSqZKv4SnNx9LUG/1uz95B6wpvHhuZM7BFZM2S89oDw4uV4/ZttMWPgMNsBisxpfLQ7wyCQI73G/XpXIbeWU36W3ykNvpAr5Ddyd3Nn42MYMH//x9o+ZtXcWSbakVPscLY70KNeDgVUHZuhcrg6uXORiho7dt28ff/zxB/PmzQPAycmJrl27MnXqVBo1asTevXt58sknUzyndu3arFy50v745MmTvPbaa0RGRhIdHU1SUhKXL18mKioq1fNufnynI7X36NGDAQMG8Mknn+Dq6sqsWbPo1q2bPanbsWMH69at46233rI/JykpiatXr3L58mXy5TNGGn3hhRfo06cPx48f54UXXuCpp55KkYxn9DVVrlzZvu7h4YG3tzfR0dEA7N27l1q1Un4oc/N7sHfvXtq1a5diW926dZkwYQJJSUk4Ojqmuk6hQkYfr0qVKqXaFh0dTWBgoH37mjVrUlT0fX19WbduHU5OTili8/f3p0yZMuzdu9e+zcXFJcV1d+7cSVJSEmFhYSnijYuLw9/fH4Bnn32W//u//2Pp0qU0a9aMTp06pTiHiIiIZEMuHhDygLEkS4w3+p7fWEk/sRMSLsE/m4wlmYMTFCxnJOfewbDmPbDZSG6zZwGwWWHh01D0QfAveR9fnEjeoYT8BhaLhXzOGZ9mokuZLszcOzPNfTZsdC3TNcPnu7mP9a1MnTqVxMTEFBVPm82Gq6srkyZNytA5evfuzZkzZ5g4cSLFihXD1dWV2rVrEx8fn+E4Mqpt27bYbDZ+/vlnatasyZo1a/jggw/s+2NjYxk1ahQdO3ZM9dwbK60FChSgVKlSlCpViu+//55KlSpRo0YNypcvf0ev6eZP0iwWyx29/xl143WSm6Snte3maxcvXvyup1dzd3dP0fw9NjYWR0dHtmzZYv+gIFly3/T+/fvTokULfv75Z5YuXcrYsWMZP348zzzzzF3FICIiIiZxcrk28FtlCO9pbLMmwdm/U1bSj++AK+fg5E5juSULbJsBzUZmdfQieZIS8ntQzLsYo+qM4vX1r6cYZd2GjVF1RlHUu2imXzMxMZGvv/6a8ePH07x58xT72rdvz7fffku5cuXYuHEjERER9n2///57imPXrVvHJ598QuvWrQE4duwYp0+fTnW933//PdV5wsPDAez9t5OSUrcQuJGbmxsdO3Zk1qxZHDx4kDJlylCt2vWpQKpVq8a+ffvSbXqelpCQELp27crLL7/MggUL7ug13Uq5cuVSDfJ283tXrlw51q1bl2LbunXrCAsLS5X0ZpZy5cqRmJjIxo0bUzRZ37dvn/0DibSEh4eTlJREdHQ09evXT/e4kJAQnnzySZ588klefvllpkyZwjPPPJPh77GIiIhkUw6ORh/yAqWh0rXxjWw2uPDP9QR961dw8UQ6J7DB+ah09onIvVJCfo/al2pPtYBq/HjgR/s85B1Ld8ySZBxg0aJFnDt3jn79+qWaXqxTp05MnTqVoUOH0qdPH2rUqEHdunWZNWsWu3fvTjGoW+nSpZkxYwY1atQgJiaGF154AXd391TX+/7776lRowb16tVj1qxZ/PHHH/a+4AEBAbi7u7NkyRKKFCmCm5tbulOe9ejRg4cffpjdu3fTs2fPFPtGjBjBww8/TNGiRXn00UdxcHBgx44d7Nq165bzbz/33HNUrFiRzZs3U6NGjQy/plt58sknGT9+PC+88AL9+/dny5YtqUawHzJkCDVr1mT06NF07dqVDRs2MGnSJD755JM7utadKF26NO3atWPAgAFMnjwZLy8vXnrpJQoXLpyq+fyNwsLC6NGjBxEREYwfP57w8HBOnTrFihUrqFy5Mm3atGHQoEG0atWKsLAwzp07x8qVKylXrhwAxYoVw2KxsGjRIlq2bElCQoLmIxcREcnpLBbwCTGWcg9DUjys+xDS6IYJlusjvYtIptO0Z5mgqHdRBlUfxDsN32FQ9UFZloyD0Vy9WbNmaSa+nTp1YvPmzZQrV47hw4fz4osvUr16dY4ePcr//d//pTrPuXPnqFatGr169eLZZ58lICAg1TlHjRrF7NmzqVy5Ml9//TXffvutvSLr5OTEhx9+yOTJkwkODr5lYtikSRP8/PzYt28fjz32WIp9LVq0YNGiRSxdupSaNWvy4IMP8sEHH1CsWLFbvhfly5enefPmjBgx4o5e060ULVqUuXPnMn/+fKpUqcJnn33GmDFjUhxTrVo15syZw+zZs6lYsSIjRozgjTfeSDHCelaYNm0a1atX5+GHH6Z27drYbDYWL15828Espk2bRkREBEOGDKFMmTK0b9+eTZs2UbSocZ8mJSUxcOBAypUrR8uWLQkLC7N/uFC4cGFGjRrFSy+9RFBQEC+++GKWvkYRERExQXgvIL1xnm3X9otIVtAo69lkVGir1XrLEazNYLFYmDdvnn1OccnbbnePZqefJ8mbcsqoq5J36R6VbG3bLFj4NDYs2GxWLBYHLNg0yrpkOznld6lGWRcRERERkYwJ7wFFH8S65SuO79lIUPlaOFbvrdHVRbKYEnIREREREQH/klgbD2fLlcW0btwax2xcfRTJLZSQS7pyeW8GERERERERU2WPzsoiIiIiIiIieYwSclQJFskM+jkSEREREbkzeTohTx6V7/LlyyZHIpLzJf8cZefRLkVEREREspM83Yfc0dERHx8foqOjAciXLx8Wi8WUWKxWK/Hx8Vy9ejXbTHsmcqP07lGbzcbly5eJjo7Gx8cHR0dHE6MUEREREck58nRCDhAYGAhgT8rNYrPZuHLlCu7u7qZ9KCByK7e7R318fOw/TyIiIiIicnt5PiG3WCwEBQUREBBAQkKCaXEkJCSwevVqGjRooCa/ki3d6h51dnZWZVxERERE5A7l+YQ8maOjo6kJhaOjI4mJibi5uSkhl2xJ96iIiIiISOZSZ2UREREREREREyghFxERERERETGBEnIRERERERERE+T6PuQ2mw2AmJgYkyO5tYSEBC5fvkxMTIz650q2pHtUsjvdo5Ld6R6VnED3qWR3OeUeTc4/k/PR9OT6hPzixYsAhISEmByJiIiIiIiI5CUXL14kf/786e632G6XsudwVquV//77Dy8vr2w9v3dMTAwhISEcO3YMb29vs8MRSUX3qGR3ukclu9M9KjmB7lPJ7nLKPWqz2bh48SLBwcE4OKTfUzzXV8gdHBwoUqSI2WFkmLe3d7a+sUR0j0p2p3tUsjvdo5IT6D6V7C4n3KO3qown06BuIiIiIiIiIiZQQi4iIiIiIiJiAiXk2YSrqyuvv/46rq6uZocikibdo5Ld6R6V7E73qOQEuk8lu8tt92iuH9RNREREREREJDtShVxERERERETEBErIRUREREREREyghFxERERERETEBErIRUREREREREyghDyb+PjjjwkNDcXNzY1atWrxxx9/mB2SCABjx46lZs2aeHl5ERAQQPv27dm3b5/ZYYmka9y4cVgsFgYNGmR2KCJ2//77Lz179sTf3x93d3cqVarE5s2bzQ5LBICkpCSGDx9O8eLFcXd3p2TJkowePRqN/SxmWb16NW3btiU4OBiLxcL8+fNT7LfZbIwYMYKgoCDc3d1p1qwZBw4cMCfYe6SEPBv47rvvGDx4MK+//jpbt26lSpUqtGjRgujoaLNDE2HVqlUMHDiQ33//nWXLlpGQkEDz5s25dOmS2aGJpLJp0yYmT55M5cqVzQ5FxO7cuXPUrVsXZ2dnfvnlF/bs2cP48ePx9fU1OzQRAN5++20+/fRTJk2axN69e3n77bd55513+Oijj8wOTfKoS5cuUaVKFT7++OM097/zzjt8+OGHfPbZZ2zcuBEPDw9atGjB1atX73Ok907TnmUDtWrVombNmkyaNAkAq9VKSEgIzzzzDC+99JLJ0YmkdOrUKQICAli1ahUNGjQwOxwRu9jYWKpVq8Ynn3zCm2++SdWqVZkwYYLZYYnw0ksvsW7dOtasWWN2KCJpevjhhylUqBBTp061b+vUqRPu7u7MnDnTxMhEwGKxMG/ePNq3bw8Y1fHg4GCGDBnC0KFDAbhw4QKFChVi+vTpdOvWzcRo75wq5CaLj49ny5YtNGvWzL7NwcGBZs2asWHDBhMjE0nbhQsXAPDz8zM5EpGUBg4cSJs2bVL8PhXJDhYuXEiNGjXo3LkzAQEBhIeHM2XKFLPDErGrU6cOK1asYP/+/QDs2LGDtWvX0qpVK5MjE0nt8OHDnDhxIsXf+/z581OrVq0cmT85mR1AXnf69GmSkpIoVKhQiu2FChXir7/+MikqkbRZrVYGDRpE3bp1qVixotnhiNjNnj2brVu3smnTJrNDEUnl77//5tNPP2Xw4MG88sorbNq0iWeffRYXFxd69+5tdngivPTSS8TExFC2bFkcHR1JSkrirbfeokePHmaHJpLKiRMnANLMn5L35SRKyEUkwwYOHMiuXbtYu3at2aGI2B07doznnnuOZcuW4ebmZnY4IqlYrVZq1KjBmDFjAAgPD2fXrl189tlnSsglW5gzZw6zZs3im2++oUKFCmzfvp1BgwYRHByse1Qki6nJuskKFCiAo6MjJ0+eTLH95MmTBAYGmhSVSGpPP/00ixYtYuXKlRQpUsTscETstmzZQnR0NNWqVcPJyQknJydWrVrFhx9+iJOTE0lJSWaHKHlcUFAQ5cuXT7GtXLlyREVFmRSRSEovvPACL730Et26daNSpUr06tWL559/nrFjx5odmkgqyTlSbsmflJCbzMXFherVq7NixQr7NqvVyooVK6hdu7aJkYkYbDYbTz/9NPPmzeO3336jePHiZockkkLTpk3ZuXMn27dvty81atSgR48ebN++HUdHR7NDlDyubt26qaaL3L9/P8WKFTMpIpGULl++jINDyrTA0dERq9VqUkQi6StevDiBgYEp8qeYmBg2btyYI/MnNVnPBgYPHkzv3r2pUaMGDzzwABMmTODSpUs8/vjjZocmwsCBA/nmm29YsGABXl5e9r45+fPnx93d3eToRMDLyyvVmAYeHh74+/trrAPJFp5//nnq1KnDmDFj6NKlC3/88Qeff/45n3/+udmhiQDQtm1b3nrrLYoWLUqFChXYtm0b77//Pn379jU7NMmjYmNjOXjwoP3x4cOH2b59O35+fhQtWpRBgwbx5ptvUrp0aYoXL87w4cMJDg62j8Sek2jas2xi0qRJvPvuu5w4cYKqVavy4YcfUqtWLbPDEsFisaS5fdq0afTp0+f+BiOSQY0aNdK0Z5KtLFq0iJdffpkDBw5QvHhxBg8ezIABA8wOSwSAixcvMnz4cObNm0d0dDTBwcF0796dESNG4OLiYnZ4kgdFRkbSuHHjVNt79+7N9OnTsdlsvP7663z++eecP3+eevXq8cknnxAWFmZCtPdGCbmIiIiIiIiICdSHXERERERERMQESshFRERERERETKCEXERERERERMQESshFRERERERETKCEXERERERERMQESshFRERERERETKCEXERERERERMQESshFRERERERETKCEXERERDLd8OHDeeKJJ+7qudOnT8fHx+eeY2jUqBGDBg265/OkJT4+ntDQUDZv3pwl5xcRkbxBCbmIiOR5p06dwsXFhUuXLpGQkICHhwdRUVG3fM7IkSOpWrVqpsWQlcnj/XbixAkmTpzIq6++elfP79q1K/v378/kqDKXi4sLQ4cOZdiwYWaHIiIiOZgSchERyfM2bNhAlSpV8PDwYOvWrfj5+VG0aFGzw8qxvvjiC+rUqUOxYsXu6vnu7u4EBARkclSZr0ePHqxdu5bdu3ebHYqIiORQSshFRCTPW79+PXXr1gVg7dq19vU70adPH9q3b897771HUFAQ/v7+DBw4kISEBPsxn3zyCaVLl8bNzY1ChQrx6KOP2p+7atUqJk6ciMViwWKxcOTIEZKSkujXrx/FixfH3d2dMmXKMHHixDu+blxcHMOGDSMkJARXV1dKlSrF1KlT7ft37dpFq1at8PT0pFChQvTq1YvTp0/b9//www9UqlQJd3d3/P39adasGZcuXUr3vZg9ezZt27a1P160aBE+Pj4kJSUBsH37diwWCy+99JL9mP79+9OzZ08gdZP15NYIM2bMIDQ0lPz589OtWzcuXrxoP+bSpUtERETg6elJUFAQ48ePTxXXuXPniIiIwNfXl3z58tGqVSsOHDgAgM1mo2DBgvzwww/246tWrUpQUJD98dq1a3F1deXy5csA+Pr6UrduXWbPnp3ueyEiInIrSshFRCRPioqKwsfHBx8fH95//30mT56Mj48Pr7zyCvPnz8fHx4ennnrqjs65cuVKDh06xMqVK/nqq6+YPn0606dPB2Dz5s08++yzvPHGG+zbt48lS5bQoEEDACZOnEjt2rUZMGAAx48f5/jx44SEhGC1WilSpAjff/89e/bsYcSIEbzyyivMmTMnw9cFiIiI4Ntvv+XDDz9k7969TJ48GU9PTwDOnz9PkyZNCA8PZ/PmzSxZsoSTJ0/SpUsXAI4fP0737t3p27cve/fuJTIyko4dO2Kz2dJ8D86ePcuePXuoUaOGfVv9+vW5ePEi27ZtA2DVqlUUKFCAyMhI+zGrVq2iUaNG6b63hw4dYv78+SxatIhFixaxatUqxo0bZ9//wgsvsGrVKhYsWMDSpUuJjIxk69atKc7Rp08fNm/ezMKFC9mwYQM2m43WrVuTkJCAxWKhQYMG9pjOnTvH3r17uXLlCn/99Zc9xpo1a5IvXz77OR944AHWrFmTbtwiIiK34mR2ACIiImYIDg5m+/btxMTEUKNGDTZu3IiHhwdVq1bl559/pmjRovakNaN8fX2ZNGkSjo6OlC1bljZt2rBixQoGDBhAVFQUHh4ePPzww3h5eVGsWDHCw8MByJ8/Py4uLuTLl4/AwED7+RwdHRk1apT9cfHixdmwYQNz5syxJ8y3u+7+/fuZM2cOy5Yto1mzZgCUKFHC/txJkyYRHh7OmDFj7Nu+/PJLQkJC2L9/P7GxsSQmJtKxY0d7E/RKlSql+x5ERUVhs9kIDg62b8ufPz9Vq1YlMjKSGjVqEBkZyfPPP8+oUaOIjY3lwoULHDx4kIYNG6Z7XqvVyvTp0/Hy8gKgV69erFixgrfeeovY2FimTp3KzJkzadq0KQBfffUVRYoUsT//wIEDLFy4kHXr1lGnTh0AZs2aRUhICPPnz6dz5840atSIyZMnA7B69WrCw8MJDAwkMjKSsmXLEhkZmSrG4OBgjh49mm7cIiIit6IKuYiI5ElOTk6Ehoby119/UbNmTSpXrsyJEycoVKgQDRo0IDQ0lAIFCtzROStUqICjo6P9cVBQENHR0QA89NBDFCtWjBIlStCrVy9mzZplb/p8Kx9//DHVq1enYMGCeHp68vnnn6cacO5W192+fTuOjo7pJrs7duxg5cqVeHp62peyZcsCRlW6SpUqNG3alEqVKtG5c2emTJnCuXPn0o33ypUrALi5uaXY3rBhQyIjI7HZbKxZs4aOHTtSrlw51q5dy6pVqwgODqZ06dLpnjc0NNSejN/8Gg8dOkR8fDy1atWy7/fz86NMmTL2x3v37sXJySnFMf7+/pQpU4a9e/faY9yzZw+nTp2yV+wbNWpEZGQkCQkJrF+/PlUV393dPUPfRxERkbQoIRcRkTypQoUKeHp60qtXL/744w88PT1p2rQpR44cwdPTkwoVKtzxOZ2dnVM8tlgsWK1WALy8vNi6dSvffvstQUFBjBgxgipVqnD+/Pl0zzd79myGDh1Kv379WLp0Kdu3b+fxxx8nPj4+w9d1d3e/ZcyxsbG0bduW7du3p1gOHDhAgwYNcHR0ZNmyZfzyyy+UL1+ejz76iDJlynD48OE0z5f8IcbNSXujRo1Yu3YtO3bswNnZmbJly9qT3VWrVt2yOn6715hZKlWqhJ+fH6tWrUqRkK9atYpNmzaRkJBgr64nO3v2LAULFszUOEREJO9QQi4iInnS4sWL2b59O4GBgcycOZPt27dTsWJFJkyYwPbt21m8eHGmX9PJyYlmzZrxzjvv8Oeff3LkyBF+++03wJhGK3nQs2TJzaufeuopwsPDKVWqFIcOHbqja1aqVAmr1cqqVavS3F+tWjV2795NaGgopUqVSrF4eHgARvJbt25dRo0axbZt23BxcWHevHlpnq9kyZJ4e3uzZ8+eFNuT+5F/8MEH9uQ7OSGPjIy8Zf/x2ylZsiTOzs5s3LjRvu3cuXMppk4rV64ciYmJKY45c+YM+/bto3z58vbXWb9+fRYsWMDu3bupV68elStXJi4ujsmTJ1OjRg37e5Js165d9q4HIiIid0oJuYiI5EnFihXD09OTkydP0q5dO0JCQti9ezedOnWiVKlSdz1lV3oWLVrEhx9+yPbt2zl69Chff/01VqvV3qw6NDSUjRs3cuTIEU6fPo3VaqV06dJs3ryZX3/9lf379zN8+HA2bdp0R9cNDQ2ld+/e9O3bl/nz53P48GEiIyPtA8MNHDiQs2fP0r17dzZt2sShQ4f49ddfefzxx0lKSmLjxo2MGTOGzZs3ExUVxY8//sipU6coV65cmtdzcHCgWbNmrF27NsV2X19fKleuzKxZs+zJd4MGDdi6dSv79++/bYX8Vjw9PenXrx8vvPACv/32G7t27aJPnz44OFz/N6d06dK0a9eOAQMG2Cv1PXv2pHDhwrRr185+XKNGjfj222+pWrUqnp6eODg40KBBA2bNmpVmjGvWrKF58+Z3HbuIiORtSshFRCTPioyMpGbNmri5ufHHH39QpEiRFNNcZSYfHx9+/PFHmjRpQrly5fjss8/49ttv7U3jhw4diqOjI+XLl6dgwYJERUXxv//9j44dO9K1a1dq1arFmTNn7njkd4BPP/2URx99lKeeeoqyZcsyYMAA+7RlwcHBrFu3jqSkJJo3b06lSpUYNGgQPj4+ODg44O3tzerVq2ndujVhYWG89tprjB8/nlatWqV7vf79+zN79uxUTcobNmxIUlKSPSH38/OjfPnyBAYGpujvfTfeffdd6tevT9u2bWnWrBn16tWjevXqKY6ZNm0a1atX5+GHH6Z27drYbDYWL16cojn8zTGCkaTfvA2M+esvXLhgn75ORETkTlls6c1bIiIiInIXbDYbtWrV4vnnn6d79+5mh5NlunbtSpUqVXjllVfMDkVERHIoVchFREQkU1ksFj7//HMSExPNDiXLxMfHU6lSJZ5//nmzQxERkRxMFXIRERERERERE6hCLiIiIiIiImICJeQiIiIiIiIiJlBCLiIiIiIiImICJeQiIiIiIiIiJlBCLiIiIiIiImICJeQiIiIiIiIiJlBCLiIiIiIiImICJeQiIiIiIiIiJlBCLiIiIiIiImKC/wdC0st51Oj/FQAAAABJRU5ErkJggg==", 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" ] @@ -1412,7 +954,28 @@ } ], "source": [ - "plot_windowed_results(results_OB, results_HAT, results_srp, xlabel=\"# Instances (window)\", ylabel=\"Accuracy\")" + "from capymoa.evaluation.visualization import plot_windowed_results\n", + "from capymoa.base import MOAClassifier\n", + "from moa.classifiers.trees import HoeffdingAdaptiveTree\n", + "from capymoa.classifier import HoeffdingTree\n", + "from capymoa.classifier import AdaptiveRandomForest\n", + "\n", + "# Create the wrapper for HoeffdingAdaptiveTree (from MOA)\n", + "HAT = MOAClassifier(schema=elec_stream.get_schema(), moa_learner=HoeffdingAdaptiveTree, CLI=\"-g 50\")\n", + "HT = HoeffdingTree(schema=elec_stream.get_schema(), grace_period=50)\n", + "ARF = AdaptiveRandomForest(schema=elec_stream.get_schema(), ensemble_size=10, number_of_jobs=4)\n", + "\n", + "results_HAT = prequential_evaluation(stream=elec_stream, learner=HAT, window_size=4500)\n", + "results_HT = prequential_evaluation(stream=elec_stream, learner=HT, window_size=4500)\n", + "results_ARF = prequential_evaluation(stream=elec_stream, learner=ARF, window_size=4500)\n", + "\n", + "# Comparing models based on their cumulative accuracy\n", + "print(f\"HAT accuracy = {results_HAT['cumulative'].accuracy()}\")\n", + "print(f\"HT accuracy = {results_HT['cumulative'].accuracy()}\")\n", + "print(f\"ARF accuracy = {results_ARF['cumulative'].accuracy()}\")\n", + "\n", + "# Plotting the results. Note that we ovewrote the ylabel, but that doesn't change the metric. \n", + "plot_windowed_results(results_HAT, results_HT, results_ARF, ylabel='Accuracy', xlabel=\"# Instances (window)\")" ] }, { @@ -1421,661 +984,59 @@ "id": "0af53c62-ce89-49eb-a7cb-6c9c09f4e6f9", "metadata": {}, "source": [ - "## Regression" + "## 2. Regression\n", + "\n", + "* Regression algorithms have its API usage very similar to classification algorithms. We can use the same high-level evaluation and visualization functions for regression and classification.\n", + "* Similarly to classification, we can also use MOA objects through a generic API. " ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "id": "56ed36b6-fd9a-49b6-965e-2ff964423948", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:39:27.455612Z", - "iopub.status.busy": "2024-03-21T04:39:27.455408Z", - "iopub.status.idle": "2024-03-21T04:39:49.299620Z", - "shell.execute_reply": "2024-03-21T04:39:49.299268Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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classified instancesmean absolute errorroot mean squared errorrelative mean absolute errorrelative root mean squared errorcoefficient of determinationadjusted coefficient of determination
01000.05.6743407.3632741.3946001.477190-1.182092-1.206386
11000.03.6290254.5491670.9165700.9341010.1274550.117741
21000.03.0455183.8610260.7595090.7765830.3969190.390204
31000.02.7030283.4059310.6952830.7138340.4904420.484768
41000.02.4365563.1111160.5999060.6228770.6120240.607705
51000.02.3988003.0355450.5743340.5926870.6487220.644811
61000.02.2121322.8000960.5478490.5641450.6817400.678197
71000.02.1333152.7514380.5289140.5529950.6941960.690792
81000.02.1075812.6979640.4944130.5241780.7252380.722179
91000.02.1109552.6383470.5275240.5292240.7199220.716803
101000.02.1140842.6851310.5172200.5344870.7143240.711143
111000.01.9158992.4700400.4730170.4950150.7549600.752232
121000.01.9752742.4908380.4752180.4879310.7619240.759273
131000.01.9057622.4815890.4792730.5069900.7429610.740100
141000.01.8286132.3411980.4526790.4745700.7747840.772276
151000.01.9145452.4467820.4710890.4937810.7561800.753465
161000.01.8747422.4059280.4479540.4660370.7828100.780392
171000.01.7991862.2917700.4422270.4565290.7915810.789261
181000.01.8010662.3287940.4522060.4759500.7734710.770949
191000.01.7882572.2633540.4404530.4482900.7990360.796799
201000.01.7087192.1531010.4269700.4382910.8079010.805762
211000.01.7593532.2476420.4355300.4496530.7978120.795561
221000.01.7089482.2542080.4345960.4616620.7868680.784495
231000.01.7954552.2944140.4336660.4503400.7971940.794936
241000.01.7976062.2779110.4415090.4585960.7896900.787348
251000.01.8327302.3556120.4455710.4674220.7815170.779084
261000.01.6889702.1104560.4177280.4228020.8212380.819248
271000.01.7165502.1658860.4327410.4425900.8041140.801933
281000.01.6740702.1569860.4077970.4304240.8147350.812673
291000.01.7620142.2454580.4514470.4692010.7798500.777399
301000.01.6801192.1374880.4060330.4180790.8252100.823264
311000.01.7045452.1855020.3990210.4191290.8243310.822375
321000.01.6494022.0874670.3957720.4118270.8303980.828510
331000.01.7001212.1540540.4067720.4182990.8250260.823078
341000.01.6511062.1346170.3994110.4225830.8214230.819435
351000.01.6660912.1364840.3961680.4140280.8285810.826672
361000.01.5672831.9852870.3928030.4027450.8377960.835990
371000.01.6029262.0820940.3925800.4167400.8263280.824394
381000.01.6441332.0735440.4047960.4180530.8252320.823286
391000.01.6292192.0851260.4063830.4227230.8213050.819315
401000.01.5583472.0045620.3827940.3985870.8411280.839360
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", "text/plain": [ - " classified instances mean absolute error root mean squared error \\\n", - "0 1000.0 5.674340 7.363274 \n", - "1 1000.0 3.629025 4.549167 \n", - "2 1000.0 3.045518 3.861026 \n", - "3 1000.0 2.703028 3.405931 \n", - "4 1000.0 2.436556 3.111116 \n", - "5 1000.0 2.398800 3.035545 \n", - "6 1000.0 2.212132 2.800096 \n", - "7 1000.0 2.133315 2.751438 \n", - "8 1000.0 2.107581 2.697964 \n", - "9 1000.0 2.110955 2.638347 \n", - "10 1000.0 2.114084 2.685131 \n", - "11 1000.0 1.915899 2.470040 \n", - "12 1000.0 1.975274 2.490838 \n", - "13 1000.0 1.905762 2.481589 \n", - "14 1000.0 1.828613 2.341198 \n", - "15 1000.0 1.914545 2.446782 \n", - "16 1000.0 1.874742 2.405928 \n", - "17 1000.0 1.799186 2.291770 \n", - "18 1000.0 1.801066 2.328794 \n", - "19 1000.0 1.788257 2.263354 \n", - "20 1000.0 1.708719 2.153101 \n", - "21 1000.0 1.759353 2.247642 \n", - "22 1000.0 1.708948 2.254208 \n", - "23 1000.0 1.795455 2.294414 \n", - "24 1000.0 1.797606 2.277911 \n", - "25 1000.0 1.832730 2.355612 \n", - "26 1000.0 1.688970 2.110456 \n", - "27 1000.0 1.716550 2.165886 \n", - "28 1000.0 1.674070 2.156986 \n", - "29 1000.0 1.762014 2.245458 \n", - "30 1000.0 1.680119 2.137488 \n", - "31 1000.0 1.704545 2.185502 \n", - "32 1000.0 1.649402 2.087467 \n", - "33 1000.0 1.700121 2.154054 \n", - "34 1000.0 1.651106 2.134617 \n", - "35 1000.0 1.666091 2.136484 \n", - "36 1000.0 1.567283 1.985287 \n", - "37 1000.0 1.602926 2.082094 \n", - "38 1000.0 1.644133 2.073544 \n", - "39 1000.0 1.629219 2.085126 \n", - "40 1000.0 1.558347 2.004562 \n", - "\n", - " relative mean absolute error relative root mean squared error \\\n", - "0 1.394600 1.477190 \n", - "1 0.916570 0.934101 \n", - "2 0.759509 0.776583 \n", - "3 0.695283 0.713834 \n", - "4 0.599906 0.622877 \n", - "5 0.574334 0.592687 \n", - "6 0.547849 0.564145 \n", - "7 0.528914 0.552995 \n", - "8 0.494413 0.524178 \n", - "9 0.527524 0.529224 \n", - "10 0.517220 0.534487 \n", - "11 0.473017 0.495015 \n", - "12 0.475218 0.487931 \n", - "13 0.479273 0.506990 \n", - "14 0.452679 0.474570 \n", - "15 0.471089 0.493781 \n", - "16 0.447954 0.466037 \n", - "17 0.442227 0.456529 \n", - "18 0.452206 0.475950 \n", - "19 0.440453 0.448290 \n", - "20 0.426970 0.438291 \n", - "21 0.435530 0.449653 \n", - "22 0.434596 0.461662 \n", - "23 0.433666 0.450340 \n", - "24 0.441509 0.458596 \n", - "25 0.445571 0.467422 \n", - "26 0.417728 0.422802 \n", - "27 0.432741 0.442590 \n", - "28 0.407797 0.430424 \n", - "29 0.451447 0.469201 \n", - "30 0.406033 0.418079 \n", - "31 0.399021 0.419129 \n", - "32 0.395772 0.411827 \n", - "33 0.406772 0.418299 \n", - "34 0.399411 0.422583 \n", - "35 0.396168 0.414028 \n", - "36 0.392803 0.402745 \n", - "37 0.392580 0.416740 \n", - "38 0.404796 0.418053 \n", - "39 0.406383 0.422723 \n", - "40 0.382794 0.398587 \n", - "\n", - " coefficient of determination adjusted coefficient of determination \n", - "0 -1.182092 -1.206386 \n", - "1 0.127455 0.117741 \n", - "2 0.396919 0.390204 \n", - "3 0.490442 0.484768 \n", - "4 0.612024 0.607705 \n", - "5 0.648722 0.644811 \n", - "6 0.681740 0.678197 \n", - "7 0.694196 0.690792 \n", - "8 0.725238 0.722179 \n", - "9 0.719922 0.716803 \n", - "10 0.714324 0.711143 \n", - "11 0.754960 0.752232 \n", - "12 0.761924 0.759273 \n", - "13 0.742961 0.740100 \n", - "14 0.774784 0.772276 \n", - "15 0.756180 0.753465 \n", - "16 0.782810 0.780392 \n", - "17 0.791581 0.789261 \n", - "18 0.773471 0.770949 \n", - "19 0.799036 0.796799 \n", - "20 0.807901 0.805762 \n", - "21 0.797812 0.795561 \n", - "22 0.786868 0.784495 \n", - "23 0.797194 0.794936 \n", - "24 0.789690 0.787348 \n", - "25 0.781517 0.779084 \n", - "26 0.821238 0.819248 \n", - "27 0.804114 0.801933 \n", - "28 0.814735 0.812673 \n", - "29 0.779850 0.777399 \n", - "30 0.825210 0.823264 \n", - "31 0.824331 0.822375 \n", - "32 0.830398 0.828510 \n", - "33 0.825026 0.823078 \n", - "34 0.821423 0.819435 \n", - "35 0.828581 0.826672 \n", - "36 0.837796 0.835990 \n", - "37 0.826328 0.824394 \n", - "38 0.825232 0.823286 \n", - "39 0.821305 0.819315 \n", - "40 0.841128 0.839360 " + "
" ] }, - "execution_count": 12, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ "## Open a regression dataset\n", "from moa.classifiers.trees import FIMTDD\n", - "from moa.classifiers.meta import AdaptiveRandomForestRegressor\n", - "from capymoa.learner.learners import MOARegressor\n", - "from capymoa.learner.regressor.regressors import KNNRegressor\n", - "\n", - "\n", - "fried_stream = stream_from_file(path_to_csv_or_arff=DATA_PATH+\"fried.arff\")\n", + "from capymoa.base import MOARegressor\n", + "from capymoa.regressor import KNNRegressor\n", "\n", "# Create a learner\n", "fimtdd = MOARegressor(schema=fried_stream.get_schema(), moa_learner=FIMTDD())\n", - "arfreg = MOARegressor(schema=fried_stream.get_schema(), moa_learner=AdaptiveRandomForestRegressor(), CLI=\"-s 10\")\n", "# There is a wrapper for KNNRegressor\n", "knnreg = KNNRegressor(schema=fried_stream.get_schema(), k=3, window_size=1000)\n", "\n", - "results_fimtdd = prequential_evaluation(stream=fried_stream, learner=fimtdd, window_size=1000)\n", - "results_arfreg = prequential_evaluation(stream=fried_stream, learner=arfreg, window_size=1000)\n", - "results_knnreg = prequential_evaluation(stream=fried_stream, learner=knnreg, window_size=1000)\n", + "results_fimtdd = prequential_evaluation(stream=fried_stream, learner=fimtdd, window_size=5000)\n", + "results_knnreg = prequential_evaluation(stream=fried_stream, learner=knnreg, window_size=5000)\n", "\n", - "results_fimtdd['windowed'].metrics_per_window()" + "results_fimtdd['windowed'].metrics_per_window()\n", + "# Selecting the metric so that we don't use the default one. \n", + " # Note that the metric is different from the ylabel, which overrides the default name of the metric.\n", + "plot_windowed_results(results_fimtdd, results_knnreg, metric=\"coefficient of determination\")" ] }, { - "attachments": {}, "cell_type": "markdown", - "id": "f41e74a0-715e-47ea-8ff6-f45beb8a1ce6", + "id": "d05f9870-2238-41db-9602-d676e2efadf5", "metadata": {}, "source": [ - "## Plotting Regression results" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "700d864e-f2d4-4e56-9e0d-d39c5f05e936", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:39:49.304897Z", - "iopub.status.busy": "2024-03-21T04:39:49.304753Z", - "iopub.status.idle": "2024-03-21T04:39:50.058547Z", - "shell.execute_reply": "2024-03-21T04:39:50.058176Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_windowed_results(results_fimtdd, results_arfreg, results_knnreg, metric=\"coefficient of determination\")" + "## 3. Concept Drift\n", + "\n", + "* One of the most challenging and defining aspects of data streams is the phenomenon known as **concept drifts**.\n", + "* In CapyMOA, we designed the simplest and most complete API for simulating, visualizing and assessing concept drifts.\n", + "* In the example below we focus on a simple way of simulating and visualizing a drifting stream. There is a tutorial focusing entirely on how Concept Drift can be simulated, detected and assessed in a separate notebook (See ```04_drift_streams.ipynb```)" ] }, { @@ -2084,23 +1045,21 @@ "id": "071f25af-0673-4377-8912-0f0f5f43380c", "metadata": {}, "source": [ - "## Plotting Drift Detection results (Classification)\n", + "### 3.1 Plotting Drift Detection results\n", + "\n", + "* This example uses the DriftStream building API, precisely the **positional version** where drifts are specified according to their exact location on the stream.\n", + "* **Integration with the visualization function.** The DriftStream object carries meta-information about the drift which is passed along the stream and thus become available to ```plot_windowed_results```\n", + "\n", + "* The following plot contains two drifts: 1 abrupt and 1 gradual, such that the abrupt drift is located at instance 5000 and the gradual drift starts at instance 9000 and ends at 12000. This information is provided to the stream via ```GradualDrift(start=9000, end=12000)```\n", "\n", - "* This example uses the DriftStream building API, precisely the positional version where drifts are specified according to their exact location on the stream. " + "* More details concerning Concept Drift in CapyMOA can be found in the documentation: LINK_CAPYMOA_ORG" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "id": "f7b59e4a-7651-4cff-82c0-bb587917e234", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:39:50.070250Z", - "iopub.status.busy": "2024-03-21T04:39:50.060931Z", - "iopub.status.idle": "2024-03-21T04:39:51.846871Z", - "shell.execute_reply": "2024-03-21T04:39:51.846447Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2109,16 +1068,9 @@ "None\n" ] }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[, ]\n" - ] - }, { "data": { - "image/png": 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", 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GjVR0z0z7tSBCah988IHm5gP5TDAYLGq8leQbr97eXuy///6W/R6DwWAwcmEh6wwGg8HIwWaz4ZxzzsE//vEPrF27Nud9LS8b8crJ3+d5Hj//+c+zPheJRBCLxbJemzlzJurq6mhI+tDQUM7vEKPRirD1c845BzabDTfffHOOp5v8rlp/EokE7rnnnpzj+f1+QyHsbW1tOPTQQ/Hggw9mlQ374IMP8Oyzz+L000833ZfBwcGc14yMlZExOOmkk+ByufCLX/wiaxx++9vfYmRkBGeccYbp9hImT56ME044Affddx/6+vpy3t+3bx/995IlS/Dee+/hsccey/kcaRfZyFCWY9Pi6KOPBgDVaxwQcuzlOeD/+c9/8MYbb+C0004z3X4tTjnlFNTV1eH222/PuSdIv4444gjMnDkTP/nJTxAKhQr6HTX8fr/uWL399tu6KvUMBoPBKB7mIWcwGAyGKrfddhueffZZHH/88bjyyiux//77o6+vD3/5y1/wyiuvUCExOXPnzsXMmTPx3e9+Fzt37kQwGMSqVaty8lY3btyIE088EcuWLcMBBxwAh8OBxx57DHv27MH5558PAHjwwQdxzz334Nxzz8XMmTMxNjaG+++/H8FgsCCjVcmsWbPwgx/8ALfccguOO+44fP7zn4fb7cabb76J9vZ23H777Vi4cCEaGxtx8cUX4xvf+AY4jsNDDz2kuiFxxBFH4JFHHsHVV1+N+fPnIxAI4KyzzlL97TvvvBOnnXYajj76aHz5y1+mZc/q6+tpTXUz3HzzzXj55ZdxxhlnYPr06di7dy/uuecedHR04Nhjjy1qDCZNmoRrr70WN910E0499VR87nOfw4YNG3DPPfdg/vz5NJ++UP7nf/4Hxx57LA4++GBcccUVmDFjBvbs2YPXXnsNO3bswHvvvQcA+N73vodHH30US5cuxWWXXYYjjjgCg4OD+Pvf/457770XhxxyCGbOnImGhgbce++9qKurg9/vx4IFC3LytgkzZszAQQcdhOeeey6nvj0Zn2OPPRZf/epXEY/H8bOf/QzNzc34/ve/b7r9WgSDQdx99924/PLLMX/+fFx44YVobGzEe++9h0gkggcffBA2mw2/+c1vcNppp+HAAw/EpZdeiqlTp2Lnzp148cUXEQwG8Y9//MP02B9xxBF47rnn8NOf/hTt7e3o7u7GggULAABvvfUWBgcHcfbZZ5s+LoPBYDBMUHZddwaDwWDUDNu2beMvuugiftKkSbzb7eZnzJjBX3XVVXw8Hud5Xr3s2fr16/mTTjqJDwQCfEtLC3/FFVfw7733XlY5qv7+fv6qq67i586dy/v9fr6+vp5fsGABv3LlSnqct99+m7/gggv4adOm8W63m588eTJ/5pln8mvXrs1qIwose0b43e9+xx922GG82+3mGxsb+eOPP55fvXo1ff/VV1/ljzrqKN7r9fLt7e3897//ff6ZZ57J6XcoFOIvvPBCvqGhgQdAS6CplT3jeZ5/7rnn+GOOOYb3er18MBjkzzrrLH79+vWqbVaWMyMltHp7e3me5/nnn3+eP/vss/n29nbe5XLx7e3t/AUXXMBv3Lgxp79q5BsDnhfKnM2dO5d3Op38lClT+K9+9av80NBQ1meOP/54/sADD8w5PhkDrTJ3W7Zs4S+66CK+tbWVdzqd/NSpU/kzzzyTf/TRR7M+NzAwwP+///f/+KlTp/Iul4vv6OjgL774Yr6/v59+5m9/+xt/wAEH8A6Hw1AJtJ/+9Kd8IBDIKm8nb+9dd93Fd3Z28m63mz/uuOP49957r6D2k3OmVcbv73//O79w4UJ6PRx55JH8ww8/nPWZd955h//85z/PNzc38263m58+fTq/bNky/vnnn6efMXrN8DzPf/zxx/yiRYt4r9fLA8gqgbZ8+XJ+2rRpWWXWGAwGg2E9HM+bUPdgMBgMBoPBGEeMjIxgxowZuOOOO/DlL38ZALB161Z0d3fjzjvvxHe/+90Kt7D8xONxdHV14ZprrsE3v/nNSjeHwWAwxjUsh5zBYDAYDMaEpb6+Ht///vdx5513lk01v9p54IEH4HQ6c2qZMxgMBsN6mIecwWAwGAwGQ8ZE95AzGAwGo3wwDzmDwWAwGAwGg8FgMBgVgHnIGQwGg8FgMBgMBoPBqADMQ85gMBgMBoPBYDAYDEYFYAY5g8FgMBgMBoPBYDAYFcBR6QaUmkwmg127dqGurg4cx1W6OQwGg8FgMBgMBoPBGOfwPI+xsTG0t7fDZtP2g497g3zXrl3o7OysdDMYDAaDwWAwGAwGgzHB2L59Ozo6OjTfH/cGeV1dHQBhIILBYIVbI5BMJvHss8/ilFNOgdPprHRzGAXAzmHtM+7PYTgMtLcL/96yBWhpqWx7SsS4P48TgAlxDiMR4NVXAZcLcLsr3ZqSkMxk8OzQEE5pbIRTxxNkCdEocM45wr8ffxzwekv7exOEZCyGZ8NhnHLccXBWyZqZYZ4JMafWCKOjo+js7KT2qBbj3iAnYerBYLCqDHKfz4dgMMhulBqFncPaZ9yfQ7td+nddHVAl85/VjPvzOAGYEOfQ4QD8fuFe9Hgq3ZqSkMxk4IvHEWxuLo9BTmhuZga5RSQjEfh4XrgXx+kzYyIwIebUGiNf2jQTdWMwGAwGg8FgMBgMBqMCMIOcwWAwGAwGg8FgMBiMCsAMcgaDwWAwGAwGg8FgMCrAuM8hNwLP80ilUkin02X5vWQyCYfDgVgsVrbfZFhLvnNot9vhcDhYqT0Gg8FgMBgMBoOhyYQ3yBOJBPr6+hCJRMr2mzzPo7W1Fdu3b2cGW41i5Bz6fD60tbXB5XKVuXUMBoPBYDAYDAajFpjQBnkmk0Fvby/sdjva29vhcrnKYiBnMhmEQiEEAgHdIvGM6kXvHPI8j0QigX379qG3txezZ89m55nBYDAYDAaDwWDkMKEN8kQigUwmg87OTvh8vrL9biaTQSKRgMfjYYZajZLvHHq9XjidTmzbto1+jsFgMBgMBoPBYDDkMGsQYEYxoySw64rBYDAYDAaDwWDowSwGBoPBYDAYDAaDwWAwKgAzyBkMBoPBYDAYDAaDwagAFTXIx8bG8K1vfQvTp0+H1+vFwoUL8eabb9L3L7nkEnAcl/XfqaeeWsEWjz+2bt0KjuPw7rvvAgDWrFkDjuMwPDxc0XaVEmWfGQwGg2Edvf1hrHj6Y3z94Xew4umP0dsfrnSTGAwGwxRsHmOUk4qKul1++eX44IMP8NBDD6G9vR1//OMfcdJJJ2H9+vWYOnUqAODUU0/FAw88QL/jdrsr1VxNevvDWLl2O3YMRdHR6MWynk50t/hL/rvbt2/HDTfcgKeffhr9/f1oa2vDOeecgx/96Edobm4u6JgLFy5EX18f6uvrLW2rXL2eqNp/4QtfwO233172c9rZ2Ym+vj60tLSU9XcZDAZjvLNy7XZcs2odOI4Dz/PgOA73vbQFK5bMw9Kezko3j8FgMPLC5jFGuamYhzwajWLVqlW44447sGjRIsyaNQs33ngjZs2ahV//+tf0c263G62trfS/xsbGSjVZlZVrt+PEu9bgf1/+BE+u24X/ffkTnHjXGvxl7faS/u4nn3yCnp4ebNq0CQ8//DA2b96Me++9F88//zyOPvpoDA4OFnRcl8uF1tbWkpR/e+CBB9DX14fe3l7cc889eOihh/DjH//Y8t/Jh91uR2trKxyOCV1kgMFgMCyltz+Ma1atQ4YH0hk+6//LV63DVuZhYjAYVQ6bxxiVoGIWSSqVQjqdzikH5fV68corr9C/16xZg8mTJ6OxsRGf/exn8eMf/1jX+xuPxxGPx+nfo6OjAIBkMolkMpn12WQyCZ7nkclkkMlkwPM8osm04T5sG5BuWvC88KL4/+Wr1uHAtjpMa84tp8bzPKKJNOzxJDV8vU67KSP4a1/7GlwuF55++ml4vV4AQEdHBw455BDMnj0b1113He655x7MmDEDV1xxBTZv3oxHH30UjY2NuO6663DllVcCEMp3kf9nMhmsWbMGJ554IgYGBtDQ0IDf//73uPrqq/Hwww/j6quvxvbt23HMMcfgd7/7Hdra2mh7fvOb3+Duu+9Gb28vurq68PWvfx1f/epXs9ocDAYxefJkAMDUqVPxuc99Dm+99RZtw5YtW/Cd73wHb7zxBsLhMPbff3/ceuutOOmkk+gx+vr6cMUVV+DFF19Ea2srbrnlFvzwhz/EN7/5TXzzm98EAHz88ce48sorsXbtWsyYMQM/+9nPsHjxYqxatQrnnHMOtm7dipkzZ+Ktt97CoYceSvv87LPP4tprr8X69etx6KGH4re//S3mzJlDf/vWW2/FL3/5S0SjUSxbtgx1dXVYs2YN3n77bdVzRK6pZDIJu91u+NwyygOZD5TzwrghmYST/DOVAsZpP8f9eawh/vzGNnDgAPA573EAHn5jG757yuyc9ybEOUylhPVBJiP8Nw5Jiv1KlqN/mYw0v43jMS03SXENO56fGfkodB6rJibEnFojGD0HFTPI6+rqcPTRR+OWW27B/vvvjylTpuDhhx/Ga6+9hlmzZgEQwtU///nPo7u7G1u2bMF1112H0047Da+99pqmgXP77bfjpptuynn92Wefzak17nA40NrailAohEQigWgijaN/+rol/cvwwOm/fNXw51+7+ih4XcaMtqGhITz77LP44Q9/mLPR4PP5sHTpUjzyyCO4/fbbkclkcNddd+G6667D17/+dfztb3/DVVddhSOOOAKzZ89GKBQCAITDYYyOjiISiQAQ8vttNhtisRgikQjuuOMO3HPPPbDZbPjKV76Cb33rW7j//vsBACtXrsQNN9yAO+64A/PmzcO6devwzW9+EzabDRdccAFtWzQapRskmzdvxvPPP48LL7yQvrZ792585jOfwTXXXAO3240///nPOPvss/Gf//wHnZ1CiNCXvvQlDAwM4B//+AecTid+8IMfYO/evYjFYhgdHUU6ncY555yDjo4OrF69GqFQCNddd13W72v1+brrrsNNN92E5uZmXH311bjkkkvwzDPP0D7edttt+MlPfoIFCxbgr3/9K371q19h+vTptP1KEokEotEoXn75ZaRSKUPnllF+Vq9eXekmlAR7LIYzxX8/8/LLSCs2P8cb4/U81hJvbrQhw3MQlq3ZZHgeb67fgqdSmzS/PyHOYYHRa7XE6t27S/4bWfNbX9+4n9/Kzep//avSTagYxc5j1cSEmFOrHGJj5KOiMbsPPfQQLrvsMkydOhV2ux2HH344LrjgArz11lsAgPPPP59+9uCDD8a8efMwc+ZM6tFU49prr8XVV19N/x4dHUVnZydOOeUUBIPBrM/GYjFs374dgUAAHo8HjkTljKa6YB18LmOn46OPPgLP8zj00ENz+gQA8+bNw4MPPoh4PA6bzYbTTz+djskhhxyCe++9F2+++SaOOOIIBAIBAIDf70cwGKSbFnV1dQgGg/B4PEgmk/jf//1fzJw5EwDw9a9/Hbfccgv97TvuuAM/+clPqPF98MEHY+vWrXjooYfwla98hbbr8ssvh91uRyqVQjwexxlnnIEbbrgBTqewz33MMcfgmGOOoZ8/7LDD8M9//hNr1qzBVVddhY8//hhr1qzBG2+8gZ6eHgDA7373O8yZMwcejwfBYBBPP/00ent7sWbNGrS2tgIQQtQXL14Mr9eLYDCo2efbbruNXlfXXXcdzjrrLLhcLng8Hvzud7/DZZddRr3+hx12GF544QXEYjHVcwAI15fX68WiRYtyIkEYlSeZTGL16tU4+eST6TU4rghLYXWLFy0Cxqlmwrg/jzXEescmvPfKVqT5XM+SjeMw/4AZOF3DQz7uz2E0Crz6KhAIAOP0eZDMZLB6926c3NoKp63EGZHRKP3n4rY2QIwUZBRHMhrF6qEhnHzccXDW1VW6ORWh0HmsmpgQc2qNoOW0U1JRg3zmzJl46aWXqKeyra0N5513HmbMmKH6+RkzZqClpQWbN2/WNMjdbreqSJjT6cy5KNPpNDiOg81mg81mg9/txPqbFxtu/09Xb8QDGjetneNw6bFduPrk/XLey2QyGBsdQ12wDjbxoWUmZJ18h7RdCTkOee+QQw7J+lxrayv6+/tpv8lntf72+XyYPVuafNrb27F3717YbDaEw2Fs2bIFV1xxRZbxnUqlUF9fn/W7d999N0466SSk02ls3rwZV199NS6++GL8+c9/BgCEQiHceOONePLJJ9HX14dUKoVoNIrt27fDZrNh06ZNcDgc6Onpocfdb7/90NjYSMdi06ZN6OzsRHt7O/3do446Km8fAeDQQw+l/yaigv39/Zg2bRo2bNiAr33ta/T9TCaDI444Aq+++qrqOSDH5zhO9dpjVA/j9vzI+uR0OLL+Ho+M2/NYQ5y/YDruf6VX9T0ewAULpuueo3F9DpNJgOMAm034bxzjtNlKb5DLju+cAGNaNsT1o9PhGL/3Yh6KnceqiXE9p9YIRse/KlSt/H4//H4/hoaG8Mwzz+COO+5Q/dyOHTswMDCQlbtsJRzHGfZSA8AXF0zH7zRvWh5fWjBd9XiZTAYplx0+l0PTmNNj1qxZ4DgOH330Ec4999yc9z/66CM0NjZi0qRJAHIvBo7jaN62EdS+z4ubECT8+/7778eCBQuyPqdMK2htbaXpCHPmzMHY2BguuOAC/PjHP8asWbPw3e9+F6tXr8ZPfvITzJo1C16vF1/4wheQSCQMt7UY5P0kmxpmxonBYDAmMt0tfqxYMg/ff3Qdzb60ifvMK5bMQ1cZqo8wGAxGMZB5bDnRiBKxcWweY5SOim4pPvPMMzTEePXq1fjMZz6DuXPn4tJLL0UoFML3vvc9vP7669i6dSuef/55nH322Zg1axYWLzbuxS4l5Ka1cYDdxmX9v5Q3bXNzM04++WTcc889iMrCtgAhD/v//u//cN5555VEKV3JlClT0N7ejk8++QSzZs3K+q+7u1v3u8RgJ3149dVXcckll+Dcc8/FwQcfjNbWVmzdupV+fs6cOUilUnjnnXfoa5s3b8bQ0FDWZ7Zv3449e/bQ1+S17Qtlzpw5OcfREnNjMBiMicrSnk789/Ez6d+nHNCKF75zAisVxGAwaoalPZ148hvH0r+9LjubxxglpaIe8pGREVx77bXYsWMHmpqasGTJEtx6661wOp1IpVJYt24dHnzwQQwPD6O9vR2nnHIKbrnllqqqRb60pxPzu5rwiKwO+Xk9nSXfQfvVr36FhQsXYvHixfjxj3+M7u5ufPjhh/je976HqVOn4tZbby3p78u56aab8I1vfAP19fU49dRTEY/HsXbtWgwNDWXl8w8PD2P37t3IZDLYtGkTbr75Zuy3337Yf//9AQCzZ8/GX//6V5x11lngOA7XX399lod67ty5OOmkk3DllVfi17/+NZxOJ77zne/A6/XSzYeTTz4ZM2fOxMUXX4w77rgDY2Nj+OEPfwgARW1QfP3rX8cVV1yBnp4eLFy4EH/+85/x4Ycf0rx6BoPBYAjEUlK1klMPamUeJQaDUXM4ZVGeiVQG05pyqyYxGFZRUYN82bJlWLZsmep7Xq+XKlxXO10tfiw/dW5Zf3P27NlYu3YtbrjhBixbtgyDg4NobW3FOeecgxtuuAFNTU1la8vll18On8+HO++8E9/73vfg9/tx8MEH41vf+lbW5y699FIAgmHc2tqKRYsW4bbbbqP1wH/605/isssuw8KFC9HS0oLly5fniCH84Q9/wJe//GUsWrQIra2tuP322/Hhhx9S0TS73Y7HH38cl19+OebPn48ZM2bgzjvvxFlnnVWUsNoXv/hFfPLJJ/jud7+LWCyGpUuX4sILL8R7771X8DEZDAZjPNI3HKP/jiSMlxJlMBiMaqFvRIpATWd4jMaSaPC5KtgixnimKnLIGYUxffp0/P73v9f9jDzkm/Duu+/Sf3d1ddF8cAA44YQTsv6+5JJLcMkll2R9/5xzzsn6DABceOGFuPDCCzXbofy8Gl1dXXjhhReyXrvqqquy/m5ra8NTTz1F/96xYwf27t1Lc9MBwZMur2X/6qtC+TnymXx9BgSBN+Vr119/Pa6//noAQm75iSeeyDzkDAaDoUC+kI1UsHoJg8FgFIp8YxEA+kMJZpAzSgYzyBk1xQsvvIBQKISDDz4YfX19+P73v4+uri4sWrSIfuaxxx5DIBDA7NmzsXnzZnzzm9/EMcccU5TxHIlEcO+992Lx4sWw2+3405/+hDVr1tRMFAeDwWCUi10j0kI2yjzkDAajBtk1kq3RNBCKY9bkQIVawxjvMIOcUVMkk0lcd911+OSTT1BXV4eFCxfi//7v/7IU0sfGxrB8+XJ8+umnaGlpwUknnYS77rqrqN/lOA5PPfUUbr31VsRiMcyZMwd/+MMfcNJJJxXbJQaDwRg3JFIZ9Ifi9O9okhnkDAaj9lB6yAfC5an4w5iYMIOcUVMsXrw4r8r+RRddhIsuusjS3/V6vXjuuefo35lMJie/ncFgMCY6e0ZjkGf7sBxyBoNRi6h5yBmMUlHRsmcMBoPBYDDGD7uGsxexLGSdwWDUIn1i6k1Xs6CuzjzkjFLCDHIYExxjMMzCrisGgzHR6BvJDvOMsJB1BoNRY/A8jz5xc/HAqfUAgIEQM8gZpWNCh6yTvONIJAKv11vh1jAqSTyZxmAkgWSKh9PBocnngttpz/9FHSKRCABk5beXg97+MFau3Y4dQ1F0NHqxrKcT3Rp1gM18lsFgMPJBwjxtHJDhgShTWWcwGDXGaCyFsBjdc/DUejy5rg8DYRayzigdE9ogt9vtaGhowN69ewEAPp8PHMeV/HczmQwSiQRisRhsNhakUGmGIwnsHY0B4ADwADjsG+IxOejRLHGhdw55nkckEsHevXvR0NAAu704w94MK9duxzWr1oHjOPA8D47jcN9LW7BiyTws7eks+LMMBoNhBCKE1Nnkw7aBCMshZzAYNQcp3djoc6KjUXDY9TMPOaOETGiDHABaW1sBgBrl5YDneUSjUXi93rJsADC0SaUz2DMah1pw+d5dwJSgGw577qaJkXPY0NBAr69y0NsfxjWr1iHDA1RVSfz/8lXrML+rCV2i99vMZxkMBsMoZCE7o8XPDHIGg1GTkI3Ftnovmv1uAMAgyyFnlJAJb5BzHIe2tjZMnjwZyWSyLL+ZTCbx8ssvY9GiRWUPZ2Zkc/+/PsHKtfuQyeSa5DYbh2U9nbjiuO6c9/KdQ6fTWVbPOCB4vDmOA1Ry1zmOwyNrt2P5qXNNf5bBYDCMsktcyM6cFMCLG/YxUTcGg1FzkNSb9gYPmgNCpCRTWWeUkglvkBPsdnvZDCi73Y5UKgWPx8MM8gqzsT+OnaMpqNjjsHHC+x6PJ+e9ajyHO4aimkJyPM9jx1C0oM8yGAyGUchCdubkAAAgkmQ55AwGo7bI9pALBvlQJIlUOqMaNclgFAu7qhgTmo5G7ZBzjuNo7lAtYKYv46nfDAajOogm0hiOCJFmMycF6GsMBoNRS5CNxbYGQUvIJi6XBiMsbJ1RGphBzpjQLOvp1PUUn1dD4mZm+jKe+s1gMKoDsoj1u+yYEhTyLplBzmAwag3iIW+v98Ju49DkJ2HrzCBnlAZmkDMmNN0tfqxYMi/rNTvHwcYBK5bMqylhM9IXud/bzkG1L2qftWl8lsFgMIxAwzwbvPC6hBSwSDKtufnHYDAY1Qj1kNcLKYvEIGfCboxSwXLIGROepT2deOTN7Vi7bQgAcNHR03Hxwq6aNEqX9nTitS0D+Os7OwEA582fhisXzVDty9KeTuwdi+HOZzYCAE4+YAquPW3/muw3g8GoPPJFrM8lLC94HoinMvA4yytyyWAwGIXA8zz6RkQPeYOQvicorYfQz4TdGCWCecgZDACJdIb++4tHTatpo9Rhl/zeS46Ymqcv0mdPOaC1pvvNYDAqizzM0yszwFnpMwaDUSsMhBNIpDLgOGBKUPCQS0rrzEPOKA3MIGcwAIxEpZJ3/TU+4Ybj0uI3X1/kD5dwgqkhMxiMwumTCSHZbRzcDmGJEWFzC4PBqBHIxmJLwA2XOIe1BARNjIEw85AzSgMzyBkMgCoDA7WfIyQ3rPP1ZVD2cJEb8gwGg2GWXSOShxwAfGIeORN2YzAYtQKtQV4vlbxtZqJujBLDDHLGhCeT4TEakwzygRrPEQrHJYM8X18GZAa7/HsMBoNhlr5hyUMOgIats5B1BoNRK9B5rF4q/9pEQtZr3GHDqF6YQc6Y8IzFUpCLAE+kkPV+FrLOYDAsggghkYUsVVpnBjmDwagR6DzWIPeQiyHrNe6wYVQvzCBnTHjk+eNA7ecIyfM18+3myh8uERayzmAwCmQ0lkRIjLJpFxeyRGk9mmSbfQwGozZQpt4AQAvzkDNKDDPIGROeHIO8xj3kIZlhrbeby/N8Vo55iHnIGQxGgRAhpHqvkxriXppDntH8HoPBYFQTJGSdlDwDgGYi6lbj60NG9cIMcsaEZziaPcHW+g5oxKCo22g0hVRGitWPsBxyBoNRIPIa5AQfDVlncwuDwagN1ELWm0RRt1A8hViSRRMyrMdR6QYwGJWGeMjtNg7pDF/TOUKZDJ+Vr6mXQ96vCM1nKuuMaqC3P4yVa7djx1AUHY1eLOvpRHeLv9LNqhi1Mh60BrnMq0RV1gtYwJay37UypgwGo7ykMzx2j+aGrAc9DjjtHJJpIbJQPs8xGFbADHLGhIeUPJvW5ENvf7imPeQRxcJ3MBxHJsPDZuNyPqsMvWKiboxKs3Ltdlyzah04jgPP8+A4Dve9tAUrlszD0p7OSjev7NTSePSpeMi9TmGJYVbUrZT9rqUxZTAY5WXfWBzpDA+HjcOkOjd9neM4NPvd2D0aw0CIGeQM62Eh64wJD/GQzxA9JMORJJLp2sx5JKXLiP2d4YFhRY48gUQC2MUPs7JnjErS2x/GNavWIcMLXgr5/5evWoet/eFKN7Gs1Np47FTJu/QVoLJeyn7X2pgyGIzyQlJvpgQ9dG1EaBaF3ZTRhQyGFTCDnDHhIQb59GY/NWSHatRLToxqv9uBeq8TgOAlV4NEAhBF5DArTcSoICvXbgfH5UZyAIJ34pG128vcospSa+Mhhazn5pBHTUTflLLftTamDAajvJB5TB7pQ2DCboxSwgxyxoRnRAxZb/Q5qXBHrYatkzxwv8sh7eZqPDzIQ2Vak0/8LvOQMyrHjqEoeJ5XfY/neewYipa5RZWl1sZDClmXPOQep3kPeSn7XWtjymAwygudx1RC0pvF9aGWk4PBKAZmkDMmPMRD3uBzotlf2zugJA/c77ajJU9fSL31aU1CqH4kkUYmo75YZTBKTUejV9d72dE4sXL2amk8eJ6nysRyISTJQ27cIC9lv2tpTBkMRvnZRSJ91DzkxGFTo+tDRnXDDHLGhIeUPQt65R7y2twBjSSkkPV8fSEPlc4maRGqFIVjMMrFsp5OXe/leRNMcKuWxmMwnEA8JehuTKmXhJAKySEvZb9raUwZDEb5UROnJJCQdb3qNQxGoTCDnDHhGYkKRmyDz5U3zLvaCYkh6z6XPW9f+kVRt6kNXpo7z2qRMypFd4sfK5bMy3rNbuNg44AVS+aha4KVpSLjIffnVut4EO94S8ANt8NOX/e6RJV1Ext9tN+yjts4WNJv1TG16NgMBqP22UVrkKuErAdq22HDqG5Y2TPGhGckIhis9V4nWsQd0FrNESIGdcDtoLu5Wn0ZFPPkJwXc8LscGIunmLAbo6Kcc9hUfO/RdQAETYfzj5yG83o6J6yhtLSnE+/vHMEfXtsGjgOuXDSjKsdjF1VYz/YqEQ95zOS8QsqPkWvh6BnNuPXcgy3p99KeTry3fRh/fONTAMD5R07DFcfNqLoxZTAY5YfOZfXaOeQsZJ1RCphBzpjw0Bxyr7PmJ9yQaJD7XA60BPT7QoTrmgNu+Nx2wSBnHnJGBdkzGqP/ntroxfJT51awNdWBVzRqeR749kn7weWovsA24iFXhnmStkeS5ucVt1PytB/Z3WypwSwvZ3ThgmnMGGcwGEikMjRysK1BO2R9sEZFfxnVTfU92RmMMpJMZ6hXuN7rrPkcIZKr6Xc7dAXqUukMhiLEIHfB7xb25phBzqgkxLADhMURA4gnpXGo1vtzl4rCOgD4ClBZJ5CNUgAIxZM6nzTPWEwax1CsOseUwWCUlz2jMfA84HLYqHNGDnmtPxTX1KJgMAqFGeSMCY180TceRN1oHXKXnfalX6UvQ5EkeB7gOKDR54JfzPUMm6gXzGBYDQkXBIBkmi14AFCxNKB670+1GuSAEKkDmFNZJ4zK5uYxi43mMdnGhtXHZjAYtQl5/rTVe1SrMZAc8ngqw9L7GJbDDHLGhIYY5HUeB+w2Lm+Yd7UTlqms6/WFbDg0+lyw2zj43YIni9QxZzAqAfOQ5xJPSfdktd6fajXIAVnIegGL1+GING9ZbpDHZMa+xd53BoNRm6iVbpTjczmoLsZAqDadNozqhRnkjAnNcERYjNV7nQBqP0eILNj9bjvty0g0iWQ627gZFI10EoJFPeRVGhLLmBj0yTzkcWaQA6gND/kuTQ+5+TrkBHn00pjF81IozkLWGQxGNjT1RiV/nEAjD2vUacOoXphBzpjQkLDIBh8xyIXJNhRPIVaDNblpyLrbgQavk5YzG1JsMPSHpfxx8nkALAyLUVF2yTzkyk2kiUq155CnMzwV48vxkIs55Il0BimT55NslgLZHm0rkHvcR5lBzmAwIEu90fCQA7XvtGFUL8wgZ0xohqNSyTMAqHM74LILt8VADU64NGTd5YDNxmnu5pJwKyL8JoWss8Upo3KQ0GeAhawTEmm5QV59G2b9oThSGR42Dphc5856j4SsA+ZqkQMKUTeLjWb58UJszmMwGJCl3uh4yFtoJR4Wss6wFlb2jDGhGYmQkmfCJMtxghG7ezSGgVAcUxu0d0qrESlkXbi1m/1u9IcSOSJ1JK+cesiZqBujCiChz0C2ITqRiSflOeTVdX/29ofxy+c3ARCM7+1DUXTLSoi5HTbYOCDDC2HrQY/T8LFHSinqFpOLurEcckZt0TuWwsqtUewIp9Hht2NZlxfdddWxnO/tD2Pl2u3YMRRFR6MXy3o6s+aEaqW3P4x3Ph0GALy6uR8LZ7aotpusmWrRYcOobqrjDmYwKsSwuOgLeqWFYnOAGOS1N+FGEpLKOiA+PPbkCrsRA514yH2s7BmjwsSS6awwwHSGRzrDZ9WMnojIc8gjVbRhtnLtdlyzah39OxxP48S71mDFknlY2tMJQNjg9LkcCMVTpvPIs8ueWdfveCqdtdnDcsgZtcTK3giuWTsKjgOtlHLfx2GsmB/E0i5fZdsmzgkcx4HneXAch/te2pI1J1QjpN0ZsbDH0x/sxtMf7FZtt1Qal3nIGdbCQtYZE5oRRQ45IE24tbgDSjzkxMDW6kuuh1xUQ67CkFjGxIAo3MoNcJZHrhR1q477s7c/TBewGVl1ugwPLF+1Dlv7w/S1QpXWlQZ5OmNNGTylt52VPWPUCr1jKVyzdhQZAGkeWf9f/uYotoYqdy3L54R0hs/6v3JOqCbk7SaQeU2t3c3+2q7Ew6hemEHOmNCQRV+9zENeyzlCJOQ8IOaEN2v0hRjoLTmibmxxyqgMRGFdrtTNlNaVZc+q4/5cuXa7ap1eQPCKP7J2O/2bKq0njbc9kcrkGPBWzU1Kj7jVCu4MRqlYuTUKjdsOHAc80htVf7MMmJkTqgmz7SZODCbqxrAaZpAzJjRSDrlkkBMhtNr0kAuLS5+L5JCr7+YSA70pR9StOjxwjIkHUVif1iSFXTJhN6XKenXcnzuGouB5dY81z/PYMSQZBkRp3YyHnGyUchzgtAuLZas82cxDzqhVdoTT0LjtwPPC+5XCzJxQTZhtN0nzYyHrDKthBjljQqPmIa/VHKFEKoNkWniw+HNC1pmoG6O6oR7yei+tdMBC1hUh61Xize1o9Op6lToaJTFMXwEh6yNi9Yugx4k6UQjOqlzvsXi2iFsozkTdGLVBh9+u6yHv8NvV3ywDZuaEasJsu5moG6NUMIOcMaEZVjXIazNHSL5YzxJ1Q3bZs3gqTcM0W6iHnIm6MSoL8ZC3N3jhcgiPJuYhV4SsV8mG2bKeTl2v0nkyISQSrWNG1E2+UVrnEb5vlRo68YhLx62OMWUw8rGsy6vrIT+vu3JGr5k5oZow227iIR8MJ5CxSNeCwQCYQc6Y4NCFn0zUraVGc4TIYt3tsMEhehjV+kL+7bBxCHqFRalkkFdHSCxj4kFqwLY3eCSDnHnIq9JD3t3ix4ol8yAXwLdxwn8rlsxDl6xckKeAkPXhiGSQB8S5yapcb+Jpb6/30r+1FuQMRjXRXefAivlByP25dk5YyK+YH0RXoHKFk8icIHc2c1CfE6oJ2m7Za3Ybp9luktKYzvAYZSUTGRbCyp4xJiw8z9Mc8iwPubgDWmuibsoa5IB6X+Th6iRUi3jUq8UDx5h49Ik1yNvqvTRveKJ7yHmezxqDalFZB4ClPZ2Y39WExT97GfFUBkt7OvHV42fmLGClkHXjc4u8+gVJW7Auh1w4dluDBxv2jCGV4RFLZqgaPINRzSzt8uHj4RR+uykCr53DJbN9OK/bW1FjnLatpxOD4QRu/+fHAIDZUwL43//qqVpjnLC0pxOheAo3/WM9mv0uLJvfifN6OlXb7XLYEPQ4MBpLoT+UQIPPVYEWM8Yjlb+DGYwKEUtmqAdOPqmSHdD+cILW0qwFiDFNBNoAoEn0kIcTaUQTaXhddpobTwTdhO8IUwEre8aoFLuYhzwHpcp8tXjICdOafNRg/s7J+2Fy0JPzGaqyXkDIetDrpKJ2VuWQk5rmU+o8tJbzWDzJDHJGzeB1CGuSOieH5QfXVbg12QxFJK/xrMmBqjfGCcQpc0B7EMtPnav72ZaAG6OxFAZCccyaHChH8xgTABayzpiwkEWf3cZRDzEg5V0nUhm6eKsFiDFNBNoAoM7toAJZRNiNeMhJOLv8O4l0ZsJ7JRnlJxRPUQ9om0zUbaJfi0qD3Gwt71IzFk/R+r1BWZSRHC8te2Y+ZL3B60SwhDnkNBye5ZEzaoiYKN4aT1dfqsXGPWP032Y24SoN2Vh0O/KbRUzYjVEKmEHOmLAMi0q+DV5nlhfc53JQr04tCbuRzQN5yDrHcTl1M8n/SUk0APDJvOpmQksZDCsgCutBjwN+twNOZpADyBZ0A1B1G4Sj4qamx2mjueJKClNZl+WQiwa5VX0fpQa5E0GLFdwZjHJADPJEFU6PG3bLDHITm3CVhjxryLNHD1oat8bSGhnVDTPIGRMWtfxxQi3ugBJD2qcIvVSqxveLnnJSEg0QHkIkTLjaFv2M8Y9cYR2QvBQTveyZckMiUmX35rDOHEqwTmXd2pB15iFn1CoksyxRZSrfoXgKO4elut2xZO3M3yQayWXIQ07KydbO+pBR/TCDnDFhGZblKSppqkFhN5JfKg9ZB6S+kNxxYpg3+bPFSPwFeLIYDCsgHvK2eiEHmZU9E8gJWU+mq6rUzohK2UglXqKybsJbJhd1C7iFY1st6hbwOKixz2qRM2oJ4iFP80CqiuaDTbJwdQCI1ZKHXNz8dRnwkLf4a7M0LqO6YQY5Y8IiX/QpoRNuDe2AEgVmecg6kNsXsskgzyGXf6/ahKMY4x/iIW8TPeQ0ZH2Ce8iJoFmdeG/yfHWFgUppP9pKw5Kom/F5ZTgiHLcUdchJeHpQZpCPMg85o4aIyXLHq8lLvmlPCIDsnq+iuSofyZQwjuY85LXjsGFUP8wgZ0xYDIWs16KH3K0Vsi56yGkOuTvrc8SzzmqRM8oN8ZC3Mw95FiSHPOh10vq+1VSacEQnyojgLSqH3CXzYlvlIReOE3A7EWA55IwaJJ5lkFewIQo2iB7yg6bWA6g1D7nQVjM55P3MQ86wEGaQMyYs1EOuapDXXo6QWh1yILcv8jrkcoghX00LfsbEoG9EqkEOSGGDE95DLm5IeJy2qtww04syIpAccnMGuTAHlTqH3OpjMxjlIMtDXkVK60Rh/dDOBgC1pbJONn9NqazXkMOGUf2wOuSMvPT2h7Fy7XbsGIqio9GLZT2d6K6R2pJ66OU/NtdgjpCUQ67wkMv6wvM8DbNqCSg85HlC1sfrdcCoPKQGeVsD85DLidNFoh1+tx2heEo3pUTrHi3VvasXZUQgOeRGF+c8z2OEhML7nNSAtk5lXZZD7q5sDnnvWAort0axI5xGh9+OZV1edNeZW5ZZcYxqoZR9KfdYl7Iv8j25eBWFrBODfF4H8ZDXzvydTBsPWSepRJ8ORrDi6Y/ZWohhCbU5azPKxsq123HNqnXgOA48z4PjONz30hasWDIPS3s6K928oiCibvW+3PxHSWW9dnZAiWc710Mu9SWSSNOHZK6om2iQqyycx/N1wKgsPM9jFw1ZV3jIJ7pBLoZ8uqmHPK5pkGvdo184ogOPvrWjJPeuXpQRgYasJ40Z1NFkmi6O671O+htW5JDzPF81HvKVvRFcs3YUHCdoA3AccN/HYayYH8TSLl/ZjlEtlLIv5R7rUp8XuYe8WgJmRiJJ7BkV1kvzpjYAECKc0hkedhun883qIG6w7BmZZwHBiP/flz5hayGGJbCQdYYmvf1hXLNqHTI8kM7wWf9fvmodtvaHK93EotD3kBOV9drxkJOQUKXKurwvpD8epy2nPBqpRa5c8I/364BRWYYjSbpJ1KrIIZ/oZc/isjBKstGmFvqtd4+uXLujZPcuLXumG7JuzkNOjumwcfC57JaWJgsn0uBFW6bO7ZSOXWYhy96xFK5ZO4oMBKVs+f+XvzmKraH87bHiGNVCKftS7rEux3mRe8WrRdRt417BO95e78GkOin6rlbyyBMGyp7J51lCmmdrIYY1MIOcocnKtdvBceo7mxzH4ZG128vcImsZiRCFYG1Rt1oS7QjF83jIQwmpBrnfnXNuyeJUWet4vF8HjMpCwtWb/S54xPBmFrIuIA9ZJ4atWui23j2qhRX3rpGyZ2YNcnleOsdxCIrCa/FUpujrgYi3OWwcPE4b6jzWllQzysqtUWidLo4DHumNqr9p8TGqhVL2pdxjXY7zUo0q6xt2Cwb5fq11WXnYtaK0bqTsGVsLMUoJM8gZmuwYioLn1Sd7nuexY6h2Hvhq0MWkWtkzMb96KJKoqrq/ekTEkHWfUmVd9JAn0hl8OhABkFvyDJDEl0KKGLjxfh0wKkvfMCl55qGvkbDB+IT3kIsh6w6btGGmIrqod49qYcW9O2ykDrlLqkNupI3EQ06U2+VVI4oNW5fXIOc4DgGLS6oZZUdY8tQr4Xnh/XIco1ooZV/KPdblOC/VGLJOapDvN6UONhtHjfJa8ZAnSci6joecrYUYpYQZ5AxNOhq9uruBHY3eMrfIWvQWk41iXnk6w1PDvdoh6ssBhYfc65K8a6QsiTJ/XPgeKU+UveAf79cBo7L0EUG3euk6oiHrqdrYDCsVRDzI7bTD51bfMAP071EtrLh3Rw15yKUa6nEDHm5lXrrDbtONDjDDmCx/XP7/cpc96/Dbdb2oHX67+psWH6NaKGVfyj3W5Tgv8imgajzkMoMckDbiasUgJx5yt46HnK2FGKWEGeQMTZb1dOruBp5XwwIWmQxPF5NqIesuhw1BcbFWK8JuJPdbmRsOSGHrG8WwsmaFwrrwPXVRt/F8HTAqzy6x5BmpQQ7Iy57VxmKuVMhzyOmGmYpRqnePamHFvSuFl+du8BGIyjpgrPSZmpFvlfiavAY5IOSRW3Fcsyzr8up6Uc/rzr+wt+IY1UIp+1LusS71eeF5virLnm3aEwIA7DclAADwOEiqSm1EORnJIWdrIUYpYQY5Q5PuFj9WLJmXs9vLccCKJfPQVcNlHkKJFBXmCGp4d0jYeq3kkUtlz3KLJ5CwdbKLraxBDsjqkCsW/OQ6UAql2sbBdcCoPH3DpORZroec5ZBLIetaG2aA+lxtt3GwccCyno6se9fOcZbcu8l0hnqs9Tzkdln4qlq4vZJhWvJMmqOsEnYjnvAcD3mZRd266xxYMT8I+ZRq44QF2Yr5QXQF8hfAIcdQwpk4RrWgNh6ANX1RO7bdgrHWOoba73Ew93t6JDIAr/i70vSH4hgIJ8BxwKzJgkFOPeSp2thUpTnkOga5fC1Ezi/HsbUQwxqYQc7QZWlPJ24/9+Cs1849dGrNl3cg9XM9ThsVklIiF0OrdjIZHhExNEwp6gZIOeMkx6nFn+sh16tDvrSnE09+41j6d1u9By9854Savw4YlYd6yOUGuZ2orFeH96dSJLLqkGvfn4Bwj970uQMBAI0+J65cNAMvfOcE3PGFQ/DstxfRz1109HRL7t1RWSoPiSbSwoywm5pQnCS+Zk0OOak/HpAZ5Okyh/4u7fLhc9OkefjcaR68cFqLqbJYn5/uhUtcxdU7OfG4nporeQYI4/GNA7INmlOnui3py9IuH07vEMbaaweunOM3PdZLu3z4bKu0SfTl/Xyax1ja5cMPDgnQvw9ocJj+PS1iijkxXgVzJIm8m9bkoxuHZF1lVMyx0iQMlj1b2tOJF75zAua0CqH5R3Y1sbUQwxKYQc7IS4MoeuYQ3SyvbO4v++LFaoyoAxOv8mANhKxHk5KQjN+tErKuMMBVPeTUA6e+4CdhnoDgWWK7wQwrIDnkWSHrzEMOQApZdzls8ItGrdb9CYB6og+b1ojlp86l9+isyXXUy3zRwi5L7l2iwVHndsCRZxFLwtaNhKwrRd0A6zzZYxoeckB/XEuFfDg+N81r2nu6PZxGIgO4bMDtPfUAgFf2JJAxmb5QLdjFEI8ZAeF6eXVvwrIcaXJsj53D8oPrCvNUy0JQrtjPr3sMlywspc1rtyxiIa4Yj2rIId8oRt7NnlxHX/M4hTmhZlTWDYSsE7pa/Fg4swUAcPj0RrYWYlgCM8gZeSGLwsOnNSLocWDvWBxvbh2scKuKgyz6GrzauY9NNVT6jCwmOS47Z5PQpDDA1UTdaJ1jDdnWgbA0DrUidMeobjIZHrtHiMp6bsi6ERGw8UxWyHoeDzmgv9FIXrPq3tWrUqGEKq2b8JA3lCKHnIq6Ccd2O+w0GqPceeQAMBCXru+RAmKPN44IbZ4VdOCzbW4EHBx2RTN4Z6A252cyBie1uzHJY8Nokscre6zZEI+KnuRoER7lsaT8fOkfR/7+rqh1RqnSIx6vgilyg5g/PqdVigog65BaEXVLGih7JqdSFRoY4xdmkDPyQpR+6zwOLD6wFQDwxLpdlWxS0RjxkLeIRmstiLoRhXW/y6GqAtqsMMBbVEXd9JWM5ZECZEODwSiG/nAcyTQPGwdMqZOuSScVdauC1WYFkVTWZaJuOkYtuS/1DPLhiDUbjCM6v6WEhLFGk/mNXrW5OeC2ykMulT0jVEppHVAa5OYNxY2jQpvnBB3w2Dmc3C7cQ//YHrOmgWVmWDTIG9w2nNEhRMw8YVFfomLFhlgaBUcQjCWl7+XbQBmWvd8Xsc4ojSkOVQ2ibpsUCutA7RnkUg65sWoVwQrOG4zxCTPIGXmhXhqnDWce0g4A+Of7u5Gq4cUyEQ7SEnQDJCXyWsghp4JuKuHqQK4BrhayLtU5Vn+AyiMF4qlMzTxoGdULqUE+uc6TFfYslT2r3TnGCuKyHHJi1OoZpRXxkBswyL0u44rLknJ7bg75aJHeKKWoG1BZT1eWQZ40f61vED3ks+uFPpzZKRixT+2IIV2DYesjosHb4LLRvjy7M56TN10IEdkxCvWSZ3nI85yvEZnxPpTg6YZAseR6yCt7nnmezyl5BtRuDrnLbqwsnVVCkwwGgRnkjLzIF4ULZzaj0efEQDiB1z4ZqHDLCkdt0aeklkTdiBGtprAO5BrgaiHrPreUo6pW2kM5DixsnVEstAZ5gyfrdRfzkAPIDlkP5EkpAaS8brV5jbxmtUGuN4cSfDRkvUgPuUVlz+rcuR7ysTIrrSczPIZlXvHhAkLWN8k85ABwXKsbQSeHvbEM/rOv+p9bSojXud5pw+HNTrR5bQileKzZXXyUWkRmEEcKNI5DSfn50j+G8nxaFbau3JyotL27ezSGsVgKdhuHGZOkXGpikMdqZFPVTA45IBOaLPO8wRi/MIOckRd5LVyn3YZTD2oDADzxXl8lm1UUZkTdaiNknXjINQxymahbnccBtyN3F5gsenleXYhlIJQ9DswgZxTLrmFSgzy7Ni8JG2SibtLcmy+lBFCv4U2gHnKL0k30wuOVUJV1A1E1VN/DZ30OeUiRQw5UztM1pEj+NRuynszw2CIa5PuJHnKXjcOpU60N9S4nZAwaXBxsHEe95Fb0Re6hLsRbzfM8QinjIeujivetClvPMcgr7CHfKOaPdzX7stYVXlH+v2Y85AbKnskJWDQnMRgEZpAz8hJPSl4aADhrnmCQP/3h7ppdMI9EcoWDlFAPebj6PQ1E1I0sfJXIPeTKfHKC12mnIrJqi/7BMPOQM6yFesjrlR5y4TpOTnQPOc0ht8tSSvKHrKt5rUsXsq4tjEnwOvXTYQiZDE/D0kujsi7mkGd5yIXfKXcuaH+OQW7uWt8WSiPJAz47h6k+ad4nRuzTO+NIVYECtxmoh1w05s7sFDbqnt8VR6TItYY8ZD1SQMh6NM1D/rV8GyjEg07K0u0arwb57txwdQDwOGosh5yWPTOWQ17HRN0YFsMMckZeqJdGDEFaMKMZLQE3RqJJvLq5v5JNKxgjCsHEcB2OJKveMCAe8oCGh7zRJzPIVQTdAIDjOBryrhYW268wyJmwG6NYdqkorAOs7BlBXWW9QFE3ca4btsggJzocZjzk+QzysXiKlm8sSR3yeG4OeZ27MgvrQYVBbjZkXZ4/bpMJeS6c7EKTi8NAPIPXaixsfZga5EJ/5jU6MM1vRzTN44W+4iLVokWGrMsF3YD854tsLuwnphP0Ra2Zy5S3v850UBY2quSPAzLdiBowyHmeN+0hr7NIaJLBIFhTGLFAxsbGcP311+Oxxx7D3r17cdhhh+HnP/855s+fD0C4SW644Qbcf//9GB4exjHHHINf//rXmD17diWbPeGQh00CgN3G4YyDW/Hga9tw+z8/wl/f2YmORi+W9XQCAFau3Y4dQ1H6WncV1mg0Em45JDM4f/zEelxyTHdV9gWQFuk+DYN853AUbocN8VQGg6E4evvDqn3xuewIxVOqNXlJyLrdxiGd4celh7y3P1wV12+1tKOU9PaHsVYsn/ifTwbw2bmTaR+Nlj3r7Q/jz29sw5sbbVjv2ITzF0zXHKdKjGmxvymfewPiZlkinUEilVFdOJZT1G20gBzyaJ4cctI2r9OeFf5qVVg5+b6qyrrJhXWx55YIutk5IM0Do0lzRuKGUWGsiMFHcNg4nNrhwZ8+ieK298YwKxhFh9+OZV1edNdVdMmnSyzN0xJexEPOcRzO6PTg1x+HcdcHITyzMy71xURXeJ7PMsILCVlXGuSjeUXdhPfnNjjxwXBqXIas9/aH8dLGfQCAj/pGs9YVngqrrJu5P9MZnm4Eug2KukmbhILmjlp1GwbDDBWdnS+//HJ88MEHeOihh9De3o4//vGPOOmkk7B+/XpMnToVd9xxB37xi1/gwQcfRHd3N66//nosXrwY69evh8fjyf8DDEuQe2kIJFd5454QNu8NgeM43LtmCwDAZuPoBHXfS1uwYsk8LBWN9WohXw75yrXbcc2qdfTvh17fhode31aVfQFkOeQqIeukL+S5vXUgghPvWqPaF7/bAYzFVb1wRNRtWpMPvf1hy8onVQtknDiustdvtbSjlCivydUf7cHqj/bQPpKwQT1RNzpO4JDhObz3ylbc/0qv6jhVYkyt+E25oKZXdm9HE+kcg1we7q0WRt4gvlaJHHKyOM/nIdealy3LIRe/H5TnkBdwbCvObX9MOLfT/Hb0htKmPeSbRA/5nPrcZVzAKdw/H42ksGEkBY4D7vs4jBXzg1ja5TP1O+WCeJRtAAKy0lNu8TLvDaWxLZSmffnJwU583uCx4xlAPrqFhKyPJZURDdrHiKV5Wp5sf/H8lCxkvUJlz5Rz+HMf7cFzsjmcqqwXUD3AqrYZvT/lzxmnwbJnZE5KZ3jEkpms+ZnBKISKhaxHo1GsWrUKd9xxBxYtWoRZs2bhxhtvxKxZs/DrX/8aPM/jZz/7GX74wx/i7LPPxrx58/CHP/wBu3btwuOPP16pZk9ISB4jWQD29odx70tb6PsZXtxhBMBD+Dd5LcMDy1etw9b+cAVaro2Ua5m7cO3tD2c9aAChj9XaFwAIE5V1hYdcrS88tPvilymty+F5norbzRB3mUfHkYdcPk6VvH6rpR2lxMj9RTb/tFJFssaJ58GDQ5pXH6dKjKlVvykvOely2Kj6fEjF0yx4aoR/l7PsmZ4OB0HykOczyIV+Kb3uVuSQJ9MZGj6rlkNu1CC36twSD/kM0WttVtRtAxF0U3jIe8dS+M2GCP07A8EDnwGw/M1RbA1VZ4jtiCxcnYTg946l8Iv10njK+3L9W2OGj630iBcSsh5SfEcv539Utrkw2+KQ9VwPuSWHNYWROdxbobJnhdyf8tQol92YWeRz2WETbXeWR86wgop5yFOpFNLpdI6n2+v14pVXXkFvby92796Nk046ib5XX1+PBQsW4LXXXsP555+vetx4PI54XMo1Gh0dBQAkk0kkk9Vx05B2VEt78kHCDB2c0OY/v7ENHDgIpl1+OAAPv7EN3z2lelINSP6j35l7HvT6R/ryzc90Aaieczgm9sfj4LLaZKQv8vNCHqKj4XjWccZiSSTFhUBXs5DvO6j4TK0hvw/NjlOpsLQdySSIWZNMpYAqOVdG+risZyoAYaGkdo2ZGadKnFurfpNshtohjIPfbUciksFIOIbJ/uzH974xwQjzuezg+DSSilBRv+g1HY4kLLlviUHuc3J5jyfu8yEUV38Ok9cGQ4KmQJ3HkfU5D/1+ColEoqDwULnmhdvO0+N7RY/YaNTYuBR8blMpoYRFJgNkMtgnulC7AqIadZpHKJmG24CoVDzNY1tI+H53nQ3JjGRQ/Lk3IohzqjyeOQ54+JMIvntQIO9vFAJph7w9RukXo7KCTqk/+fqS9bs6vzmmuBfCyYzpNg6J7SPNGU5oH4P0pd7FYbJHaOiuSLqgcVESVWxSRtPm+6JHUtzV03tmGLkHZk8WNu6jiVRZ1wmF3J/hmLB+snEAn0kjmTG2iRBwOzAaS2EoFEOjt7o85LVmZ4xnjJ6DihnkdXV1OProo3HLLbdg//33x5QpU/Dwww/jtddew6xZs7B7924AwJQpU7K+N2XKFPqeGrfffjtuuummnNefffZZ+HzVFaq1evXqSjfBENt32QDYsPGjD/HU4Ad4c6MNGZ6DML3lJ8PzeHP9FjyV2lTSdholnQHCceHS/88rL2G9wsGj1z/Sl9ViX6rlHG7sFc7R9k824an4Rvq6kb7Iz0t4WDjO62+9A2yXHmh7owDggNvOo3/HJwDs+GjLNjz1VG+pulQ2Vq9ebXqcSoWV7bDHYjhT/PczL7+MdJWk+RjpY3t4EwAHYskUnnrqqYKOQcapEufWqt8MRe0AOLz+6ivY6gO4tPD36hdfxqZsDSV8GgIAB1xQH7OBmPD+YDim+r5ZBkNCW95+/RVsy3NpbdrLAbDj0527dX/79bfXAbAjNjKQ9TnBvnEgneHx+BP/pAa+GUj/nTYeq595mr6+pV9o29Y8bSMUfW4HBd2Ej0aEuXY0FQIHG3hweGx7H4L5ReuxMwykeQe8dh5vDe7JMk7f7M/Tvv4Qnto1mv9HimC1zhpNi/cHhfMALoWndu0CkL8vhGf6+nTntz3i84uwdnAYfveQqfa9Ll4nQRePkQSHvTGpnUq2jAq/Z7dlsG5kLwAHwikeqz7dBW+Rq+73xWe0284jnuawKxLTbEcxrP7XvzTfM3IPJPfwAOzYtbffkvnGKIXcn4NxAHDADt5UW+0ZYQ585oWXML0u78crQrWsUScykUgk/4dQ4Rzyhx56CJdddhmmTp0Ku92Oww8/HBdccAHeeuutgo957bXX4uqrr6Z/j46OorOzE6eccgqCwaAVzS6aZDKJ1atX4+STT4bTmT/cr9L8Zd9bwNAAeg47BKcf2o71jk1475WtSPPGPOQ2jsP8A2bg9CrxkA+EE8AbawAAnz/zVDgUIUp6/SN9OfkzXVV1Dp98+F1g314cfshBOP1IKUfKSF/k5+XZsXVYP7wbM+YcgNOPnk5ff2vbEPDum5hS78NRh8/A37Z9iEDjZJx++uEl7Vcpkd+H6x1bTY1TqTB7vnQJS6F5ixctAlparGpmURjp4+JjpuOGt9cgw3M49dTTYLNxpo9BxsnSMTWIVb95zdrnAGRw8oknoLPRh//Z8m8M7g3h0J4FWDizOeuzr2weAN5/C62NdTj99IU5xxqNJnHzOy8imeFw4smLadWMQogl00i99jwA4JzTT8lSLVeD+2A3/m/LOgQamnD66UfmvE/uxands4HeT7BfdwdOP/0g+j7P87h27XNIZ3gsPP6zmBI0v7n0Ud8Y8M5raPC5cfrpJ9DXfRv34Q+b3oE70IDTTz8q73EKPrfRKPDqq0AgAHg8eGDDIIAUPjOlCat3jmI4waOnaTJmBfMvy/7+aQzAKA5ocOGMqY3Z7RsM4b3BCNRSi20ch/ktfpzeXjoP+erdu3FyayucNnMZkbFkFMAYpvndOL29AUD+vhAWt7UBXm/uh0Q+GEoCkAzwbn8Qp7ebE3TcE44ACGFWnQtvDSQRS3M4ra1NNVrjecQBjKDd68S5nVNw6zv7MJLkcXD9ZFozvlDeHwgBiKDJZUdfNIOg043T2xvzfs8oyWgUq4eGcPJxx8FZp25lCvdAr/Y1dsAMzO9qwO82vgNvIIjTTz/asvblo5D7s7c/DLz9KjxuJ04/fbHh37rnk39jaE8I83oW4BjFfFxpas3OGM+QSO18VNQgnzlzJl566SWEw2GMjo6ira0N5513HmbMmIHW1lYAwJ49e9DW1ka/s2fPHhx66KGax3S73XC7c8s6OZ3Oqrsoq7FNahDREJ/HBafTifMXTMf9rxj3jPIALlgwvWr6GkkKKQ11bge8ntxrRa9/yr5Uyzkkwin1PldWe8z0BQDqxHzQeIrPen1YFCBqDrjRFBAWwyOxVFX0vVjyXdPlvH4tbYfsc06HI+vvSmKkj36P5CbkbXY4FcajmXGqxLm14jd5nqeibgGPG06nE37R8I2lkfP9UEJ9DiA02h2wcUKeZyQFBAyoo2sxIApU2W0cGgOevCHkdT5hno2lMrr9HhNDfRt97pzPBdwOjEQFQ6iQ80XyiOs82XN2g1+Yz8KJtKHjFnxuk0khztpmA2w2DMaF9kzx2lHvtGE4kUY4BUOG7JYxYZzm1DtyPn9+tw/3b1D3yPA8cMEMn2lj2SxOm830b4yJUZ2NLum7+foi/z3o/F4ik319xjPGxllOREy97/Tb8dZAEskMkOI5+FRyjkmafoPYlzafHSMjKeyN8TiwsbixJ+nO9S4b+qIZJAroiy7ivex0ODTvh/MXTMf//kv/Htg9KqSfxPPc81ZTyP2Z4YTxcztsptpK9CeiSb5q10LVskadyBgd/6qoQ+73+9HW1oahoSE888wzOPvss9Hd3Y3W1lY8//zz9HOjo6N44403cPTR5dttY+SWPetu8WPFknmwccKCjPyfBAnJnVk2DlixZB66qqhk03CeGuTK/skf5dXWFwJRWfe5svfYtM6V1nkh3w8pVNaJoFuz301FnMaTqJt8nAjkWi7nOa+WdpQS0kcCp3JNyhXE1ZTWyTGUdiCH3HHqbvHje4vn5HyulGPa3eLHjZ87MOs1G2fuN1OiEBEAWgKMiJFFVETd8lWOsNk4BC0SdpP/lpF8bp9BlfXRmLqoGyAvfVZY20MqNcjlfxs9Lr32ZK+ZPbeAJOrW5LahQSzzpScUJmfjiLqgGwB01zmwYn4wq312TljsrZgfRFegOkufjVJRN+neJ32RL1RJX245wniMsFLErbA65EL7pnjtIELcWkJ85DyS89ruE67/vmjxAmdE1C0oakJUouxZd4sf+7cJ48+J175yDvfSsmflVZ0r5BmaFK8Ho4JuBDp3sFrkDAuo6Mz8zDPPgOd5zJkzB5s3b8b3vvc9zJ07F5deeik4jsO3vvUt/PjHP8bs2bNp2bP29nacc845lWz2hCOeJGXPJC/V0p5OzO9qwiOyOo/nieUkfrp6I/7+3i7Ue53421XHVJ0RkW/hCmT3b9OeMTz30V4AwHGzJ5WljWYhZcoCKnXItc6V2nkJuMnCOfsBMyiWPGsJuOhGxvA4MsgBYZzmtNbhc796FQDQWu/Bw1ccVfbrd2lPJ9obPPjib/4DADhoaj1+ecFhVXcfFcPnD+/ADx5/H4kUj8/MmYw5rXVZ16Tc45PUqEW+tKcTNo7Dd/7yHn3t0GkNqmVtUorYygPag/ifCw8v6Zge0JadInXaQa343uK5hn9TXoPd7RTGg6iVKzfMAHnlCO15rcHrxHAkWfS9S0oeGil5BkgbffkUl/VKqRWrtE5U1Os8uZ53+ftGWNrTiU8HI/jlC5sBAOceNhVf/+xsw+c2luYRFo2AZo8NQZcouGdQaX0jUVjXCH9e2uWD387ha6+PwGvncMlsH87r9latMQ5Ifa93ZW/wLO3y4bAmJ056ZgAA8F8zfbhktg9dduPXcFRx/xdSh5yorNc5OdS7bBiIZzCcyKDNl5v6MaLYXGjzCv+3oha5ZJALx6xE2bORaBKb9oYAAMt6OhFJpHPWFVLZs/LXISdrngt/8zp2DccwY5Ifv714vub9mUgLbXQ6zBnkAZMVGhgMPSo6O4+MjODaa6/Fjh070NTUhCVLluDWW2+l7v3vf//7CIfDuPLKKzE8PIxjjz0WTz/9NKtBXmZISQiyKCR0tfix/NS5OZ//zin74e/v7UIsmcb05uoS0gOkOrx6C1cgu39Lfv1vvLVtCE++34cvH9td8jaahZQp82nUwtQ6V0p8bvVF70BYWIA3B1xSPeNoktb4HC9MrpPmFoedq5gRLK8jPWtyYFwZ4wCwfTCCRIqH22HD/Rf1wK7IEbfZODjtHJJpXrcWObmH65w8xpIc3ts+jH1jcUyqy05FeWJdHwBg9uQANu0NYU5rXcnHdOOeUNbfx86eZOo347KFLPHckLKGERWj1MhGIy19VmQtciO/JYfU6M3vISfRS7nKZsXWIicecOWmJTHQ46kMEqlMTn13LeR1h886pN3UuSXecZcNqHNwkofcgDcxksrg0zAJWdce//3E91w2YPnBVao4JYP0vcGVO/6zgk40uDgMJ3hcOEPcWDCxqZTjIS+iDrlgkHMYiAMjSQ0PeTJ7c4F4yK2oRU4NchfxkBd9SNM8++FuJNM89psSyIp2kiN5yMtvkAPCmmdK0INdwzF0t/h170+y+VmohzzEDHKGBVQ0ZH3ZsmXYsmUL4vE4+vr68Ktf/Qr19fX0fY7jcPPNN2P37t2IxWJ47rnnsN9++1WwxRMTZch6PlrrPfR7Q0Uu/EqB2cUkAJw5T9AxeGKd9WqmVkBC1pV1yM3iJwtnhQeuPySFrJNxS2f4ouoCVyPyxcPukRgyFQgHBLKNDqvqRlcTG/YINYRnTQ7kGOMEsjhKaHjIASkccrIHmNcRRIYH/vlBX9ZnNu4Zw4Y9Y3DaOZx7uFBOLVyG63bjnuw6yX3DUVPfly8SiaidX/Q0q7WfeK0bVIxZAglZL9ZDbnYONVuHXN1DLrxW6OJ3TCNkXW6gm5nP5H0xe48OEE0Otw0cx1HDbdiAdbV5NE2/2+zWfibXiSHNoRQP3qAAayUhfSeh2EpIX/vj5i1QK+qQj4lGdsBhQ71TP8VgWBGy3kZD1ou3nsmjmXjI4xXwkP9D3OA8c1675mc8Yt+jyXTFrj/yfIjrPEMA0JKuRjfjCHVFptEwGHKqIoecUd3EU7kh63q4HXa0BIRF4S6Ti9ByUIhBfvrBbeA44J1Ph7FjyFgJg3ISFheHRRvk4vfDipD1gZDkIfc4bdRYGm/GovzBnUzz6Bdz58uN/AE/3sYYADaJxuqcKdqeOxI+qGeQk7nJaeNxxkGCEOgT72Ub5E+8J2yiHb/fJLTXC0rM+Ty1VkAM8m7RM7NrJGbq+2obodL9qR2yHtSZ14ixblUOeb4oIwLxliXSGaR0Ih7ocVX6QAzn0QIXv2STK6AwyO02jm5EmllYy68hs3oaxENOjExi4I0aCFnfMCr8llr+uBxikKf53JDtaoTkY6t5yAFprAYKMMiJR5wcuZCQdWKQ1zm5vDn/I4rNhdKErFcmh3wwnMCrm/sBSI4KNUjIOs/nN4hLBXk+xPNEnpBnjLNQD/k4c0wwKgMzyBl5IZOZUQ85ALQ3CAvfPpOL0HIg5SkaKPgqMiXowYLuJgDAk+v68ny6vCTTGfpA8WuErBvFp+GBGyQh63634NHxWSMOVW0ow+v6hitz/co95MTzOZ7YIIZz79eqbZBTD7mOAUc8IA6bkKMNAG9uG0TfiLARyPM8DVc/c167LAe7fB7y4/cTdCdIm4xCN0JlqUI0gkVH1E3NmCXUi0WQrRR1M4I8vDuiE8I6qnPcYkPWQxo55IBkpJs5ttwgHzYZCdYvujmbRCPTjKgbFXTLUz7La+dgF53NYxqh1dWEmqibnBaP8PpgER5yMt6FhKyHxLkm4JQiGrRE3YYVmwvykPVivcVSyLo4P5bZ1n36g91IZ3gc0BbEjEna5fO8ssoY+QziUhGnHnL9jRCyfjLrIS9Ef4LB0IIZ5Iy8xDVyyPVoE8PWzS5Cy0EhHnJACs96osoMcnl4uVJl3SySirOGyroY+WBVLmq1kWOQV+j6lRuMJIx3PEE85PtN0V7QuQx4yMn5ctqEOadneiN4Xto0W983ik/6w3A7bDjpgCnS9a0iimYlA6E4+sWokkX7CfXfzW7uSBuh0sJW0njIbb+eIBqB6j8Uuclj5LfkuB02qnqsFbaezkief7XjBooWdRPaXKcSRVRXgDhTNFF4WgkJWSdGZr0JUTcq6JbHQ85xHAKiHHgtGORSmLd6yDoxpsnYmYFECJDxLsZDHnRKqvhaKQbKzYVWr3APxzPAkEHhPi3iGYWHvMzRDyRt76xDtMPVAcHb7BBv+koIuwHS8yGfh56IuplxOgGyeYN5yBkWwAxyhi6ZjCSqZDRkHQDaxNDQXRXyMOoxEiW5luYM8tMOaoXdxuH9nSPYNlA9YeskvNxlt5ne4VXic+d6ENMZXvKQiwZ5g0Xlk6qNmOLBXanrNztkPVETOaBGSaYz2LJP9JDrhKwbMshTkkEOyLUe+rL+/5k5kxFwOzRFC62GCLpNa/JhRouw6bBrJGrqPKqFrNMqCCrt1/MuE+pLUPbMCBzH5VVal0fzqoXdB6nRbG3ZM6CwkmpZHnKT4zmoDFk3IepGPORz8njIAcGbC0iCZNUKz/MyITT9kPVCcshJzjg5RiEh/ERlPeDkqKCa1vlSbi647RxaxN8uVthNqbKe5IFMmZ4P+8bieP0TQe1eL1ydUEmldcC4QU7KnpkNWQ+YLJnIYOjBDHKGLvJwUTPGXnvD+POQNwfcWDizGQDw5Pu7LW9XoUiCbsWFqwPqHvLhSILWQ27yZXvIx1vps3iVeMjlO+7JNF+xBU0p2DYQRjLNw++yY6qY2qIGCVlP6iyeiReZGORE6+Hd7cPYPhih3pwzDxEWj3oh31ayURYBQEQuY8mMqdBmEmYpn3eJUau2oTBsIK/bqpKFwwXMofmU1iNil+o8DlWhv0CRmymjGjnk5DfNHlt+T5rd4OjXMMjzibqNJjNUGGx2Hg85ANSJN0aoAI9wOQmleJDbvF4jEq9FfL4VlEOeyh5vs6JuaV4qU1fnlETd1CIatDYX2nxiHnmRtciJiFudTPyuXGHr//ygDxkeOKSzAZ1N+SvoeCqstE4Mcb1NXQCIp4tTWWch6wwrqN6ilIyqQJ77Yyach3jIK5WDq0VvfxibxfqZT3/Qh/3bglR0yQhnzmvDvzb148HXtqHLa8N6xyacv2C6qWNYDQnzLDZcXThGroeclDxr8DnhEB9YRnLIe/vDWCmrfb6sp7Oi42SEHA95hTQQlA/44UhS9/yqjrW2rVs0xZzbDbuF+2/2lDrdknnUQ57WXswpPeSTRa2H1z8ZxMUP/AfbB6Nw2DjMmCS0jYqilThkXTLI6+BxCiKX/aEEdo1E0eg3pl1BQ9ZluZhaKSWJVIa+Vk4PuZ6iuxKqtJ5UX7wSg1xrQ6GUOeSFHDtLZd1k6g4VdfOQHHLhPtATdesdS+EX64V7x+8QvOxa3mRCnbM2QtZJLrbLBng09pWbi8khT0s13wHzIesh2fgFHPqibmGNzYU2rx3vD6WKFnaLEZV12bmPp3l47KUrP0rm+0fe3A4AOEZ0TOTDK1NaLzepdAYp0ZNQqhzyOrexyg+1uBayos212O9Kwgxyhi5kIrNxoPlARiAe8p1VpLK+cu12XLNqHfX2PrGuD0+s68OKJfOwtKfT0DHIg2UwksRQhMN7r2zF/a/0mjqG1RAPubK+biGQYyRSGSTTGTjtNklhXWZIUA+5xkKUjDXHcbRW+X0vbanoOBkhV9StQjnkigf8SDRJhRKVaI31T06fhc+XoG3FntsNBvLHAWNlz5QeckASlPxkXxiAkHJx5i9ewYol83DKAYLwWyJtrua0WYhBPkcUrWur96I/lEDfcAwHttfrfZVCU4Xscg+5YK0oRReJgcxx6gYnwSqD3Eh4vBIi8qTtIed0j1msgNJYXL0OOSBbWJvwkEcsKnsGQOZxzdB7Ss7K3giuWTtK/w6ngBOf7seK+UEs7dL2VFIPeZWHrA/Lcq61Numai8ghJx7xFpmom9o4a0E2NFw2Ify8XmcDZVhjc0ESdivuXKh7yEu34ULne4BuNNz70hZ0t/jzzvceMc0xVoaqFkrkm+v5ROWS6QINcgMh67W4FrKizbXY70rDQtYZukh5jHbDDy9A8pDvGY0hXaFaznJ6+8NZxjgAZHjhv+Wr1mFrf9jQMW7+x3r6Nw8OaZ43dYxSQBbnPgtC1uVeWCJ8JQm6uel7egt7+VinM3zW/ys5TkYg17skSlj5HHJAe+NDb6yvf/wDy9tlxbndJPMe60Hy+fTy/+Rlz0j7Hn9nZ9ZneEj3+b6QdD7z1cQuFJ7naQ757MnEIDefwqOqsq5RlpDch3Vu9XBvAvE+FyvGKNU8N26Q+wyGrDdoVL+oKzaHXDTkg2o55B7zJdWKCVmXcsiFMSEGXorPVQDvHUvhmrWjyACQ3wkZAMvfHMXWkPYmAhF1G61yD/loMrtutxpW5JATYbg0by7Mm+Tgk7xtPVE3kleu3FxokymtFwPJIffYOZDhKlXAT9Z8r1g7GZnvva7K5ZDL08/yiroVWfYsnEirrnNrcS1kRZtrsd/VADPIGboUorAOAJPr3LBxQCrDoz9UmVrOclau3a65ocBxHB5Zu70sxygFZHFuhYfc5ZBqjJPjEg85qS0PSKJuavV3q3WcjEAe4iSsas9oTLducqlQeuq0Fvz5xtpqrDi3GxTeYy2It0Ivhzym8JDna9+qt3fS44ZKlEe+dyyOkWgSdlmoPPHa7zSRwqNWblIr5N5oCLl8I61QocBMhi9IhyOfqBspJqB1zGJq/vI8r1mHPOvYpsqeyUoTRo2r1vM8n5ND7pUZV8pSWiu3RqF1K3Mc8Eiv9iYP8aKGqtwgJ32ud2rPWWSsRpO8aY8wCVlvdNtyXjOCXNAN0BfhG0moby7QWuRF5JCnMjxItL3HzsFtK20t8mLneymHvPzPULmHPJHOIKMzRgkVAU0jyOcStXmpFtdC43mtXO0wg5yhC/XSmJyoHHYbpgQFr9CuKghb3zGkrXDM8zx2DOVvoxXHKAVkce4rsgY5gXjaied9QNxQaZKHrFNxqNyFaLWOkxFIyPrUBi+cdg4ZHtgzVv4NJWI8EONRbeMDyD/WVlPsuY0l07RCQT4PudmyZ0bbJ5U+K41BvmG3sOEwvdlHF6SFechzq1sQ4cZwIpXVT1I5Ip+BTLzPqQxPtSfMEkqkaKSRGYPckydkPSyeDjWFdaC4HPJ4SsonVa1DXkA4vLwfsWTGsHBVKCV5Z4mRyXEc9b4qva47wmlo3co8L7yvRUA8ZrWrrA/nqUEuvCfVVTebR0485EEnB2LzmxF2I+NXRw1yqQ65UuFca3NhqgUe8rjMqHTbOLjtpTXIi53vK6myrrwfEzob64kCQ9bdDru0wavyPKnFtdB4XitXO8wgZ+iitig0SqXDfuV0NHp1d+w6GvMrYFlxjFJAVdYtEHWTH4cs2ImoW7NfClmn9YxVDMVqHScjkJ18n8tON5QqkUdODIMO0bOq5YHLN9ZWU+y5/WRfGOkMj3qvE5Pr3LqflXLIdUTdFAa5kfapCRdaCc0fl204tDWYF7lU2wwl9ybPq4dM5wsh9zilCJhC88hJuLvbYaMLbiP48ijcR8Uccm1RN6f4ffXwUD1IKDrHAT6VNpOSaqZU1hUbC1qbZkoGROPT7+DgdUjXagM18rINhw6/XddD3uHXPgekVnW1q6yPUINce86ycZxUi9ykQU7CvH2yMTdnkIseckd2zj+PXMG8YS0PuWiQ74lmkC5wszQmu+TcdtCoilKlaBc733udlRN1UxrkennkUsi6+WdmnU7JxFpcC43ntXK1wwxyhi5qYZNGIYvQavCQL+vp1N2xO8+AyIQVxygFxHD2WxCyLhwnu9axWsh6UEfUrVrHyQjECPI47WgXdRAqobROHu5TxQeXlvGUb6ytpthzu2mvJOiWb8PASMg62TAkdo2R9mkplVvFRpUc+XZxc3KXGQ95MjddyCszJuVh6+Q+1PIuEziOk6JbIsbDrOUUWjaSqqznySHPJ+oGmAstl38+4HbAplZSzWQ94YTM405y9o1ucAzEs/OZCTQMWhGyvqzLq+shP0+nnEKt1CEnQmj5VOMLFXYjxrfPzsEnThZmQtaJ0U085G47RwXblBso5O+gYnNhsscGGwSdgP4ChOkAaWPBZRM2KFyiARkvkYe82PmezFfKcqLlQJk3rqe0Tj3kdvOOJ710l1pcC43ntXK1wwxyhi5qwkJGaa8iD3l3ix8rlsyD/BFpt3GwccCKJfPQZaAUAzmGfD1n42DqGKXASlE3ILfWsZqoW4NO2TMyTnLsnLmxrhQx2QZUW0NlPOQ8z9Ox72gUFJS1RN1Ux1q8rm855yDL26Z2H5m5B0g4d75wdUDmIdcJNVR6yOX3KBkH5X1eeg+5IOi2n4qHfM9oTDeXUY5adJLNxqnWUjdjJBertG7UG68kn8ATieRt0OiDy2GjG8NEMd0oY1TQzZpwePmmQqsYSWO0tvuAaHw2KwzyBo285O46B1bMD2Y/uzhh8bZifhBdAe2N2Doasl7lHnIDom6AzCAvMGTd6+DgE41YM6XPiEp9QBaGLp0vRci6+LeyLw4bhyliHnmhYevEICeh6jSH3MTmghm6W/z43uI59G+1+VQPGrJeCZV1pYdcJ/Wp0LJngHwzL3fuUHs+A5VfM+qh9oznYK7Nav02e4yJCCt7xtCluJB1MUzThFeolCzt6cS/N/fjsXd3YdbkAE4+YArO6+k0NTks7enE/K4mfP6eVzEYSeLUA1vx/VPnVnSCIQvzgEUh60oPol7Zs7FYCukMn6PsvLSnE7995RN8LNac/q+jp+OShV1VPxGTh7jbaZddv+XdUIok0jRHtyOPhxwQxvonz2zAnrE4mvxOnDd/mnBdlygqbGlPJ7YNRPCrFzcDEHbD//v4mYbOrZr3WAuyONJbSJENFPnal9yjj8jqn8rvcz+9vq03yHmepyryc1qlsm5TRJHLZFoQuZwsGnF6aOl3+NwOhBPprA0FsmGjZczKqdcRZDRCsR7yQsueAYLhHA8lTOeRh/KUhjSbQx4Ra6k7bBxaAi7sHI4aVq4nxmRLjodc6P+wSimtpV0+jCUzuPndEJpcHM6b4cN53V5dYxyQVNar3iAnIes6om6A0iA3ZjyleR7Efvc5bFLIegEe8qDMMVHvtGF3NJPjIdfLh2/z2dEXzaAvmsFhhn9dIi5TWAcAIhtjRjHeLCnxNzsavThsWmPOfKpHJXPIlSHqeh5yUvassJB1cS2kscG7tKcT/9/TH9N1FAA8+t8Lcfj0RtO/VS6W9nRiXyiOO57eAEDQQ/n9pUeaXiv/7LmNVMi0o9GLh768oOrXgJWEGeQMXeIFqk8CUi3yXSbyJktNSFwMXrywC/911PSCjtHV4seMSX4MbhvGaQdNqfgEQ0XdLApZV3oQiUp+cyDXIAeEhX2jP1fdeSAsLVD/6+jpFR8nI5Dr3eO0U69ZuVMuyLjbbRz1vuXzZo6IobZt9V4sP3Wu8GK4dKVF5BswFxw5zfC5VfMea0FK0CR1POTKsmeErha/NA4K/DQCxPpF4s7hKMKJNJx2DtObpTFx2G2YXOfB7tEYdo3EDBrk6nNvwO3AvrF4lmFrpi54g066iRHI9+o1ypNpkU9lPV/IOiDkkfeHEqajG0goep2Kwjo5LmA8aoKMvddlp2kCZkPWmz3Z55UYe0oDj5DKCPfcMVPcWH5w/vsHkKusV3fIOgnTz+shF8dMCPk2tiaRe8KzQtbNeMgVKuuAfAMle2xHdRTj27x2AMmiPeTUIBfn4XiJPOQA8MS6PgDANz47G8vmmws1JlExlVBZVxrgem0oVGUdyJ/uwvM8nZ/b6j3oG4nhw10jVW2QA1INeUAoc2d2/RZLprPS/VIZvibWgJWEhawzdCG5PwXlkFeZhxyQ2kLC6QulEFXeUhGm3h9rQtYDMg9iIpXBqNhHuaib026jobNqoZqJVCar3F2h4bHlhnjIPU5bxTzkcuNBLzWAEEmk6GKjXKGB8vYoa2JrEUmk8OkgUVgP5Pm0UZV1MYfcxPTkU2gkWAmJAJg5KZBT09ZsCoSUQ559X6uF3JsJI7cqZN2sh5zkk0Y0vGW07JlOHwI6Akp6jOqUPAOyS6oZ0V4g95nPZafjYDRkvT+RXfKMoFfbGpDCnNt9xuf5mglZN6CyDkhRBWZU1oknnAPgsQsl5oBCVdZlHnKNnH8tUTcAaPeJpc+sNshLlEO+ac8YNuwZg9POYfGBraa/762oyrrSQ16akPV8JRNHYymqg/Il0Qn0D3GTo5qRr20/HYyYjijbvDcEngddJ/aNxGpmHVgpmEHO0KWokHVxAbp3LK7r5SonROWYGFuFQhaGpcpDNQMxiHwWhawTgyUUT2NIFH6y27icBbjewn7PaCxLiMhoOGelicmu90JKVVnBmEyAyojxJA+FK5VQmZIsg9ygp3nzXsE73hJwZekRaGHEIJc85IaaAEC6d8MlMciFPs5WiQAwKxKoFbJOQ+7lom5mcsh95gxIJUTx32wOuSTqljvuPM/Tsmf5QtYB8xuhZLGsVvJMftx0hjdkPJDPeJ12Q5tmcrRF3aRSWmqQ+tWknrUR6mpEZZ2E6SuF0JQUorJOPOFeOweOkzzkhYSsB7JU8dU3UPQ2F4jSeqG1yMktTy4dNw1ZL835JYbjotmTdDfKtPCIE7PRkoBWkptDbkDUrRCDPI9zhpSNrXM7cO5hUwEAb24dxJ7R6okcVSOk0OnYJD7bjEL0Yg6aWk/XUiSdi6EOM8gZusSL2Dls8bvhtHPgeVTF5BNLpmkJLxJOXyj5dkXLCTGItPIjzeKX1Wnul9UgV6oT1/u0S58pw7xrZWdU7iFvF4W4+kMJ3Ye51YzJjIcGX/7wYnkkQinyotWQn0+jv2lG0A0AVRA24iE3Y5D7FGX9rGTjblLyLDcCgG7wGPWQa4RREo9DWMVDbiSMvFgPuZnweDlenRzyWDKDNE/Knmn3odDIJPkml2rbnHaahmHk2FLIumzTzKBq/aBofCpzyImBN6oRWku8qm0FeMjDKb7gUlvlwKyoW78ZD7lM0A2QPORmQtaVKuuAtIEymlP2jM96X067l9QiL8xBkZNDTkXdCjqcLjzP44l1uwAAZx7SVtAxiIe8Ega50iOu9xyRyp4V4iHXT3ehZWMDLrQ3eHHE9EbwPPBklXvJlfPgRpPG9Ma9REuljj7zN5o06icazCBn6KLlpTGCzcahtYqU1neLbfA67aYXk0qq00NuTci6VIc8pSroRqj3Cp9TK5+kPN+FllgqNzSH3GFHo89Jr/vdZbx+yTVV53bQ/NTRWFJTnVvuIS9Xrp78fBq9BzbtNZ4/DsjLnlntIdevh10MG3RE62gtcsMecvXoJLJhJk8VkPK6TYSsF51DbtZDrl1ujnjr7TIVeTXM5noTiLcnqBGyznGcKWM/KptzG7zaG5NqEO+uModcKyeZQIw4MyHr8pznUJWGrad5nhq8eUXdPObLnhFPODHEC6lDHlILWdfI+SebC7oecqtC1ktY9uzjvWF8si8Ml8OGk/afUtAxKinqZkplnZY9KzyHfFQjjWYglF2l5sx5wuYG2eyoVohIHRkT0wa5uDk9e0odTVEze4yJBjPIGbqo1cI1AwkNr4Za5KQGcFuDJ28N5HzQ0MlqMMjFNlhXh5yE9KZlJc9yDXKyEFVTa1bWWx6JVn6cjEA1E5w2cBxHveTlFCaU55ATo4fndULiwpKHPJHOIFWG9JAsD7nBkHXzHnJRZV2jP+kMT3PzCvGQWy3qls7wNCxfrY9ma5Fr5TXSDTPxvpeLBhkJKzUbYq2k4LJnOiWQJK+7Q3durjNZL5yQz0Nu9tiRonLI1cueaeUkA4JnlHiFzYSsu2wcDW+u1jzyUVl/S5FDTjzhJFTdijrkgHrIunxzQc3b3ybmkO+NZZAswIhWlj0jP1GKsmdPfdQPAPjMnEmaqR75qGzZs+rIIe8XN8ybRKfG6Qe3geOAtz8dxo6hiOnfKxdkzjy4ox6Aee82+fycKXIPOTPI9WAGOUOXYnLIgeqqRU7yx9uLzB8HqkvUjRhElhnkspBYyUOem/Nbr6PW3KcwYEneabUjhawLY1CJPPIxmQCV22GnhoyWAUVC4ghaollWIm+LUW+lVPIsv6AbALjEOUcr1FDuASkkh9xqUbdPByOIpzLwOG3obPLlvE895AY3d/LlkJOQ+2gyTT08ZsqeFWuQBwsNWU/mjjupElCfZ+FfaKoQ2TjVUlkHzEU90ZB1p51ughgZzwzPYzCuHrJOPK5qHvI9Ys6xy5ZryOdDEnarDh0XJaS/fgcHpy2Ph1zseyTNI6JjYMkhhjfxjPsKEHUjOfhygzyokvMv31wIqnj7W9w2ODmAB7Anav58EE848ZATw9zqsmc8Dzy5fh8A4Mx57QUfh4asGzxXVhJTpJnFdZ6LyWI85HnWgoPi87lFdGpMCXpwZFcTgOoOWw+Jc/IRohq8GWM6FE9hp+iE229KgBnkBmEGOUOXYspBAPJFaOU95MSoaitSYR3IVuWtJDzP09BVvVBPM/hkIbHy/CclegtRMtbTRMOkdnLIpZB1ABVRWpdyyIXzIHng1Dc15CHrQOm9ETzPm84hH40l6RiqCZ6pQWrCaoWsyz0epjzkVLTQ2nuXLDZmTQ5klYUjkM3JvWMxQ1EMdDPUqTTIs3PIyblw2DhDaSskz7zQTTIzNc/lSKJuudfniFjzLJ+RX3QOuY7BHxTfMxaynushNzLHDScBcuYbNUTdxpK5+d67olK4utnormoXdpPyx/P3y+/gqFfYqJecGN4+Rci6OQ+58FsB2b2o5iEnfdHaXLBxHFqLEHaLEVE38TZ3kxxyi0PWPw0D24dj8DrtOHH/yQUfh5Y9q4CHPLcOeWk85MF8OeQkZF3m1DjrEGGT44kqNsjJPHj4tAYAwhpIKyxfCRFvm1znRoPPhdniJnx/KEHHg5ELq0M+QentD2Pl2u3YMRRFR6MXy3o60a1SI1Dy0hTnITeqLFxKSBvIJkExVIuHPJbMgDyLrfKQSzm2aTp5tqioYuuFapIQ77mtdfh0MKIa1m41Wte00WsdkK53og5LxP/KmXIhhdcK49vgc2L3qHbJEOUDrlCldaPjFE2maag4kD/0u7c/jF88vxGAsGk0GE4Yyj/Op7JOPOROO4c8jrUs/Dq5zIXS2x/Gb//1CQChvb394ZyxawkIIpfJNI89Y3FMzTMP0XQhxdxLRenEcZeHkBsx1orNIS9U1E3LIO/tD+ORt3YAELxJamNHIKGzZlOFQnnqkANSLqgR77u6qFv+8RwUP1LvzDXYSLg2D8EolxuoVNDNa/45TPLIK+Uh7x1LYeXWKHaE0+jw27Gsy4vuOuk8EA9z0MCuGsdxaHHbsCuawWA8gw4Dv68Zsm5wgyKe5qkHWl3UTRpXqeSZ9n3Y6OKwPQzcvm4MCya5csZDj9yyZ1IbraB3LIU/b4rhsZ3CdbZgRlNR1VvIc7QiOeRKD3mJQ9a1Ul36VZwapx3Uih/97QO8v3MEl/3+TcxprdNdl2hhZm1jFrLBMLXBR+unb9ozhiOmN+X97kaFlorP5UBnkxfbB6PYuCeEow1UWclHKfteKZhBPgFZuXY7rlm1DhzHged5cByH+17aghVL5mFpT2fWZ7W8NEZpb6ieWuTES19sDXKgejzkcmEnr9MiD7lL6tuAIv9Jjp5niJzv/duCeHb9Hl2VcCvQuqa/cEQHHn1rh+q1fs4huXVVYwojqBIeciJARa6xYB4PXE7IegFiZWbmBOW51Ps9clxCOJHGiXetUT2uErdBg1w4V8avL7/FZc9IH4lTc9PekGoficjl9sEo+oaj+Q1yjZB1pSgdOR9GQ8hJ7vdoLIV0hlf15muRSmeoMaynhq6GFLKeptcYHTvxMzuGorrXR6DIHPI6AznkRrxAJOze58oue0b6pUW/eGiloBsg5Hv77BwiaR4jiUxWDvIuqrBegMFQwVrkK3sjuGbtKDhOCIPmOOC+j8NYMT+IpV1C9NSwwRrkhGaPYJAbLX0WUYSsm61DLt/I8MvKnkkpBtJx8m0urOyNYN2QcO28M5DEe4PJnPHQI67MIbcwZF1+roh9/9KGffjL2u1552otPBVUWTdX9kzocCEGeSBPKUalqBsAPP/xXupIefHjvXhp4z7N560WZp7ZhSBPnZs9pQ59IzFs2B0yZJBv2J2rpTJnSp1okI/h6JnNRbWt1H2vFCxkfYLR2x/GNavWIcMLIkTy/y9ftQ5b+8NZn9cqvWMUatCUURRLC+K1tdJDXumyZ1TQzWXPKUtWKAFZnWMasq5ikGuJQ0UTaQyJRsL+bXWqn7ESvWt65dodmtf6toFsQRWe5+muOtnZb6ugh5wYCA06ufqAJBpDMBuybnZOUJ5LrTrk8uPKIyq1jquElKBJaIR30/QCk5uFxFMbtkBlXd5H0kWe1+5jm4la5Fr6HfINM0DmITdokMs922YN21HZfKelWK4FaTfPC33LGjtx8HjoXx8F1yGnOeTaY2ROZT03ZD2V4fOW0hsQb1Vl/jihQaMWOQlvNqOwTiC1s8ttkPeOpXDN2lFkIBh48v8vf3MUW0PCOJO8ayMh64CUR240ZD2qCFmX6pAb+z4J9Q84ONhlmy1kwySc4qlAm+Qhzz2/ZDwIPNTHQw+lh5yGrBfpIVeeK4CjbTQyV2vhraDKutIjrgxhl5Mg1TqKKHumFbWjrFRD5j0CD/3nrRpmn9lmSWd42ZzpoGU8jeaAb6IlzyS9GKvyyEvd90rCDPIJxsq12zV38DmOwyNrt2e9Fs/yQpmHhPwOhBMV2SWVQ9SNrfCQB6rFQy4aQz6LwtUBmcEST8lU1rVD1pWhmsQ77nfZ0dEoekFKaJDrXdNacByHv7y1M+u1ZJqnhoFbXEi0V8RDrp5DrrWpMSieIzIEZkOxzc4Jyo0BLU+z2eMqyReyXmhJxoA7O+S7GMz2sd1ELXLtHPLskPsRk2XInHYbvcfNRq6Qcnd1bgccJhev8gieSCJd0PVRV2C5Sbm3RwszJdWiNGRdEF0kYlD5Nh4HkuoK64SgSl4yAPRFiMK6+ecwzSEvc8j6yq1RaE3LHAc80ivcA2Y95E0mDfJIkSHrZCMjoBBpC8o2EEgt8lGdGuRGx0OPUpU9029b/rlai0p6yMnalcz3JSt7Jh4/kcqoeuEHFSHrxT4XrTqGHvLN6oDbQXVfjBrTG2QlzwhWGeSl7nslYQb5BGPHUBQ8rz558zyPHUPZD4ViPeT1XiddiFVSaT0UT9FFmZUe8nAijXQJaoAahUyceuV8zBKQibr1j2UrhMrRqr/bJ8vVV4ZzlgK9a1oLnuexQ2EUyXPOyPVOPOQj0WRJ6larMaqSQ07aoITneboD3xYU2mrWIDc7JyjboTUuZo+rxGXQQ252s5CIuoUTqaKvSbN9NFOLXNoMNSbqZianu6FApfVCFdYBocY46UskkSro+qgzIbxGyCi8PVqYKnuWlFTWOY6j40E2LLToF99u0vSQi4a9wngmIeuFeMgrFbK+I5yG1u3F88L7gFzUzdgag5Y+MxinbVXIep1iY8zOcXSzg2wq6HnIjY6HHrTsmXh4KYc871d10W9b/rlaC7L2S6b5spTjlEOeDySSRzdkvYh1rnztpYyYTGd4DEayK9UU+1y06hh6kPnVZbfB47RjDjWm85c+G44ksHdMcBLMnqzmIQ8V9dwtdd8rCTPIJxgdjV5o+RI5jkNHY7axSr1QBeaQcxxHjZpKKq2T367zOCwxXrMm4Qp6ycmi3Ii6slGItz3DS6Fmeh5ypVozCe9uq/fQzyRSmZy6oFbR0egtyEPeodiYIbv4HCc9mIMeJz3X5apFrhSg0hONGo2mkBI3hEg0QlSlrJQeeuOnNieMiOebXHNa17/Z4ypx5sshV6QXGIWcT54vPpTSbB+pyKUZD7liw8Ev2zADpPvPTE53Pl0CLQox/uXIhd0KuT4KKXsWUnh7tDCjCyIPWQeM13Yf1MkhByTP6rBGyHphOeSVUVnv8Nt1PcIdfjFKg+ZdGwxZ9wjfG4yZC1n3FqiyTj3kjtz2kTzyEdEQJ5sLat5+o+OhBzG8JVE3a1TW9duWf67Wwitbl5S79Bl5npO5TstDTsKdgcJC1u02jla4UW4UDkUSVDuhUZwjin0uWnUMPUKKiCJJJT1OPf5aEKN9aoM3K0VoxiQ/bJwwRxKDvRBK3fdKwgzyCcaynk5ozd08z+M8LVG3AkPWASnst5JK6+S3rahBDggGm4MTBtJsHqaVhC2uQQ4APoU4nMthUy2pRsqexZKZrJC0PtlYB9wOKhpVqjzyZT2dBXnIlx4xNes1SdXaljXhl7sWuaSynr/sWb8Yrl7ndtDzETWp8KM3fmpzAjmPRLBRyyNv9rhKiIdcs+wZzSE3NzcJXk3h38WGrZvto1GRwFQ6QzdacjzkGirrZrzWxIA0m0oiV3QvBOIxiyTSBV0fZIGYSGcMh8GGFN4eLczkkJOoEK8r+x7NV01iQDQ+tXPIhddHZfdwNMVTo7UwlfXK1CFf1uXV9Qif1y3cCyM6XmU1SLi/YVE3Zci6SQ95SDTI61Q2DJQ5/yM6mwtGx0OPuDKH3G6NQa7ftvxztRbyuavU5TiVkLUruTe1csjlG76FiLoB2ukuJHqtweukKT7FPhetOoYeYwqnAFFJB/KHnEsK64Gs1z1OO7pEFfRiwtZL3fdKwgzyCUZ3iz9nB8luE8oGrVgyj94wBDKJFTpRATKDpgo85MRbbwUk+rGiHnKLa5ADghq03OPe4nep7kjWuR3UsJEvRGm99wYPOI7LW0e7WLpb/FixZF5W6StyTS/r6ch+nZOu9enN2aq2Usmz7LGkYcbl8pCL1xOpb1rvU08NAGSCMQEXPWdmQ+vJ+MnRmxOUBrnW9a96XjhoHldJPpX1QnPIOY6TlT4r7t4lfZTfHXpjZ1QkUB6mn78Ouah6bsIgN1M7W06xHnKqtJ5Io7vFj//v89J1x4HPe30EXOYjk5QiiVqYCYenHnJxrqjPI7xIGCAecrf6fF2vCIEGgF2id9zv4Ax7keXU0bJn5fWQd9c5sGJ+MOs1m/jfivlBdAWE8zFCc8hNiroljPWHeMKVHvJiQ9YByRM+YiBknYyHDciaE5XjoYdW2bNibV1522i7TMzVWnAcRyOYyp1HTn6PGuQaIetWGOQBjQoNaho88uciuQw4mBtrtWcrTB5DDyJQJ48ommMwB5wa5K11Oe+RY5Ac80JQrlc46D9zawlW9myC8elABNtlORYNPicuOHIazuvpVL2QC130yiEGTTV4yNss8pADgNcOhJKVrUVOVdYt9JCT4xHPp1q4OiAY7kGPEyPRJEaiSUwOEmMjOxqh3uvEYDhRcN1jIyzt6USDz4kr/vAW3A4bLju2m17TFx/dhTN++QoA4LJju/DFBdPR1eJHMpndnpjMQy5nKjGiyuAhT6UzdNwDOaJuudfZoOyBr1Xn2QhLezrxt3d34pXNAwCAK47rxvnzp6nOCcToIOHXkYRUxkrtuAe0BWXj303HPx9G65AXMjf5XHaE4ilLNtOW9nSitz+Me9ZswdQGLz53aLvmfEruCSJyqeWxlXtzlEJDZDMhnsoglc7QvGVzOeTiJk+enGcl5NwX6iEnSuskrYKMkcvO4YCGDBYcMAMX6FwfNhuHgNtBNUFaDNSzJd4ePUE3QO4hN5BDrgxZN7jBQXLItUPWiYEnGYtSDXKb6dQcoHIq6wCwtMuHf26P4YXdQsePnuzCrUdkG58jVAjNnIfctMo6FXUTvh/PAGmez1JOV4OE+qt5yKUUAzFkPc/mwtIuH+a3uPDIJxH8YUsU4RSPa+cFDJU8E9qsXvasWFE3edvOWt2PsRTwuQMn41unHlC0geN12nOi6MoBSWkKytLm1JBvfjoKrFSjlUqjVFgnLO3pxPyuJlyzah1e7x3E7CkB/O9/9Zga66U9nWir9+BLv/0Pfe3v/+9YHDS1vqA+yFHbxJw9pQ7PfbQ3rzFN3t9vcq5BPntKHf75we6ihd3k65WDptbj2Nktms/cWoIZ5BOMJ97fBUBYSEQSaUyuc2P5qXM1P1+sqBsgUxauYC1yK2uQE8RUtoqWPiMLQ7/LYoPcZcc+8d/NKoJuhAafZJAT5B5yoHBvnFmIR3lqozfrmp7bJnlpvnrCLNWa6oBk4OV4yMtYuk8eQk0MBLrYVzGe+mV14r1O0etb4MJHvti/4rgZmhsxSg95OsMjnspoGpdN4vXjsHG47vT9DRsVThqyrr7YjBUYsg6IG1hjcdMCeFqQFp5y4BTd+bTB54THaUMsmcHukZjmAoLMuw4bl6Nm7pN5V8OJNI1OMWMk1xvMeVZSjKgbIHnISVrFE+uE59EZ89pwgudTnH7KbDid+seu8wgGudF5d8yAoJv8fSObNBGZyjogjUe+FADJQ57HIJdtyEg1yAuLgiKe3XKrrBPk5Tg7/fYcT7BZUTeymWE0h1wp6kZC1gHB4+xXyQ2Xo6WyDsjPV3bIul5fugIOLJ8XhNPG4RcfhfHGviSumGOoKyUreyZvm9PGAeBxxVEdlhg4wvycLHvpM7KpWZ8nh5wqrDsK2/ACtNNdSA1ytY3DrhY/Pn9EB17vHURbvbegsVY6YvSE68wQUgjLApJ3e5OOsBvP89TYnqPjITciDpcPcsV/+dhunHPYVN3P1gosZH2C8cR7fQCAM+e1AcgvamRFDnm5Q37VkCt/W4XHLpY6qWgOuSjqphECWSg+mYGvZcAC6qGa5Dy3yTzkQGlLnwGyXV3FQ8pu46i3Ve96J9e6x6E0yMvnISfXkttho23W29AgO/AtARe84iKw0Fw9+fcGdIRbSDvaZJtbWqXPgOzddjMLHuohT2dUc8aKid4hYd9WpZsM0vOg77HlOE6mqaF9PRFvjloIpdthh1NckIfjKXpfmfGQGw2xVkI95F7jAnJy5GkV6QyPp97fDQA446Apho9hxpMtfM5YJQozNc7JPELmSSOibsl0BiPiobUN8tyQ9b6o8O9CFNaByoWsE2Ky0PCNo7ljS8ueGRV1E8fOaHciijrkHrsUKmwkbH1UL2TdhKibkjM7hfnzpd1x+v18xMQpmjzuyf9NyoboEibjZVEaHK1FXuYccrMh6+4CBN0IQa0cckXJMyWkeg0JbTeLcr6xwtAFpLk1mOUhF3LCN+wZ08zh7g8lMBRJguOAmZMCOe+TvPJNOscw3kZjG621BDPIJxCf7Athfd8oHDYO5x7WASD/JElL7xSosg7IlIUr6CG3sgY5gXrIq0Bl3cqyZ8rj6RkZSmNxNJakHilSg54sVvMJHhWLVNoo1zCRwrm1z1VM41onnmAjytjFotYHMn7hRDpH4IzmqPnd1DgoNC9a7i0mhr4a5FwLXnkpJ1gLIzWg1ZAbo2qlzyQPeSEh6+JYWVCLHJDOg97mFUGqOqG9QZlvs0F+rgsRWqtUDjm9D5NpvPHJAPpDcTT4nFg4s9nwMajhbHDeDcW054Xs4wrvRwyUsiT3GOmPXiUEwpD4ng2SGJiSBpWQdeohL0DQDaicyjohIvPebhzJLjUYS/PUyDQasu6xc6qK51ooQ9Y5jqP55EZqkYd0VNYbFBsoUg55/vbtV+/EfkEHkjzw7C5jzorcHHJrRN0IqQwPkglglS4NrUVebpX1lLLsmb6oWzE6SVqbhPIINjVIKbRBneetHsr5u5jcbDlkHSJ/Zs+cFKAq6fs0VNI3id7x6U2+LIV9QleLH047h3AijZ1FrqdCBjdaawlmkE8gnlgneMePmdWCqXkUkglWhKwTr/RYzJqcTbPwPE8Xv+0Wesi94jxQ0RzyRLanxirkHndl/pMcpfebjHODz0nbVKg3ziw0V1RlgvYZMBypgafhIe8biZWsljpBbddXbkgoH8ByUTcjxrEe8ugBvR17ch7rvU5Dnmaq2Oo2Z8TJc6fV8v+kHPIClKdJ6TCL5qN+jVxBNSSlde0FSb7IJHmIZCFh5IUb5KTEWqEq62QjIY1/iM+jUw9sNVVuKGCyFrl0/RnLIQf005AyGZ7OFV4TZc/6w+JGlpuDTSNSROlxBeQ1yAsUnKqQyjpB7oUOpXjsikrtIGryHNRztLXQijBQ/X1FyDogGecRA6Hekqibdsj6aILP2lwIGtwkJF7yJ7YbM8iJyrpbYZDHLQpZD8vOlVUecrJhWk4PeSbD02cGSc/RUlknm9yFlDwjaG0SDqqIuskhhnp/OFHQ2oLMN2Q62bTXGoNcbR0iV0nfoJEDvoEqrOeGqwPCGBPPebF55KMGN1prCWaQTyD+8Z6Qr3fmvDYply+Z1i0hQDxTxYSsB9wOemNXQml9JCrlL7WWwENe2bJnZJfQ2pB1eW6S1sMEyF2IkkgEuXheuXLI9XJFPS4jBrm6h5z0JZJIY1RFWM1KQvHsciOAEHJPdvmVmxoDFom6AdmedSMe8gafk14nel55td12I8gNcrU8cppiUJCHXFQqL1JlnaCmpquFFDFkwEOu0TfS/j2jcVquyJSoW5E55MV6yEejSfzzA5I+1W7qGJKAkrG2hwzmkLscNrrxPBbXPnZMFvqa4yHXGU8Svtqi4z1VltECZKJuRYasJzLWGW5mUHqhN45I9xzJva53aW9SqNFkwiCPKkLWAXNK61LZs9zfJBENw4lMQZsLxCB/dU/CkEhdTg65xSHrZDzsHF+UE0YOWWuWU9RNHlGVL2Q9boWHXCPdhaaUaXnIxZD1RCpTkLOKrAfmtgo6ORt2WxWyrm7sEqE2LU/8xjwGOSAIuwnHKK6tamulWocZ5BOEDbvHsGlvCC67Dacc2EoXEjwveQaVJNM8XewVE7IOVLYWOVH9bva7ChKA0qIaRN1K5SGXh6vpibrVKwTHaCSCbOOj3DnkaoafEWNVMvCyrxGvy45G0YApddqFVl6UlgiXXMXVa2DTQY/skHV1D3kmw9M896BXioII6YR+kz4FTT44bTaOqt7qe8jNz01We8jlufz5kDQ1dDzkGor/BLIRQtIovE67qU3TSoesP//RXgxHkmj2u3DUjCZTx6gzUS9c/jkjG0JG8sjl9wmJpjFS2nFAnCP1vLvE4xpJ80hkeCG6S/QoFxqyLhctq4SXnHihZ9QJ7ZfnkVNVcpPrCy2VeiU8z1MjU+4hNxOyPqZTh5zk/I8k+YI2F2bUOXBAgwMpHnh6Z/61kWbIusUechP7HXkhkVvlNMjlv5VX1M0Cg5zWIVca5DSHXH2j1udy0DlRbxNcCzIfz+9qBAD0h+IY1NF/MYpWtCEpZaYl7EZy2NVKnhHmyPLICyWZzlC7hRnkjJqDqNku2q8F9V4nnSQBbe+WfEex2N1SKW+y/B5ypeq3VRBRt/Fa9oxgJGSdPBjUxrpcHnK9XFGfLFRWCz0Dz0iYsRVoCVCRMVTm4ctFY6SSUqUTdRuLpbI8siQyI6Jj2BaT66VX+kwqU2feUCFjFbYgjDKaSNPrylAOuSwFQot8IeskVYAY5GYNZCLKVqioW6EGOdnsIqGNpx3cmqMinw8zauiAtrdH/djq4kxyyH3icdqogng9LSOn5yEXQ9Z1POR1To4Kjo0kMhhN8tRIKjRk3c5JOdeVyCMnRu+hTcLYbhyRxmjYZA1yQotBizGeAcjM4VXzkBswZEMpbVE3Epo+nMgUvLlwlsGwdZ7nqSecdN/KsmeAZJBbFK0OQLrny6myTp4NdhtH02S0DHJLQ9Zzcsjza4s0FyHsRubjtnovOhqFNUqxoeCAdlTRfjJhNyU8z2MjKXk2JVfQjUA95EW0U77xYfXat5Iwg3wCwPM8zR8/6xAhPNBm46jhoWWkyCcwZS1cs7RV0kNeghrkgCyHvApE3fxWh6zLPO66IetkIUpC1odzx7rBl/2ZUqGXK+qVqTtrQUJR1aIoiEDdrhJXCtAyHqgBJfPApdIZDBGvm7+4kPVEKoOUbFGntVtPziHxyEoecgM55AXkepFFkpqoWzwlGUZmIRsJVnjIyULK5bAZ2nQwIhKYT9SN3J8kYsNsTjcxqKPJtOFSObFkmj4T6guuQ559b51lMlwdkErxjJrMITdybowouEdUopLohlkspSkIRzfP3NrGp43jEHRKYet9UeG3GlwcrZ9dCIEKKa3LPdTUIM/ykJurQU4wmkMu94D75DnkduMh66M6Zc8kEb6MTNDNXF9I2PrrexPYF9OJ4JJNgZKHXPg7kYEl+iZhEiVW4wY5fTY4bDS6M67x+5Z4yN25z8F4Kk2f53qRU02isFsxHvJ6r1NWUqx4g1wrUk8qfZarkr57NIaxeAoOG4cZLdoGOTnG5r2hvOKZ+drnddqL2kipNsZPTxiq9PaH8b1H16G3Pww7B8yaLN0ocsVbNeSCboXWZyR4xcnusbd3YMXTH6O3P1zU8cxQihrkQPlyyHv7w1jx9Mf4+sPvZI1db3+YKlU+9vZOS8dUvhnz+1d7NY+trL9LPMjtah5ylTraVqKXK5rvWgekMGE1A48YQA/+e2tJr1+SF6XlIZd74IYiSfC8IOjS6HNKmw5J80am0ojX2q0nGwKkPQF3/siDsQJzyIESeshpyHrxi0R5nqCReTIhLhZHYyn8+In1qtcSnXs1NhuIV2CnuEFkti64UIJO+Ldyo0xrvnl/5wgAIUf21y9uLugekFc58LnsecvEqUHG76WN+/Lei739YXy4S2j3mg17836WiDD96Y1PNT9LNvXkUWbyiAG150Fvfxgvbh4AAKwfSaN3TPserZcZeX2R4sLVCXUVEnaTe6gPbRKMkk2jKWTExbzkITe3FDWaQx4VPeBODmJ9bQFinOcLWed5XpZDrh2ynsgAe8TUgqBJb3+n34G59Q5kAFz2ryGseH9M9fqIybz5Ug659FtW5JHTkHULDXIpZL18155UgcNONzXz1SEvpuxZnYrQ5JAYEeOwcbQsmhokv1yv1KgWozI9l9kWGuRaFWu6Wvxw2ITIssv/sJbOv739Yfz4ifUAhOe8noJ6Z5MPTjuHeCqDKxTHUHvuqEE0PgpZU1Qz46s3jCxWrt2Oa1atoyGmGR4465evYMWSeVja0wmfy4GhSFLbQ15EjqayHQ/8eysAYPtQFP/78ie476UttB2lphQ1yIHylD0j55DjOPA8D47jcN9LW/CFIzrw6Fs7QDYYH393Jx5/d6clY7py7Xb85l+f0L9/9+pW/PaVXtVjK8Wh+lSiEQoVkDLLqE6uqJH86hj1SmavRlau3Y6/i4KIm/aG8El/uGTXr1a+NfFIyvPwSa5Yg9cJh91WlIdcacTn85CTc0p+U99DXni9UFeJPOR+AxETRjEj6EbuZ8LvXu3F717NvbfieTYblCHrDSYNcpu4SByJJjEaTWJynSerfWrzzV/e2gEA4AH87796cd/Ln5i6B1au3Y6fP7+Z/h1NpHHy3S9hxZJ5OOeQVsPHuGfNFgBC3/WeJaQvZI584eO9eOHjvbqfJc/K53U+G6UecuncuBzC/RdJpDEcSdKoILVjrx1M48Sn+7FifhBLu3w5fWxwcfg0LNS0JgZ5oTXICZWqRS43eOfUO+CyCbW0t4fTmB5w0LrdRsqEyWkxmEOulj8u/ztfyHo4xYN8ok5lHRRwcLBzQJoHPg2TaAZz89HK3gg2iEJ37w+nsH4khfs+DudcH8Qgd3Cg2houm9wg57MM9EKIUIPcuuukEqJu8vQzMofGUxk6p8mhIesmSukpURN1k4er22zax6Yh6xq6LXrIN8jntIrq5RYIu2lFFT32zk6QfY0XPt6LNRv24V5xPia5NiORJE68a43ms2HV2zuoSOuLH+/FSxulY9hs2c8drWOMxxrkAPOQj1t6+8N0MUKmVh6CUb581Tps7Q/nDeOVvDSFLwZIO+TTezrDZ7Wj1JBFa5vlHvLS5pDLzyEZM/L/lWslYxwQzqsVY2r2fMk9tzzP07Fu11BZz1iU66aGbg65EVE3FQ95ua9fLQEqtTx88gAnhqCRPHktlOPSr7E4IDlrxCNrSGWdnBfLc8gL3zD0q4QYFkq/rPScHvL7maB13xoNWSf1YAvJ6VZeU/nmG3mEotl7QO0+kj+Ptg1ECjqGVjvMjLXqs1JnPo2oGOSAtCkiv0fVjp0R/1v+5ii2hnKvv3qq3C2FrLcVmD9OIDnk5TbIicHrsgne3NlB4bolBigNWTcr6mbSQ+5TGFs+g6JuJOfewamHcXMch3pxs2ObeC7N5MP3jqVwzdrR7GuaV78+lCXPAClkXf5+MZBoACtF3TyO8pc9I88Gj9OeFWWkvrErhqxbnEOeT9CNQN7vLzJkfbaogL5xb244uVnUDF4ylxF48fnAQ5jbyE8q7Qw5OcdA9jGUzx2t50sxa4pqhhnk45SVa7drhk9yHIdH1m6nCwqtnUsrJioj7Sg1xGtrZQ1yACBzValU1vXGTotix9Ts+ZJ7v4ciSXrNTKmXHkJk4Z/hgZBFZabUGNMI9wZgSPCMPsRlXslyX7/aOeS5Iev9YUlhHQA84upMr5ShFsTIIAbgaCylagRTDzk1yEkutl7IeuE55GTuSeospAqpnECuh0IV6eWQSIV8gm5mriV5upAayqoKhRjk5N4lmyylnG/y9f0vb+0s+hjydpTqswAQEecJr8IgD6oY5PrHBh7pzQ3tlIeskxrkVoWsh8ocsk4MXiKotp9okG8aJQZ5YXnXRg3yiErJM0AWsp7HiB2T5Y9rnUfS9m0h4VyZ2VxYuTUKrVtOeX0oFdYBQXOARNJbEbIeKUHIuqcCHnK5M0k+h6qFrVudQ06evXTDPM9zobmIkHW5yOasyQHYOOE1slFbCIlUho5TnVt6rph9PqjNnVYcAyhuTVHNMIN8nLJjKKpbX3zHUJTm9uQNWS+i5JmRdpSSTIaXlL9LlkNeGiNTb+y0KHZMzZ4vYgikMjw27xVCpVoC7qxQW3kel54KcbGENMK9Aciude1zpWbglfv61QoV0/OQkzxcYqTplTLUgmxUtNV7YBfD64ZUcv6VZa/8BsqHlU5lvTrKninPgxZmriWjKusEs6JuQO41Vcr5Jm/fDVTfMDN+pfosIOXBa22KyNNK9I8N7AjnPnuJx3U4MQ5C1tPZIeP71Wt4yE2GrBste5Y3ZD2Ph5zk3KsprBOCokFeSMj6jnAaWrec8vogem9KTz3xmCcsiD4jivKlyCEvr8q6lM4kdyipPUckg7zwThPDMMNL6+kBg5FT5P1BkyrrcpHNBp8THqcdXc1+AFL5sUKQe/nlkXpmnw9qc6cVxwCKW1NUM8wgH6d0NHp1d/07Gr3UQ55PZb0Q0SQz7Sgl/eE4kmkeHAdMCVprkBOnRSKdMaxUbAa9sdOi2DE1e74ElUvh8x/vHgWQLehGKHXpM57nDdUhN1T2TLb4Kvf1qyVMp5aHr3zgGyllqIVcOZp4etXC1nMMcgOe5qJyyHUEeeTCPWbxEc++FTnkoexIBS3MXEtkM1TLa6Ms9VKIh5wKMoqbZKWcb/L23UD0kpnxK9VnAelaV3rI1e5R/WMDHf7ca1fykMtD1q0xyMtd9kzpoSYecqK0TkTdgiY95I0GP19syDr1kOvkF5PNBCKIZmZzocNv1/WQy68PGrKuyEemSusWhKyXxENeiTrkCkFiPWE3qexZ4TnkHqeNbmST5x0NWffnCVkvUGWdzDN2G0cN09k6ZcmMQtYgPped9gkw/3xQmzutOAYg6QWxHHJGTbCsp1N31/88UdQN0M7tyRc2aVU7SkmfqEI8uc5teXkE+UOrFF5yvbHTotgxNXu+OI6jNXg/6hMMcrVIhFILu8WSUtkutTAmr4EccrWQ9XJfv1rGq1LNHpAe+MSAthsoZaiF5PWzSyF0KgsEEuFgRtRNT2wvH2SRpB6yboWHvPhFYr/BXEEz11I8rT/35hjkvvz1z5Uoc55LOd/k6/vSI6YWfQx5O0r1WUDy9PkUG0Fq1ST0jw2c15272GzIUlkXfqvdW2QOubjJOFrmkHWlh5p4yD8ZSyGV4WWibub657Rxhgxf8vsehbFlVNRNT2GdoGy7GcX4ZV1eXQ+5/PqIqeSQA/Ja5IZ/VhOyYeO2WbdxUwmV9bgshxyQ5lG10mcJC9a5HMfRZzaplCJpvBjzkJvNISfzdtDjoEauvCxZoWitQcw+H9TmTiuOAUjrjfGmss4M8nFKd4sfK5bMo39zEBbsNg5YsWQeulr8eZWni1nwKtsh39S1cchqRymRwtWt98TbOCl0tBR55N0tfvzgjP2zfo+cw2U9HVl/K89tMb9JzpfRY9eLBdk/6hMeAmpjXa/wxlkNySniuNyFMoC86RmAeqkp5XgQuBJdv1rlRoyIugHGyrupIff6SSF0uQsE7bJnOiHrcbJwKCCHXNwc0S97Zn5+IuNkZch6Pg+5/FoiTgIO6nMhVVnXKnvmUjcGzaC8prpb/PjC4R30fSvnm3zzyvTmXLVxvWMQ7BrPEvJZuTNGq83y4yqfU2r9i2p6yIXzL79HaTvEvznSZgAr5gfRFchdUBJDszeUpkbWlKJzyEUPeYVC1omHeqrPDp+dQyIDbA2lZaJu5r2TRvLIiQdc6SH3GqxDbiRkXZkzbsYg765zYMX8IGxA1jWidn2o5ZADktJ6tXvIyxqyTtLPxOcHESZWzSFPF6+VBEjPwtEcD7n+c4GkOg1FEqZEb6WKJ9LxSemzYjzkYxrh4FpzOIds+0Lv2ZDvGPmei1Ibx2cO+fjaXmBksbSnE//3xqd4d/swDp3WgKNmNOO8nk56gUvK0xoq63RRWNzsvLSnE/O7mnDR797Ap4NRHL/fJNxw1oElN8YBYNcwEXSzNlydUOd2IBxPlyyP3CbOUFOCbhzZ3YyORi89h187YRYeWbsdO4aiWa8XCzlfRo8tPBDC2LBbeAioh6znLlatRP4QUSsxki8aBJDnJGdf7/LxeHJdHz4djOCMg1stL3kmhN2r55DTxb6oZs9xHH3gt8ge+KSUodBP4wsMapA77XQBpRuyLrbHR8Vs1Mc1mc5Qw7mgHHKdsmcxhRfEDKQt8VQGqXQGjiIWY4NhY7mCgHQt3fzEerzw8V5MbfTij19ekHNv5c8hV1wfRYi6ye/JvlFhvjyyqxFT6r2Wzjd680oyaWxeIMdY/LOXEU9lsLSnE/99/EzVdizt6YTDZsO3V76LgNuO/zq6S7PN5Lg/f34jHntnF4IeB/7+/45V/axWyLrWpuPSnk488uZ2rN02hHl1wMIpbpw3u07VGAckg47kWbe4bUWXs5JU1ivkIRfbb+M4zK534L3BJDaMpAoWdQOM1SInHnBNUTeDKuv6HnJO9+98LO3yYX6LCz9+dwzP9cXR7rPh/45vyrk+tAxyN/WQF2+Ql6IOOalaUk6VdaX+kV7IeoKGrBdnkAvGYZQ6Z9Q2zNVoFJ+l6QyPkWgSjXkMeIKy4gkAzGklHvJQwUrresau1hwOwPCzQe8YP3j8fby6eQAzJvnx24vnax5jvKqsj6/eMHIYFsPnlp86F0fNaM56L6+omwWhPISuFj/mdzXj08EdOLK7uSzGOFBaDzlAFvZx6qG1mifW9QEAvrJoJi47tjvrva4WP5afOrckv2vm2GQhKgmD6XjIo+aVRI2Qb4KmOeRJ7Y2TmErZMwIZj2NnteCLv3kDr2weQDKdsTQNIp7K0PqcynAxMn4J0cD1uuyqD/zsqBfjRpq8tnKjjuqrXNUVAAJufbE8eeRIIeFlLod6yDrP8zIRvkI85FJbwok06gsMCeZ5XpbLn78OOSBcS19cMA0vfLwXTX6X6lxotOwZwQoP+UAojn9vGQAArPjCIehWtMuK+caqY7TVe7B1IIIlR3ToPkvI/TCnNZj3d7ta/LjqM7Pw2Du7wHGc5nGp3oIzv/AiYfuQUNbthlk2HN7hlUp0qEC8xcS73F5kyTMACFKV9TJ7yFU81HOCgkH+7mACxB42K+oGAM3u/N+JFlmHnIT4B3QMcmX+u9kSbgDQFXDgizN9eK4vjnqXTXWzhhjcSmOZ5pBbsNcSTlpvkHsrkUOuSD8zErJejMo6IK09iHPAaDlMl8OGoMeB0VgKA+G4YYNcWfEEALqa/XDYOITiKVpdyCxaOjb0NzTmcDPzutYxvnBEB17dPIC2eq/uvM7qkDNqDp7npZJfKkYSXbxrTJQJC0LW5bQESG5q4SUZzLJL7L/VCusEqf6k9R7yncNRvLVtCBwHnDGvzfLjW4XSO1cJUTetcmGEfOkZABBL5fe4LuhuQkvAheFIEq9u7i+0uaqQPnBcrsHld9nhED3/ZFNDTcVVClkvTNTN63LQEDq1+3RUsQgghq1W6DfpkyD+Z34eoR5yhWdD7ukoRHTS5bDR/PRiwtbH4inqYckXmignn6ZCvs1Qpcp6YQa50F6yafv0h7uRzvA4sD2YY4xXG1K+tv58MqJIscgH8TaNxpKa4aNyvQXVNinO6UgkiT2jwr0028CwKr3FxQq6AZJBWak65HKDnOSR/6dfGCeXTfKgm6HRSMh6HlG3fCHrUg659m8pz1ch3n7he0KbRjQsa0llXT1k3Yo65KUIWfdWsOyZh3rIdULWrTLIFTnkgzSCLf9GbUsBtcjJvC2f21wOG2ZMEpXW9xamtF5JY9foOjHfeq9WYQb5OGYwnFCtC03w5RG6skJlXU5TEfUWC6VPLKdjdQ1yQsBdOoP8yXW7AABHdjVZrhBvJUHFYlfNQ04NkBLlkIdoXUp9D3lML4fcgGq3w27DaQcJmyMkesEqqFCJKzfsXhDPkx5W8VQaY+Ln5YagkVx5NUjkgM9ll+5TNVG3qNJDri+ORmvDF/hw11JZjyflBnlhjzG/gfz3fJAxCrgdpkLn82kq5EsXUoasK+9BM20g5/SJ94Tr+axD2k0fq9zUq+Rrq6G8XvMeV/wcz4PeX0ryhawr27Rxr5DKM7XejTodtW56HKVBXmT+OCCrQ65ikJSSiPh7coObKK1/OCSG3DptptX9AYMh6/nqkFuosg4ImwvKsmRGkavrqxHXyiEn1V6sLHtmoWVQkRxypaibUydkPWVNyHpA5pyJJFK0v0ZSmZoD2s9cLejmuKLk5X5E2K1Ag5yuQyoQDk43ifNEUo4xUTdGrUG848q60ASvUZX1IuqQy2mmu4Dl85D3lclDHopZb2gSg+/MKl8gyxe7Nk5QtNf6TKk85PmUvH15okEA4yKGZ4rRCs98uNvScnc0f1yjD/WyTQ2y++6wcVliaUYiAdSIyULWmzU2zpLpDMLi5+qph1xabKVVFoTF7rY7NTzkJJrBxhVersbv0t9MMAKpHdtkwjsO5PfE5rsW5d7ZOo8jS3DQKPJ7cu9oDK/3CuHqZxxcvdE4BLWa32ooUyzy4XbY6aaW1uYhVVk3UPYMADaKAkuzJxmLOlCGbxdbgxyQPOSjZfaQE6+umoe8mHB1AGhx5R8XuiGgEbIezauyLpZlMyjqVujmAiB51kMpnlYMkaOlsu6mom4F/WwWkiq99Srr5TXIsyOMSKSV2vM6maeihVHk0ZLEsHY7bDnzhBq09JmJWuTDGpuN1CAvsBb5aAUF04xGPkl57swgZ9QIu6h3WN0YJWrUWkaKFSrrcvTUm0tBKp3BnlEi6lZbHvJtA2Gs2zECGwecdlCrpce2GvkO7ZSgR1Ugi3ymVCrrobwh6/nrZRutaz2/qwlTgm6MxVL410brwtZDeYxXuRFCHvhNfleWNz1f1IsW2Srr6osDuaFBDEq5p1bN01ys+ArxkCtzyOXRDIUugEnYdzEh60bzBJVkeWJV5o580UnyHHilh8QocgPyqff7wPPAoZ0N6GzKr3heaUhlB6s95PLPah07klA3yLWiHjaKYpdzDBrkPjsHecpymwU55HKV9ULFngpBWfYMAKZ4bAjKOlhoiHeTp/iQdcMecoOibmYF3eTIx0Rt40TbQy4a5JZ4yHnxmEUfiuKRlT0r17VH167UQy6GrKuIGlKV9SLXuQG3cP+PxVLU6dQScBt6PhXiIdea2yQPedjwseSENFTWywHpy1g8pbrBT6B57uNM1I0Z5OOYfN5hwyrrFoWsk1waM5NOMewdiyPDC17EFoOCS2aR8oasNciJd3zhzJaStd0q5A8ErWstWGIPuVapDgLZfEqkMpoTfZzmkOtPizYbhzMOFqIW/iGmFVjBaJ4+yA2Ffg0FV68z/8aDGhFZbeUWjcUBMTTkHlm3w0b/rfabY/HidtvJIknLQ17MZiHNfy/CtUTz+A3kCcrJ8sSq3BP5csjtNo5+v5D8cfn3kmkeK9fuACBFf1Q7DaRqQ0T/WTKsEdape2yyeagRNinXW1BrUzSZzvLEbRQ9VbMnGzPIOY7LCltvtyJkXbyOeEhK2uVALWSd4zgatg6YKxMmx0zIujJH3ScTddMzEscMqKzL219oXwBhnUJC44dV8sg1VdYtyiHneV7mIS/qUFnIn6dqIeOlQLm5rquyTnLIi1ZZl3LI1fRd9JCi0kx4yDWif/abEgAAbN4XQiF7NNWQQy5sVKuvFYVqNCyHnFFj7MqjMJ6/Drl1KuuAbBcwHC/LTilRWJ8S9BQU0mkEv6L2pFXQcPUaWCDLF7ttGpEIDSU2yKVa1+oPEXm+p5onN53hqcK5kQ2oMw8Rzstz6/dYJlajVYOc0CAL55IMwewHfr5NNi0klXUHDb+OJNJZYzWiYtxwHEdrYqttShW72+7WKHtWTMkzgpT/XkwOOfGEmPOQA/qGn7Jsjxpk7iGGoFl8LjsN91/fNwqgusUj5RhNgRktwEOeb/MwphGyXudx0Dq68u+SkPU5kwKG2yAP47ZC1M1jB4iTOFRGg1zLQ03C1oHCvcpNBozfiFYdcvHvNK+vTm6oDrmsHYV6+6VjaQu7kfD/UqmsxzPCeAClySEHylf6TCphqix7pqKyLna62BzyoCxkfdBgDXICjUqzwEM+vdkPl8OGWDKDwQKyQ8lzPFgBY9clC/HXnn8lp8p4yyEfX71hZNGXpwZ3vtrMZPIqNpSHQBb6yTSP0VhKc5HU2x/GSlmNwmU9nbqqv2qfB4B713wCAEjzPHr7wyVRDi7UQ67Vx97+MO57aQs+6hsFB2D/tjrL22w1coNmx2BEdazLpbKuZfi5HTZwnLDzGk2kc4xeuVFtpIzWYZ0NmFLnxp6xOL70mzcwv7upoOtU/vm8OeQqY6jcgfcVmENODG+Py46A2wGXw4ZEKoOBUAK+JhIerK5Y7XcLJVsiKrnYoxblkCtD1o2mF+hBxiqssXlhZB4iefZmc8gBYRz7RmKq90QiT8h6b3+YVsHoG4kWNL9tHYjAYePoRtTBU4MlKw9pNfV0M8NYyLopD3kewT1yr3gV155N1HMYiSYxGk1icp0H/aE4BsIJcBwwa5IP2GysDfI84Qc3h3F+tw/ddYUv1ziOQ8DJYTjBYyzJo7VMp1nLQ90ss/g2jaTQO5Yy3b+wzNt55wej+MJcZ84xyIaA8vflf0fTvGad95CBkPW+SBp2AGkA28OF9YVQ77JhZySDYRVhN+IBdyucC26LQtblkRNWhqw77UJFi2Sap5FNpSaWUnrI9VTWrVnnBmRrwX6qLWIscqo4UbfsZ4/dxqGz0Yst+8L4v8027H12E85fMJ2uL/M90/KtQ0pNg9eJSCKN4UgS05tz3yftE6rRWHihVgGmR7yrqwuXXXYZLrnkEkybNq0UbWJYRL4a3PkW71Z7yD1OYbEfiqcwEIqrGuQr127HNavWgeM48DwPjuNw30tbsGLJPCwVDe18n793zRbhTfG5tWckhhPvWqN5jGKQcsiNG5paffzCER149K0dII9FHsC59/y7JO22CtIXwrqdI6pjTR4aoXjK8vrdgKS6qWX4cRwHn9OOcCKter1nGeQGPOR/eWsH9o4JD92124bwzvZh09ep8vPEm6zl5SfK0sPRBJIZUmpLEbIuE1kzA/WQiznZLX4Xdo3EMBhO0Jxiqe6p+iaAqoe8SDVUTZV1C0LW9TzkRuchYpAbrUEuR2+TSm/uJW0j6+9P9oVNz2/KYwDABztH8Ze126t2rpFjdIPPrKibkWNrqayT745Ek/S7xDs+rcmXY8BrsbI3gvXD0jX5m40R3L8hghXzg1jaVXh+f53ThuFEmnp9y4Gah3plbwS/+kjKb/1gOIUTn+431b+VvRHc9O8hfCj+/eCmKH69NfcY/397bx4nRXXu/3+q1+npngUYlhkZmFEQFMENJJpdFBVjNkT0+vWa5Zflaq7xJjGY3Jg9V/HGRONNzDW58eYmRhmjMYtEgwsi7qMCKso6wAwzAwwwW+/L+f1Rfaqqu6uqq7qru6t7nvfrxQump6g+VefUqfOc53k+j1oOOwC4HWKefpyJxzRr7KfxHPJGDYO8qyeEm7tHwe/ortGk6WtRwj3soyp9pBWy7pFE3Yo0yOM8vUAUy7SSOpcT8WSibB7yqBRBlfaQc5V1tRxyy+qQc6FOWdTNaOQUf48PWSDq1tXdiz1HxOdr75iAX2/eh19t7pHWl/neaZWu8d3oc6NfY6MaUCise10F68fYFdMj8MYbb8QjjzyCE088ERdeeCEefPBBRKPlU80mjNOfx0NeJ5VIypNDXoQXKhs9YbeeoaC0SEymWMbfax7ehn1DQUPHM4jGLI+KZ4DmOYqFi0qMGwxZ17vGru4+pJjcbpSw3VagvBYOY+ptVhqZoyXwkksecp0wKz1ht6gijyy75Fg2/LqVy59Cxmn28WN5yo3IhkJCM0etWFE3/v/VhN1GNIybgE75sLEiFVs1c8gtmJvqJVG3zHtlZh4qJmRdr/SZVnSS6jMHc/OE2jn4eew612SjTN/QoxBRN+5N15qnwhqibsrv4X3KBd240FI+esYSuLl7NOOzJANSANa8Oop944WnV/D85HLWIs8OWefXp2wBg7nr4+dQzgha9yiiETIPyEa6Vi3yRIpJ7Q+oGGtq7TB7Ldk0uc3nkPNhGC1ynyWoUSLOCuoK3CguFMlD7srMIY8l1VTWuZCdVWXP4tJ7wWgOeYtJwWPGmGr0D5/bZQQkWeb6Mt87LV+0YalpzhP9NBapXEh9qSnIIN+yZQteeeUVnHLKKfjXf/1XtLa24ktf+hJef/31UrSRKIBkikkK4/k85FqTpNUq64CcUzOkEprT1d2rueMlCALWdfcaPt7oOYpFWXvSCGbbDJSm3VZgpr9cToc0wZcibH3cQBkMebzn9lV2zpkeVo5T5fH5hEqUefhahqARNXk1sr1+fCGhfE75CzK75jVPfdHLIS9UDVWz7Bn3gBQxN8llzzLbbaZ/CxV1AwrzkJsde2pYcY5K06RRYkxJKsWkEj5NJvLs9TZKYomUVJKq3p07prNLn+1M1wLmQkv56NoXhtbrQRCAdT1hQ+dRQ6m0Xi6yQ9atuD4z59CqQw4oapFreJaVufZqIeul6KtmnVrk+cqeFSvqxu+VvwR2mE+htF4OsjU4pJD1UnrI62TnjBQ5ZfC9wFOehkPxnPQsNcYVKuTKzUYr1pf5tGxKTb4IpUqqwJeagkfgWWedhZ/97Gfo7+/Hd77zHfz617/GkiVLcMYZZ+A3v/lNWUtrELkMjUeRSDE4HYJqXWhANlDiSaY6CVgdsg6oe944fcfDmuOGMYa+42HDxxs9R7FIHnKDOeRm2wyUpt1WYLa/jNYOLoQxA4af7D1W8T6Y8LhaOU6Vx0u5W/k85KGYInc589nOV8pQC7m2sit93tycNq18XL9XexOg2PA3zbJnWTmChcDbna2ybqZ/+TxmtuwZoF23GlDMvVnXZ3bsqWHFOSqNctGmdS1jkYQUbWRVyLoy8kQtZL2xSA95XzAJrdcDY+LvC4ULk5UzZD0shayL323F9Zk5h1bIOiAb6Voecn6f6pxiiHsx7TBKo2SQq6zHUuoK6FaVPRvXEMCzAtkgL1PIek4OuY7KukVlzxoUZc/Mqqw313ukNIHjBrzkfG7yuBwZ78Bi15eMMYVBXhmDV7nOUaNWa5ADRRjk8XgcXV1d+OhHP4qvfvWrWLx4MX79619j5cqV+OY3v4mrr77aynYSJjmYrkE+vcGrWhcayFxQqHnJ89XCLYQpKgt9zsxJPl3PzcxJPsPHGz1HsQQklXVjRqbZNgOlabcVmO2vUgq7GdnVlasKqHjIDZY8A6wdp8rj8xmvSgMuf8h6oSrr4v/npfaOGQhZ16vnPVZkDrkcaqjuIS+u7Jl6u432VyrFTKvpKmnSCLtmjClE3TKvz+zYU8OKc1QarmOQSDHNsnVcvb7e4zS12FZqNWQTSkfXuByC6jmbszYKeA65UYN8pt+p63Wd6S/8Xcw95OUMWQ9lhYxbcX1Gz5FkTArjrlfpq3wh6/w+aSmsl6KvuMq6esi6+HduDrn4t1Ue8kApQtbT97DcKutSyDrPIVdTWbe47Fk4nsThMTE61aiH3OkQpE1wtejRbPiGX3PWu7jY9WUolpQ875UyeLneUN4ccjLIgddffz0jTH3BggV46623sHnzZnz605/GLbfcgieffBJ/+tOfStFewiBcYV2rDBUgTkC8HJjaRCmFrBswUowiq0nmesivWNyu67lZnSU2pHe80XMUi1Jl3UhbzLYZKE27rcBsf+XLzSwGOYfcSMi6yliPG48GsXKcKo/PtzOtjDDgntmWrBd+XQEq66kUk+6JFLKu5yFXUVkHcnOxgfxh+Pnw5AtZt6DsWfYGjdH+Gg7HpTzsSYUY5BqGn9KLkz0ezY49Naw4R6WpczuksaG1cCskf1x5/Eg4d4NJT9At8//GcWg0itFIAk6HgBOnGlPAv6LDp+t1Xd1Z+GYJD7seK1MtaED2kPOQdSuuz+g5wgpDWy9kPaJhyEoGuYbHuBR9JYWsq2yaRPOJuhXZrePpcVFfAjuHz9NlU1nXClnX8ZAXKzSrXHsMmfSQA7LxbqQWuVY5x2LXl3wN4nQIhkUorSaf46ZWa5ADBRjkS5Yswa5du3DPPffg4MGD+PGPf4z58+dnHNPZ2Ykrr7zSskYS5pEV1tUF3QBZeRrQELoyYaQYRZ50cncBO1v8WLtyUYbCpwBR8XPtykXoyCrPwI9XHut0CBAU/3YI8t9q5ygWvqhnLDf0VQ3eZuUrlbfvisUzM9pbynZbgbK/jLRZLzezGFIpY2FWPrd2aLXsIc//Esq+bo7RcQqIHpTs4/MJoCnvHw+xz/GQu82LuikXSdmibkPB3BzyHA+5TuRBvjD8fEg55FmLZjkksQgPucZGglp/OVT6i28qNte7C1rMaS08Mg3yzPFo9plTw4pzVBpBEOTSZxqhjYUa5M06IZN6gm5AZhQL9453TKk3HGXW2eDC2iWNcABwCsj4e+2SRnQECreW5JD18njIMz3U4jxpxfUpz6Ek+xzcOy8gN8wbkDcJtDzk43lqkJeir5rcOiHrWjnkFoWsyznkJQhZL1BstFCksphZom6lzCF3Ox057yMz5TD5sUaE3YY10sey53YBTByTivUlxynkzvnK93WlFMzzrRNrOYfc9BXt3bsXs2fP1j3G7/fjvvvuK7hRRPHICuv6u7Q+jxNj0YTqYprvHFoasp6n3iIvv3DTH0WlyEUzm3DXlWdqLhJXLW7HX7b047ndQzjthEa8b+5UabdvnaLe4urF7SVZaNa5HXA5BCRSDOORhKFJYtXidrx+YBgPvHIAnS1+XHzaDKl9131oTlnabRWrFrdjScdkQ23Wy5kthnHF2NW7/3pl/qImPa7K6/7di/swHk3iNp2yU5efPRPf+cvb0nefPK0B/33N2Rn3aTxPyHq2mFqd25FjGNQXIOqmPJYvYGQPuSJknRs4GjnkemXPtEq55UNLZd1sf6kR4KH2KnPfqsXtWPv4u5Kn49wTp+BHn1iY0V+SF6QA7zigDG/O/H4emSQIgFvFs2fmmdPCinNUmiafG0fGoprzSSElz5THq51XrkaQrxKCbJDPm2EsXJ2zqqMeS1o8WNcTRl8wiZl+J1Z3+ooyxgFZZb1com5KD7Wy7rcV17eqox7n+KcAPxV/nuQR8IdLWjLOofTOqxkXfJMglMdDrleD3Oq+ak6HrOuJummFrBdr6/JxURKVdVf5csgZY9IcKpU9k3LItUPWrXA8BbxuROLiO7PB6zL1flITUtVCb7ORz+0PvLwfr27fgyWnnoirls5GR4sfn//AibjgJ5sAAP/vPbPw6fd2Zsz5lVZYB4x4yNPisjUYsm76ig4fPozBwUEsXbo04/OXX34ZTqcTixcvtqxxROEY8ZAD+mWSSuEhb9ERdeOkFCE3p7Q25l0k8qM/+74T8fEzT5A+X3PxfPX/YCGCIKChzoXjoTjGInHMyHO/OXwD5Mol7fjCB0+SPu9o8Zel3VZitM3ZgkdWwV8iHqdD9wXok7zHuQZYIR5Xft37jwax/s1B3VD8/pFIhuHrdTtyxnW+l2Gd24k6t0P2jvu9OQtNvTx5Lfiz73M7pZJvahtnWosAv8YmAGPMUCqBHrJBnnnuiAWLqHoNlXUASCRTGZ6KWVPqc/pLEnQrQGEd0BavUc67Wl4KK+aJapxrlOQrfVawhzy94RSMJRFPpjKiH6TUDo15RvbuxLDDpKCbko6AC2sWmv9/ejSWWdRNz0NtxfXNVhi9CYYcI1hP0A2QNwnCWjnkCZ5Drm+gWtlXTWnr2kzZM+4hj1aBh7wcKuvxJJNSibgopkdH1C1uUcg6IBqJQ+lN7MkmhT6ltbFKOmc28tym/h0dLX58bflcrE/sworlc+F2i/PSnGkNmNFYh8HRCD5x1kzNNUglBdPyOW7G85SHrWZMj8Drr78evb25ZVEOHjyI66+/3pJGEcXTP6Jf8oyjX5vZuNCVUdTUm7Ph3n3AWDmxYsNii4UbG6MGS58BxnL8a41SibqNGzT6fDoeclkkzLzHlS+4+QJcDa62zMO7dx0aR0qxgEqlmOTp18uNala8gNXy0/KVMlRDLS92iiTqFgNjTKx7qinqpu4hj8RTCoGYwvK9+CIpnuXFsjKHXC33/dBYNKNOt1rfSoJuBSisA/lD1q2MTKpF8s0nWlUB8qEcq9nn5pt52jnksiCRXPLMWsO6UMpd9iyfh9pKxuJMCjHn8A0Bn4bHN7+om3i+gIXrn3w0SR5ybVE3b1ZzpBzyIkXdeB3yInQDNeHzdDnqkCtTsPiGrVYOeSrFpDKGxYasA5lrELORU3qCx9kUGv0DAK3NotNoYDi3mkalFdYBMznkZJBj+/btOOuss3I+P/PMM7F9+3ZLGkUUD3/Y2pqNecjVDfLShawfC8WkxXo23LsPyIqKeoxVeBLh5S6Mlj4DgP70NbYZ9KjXAs3SYjX/C8cMRstg6I11KeesgMUXX3DzBbgaO9Lhqx84eSo8LgfC8WRGealgTC7RpHcdyhew2gs/XylDNbg3Xen14+eOJVMYiyYQiaekFBaugsrhKuvZXnneL4Igb0SYxasZsl582TNJZV0lmoDPn1wjYNeh8RyxnEKEe5Rke2I5kpimhZFJtUi+WuSFesidDkEKh8w+dyhPDrky/3GXSYX1UhMos8p6qIRltNQYCGfOEeE835+3DnncmIfcSriHPJrKFJtjjGmKuqWn36JzyIMl9JBLKutlMMj5u0EQFAa5hsq6snqHFQa58t3NN7WNolcSOJtC5zYAaEs76bjTTkk+HZtywNeJWpGUYxWuk15KTI9Ar9eLQ4cO5Xw+MDAAl6v2diyqkVgihSPpsJe8HnJp51I7jNfKheHk9GKeMW0xnoERpYc8vze12LDYYuHfa6StgLgre2iUPORWMWYwhEkvPSO7TIoZ+IJ716GxDK+3Ep5PemprI+ZMDQCQjXRA3sxxOwXd502Zv632ws9XylANNaGqOrdTup9Hx2OSErjTIeQY1zz0ezzL06zsl0I9ZB6tsmcWGK2yOnxuhQS+WFk0swkuh4CxaCJjXgLk0MJCQ9a1PLFyDXIyyPVQVh1Qg79fsjeQDJ27Xj29JqRI71CDb7IcDcYQiiXhcTrQMaXe9PeXAknUrUwq65KHukwGeX8oc/4J5/n+/HXI9VXWS0HAJUiLcqWXPM4A/lO2qJtVKutBSWW9uuuQK0ti8veOlqib0mOuptdhFuUapMXkRq0UPWqoDjmf2wrwkDdpe8jtlEMejidVc/6liFjykAPLly/HN77xDYyMjEifDQ8P45vf/CYuvPBCSxtHFMah0QgYE3Nq84XNaIXxJpJyuKkVO4ccl9OBSYpFixr9ioli3EAYOD+msUI7ZtybYqStADA0HkU8yeAQxDrxE4VSiboZDWHi6RmqZc+KMII6ptTD43QgFEvioMpLDpAN8rnTG3Dy9EDGZ0Dmi1DPeM3wkKu88POVMlRDy+s3WSHspix5lt0+yUMezfaQF/9cuvOUPfMW4SHnBnmK5YYy8sXKrMn16Ezn2Sk3UAA5tNDswouj9MQqDT85h5xC1vUwGrKeLYZo5tzZuhD5VNazPVYnTvXDZUFuqhWU20MueahLIBKmxkCWQR7K8/1yyLq6JZtPZb0UOARBNWxd6S3XKntWbB3yoHS/ijqNKuU0yOUII/lCtELWlZFJxdYhBzI3Wc1u1LbkETxWUoyHnDuBsjeYAXuEgzfUucCXGGpzez7x22rG9Aj88Y9/jN7eXsyePRsf/vCH8eEPfxidnZ0YHBzEHXfcUYo2EibhD9qMpjpJpEkLLa+hXumdYuEL/SEV8QrGWJaHXN/IjSdTkoFVsRxyL/eQGzPIufdtWkOdbRZr5aBUZc/kMhj6Lyf9kPXCc8hdTodUZ3hnltEGAMkUw+50OPu8GQ04Oa26rGaQ5wvDas4Tsq4sZWjYIM+qQS6dPyDv2OvlrGmJullRnkTTQy6VtSlC1E1hzGenmwwoNDh4f+3KNsjToYWTC/SQA+ph1xSybgyjom7NBSxapbDJrPQaWW9BfUzXe5wZnja7hKsDQGOZc8jziapZTX84y0NeZMj6WAVC1gGlsJvcrqhCIM+TNS3wadvOZc/qCijHWShq6WdaKutSyTOntoCmGZRGopmSZ4AiZN2AqJv0Pi7AQ87TJPtHtHPIK+l9djgEaRNfTShXWivlWe9VI6bf+CeccAK2bduG22+/HaeeeirOPvts3HXXXXjzzTfR3q5e8ocoL0YV1gFjBrmVHnJAOfHk7gSOhhMZC/t8YeBKheRKTSLciDKS7w7I3rfWPPn9tUbJQtYNlsGQBc9y+ylSZE4yL22U7UUFgN5jIUTiKXhdDsyaXI+Tp+WKwBkVJszMIVc3BOv4xoPKdaoR0SjlxM9/dDymWfIM0BZ1M5rbr4dH4SFXhpVHTdSN18LhEORNmqxw+36FBofcX5kaAUeLFHUD1HUVohbVxa118uWQFyN81KRh7PNnSstDLghCxveZLXlWSgLp8RROMsSLNN6MwA3dsuWQhzRE3fKqrKufj4f265U9KwXcIB+J53rIvU7kGI5eyUNe3PfyjZqSGOQFiI0Witq7gRvn2ZFWVtUg5zQo3t9m3wv8+GAsmTeSwBIP+bB2Dnmlok05emtFOwjPlYqCrsjv9+Pzn/+81W0hLMJoDXIA8LnT3q14tkEu/ux2ClIIrFXw0JxjKiHrfNeO1/YOxpJIpphmG/huWZ3bYUnZikIwm0POPeRtefL7aw2+gI4mUojEk0UZU0qMTtA8bK4UFQXkPPJcYTdupM+ZFoDTIUiL9L1HgkgkU3A5HYavIV/IOiAbCxHDIevqytFyCF0UrvTiVdVDriXqZsFuu3KhFE8yeNKLxYhFJRnrPS6EYkldD/m0dFpJdvRDsSHrgPrCw8q6uLWMlhebM1rEolU29jPHRb6QdUAMkeeCf3bykCsNy2CCSTWvS0W5Q9ZzcsjzfL9ch1w9ZF32kJf3OWxK95Oy9Bk3trPD1QHA4+Q55BZ5yJ2Adr2QwpBD1kuvXyBHTxkPWbcifxzIjHBrMSnq1uB1weN0IJZM4WgwhhN01u/FRP9wD/nhsYi0/uDYpaRYc70bB47lRlMmU8wWXvxSUfBMs337djz++OP4y1/+kvHHDMlkErfccgs6Ozvh8/lw0kkn4Qc/+EGGJ+RTn/oUBEHI+HPxxRcX2uwJgSUe8hLmMcqet9zQHN72TkV9RD31cqOhvqWkwWQOuVEF/Foj4HHBoZMbpEbPUBBrH38X//rAG1j7+LvoGQrmHGNU1I97gNVF3Yob73qlz7LVlk9o9sHndiKWTGHf0RAA47lbyZS8oPjbtgHV+6G38aAG34yrz9ogUYasa5U8A2QPeTzJMkICrXg2lXl9ynw/K8qeAUBAYzNBOYdKmy2HZdG+WCIljeFCRd0AdU8slT0zRmOeiJvhAsueAUrBOK2Qde2+UaZRbNxxWPUZrQRuhyDVA8+uRd4zlsDaN8fwry8NY+2bY+gZS+h+boRyi7pp5pDnC1lXEXXrGUugLyie79EDYVPXXSySh1wRsi55yFUcEzyE3bIc8ipXWZf1RdRC1tVF3azykCtLrv15y0FTz74gCBm6LVokU0x6txay2dgS8MLtFJBiYnlPJXbIIQe0PeTKiiiVbmMpMH1Fe/fuxSc+8Qm8+eabEARBMp55GE0yafyBW7t2Le655x789re/xYIFC9Dd3Y1Pf/rTaGpqwg033CAdd/HFF+O+++6TfvZ6J44QViH0m6hxLYu6Zb5wSqGwzuEL/SE1D3m67bOn+LH/aAixZArj0YTmxCOFxVZwR6/BZA75gMEa8bWGwyGGcx4PxTEcimN6o/6GRFd3L25+eJs0zwiCgP9+dg/WrlyEVYvl9Bijhp9uHfKiPeSiUNvuI+M5ER07DmXWI3Y4BJw8PYCtfSPYdWgMc6YFFEIl2tfQ1d2Lu57aLf38p9f78MjrfTn3Q95kM+aN0PL68dzoofGotGOutiOvNORD0aRkSFqZQw6IRjC3fbkXuViDnG/SBBVjIppISh7OtmYfmnxueFwOROIp9B4PYfYUP46HZNX5QhZFHEnNm3LITdOsoYQOiOODP+eF9E+zxoJQelY0xl1Xdy+2D8ibcg++2osHXjkgPqOnTjHdDqtpcDsQSaYyhN26ekK4uXsUgiBWPxEE4L/fDeLyjjr8cV8k5/O1SxqxqiO/cryyDnk56A8npfcEIG8IqHmVlZ9nq6zz+8Fnzz8fiODP+yOGr7tYmiWDXOkh174Wr0JlXXn9Zskoe2bx/kN5Rd20PeTJFMvwCnNtEisM8q7uXvzkHzulnx9+7SD++Fru+1mPKQEPBkcjusJuyrzqQgQrHQ4BM5rq0HssjIHhcIYn3g4q64C23hBvn8fpqMkNa9Oj8Mtf/jI6Oztx+PBh1NfX4+2338amTZuwePFibNy40dS5XnjhBXzsYx/DpZdeio6ODlx++eVYvnw5XnnllYzjvF4vZsyYIf2ZNGmS2WZPKAZM1LjWEroq5aJwis4uoNT25jppB0wvFNwO+STciDJah7x/ZGJ6yAHjeeQ9Q0Hc/PA2pJj4ElX+vebhbdin2Hk2mn+tG7JepMe1fVI96twOxBIp7D+auSvOPeTzZgSkzySPevp3+a6B3w/l0jHJoHo/pEiAhLHxqCVUpUwt4Z5CNePG5XRIGxnKZ8Bobr8eToecMhNT8ZAXOz8FFKXPOIPpDTOvS6wI4XQIUqm6nenNFS5IOanek1c4Uw+150GKTrIopaNW4fduLJKQKoJw+P0UhMIiNLRyyLmHL1tvAZCfUSUZc9axkOl2WA0v4cUN8p6xhGR8JtOltfjfXfsiqp+veXUU+8bzzy3lrkMeSWYKoRUi6qa8H5yUyesuFlllPddDrmqQpz9jADQquOUlyZic81/lKuuqHnLFv5Ve8niCh6wX9x5Rfz+rr1f04PpKaoLHHL55G/C6Cm53q0YtcjvUIQe014m1rLAOFOAhf/HFF/H000+jpaUFDocDDocD73vf+3DrrbfihhtuwBtvvGH4XOeddx7uvfde7Ny5EyeffDK2bt2KzZs34yc/+UnGcRs3bsS0adMwadIknH/++fjhD3+IKVPUd5uj0SiiUXkwj46OAgDi8TjicWvFpAqFt6NU7eGCRFP97rzfkY7YRDCaeX+CEXEB7nE5LG9nUzpubmgsmnPug+lFy7SAB36vE0eDwPB4BPG4ujf5eFrp2O9xlrV/lX3I54bRcMxQG8z0T63R6EvXth4L6177gy/vhwABQO4KQwDwwMv78bXlcwHIL5F6t6B7To9DPFc4lsiYD+LxuOT5cgms4D6ZMzWAt/pH8U7/MNqbxRdrPJnCniOiEdc52Sed+6Spoqfl3YFRxONxSdTL71F/3szcj7r0QjOoeJnFEwlA47rG0/fP48yck5TPKS9VGPCqP2f1Hici8RRGQxHEG7hCqnhNPndxc4jbKSCZYghFYoj7MsWBXEKqqPnUl16ojYbkuaj3qNhfrU11SKQ3NeZM9WP7gNi3H5o7GYfTm2pTinyGG7zi9x8fl78/FBX/djtK946wG4X0Yb1i9XJsLJwRmn50TOyfBq8LqWQCKZN2gJ8rXYcy5/Sg1De580TeZ/S1g/iahwGplPinAgSk/OQk4qkUHuwJiSWGTBhyggA8sDeEr50WyPldPH1d8VQK4zzKzil/bimpFHiPT/IKCDPgQDCOgDu9Qc6/36H+/TzUO5Rg0u/17ofedVsJ3zQ5nu4jALr3UlA0NphIwl/AJqUyhYGvCfXeGWZxCWIbQ7Fkyee0oPQ+k9cDgmLDbjwcldYCfK71OPTXDvkw837WY3J6fXR4VHt9xOe2xjqXbpv15tQZaV2UvmPjGb/nBq/PVdl3T0N6EB4LZtoIx8fFa/drrEPsitG2mjbIk8kkGhpE705LSwv6+/sxb948zJ49Gzt27DB1rptvvhmjo6OYP38+nE4nkskkfvSjH+Hqq6+Wjrn44ovxyU9+Ep2dndizZw+++c1v4pJLLsGLL74IpzN3K+/WW2/F9773vZzP//GPf6C+vvThRmbYsGGD5eeMJYHjIbFb33rlOfTk2eh6d0gA4ETfwGGsX79e+nzHsPh5NBzM+NwKdo8CgAu9h4/nnPutHgcABw71vItU1AFAwNObX8Kht9VXDC8Niu0cHx6yvJ1G2LBhg3Q9g0dH8rYhyYDDo04AArZ3P4++bbqH1xyxMbF/n3v5NcR6tFeBr+50IMUEiK+zTFKM4dXte7A+sQsA0HdIvJ/vvrkFzj7tDcGRGAC4EIol8Nhj66Valxs2bMDA4XSfvLkVnv4tBV1bfVy8tsc2v47EPvHaBkNAPOmC18Gw5YVnsC39ncPp5+uNvYNYv/4g3tkt/t++fbuxfv2uou7H8SHxXG+8+TauSh/zxKZNSNapR2Ts3icev3/PTqwPy3P4wSAAuNB/fAyO6Kh4zM7tWD/8ds45hIR4/zZsfA670zpWO9Pn7d2zA+tD7+reOz2EVPrcTz+D6el9uWBE/Oyl55/D7vRlFTKfjhwT2/jqG9vgG9wKAHj1iNg37vi49Dyn0v218Y2dmB18F93pY1hktKh558Ah8Ty7DhzE+vW9AIC3esXPDvX3Yf36AwWfuxox24dehxPRlIA//30Dpir2bHvGAMAFN4sX1D+7RtLvxSOZ7yhpnti2BY6suSbvM7rzANafnAKOHTPdHquIpMTxvvnwMUQYw6tD2m0WjQuNaxkax/r+Uc3v2TA4iJ2j6ec/NKZ7bKE4IxF8JP3velcKiAN/6xvCvpA49+4fF79/T3AE6/uHc/7/0QgAuDAeT2F9fz8A6N4PI9dtBftC4tjbPRbG+n7Rs7rlqPhZMBGX2iq3S7wOAHjs4CACBTg3h6PiORxgeHZkGIIAbHjuuSKuIhP+LhkZD5V8nfb6gHivjh05lPFdTsGJJBPw+D+eRHq/HG8dF48NjY8V1S4z72c9Rg6LY/a1N3dg/dg7qse8k26zEA8barPanBpKrxFe2roDJ4zK33M8KM5vr720GfsrGMA50C9e4zu792H9+r3S59vT156Kln4cWUkoZCw6yrRBftppp2Hr1q3o7OzE0qVLcfvtt8Pj8eDee+/FiSeeaOpcXV1duP/++/GHP/wBCxYswJYtW3DjjTeira0N1157LQDgyiuvlI5fuHAhFi1ahJNOOgkbN27EsmXLcs75jW98A1/5ylekn0dHR9He3o7ly5ejsbHR7OWWhHg8jg0bNuDCCy+E221taMi+o0Hglefhcztw+UcvyZtP5H3nMP5v1xb4GpuxYsV7pM99O44A77yBqZOaMj63gj1Hgrj77ecRgRsrVlyU8bsfv/scgDAu+eB7sPPp3ejrOY5TFp6BFYtaVc/Vu6kH6NmFuR0zsWLFaZa2Uw9lH3YMhXH32y+BubxYseJDuv+vfzgM9tJzcDsFXPHRS4oKd61GNoxvw7tvDqLj5FOx4rzZmsdtd+3C1s37kGS5RrtDELDk1BOxIr3j/NOdm4FgCB9+33uweLZ2OstYJIFvv/Y0GARcsPwiOJCS+vBX+18DxkZx7jmL8eF5Uwu6tv7N+/DKEzshNJ2AFSsWAQD+/tYgsHUb5rU14SOXys/RmSMR/PKdTRiKOrBs+YV4bGQbcOQwFp9+Glack5tvZuZ+vPDnt/Ha0EHMOlHekb/oAx8AWlpU2/23P2wBhg7jrEWZ3314LIrbtz2LUEKAy98EjIzi/e85G8vmT8s5xz17X8DRQ+M4/eyleN8cMXrpj0deA44exTlnLcKKM08wcAfV+cGbGxEej+Hc974f89MK9V95eQMAhosuOB+Tfc6C59Pnom9jy9GD6JgzDys+KL6/9j+7F9i9G6edJM8pdTuO4G+/fwMhVyNWrDgPh17YD+zegZNntUl9XQjOtw/hwb1b4W2YjBUrzgEAbP/HLqCvB3NP7MCKFfMLPnc1Ueg78bbtmzAwEsGZS9+LRTObpM+f2XEEeOsNtE4p7P21fWAU/7X9JaScmXP63bufB4JBvP+8c3DuiZlRenmf0ZPbscLTDwQCgMbmWKl5bP8Ido5EcVKgCSva6rH92Di2HgtBXQ9M/d3kEAQsafFjRZu6h3zD4CAunDEDG/rGAERxxiTxuywnLNdRPrnBg93HgBN88net23McQBznTJmEFW259/toNIXvvzGEOBNwUWsrnIKgez/0rttKPIjigT0j8Dg8WNE2GQAQi4cBjKHV58WKtuac/3OTcBgJBnxg2nTM8JmPOd87lgBwDAG3A8snN2LD8eO48P3vh7vBmioB+44Gcfu258EcuWs+q+nd1APs24XOWZlrwm++/hSC0STe+4EPYfYUcYw43j4EvLsVU6dMkubfQjDzfs7X9mcGdqFp+glYsWKh6jHJbQPAu2+iffpkrFixRPNcenPq8ZcP4Mn+d+GdNAMrVpwhnjfF8OUXReP9IxddYLqOupUEX+vDn/dvR2DyNKxYcZb0eSp97SdM0792u8EjtfNh2iD/1re+hWBQ3LX7/ve/j4985CN4//vfjylTpmDdunWmznXTTTfh5ptvlozuhQsXYv/+/bj11lslgzybE088ES0tLdi9e7eqQe71elVF39xut+XGb7GUok1H0jlOrc0+eDz5H6iGevFeReKpjLYkmfgyrnO7LG/jjGZxMhyNJMAEpySokUoxHBoVQ9DbpwTQmC5rE0owzTYE06FWjT5PRfrX7XZjcvr9PB5N5m3D4aCYMzyjqQ5eb+UmvEoxKa3KNZbnXl25dDZ+tblH9XcMwFVLZ0v/n+ctN/vrdM/Z5JSnuzhzIJDWT3C73VJ+cqCu8HE0v000CHYdGZfOsXtIXDTOn9GYcd72KS401LkwFkmgbyQqiYo113tVv9/M/fCnx5WyDI7b5QI0riuSDodsyHqGpjWJ9yfFgP3pVJIpDT7V9vnTOWfRJOR+4deUp1/ywcVbUnDA7XYjnkxJOcMBnxfudBhuIfNpIN3usGKOOTQmhtrPnFQvfXZqehG8dygEweHE8XQ5rJaG4q5tcoNoKIxGEtJ5ePSoz2u/d1apMduHzfUeDIxEMB7PfEfw90JzfWHPc0uj+I4aiSTgcrmkje1wnD8ruc9p3mf07BPgfmsAcDjEPxWgMR2nHUoCbocDV3bW41c7zOW2MwZcdWI93DrX4HY4EEmnCQTcDt1jC0Zxzja/CzjGcCjCpO/K9/1NijJwiZSAOrf+/TBy3VYwOR2uOxqTryWeXo/5XILq93scAhJJBsbUf5+PWEo8v98lwJ0e626XdWu/Bp84z0Xi+ddIxSLNn57M9te5nAhGk0im3yMAkEpvOnndzqLaZeb9rMe0dG73sVBC8/jxtNjfJL/6WiEbtTl1ZnrROjgalX4XUuhlTAr44K6gqOjkgHgflO9FQNalqNR6v1CMttX0Hb/ooovwyU9+EgAwZ84cvPvuuxgaGsLhw4dx/vnnmzpXKBSCI2vycDqdSOnkG/X19eHo0aNobVX3mE50zNa45srT2eUoJJX1EtTgbKxzS0JNylrkR4MxxJIpCIJosHL1cr1yYkbUqUsNF5gIx5MZpZnU4PnjE01hnWNU1K2zxY+1Kxdl+Ggcgvhn7cpF6FCUxTNaqsPpEKTNn1DWeJfKnhUx3udNl+uLcxXw7JJnHEEQpON3DI7lFSvh98MhiNeh/Dv7fmiVMtRCS2Xd7XRIebn5yqz4VcTRpGsqUrGV14jlz5ZSGKj4smdi25RCf2pVEE5o9qHeI5eqO2ZBDXJAQ9SNVNYN05TOucyeT7g6b1MBJc/E84r/L5ZIZdRO1hN1y/uMTq58ylxD2ggdT4u6dTa48JF22XvsAOAUxL+v6KzLmH/552uXNKIjkP+ZLmcd8hl14rOiLH0WyvP9dU45BoArsnc2uLB2iRxJKcD8dReLLOqmELHkZc80roVP3dECU/XH02O8VAJ8XPQzkWJ510jFIldMyXw3yKXP5DESs6jsmZn3sx4tUqlRbVE3vRKkRmlNCwpzEWUAGE3n3ntdDsvKwBVKXlG3CqvAlwpTVxWPx+Hz+bBlyxacdpocCjJ58uSCvvyyyy7Dj370I8yaNQsLFizAG2+8gZ/85Cf4zGc+AwAYHx/H9773PaxcuRIzZszAnj178PWvfx1z5szBRReVNuylWhmQDD5jIXGadchLuCh0OMR6i0fGohgaj2JGU+bkMDXghdvpUKis69Uhr3zZM7/iu4PRBJrrtRfpA9KGycRTWAf0SxVls2pxO946OILfvrgfAHD+/On41qWnZLzcYomUtHnU4M3/gqr3OBFLpBCOJQC/3G+yanfhBl5rehNpLJrAvqNBnDy9QVJRzzbIAWDu9AZ07z+OnYfGDJUbWbW4HUs6JmNddy/6jocxc5IPqxe357zs9cq7qaGlsg6IFRGUfaVW9gwQRRXFcylV1q3ZLOOLA754Uqrkel0OJBKFL/Dq094opTo83zRTVkFwOATMnSaWqtt5aExaMHFV3ELhc8VIKC6VLKI65MZp9vH7l1kmiC/kCl20+j1OuBwCEimGkXA8pzxo9uYVR/cZNZhHWEqyVdYBoDddb/u90zyY7HVgpt+J1Z0+dARc+ND0MK57aQQ+J/CpuX7pcyOEy1iHfEa9E0AS/QqDPJ/KuiAI8DkFhJIMEYU8+aqOetz59jgOhlJ4z1Q3zpjiMXXdxSKVPYszeU7IU8LN4xAFxQqtRc43LwIlMsSUxnEknixa1VwPXqUiu4Qpr1qhfH9IZc8saI/R97MeU9IRhHplz4qd2wDZYTc0HkMknkSd22mLikUcvk7MNsjtUie9VJi6KrfbjVmzZpmqNa7H3XffjVtuuQXXXXcdDh8+jLa2NnzhC1/At7/9bQCit3zbtm347W9/i+HhYbS1tWH58uX4wQ9+QLXINZA85AZqkANAvTvXQwTIk1apdsqmpA1ypYc8u356IP3Q6ZUTs8Mk4nY64HM7EY4nMRbJY5DzDROD/VNrNBr0kHOU429Jx6Scl5tybAQMjIF6txPDiOeM90iRZc8AcYE3d3oArx8Yxo7BMcyaXI/9R8VF+LwZuQb5vOlyKa1Rg8ZrR4sfay7WzyvmJWYMe8jj6h5yQDQ49xyRS7Zo1T3lm1LjUfk7pVJuRT6bfAxEszzkHpej4Jq7HNlDLo+jAY059OTpDdjaN4Idg2NSnfIpRebZSZ7YpOiJ9XmcCoOcPOT50PKk8E0krQ2kfAiCWF/+aLrk34ymOqRSTPKW+zQMcsDYM1opGtJGClfV7gsm8caxOAQAPzmnCdOz8o9PbhLvn9shYM1Cc/nE5Sx71pqu1TUQlo2tkIENgXqXaJCHFIZsJMnQHxLP87P3NGNqXXk3xprSfZRkwHiCocEtKMqeqf8f7jmPpQozyMdL3Fdel0OqZx+OJ0sa0cidSXUuDQ+5IuJFKntm0Vxb7LM/RfKQxzRryvOyZ4VG/wCiwVvndiAST2FwJIKOFr9lG+hWoJzXlfeBr/eKXVPYFdNX9e///u/45je/id/97ncFe8Y5DQ0NuPPOO3HnnXeq/t7n8+GJJ54o6jsmGgMma1wrQ9aVA1+qhVsiL01LwAtgLCM0J7t+Op8YRnXqkHNDptIPaKDOhXA8qdtWQJlSMEE95OmJdtigQa408AayamYCipJnHqeUBqGHlvfYKiPo5OkNeP3AMHYdGsNJUwNIphga61yY1pC7gci95jsPjWE8yut/Fj+O6zXSULTgxqhPZTNCaXDWuR2aGxbZHvJkikl58cVeE/em8MUTN4rqLFhE8dBjPs6C0YRk3GVHGfH+2nVY6SEvziBXemKHwzH4PD5EFRsOhD5NGhE3oxZ4kbhBzkNElc+Tlofc7gSyQtYf6xPn1KVTPTnGOJAZ4q5lIGjBPdS+soSsi20fDCWRYgwOQTAUMu9zCUBU3jwAgN2jCTAAkz0CWrzlfwbrnGJJtlhKrEXe4Jbz4bU95OLfsQKDhfj1+92l6StBEFDnEp0WkUIbaRCt9DPVkPUkLydnj7mWe8hjiRTGowlV49gKD7kgCGhr8mHvUBD9I2F0tPilNYhelF654NcWTzKEYklpw3/UJnXSS4XpO/9f//Vf2L17N9ra2jB79mz4/Zkeq9dff92yxhHmGRjOzX/Ugy8sGIPkoQFKn8co7QQqQnOyczcDVZJDLn6/C0fGorptBeRNh4meQz5q0CBXei4PDodzfm82hMmnkqLBGJMM8mJzkrnRtuPQGE6cKnrA581oUF3Inpz2mh84FgIXZ7XCIFe7Rj1CGjnkQKbBqbcAkD3kiYy/geKviYcTxrI85MX2FSDWVQeAULq9/Pls8Lpy5hTeXzsGx6R5iy+gCkXpiR0Jx9Ha5CMPuQm0PORWLFolYz99LuUmXrb3rVrgBvZo2mj5W6843pV55GrHpyB6nP0mPKhGPNRWMc3ngAAgzoChaApTvQ7JyNT7fr5ZEFYY5DtGxLlgbpOr6AicQhAEAU0eB45EUhiOpTDT75RC0b0am86e9OeFhqwHuUFewr7yedIGecKaCFsttN4P3LkUU4aslzgS1Cw+jxP1HidCsSSOjsfUDXIp+qe4zeDW5jrsHQpKNoOdwsHrPU64nQLiSTFlyJ9lC9ihjaXA9FV9/OMfL0EzCKvoN+shV0xaoVhCYZCXdlHISyoMjStD1jPbbiiH3Ca7ejyHXa+tgGLDxGD/1BpSzqxBgzyY4SHXNsiN9r9aioYypyw778wsPDR916FxnDRVO38cEKNEpvg9OKpI2/BbMI651zdbuE6LsJRDruYhlw1OvQUAb3co3V/cIPc4HUVH2WjlkFthkPN7xb35/TrPJxfh6xkKgkeGFushB0TD72gwJnl5Y5KgZnUafeVEM2Q9/XNzEWGd2efmC32f21m15SoD6fltPMGwbzyBN48n4BSAS2aqv498TgFOQQyfHoszpexGXsop6uZ2CJhW58ChSAoDoRQa3Q7wWV3PQ8+NdWXI+s5Rce6a11i5Tf7mtEHON07y5pAXGbJeFoPcZCpVoUjvh+yQdbcj4/eAPNeWMqfdLFMCHoSOhXE0GFXNP7disxGQnUJ8XWV2LVVK+Eb10Lj4XuTpY3ZqYykwfVXf+c53StEOwgLGInFpwBr1wDocArwuB6KJFEKxJHhl1WiJF4UtaTGkYxkh65kecskgj2obb3zHrLHCO2Z8J1Mv3z0ST0rGl1EV/FpDKzdIC6VqN9/MUDJmMoQpW5wJyBYJK268z03nhe87GsSbB0cAaBvk/Pije4+JbXM7LVkYcE93xMDCJ5ZIIZFexPHNCiUtRj3k6e8cT99XuV+Kfy4lDzk3yOPWRe/40x7yYJaHXG3+nN7olUrVAeJGgRULg2zDj1TWjdNcr54Cw++lluaBoXNnRfOEdDauqgXu8R6LMzzWK86n503zYIpGaLYgCAi4BIzEmRjmbvC1xRgraw45IOaRiwZ5ErP8ch/pfT/fLFCGrO9Me8hPbqrcmoKXZBuOie2K5DHIefcVauvyMoGlNMi5QWw0lapQJIFWQyHr4n21i4ccEDfBe4+FM5xVSobD4ufFbDYCctokT6O0Uw45AMkgV2628vV1o03aaDX2GYVE0XCDtrHOZcrTppZzauWiVw2em5oRsi4JnmXmkGuFgTPG5B2zSueQSx5y7c2DwXT/1LkdRU+m1Qq/7mSK6W5ecJSG89FgLKPkFWBe1E8yVhXn4f92CHKJrUKZGvBiUr0bKQa8uOcoAH2DfJ7id1aNYSlkPZH//iq9FaoecoWKuJ5xUy95yNMh6xY+l3yxJJU90yhrUwj+LFE37iFXizBSlqoDgBa/x5KQ1lyDnELWjaKVAjNsQVgnPzc/l57WQrXAVdbH4yn8NW2Qa4WrcwJZYe5GiKUge6jLZJCfkBZ26w8nJYV3tyB6z7XgxroyZH3nqNjfJzdW0CDnSuvpfGu57Jn68cWKuske8tLNOfy5yX6HW41WxRT+s5qH3F4GuThnKQWPlVjmIU97nfm620odGytQi36ySijWrpgehQ6HA06nU/MPUTnkkG9z3lcpxFUljLd0OeTiQn8oPekkUwyHxkRv+QnNmTnkWmHgUYV3r9K7erI3X9sIktIJmnwVyU2zA3Vup/TyM1L6LNtoH8wSdis0h1w51iOKEOhi+0VUWheNNj42T057zdWYqzDwrHoRmqlDHoqL98+lqNGuZLJC1E1vE4k/qzz028p8NEllXfKQq5e1KQS/JOqW30MOZPbXZAvC1QHZE8tzA6nsmXG4wa2cSxhjsqhbMSHrWek1YR2thWqBq6wfjzG8O5KASwAuOkHfIOf/Zzxu3NhTepzLEbIOAK31ci1yI/njyt/zkPXxeAoH0wrrFfWQe7iHPD3npW1I/bJnNs8hL5NBLqc0aXjIFRtLsXTFKLuFrAPA0fHcWuSReFISrSsm+geQRUsHcjzk9jB25fRGeWPCDlWVSonpq/rTn/6U8XM8Hscbb7yB3/72t/je975nWcMI88gh3+byk/XCeEu1KMyedA6PRZBMMbgcghTOns/I5YqLgiCWs6okAQP57hM9f5zT5HPjyFgUI+E42vMcm62GzhVBOVIZDKM55CoGedRCkTBA9Hq/0iOGobcEPLq1qpXl0KzaVOKh52EDarZ6+eOA8ZB1fl956PeYyX7RI0fULaHuASkE7iGPxFNIpljeOXSeYnOlWEE3jmbIugUbDrWOmhclEk9JY6XQsmfKc2eLulW3QZ5pcL1/uleqe53v/4yZMMi5h9rjEDf7ykFrWiW+P5SSvj9fuHx9lqjbrnT++LQ6R977UkpkD3lmyLo3r8p6kQZ5iVTWAfn9Wq6Q9RxRN3duyHo8fd12ikaSnFUqIet8o9EhyLpFhcIdd9yRN26z/Gy1uX3UZm20GtNX9bGPfSzns8svvxwLFizAunXr8NnPftaShhHm6BkK4o+v9QIADo1G0TMURKeKIIQaamG8pV4UZoes81DR6Y11UvkqbqDEEilEE8mcBbhyAqm0yE6+8HpAWdZtYuaPA+I4TaQXy/+9aQ++cuE83XHKDe62pjr0j0Ry8sjNlsHg0SAZ6RkWR4Mohb48Lofus3jyNNkgP3g8hLWPv4srFrcbfnbVqPMYz9XLZ2Qox/ObfcOa1yKHfnMPuXXlSXiNWL54iljoIVdedzCWyBtl1KAw8AZGwqbmWS24J5bnBsolJ+2zSLQr3AMejieldwS/jy6HUJTx3Jy1IOQiidWcQ34onDknvGda/udT8pAnjIesl7PkGadNqkUue8jzeee5wc6PlwTdKugdByBtBmSHrGvmkEsh64V9XyjDQ16YUZ8PySDPamTPUBBd3b3oOx7GzEk+XLFY3KbP/szoPBvRmD9VQ9bTaxGPjTzkvIf/sf0QfB5nxrUrtTGKXfPyTefRSALBaEIydisdbcrJThmKJpJSioFd2mg1lo3C97znPXjqqaesOh1hgq7uXiy7YyNe3z8MAHhncBTL7tiIh7p7Df1/HkqU6TUsT8h6OJ5EKJZQrZ+u3AVTM3SlEBsb7JY1GMgh5+IZrSZTCmoFPk6PpyfYx7YN6I5Txpg0Jk+aJnoms5XWze7qymNdHk+ygVf8Qruruxd3PbVL+nlgOKJ7jU9sH5T+PTQew72b9pp6dtXgmw7MwLqKG+38/yjp6u7FJ+95Qfr51f3HNduWHfo9buGzKXvIxbbKoj3F95fX5ZA8eMFoQtdD3tXdi5se2ir9vOvQeNF9BSg9AeI9o5B14zR4XeBZJnyxqsyxLCYFReqXkGjgh9NzhtqzUg109YRw4RNHMz5bu20cD+0L6f6/gKtwD3m5BN2A4kLWeXt5ybNK5o8DsqjbSJbKuraHvLiQ9fEyCPD5VBw/fE1w76a9eGxbP+7dtBfn/3gjzv9x5mdm5tmohsaINyv1CbBfDnlXdy/u2bgHgOi5zr52qXpEkeHqgGjU8ijUgZGwXIfcJuHg2R5ypQ1Qqx5yS0ZhOBzGz372M5xwwglWnI4wQc9QEDc/vA0pJu9rMgakGLDm4W3YNxTMew61MF6+c1iqRaHf45QmyKPjMdX66U6HIKk3q4WCy/kkld8t4xObnlAZF89oM5lSUAsoxyknlWecRhNiGDEAzEkb5P1F5pCrjfWIRarW/BqVhjCD9jXy45UkU8zUs6uGGdEpSTk66/+o9ZfevOLPqudtZT6aV6vsmQVzkyDIXtSBkYh0P7JzyFXvB8zNs1o0S56AtIecVNYN43AIkuIuD+fkHpVi8seV/3+kBlTWe8YSuLl7FNkO1BSANa+OYt+49ntLDlk37n41ahBbSVs6ZP1QOCW1tdCQ9UrmjwNKD7kxlXU+JKMFhqyHypBDXufKjNxSzqn8vZdMMTCIc6vyMzPzbFRjg13SIlGJjrNDDrm0flB8ln3tXGekWEE3Do/W7B+O2C6HPDtliK+t/R6nFEVba5gehZMmTcLkyZOlP5MmTUJDQwN+85vf4D//8z9L0UZCh67uXk0vgCAIWGdgV1EK4y2jh1wQ5Fzxo8GYJHiWnV8d0DF07aS4yNswqhuyPnE95IWMU2XJM26Q800NTqEq68qxHuNjvUiPq9lrtOLZVcOZLmVoBNnrl3ntZtvmV4i6pRQK+lY8m3yxFE/ykHVr02n4bvvuQ+MAgEn17hyjq1R9BWQqhTPGFCUnK79IrAak0mehXA95UefNDlnn6R1VqLLetS8MrWABQQDW9YTVfwk5ZL0QD3k5Q9Zb6hxwC+Imw/7xtJc0z/dni7rZxkOeNsiHc0LW1Y/nHvLiy56VUGU9y0OuN6eqYXSe1dpgV/OQ88oddvCQG3nHDEtildYIivL1tught0/EKSDP63yj1S4VlUqJ6Sv76U9/mjFoHA4Hpk6diqVLl2LSpEmWNo7IT9/xMJhGbCpjDH3HtV+0HDXl6XIIC00JeHBwOIyj41HJQ56dX91Q58ah0aiUK6zETjt6RnLI+yewh7yQcar03nLl/YEcD7m5XOU6lfQMSWW9yJey2Wu04tnVot7jREQ/EhWAttfPbNv8ijDecDxpOrdfj2yVdSnFwKLoHV6ybfcR0SBXU1gvZV8pPbHxJJMiLChk3RjZoY0jIWvCOpXnTaWYZEhUo6hbXzCpmcLCmPh7LbiHvBCV9XKGrDsEAdN9TvSFktgzljD0/T5FHfLhWAqHI+LcMrfCBnljWmWde8ij6e7xangGrSt7Vj6Vdb05VQ0j82wyxaSN29yQdXuXPTPyjuFOLKs85K2qHvLKR5wCuTnkdmtfKTA963zqU58qQTOIQpk5KV1CS+VBFgQBMyfl98bKXsNclXVPCUvZTVYIu0ke8ixjlXuv9HLI7ZBPIpVoi6rnkI8rRDMmooe8kHEqhSh5nTmKoBzzIeu50SBaqqxmMXuNVjy7WtR7XIjkP0wzZN1s2+rcDjgEMYQ7GE1YqtjqyQlZ5/1lzSKKp8XsOjQGQL0GeSn7qlkRmqdUAKaQdWNkL9ys8pDzskIpBozHEtKzUleFBvlMv1P0kKus9wVB/L0WhYSsV8JDDgBt9Y5Mg9ygqFs4wbAz7R0/od6BQIWjU7JF3aL5QtaLUFlnjJXFIPdmqazPnOSDAOMickbmWeX8mVP2TEVlXRZ1q3wItJF3DNezaPJZs+Zta1J4yG3k4AJkD/mI5CFPR8TaYL1fKkzPOvfddx8eeuihnM8feugh/Pa3v7WkUYRxrljcrrurtnpxvsJSGqJuZQib5GWDjgZjksp6trpxg045MTvlkDfWaW8cAHKodUOdq6YnFC0KGadceM3vdeUognLMbspIm09xFQ95kWPd7DVa8exqYTTPVau2stm2CYIgecmDsaS1dcizy57FrdW38BvwkJeyr5Qh68pxaSflXzuT4yHnwkdFhnXWuZ3SnDASiitC1qtv/r6iw6frIV/dqW3oBCSVdRMh6xXwkANAa1ppfe9YeqPRRB3yHZLCeuXXE1zUbSzBkEix/Dnkkqib+e+KpQDetaUse+bLUlm/YnE7UiY95Pnm2Yhi0yj7/aBWh9xOIetG3jHS3OazKmRdfO73HQ1J71e7hITniLrVeA1yoACD/NZbb0VLS0vO59OmTcN//Md/WNIowjidLX6sXbkIykgmpyDWKVy7clFGzWYtpJB1pdgFz9Ms4UTFaxwPjIQxlK5Hnu0h1xNLk8OVK/+A8k2BsUhCdVLlYmQTteSZcpwqx6reOB2PygrgDXVuKbdJqbRudlNGTs+Qx1PMIlVr5TU6HULG32rXaPZ4MxgNq5VD1jOfoULaJuWRRxOWvjzlsmdpb1HcWg85j5rg4ZDZOhZAaftK6YnlZSA9TkfFSzlWC1IOORd1S5c9a7QgrJMvfEfCcU29hWqgs8GFtUsa4UB6faD4e+2SRnQEtJ9T7iEftXnIOiDXIg8a/P56Rcj6rrSHvNLh6oCcQw4Ax2Mp8Fufv+yZeQ95ULHRki+ioBh86fma53h3tvhx2gmNAMRSX3xOFdI/88xY/jsj8yyPdnM7hRzhL/5+54YnoAhZL2EkqFGy3zEcAfK1WxX9w+Ee8p3p6DAACNikikRTeu4djYgpQ3ZKUS0Vpq/swIED6OzszPl89uzZOHDggCWNIsyxanE7TmltxEfu3gwA+Mz7OnH10tmGF4lqQlflKL3D6zW/3T+a/i6HFMbOafByQzc3FNxOIhR8VzGRYojEUzkeSu4hV1vsTxRWLW7Hko7J+OFj7+DJdw6htakOD3zuPZrjlCt2B9IK3q3NdRg7NI7+4QjmTGsAY8wSUbeIhQYev8Z1ihqqqxe3a16j2eONYlRpPRTXNjLMtq0+3U/BaMLSOuTebA+5RlmbQuHji++jaW2alaqvuCc2Ek/h8Ji4MUnh6sZRRhgAcvk4KxatTT43BkcjGFZ4yKtRZR0AVnXUY0mLB+t6wugLJjHT78TqTp+uMQ4oy55VR8i6EjMh6ztGxfFT6RrkAOB2CPC7BAQTTMprBwCvxtDjueWxAsqeBXmEmBNwOQRoF24tDj5fR9LP0Xg0gZ1pIc2VZ89ENJGS5lQA+PdH38Tzu4/ixKl+/M+1SwzNs9K7XGXdquYht1MOOZD5jnnirUHsHQriw/OnYVX6nsiibhblkKc95DzdJ+B12WYjmM/fjImOLnm9X/kIllJheuaZNm0atm3bho6OjozPt27diilTpljVLsIkUxvE8G+HAHxzxSmm1Ct9airrkkFe+pD17WmDvLWpLqfd3NAdU/GQj9pox8zvEXP0GBPzyLMXbVIN8gnqIed0tPhx9Xtm4cl3DmGy36P7kuUTMPdgtjb5sPPQuOQhD8eTUlk002XPlCHrFtYhB8RrXHPx/JIdbwSjXryIRsg6x0zbeNpASBGyXpIc8rg1KQac+qw2qtUg55SirwDREzsYj+DwqDhPkMK6cZqyysbxv62o1asMmwxXsagbpyPgwpqFDab+D1dZNyfqJv5dqZB1jpmQ9Z02UVjnNHvSBnlYNiDzlT2LGd8zkZDzx0s75/iy0sWeeucQookUOlv8+M/LF+Ws/T6yqA3P7z6Kzha/4U1POdVSxSDXySF32yCHnMPfMctPnY5P/OIFvLT3KMKxJHwep+Uecq2IVDvgcTngczsRjicxHI5JQrF2CakvBaafwKuuugo33HADnnnmGSSTSSSTSTz99NP48pe/jCuvvLIUbSQMIOW3eVymjHFALuOSEbJeBpX1yWkPOZ+g1YxV3RxyqQxC5XfMBEGQhd1U2jqRa5Bn06Bzn5TwMc3vKxfb4noD/P87HYJhjzDffFKtKGCTXXIrMOrFs9Lrxw2V8WhC2kCzJGRdw0NuVfRO9qZBto5FOeALLNlDXr1GX7lRhpUDsqfcEg+5FA4fU7xjJ1bfyKJuxg1ynvNczjrkgByyzjEasn40ksLxGIMAYI5NDPLG9NrrUFgcdx6HqCSvhpxDXnjIeqk3T+qyVNb/unUAAPCRRa2qa1a+4RoxEZkR0Um1tLvKejZntDdj5iQfQrEkntlxGIB1FSQ4dW5nRlSq3fSNlMJudhOdKwWmR+EPfvADLF26FMuWLYPP54PP58Py5ctx/vnnUw55BQkXsbDOVllXlo4o5cKwJe0h56iFc+sZuXbKIQeARp3SZxO5Bnk2Uok4lagHJbKHPB2y3sRLn4mbG0ovrNFNKL75FEukJO96NGGth9wO+AwKT/FNOCtqK3NRt+FQTFroWBFelu0hj1jtIVfMmYIATG8s/6YZNx6PUMi6aRqzxH+GJVE3az3kWnoLtQ73kIeTosCYEXhd71LmJKvRlu0hNxiyzk202QFn3trl5YLnkQ+mPeRenXYVk0MeKoPCOqAQdYunMBKOY9POIwCAy05vUz1eNqCNK9XpvRvU6pDzTV47zreCIODSRa0AgL9u7QegqCBhUcg6kOklt8tamqOsoGGnqkqlwvQo9Hg8WLduHXbs2IH7778fjzzyCPbs2YPf/OY38HisUf4jzBPWyQXNR3Yd8phiwippyHogc7yo5W7KRq5OHXKbPKB6mwe8rBt5yBVpCJG4bh1Spco6IL84+OZGIWUwlBtWfLxbHbJuB4zOA2FFZE2x8H4aHJULrlkRXqZV9kwtLLEQlOOnJeCtiLeEL7AOpe+dHT02diVb1M3KsM5mZch6FYu6FUNAobxtVGm9UirrkzwCvIpHJ5+HPtv4tku4OiCGrAPAoUh6vtPJ7eUacNFCQtbj5THIlR7yf7w9iFgyhZOnB3DydPUUikI85FEdfRHJwFdEgvJ3itumZhQB8QAAV9dJREFUFS0uWyRuVjz97mGMRxNyDrlFHnIgMzLVDtGmSpQbotxB02izNlpJwbPP3LlzMXfuXCvbQhSBVj1hI2TXZi5XLdxsATdVD3mVlD0DlIrwmZsHjDEMDJOHnMPvUzzJEE2kNA3hYFpl3Z9WssmuRV6I6qbXJdfL5qkSemFu1YpxlXXxHloRss77aXBE9PL6Pc4cpdtC0C57Zq3KOlC5DbOckPUa2hwqNUpRt1SKWRuyzheESlG3CdY3Hodo5EZTorBbsyf/c8cN8nJ7mwVBQFu9Ez3j6Y1Ggx5yjh0E3TjcQ85zyPXupRUh6+XykEfiSfxtGw9XV/eOA7IwWyReiIdcL4fcnmXP1FjQ1ojOFj96hoL485aDUlSfVWXPADkVELCvh3wkHLddRGwpMD0KV65cibVr1+Z8fvvtt2PVqlWWNIowTzG5oPVZHnI+YTkdAlwl3DmsczszvNtqHnL9smc8h9weDyhvx2jW5oFSEEhPMGqi4FcYQHp55MEcUTc5h7wQhXVAXLDJ9VAzx3stGUFm65BbYWTwfj08Jm4+WfVcetILxbhkkFursu5XSBdXSnSRe2K5h7yWNodKjeQhD8UxFk2AR+1aUvZMkcNYC6JuhcLD1o3mkUsh62X2kAOZwm75PORuhwBl6W07lDzj8I0PnkNepzPsPEWVPRPn1ZIb5OnrORaM4fndQwDE/HEtvFll0oygpweTHbKeUqRmemzqIRcEQbpH978kVrHyOB2WpWsBme88u0SbcpTzr93W+6XAdK9u2rQJK1asyPn8kksuwaZNmyxpFGGecBGCM3xhy71lUYs9UHpMVoStm8khT6UKM8hKSYNGDjkXIZvs99RUWHShOB2yAJ5eHnm2qBt/cYTjyawdU3MLb6mqQDwzIqSuhowgs3XIrTAyuFr5YDqlwKrIFV4jNieH3CJ9C+UGUaXKEuaKutXOWCw1/N4lUkzSl6hzOyyZaxsVOYzF6LRUO1zYzajSeqVC1gGg1Sc/O0a+X2m028lD3ujmIesGcsjTlxwzbrtKSB7yEld2UIqqJVIMp7Y24sSpgfzHmwlZ1/GQeySDPJ2aqahH7rbxfMujCLYPiNWImurdpoWb9ageD7m9ImJLgelROD4+rpor7na7MTo6akmjCPPI4XTmH6j6rHIUsWT5Qnj9isVN16t96BkKZvyeP3zZdcjHY7IhZxeRB54Pve7VXqx9/F30DAXRMxTEfz29C4BYki77+iYq8kaLdtXTbFE3n8eJSekd0/7hSMEiH9m1yGsxh9yo8JSVKuu8njfPIbfqudTKIbfKSzCiGIM7Bscq8oxyT0BMKjdZO2Ox1PjcTqls0f6jIQDWhXTyBeHQeFQSNKsv4B1b7QQkpXVjxlGoQnXIgUwj/IG9IfSMaW/69owlkEy/t4X0H7vAPeRDEQMh60V5yMsTsn5kPJrx83lz9MskZ6uyGyGi827gc2o8ydLCxfJYtquHHADmzWjA3GnyxkU4lpTWl1azpXfYVmtUZUnLQjSDqg3To3DhwoVYt25dzucPPvggTj31VEsaRZgnVITgDP8/8aQ4Sck5mqVdFHZ192L7wJj0829f3Idld2zEQ9290meNipB1pQAY90KL4TuVX7x2dffisXRe1M5DY7h3016c/+ONOP/HG/H3twcBAEfHYznXN1GRUhF0Qtb5mFZOwEql9UJyyIHcWuSRGix7ZlQ1XQ7DLf4lx89RaL9owY0t7tGQIngseO67unvxtYe2Sj+/tPdoRZ7R7PBqqkNuHEEQ0JQ2wA+kDXKrRI+a68Xz8qgPYKJ6yNO1yE2LupV3HHf1hPD7PWHp5z/ui2DZ40N4aF9I9dhljw8hmH4FMQAX/+Oo6rGVoCkt6sbvuJ5B7rV5DnlXdy8uv+eFjM9+s7lHd56VRN0Sxj3ksh6MmqibPBZjiVSGeLGdDXIA6FTUYR+PJnDvpr2WvKe6unvxb+u2SD937z9uqzVqU3r+HQ4pRd1q1yA3fWW33HILPvnJT2LPnj04//zzAQBPPfUU/vCHP+CPf/yj5Q0kjFFMyLpygRGOJ6Ucm1IKXfQMBXHzw9syPuOCFWse3oYlHZPR0eKX8kVSTPTm+bNC2O2QT8Kvhb8KGeRrkT5I/8VY5vVNVLTy7ZWMp0Xd6pU6A8112D4wiv6RiDRBmx0DviwPeawGPeTmVdat8JBn9oNVBrnSQ55MMckwLzbFQHpuFY8qf2zL/YxmG5C1tDlUDpp8LgyNR7H/mOjdsaosEO+XsfRc43IIthWAKiWBtLE2ajRkvQI55D1jCdzcPQplC7l9uubVUSxp8aAj4Mo4NtvUS6kcWymassTz9EXdxL9jNlRZ5/NstvM+lWctxI3qWCIFxpihMG0jZc8AMcqKv0fcTgEOC8RHS0XPUBBPvnMo4zO1tXIh583uF8bEdapd1qh8/h0cjUjttMOav1SYfrNcdtllePTRR7F7925cd911+OpXv4qDBw/i6aefxpw5c0rRRsIA3NtXyO69x+mQ1JDDsaSuMIZVdHX3ak6wgiBgXXqHzueWlZqVeeRcydwOOS9616KG8vomKkZqkcsecnlMc6X1geGwFMJktgxGtohhpBbrkBuYB1IpJnnIrfD6ZRv1VtQgBxQGeTKVUQGi2P4yOgeVA+6J5ZBBbg5+/w4cE72jlnnIs84zEb3jgDKH3JjFxz3k5QxZ79oXhtZrWBCAdT3hgo6tFNlq9l6doVdMHfJSe8gLnWeVRnXUoJdcXrvm3iyXYp0bVXjI7VryjFOq95Sd3n9a8Pm377j4PDodQk1XuShoJF566aV4/vnnEQwGsXfvXlxxxRX42te+htNPP93q9hEGKcbTJQiCFOIaiiUVqtOlm6j6joc1a1AzxqQHUBCUAmByrudogfnDpUDvWtRQXt9EpaEufw55tso6oAxZV3jITY4BrrOQXfbMSuXSSmPkpaVUry2Fh9wylfX0gokxuRQeULzRanQOKge5HvLaXXSUAn7/DhwNZvxcLNmpBBNRYR0wp7KeYnI97HJ6yPuCSWi9hhkTf1/IsZWiyZ1574yUPUsySFoHRuEq66Xqq0LnWeWGq9E88nx6MF5FtJXdS55xSvWestP7Tws+jx8LxgCIawwrBe3sRsEjcdOmTbj22mvR1taGO+64A+effz5eeuklK9tGmEA2yAtbBPskr2FCobJeusXHzEk+3d25mZMUpRhUwpvHLc5TLQa9a1Ej+/omIry8hl4OOTe+Alkh64BYi7zoHPLssmc1ZAQZmQdCCkleKxTL60scsg7IGzguC0oympmDSk22J5Y85ObI9qRk389CcTqEjHFshdZCNWJGZV0ZNl1Og3ym36nr9Z7pdxZ0bKXIDlnXU1lX7hOZ9ZKHJA95aeacQudZl0MAjyQ36iGXc8jVr8WrUFqXUjNt7iEv1XvKTu8/LZqzUo/ssN4vJaZG4uDgIG677TbMnTsXq1atQmNjI6LRKB599FHcdtttWLJkSanaSeRBClkvMJxDqTxdjpD1Kxa36+7OrV7cLv0cUDHeZIXtypdA0LsWNbKvbyIiecg1QtaTinBqpVdK6SG3SmU9mtDOO6tWjITWKmuQW5FD58/6TstU1hULJr4pZ0V6gZk5qNTkiLqRQW6KRkXpM8A6D3n2uWo5XFIP2UOe3zDi+3wC5HJc5eCKDp+u13t1p6+gYytFg1vIUH336szRHsXvzOaRy2XPSrN5Uug8KwiCaaX1aJ70M77pHolXT8h6qd5Tdnr/aZE9j9shIraUGB6Jl112GebNm4dt27bhzjvvRH9/P+6+++5Sto0wQTidb1tojhsvk5QRsl7CRWFnix9rVy6CQxC9EMq/165clCEm0SiVPsvNIbeD4qLWtfAyKvmubyLCN1Ky68tzQoqydv4MlXXRQz44EsFogXXI+cs6HBfDFuWQ9dpZbBsJrbWyBjmQ2U+A+dx+LVxOh+QpGQ2LfW7F5omZOajUZHtirVCQn0hke1Kyf7bq3BM1ZF0qe2ZAZZ0bhD6nUNbw0s4GF9YuaYQDgFNAxt9rlzRmiLSZObZSOARBUloH9EPWXYK8mI+ZVFovdQ55MfOsbJCb85BrvR94GmY0kUI8fZ/svvlZqveUnd5/WjTUuTMiWaxaU9gVw7PO3//+d9xwww34l3/5F8ydO7eUbSIKoNjFtTKMt1whvKsWt2NJx2Ss6+5F3/EwZk7yYfXi9pyJICCVPpPzje2ksg5oXwuAvNc3EcmXQ87Hs9MhZLwwZzTVQRBEga++tIBToSHr4XgSSSYra9v9xWwGYwa5dd5mAPB7SpNDDohejGgiJT33Vs1NRuegctDkcyuur3bGYjnI9qRkRxxYde6JLupmxEPOZR7KGa7OWdVRjyUtHqzrCaMvmMRMvxOrO32qBraZYytFk9uB4fS7UM8gFwQBHicQSQJR0znkpS97Vug8yytpmM0h13o/KEPWU+mhbPcccqB07yk7vf/UcDoENHhdsmaUTdb7pcLw1W3evBn/8z//g7PPPhunnHIKrrnmGlx55ZWlbBthglCsuJB1n+Q1TCDK83DKEMLb0eLHmovn6x4jG2+5Iet2yinRupZ81zcRCSjqy6sxLgm6OTO8LG6nA1MDXhwei0plSwo2yGNJJBRDvJY85GZC1q3y+tVnyQBb+Wx6XNwgFzdwrJybjMxB5aDJ55ZyoMkgN0e2QZ6tWm/VuSdqyHogPR7N5JD7KmCQA0BHwIU1CxssP7YSNHkEQNQpRF2eoedxCIgkGaLGbFeJUpc94xQyz/JIIbMq65oecpd8Ph6ubfeQdU6p3lN2ef9p0VTvlgxyO633S4Hhkfie97wHv/rVrzAwMIAvfOELePDBB9HW1oZUKoUNGzZgbGyslO0k8mCdqFt5QtbNwPNGRm2aQ06Yp1Flk0VJSEXQjdPanJnfZ1plXZGeocy3s8t4twJlKUMtrA5ZdzsdGd4GK/O9eN/wNAUrROjshjI0upYEBstBdoi6tTnksnE/UUPWGyUPuQGDPCkeW1/Gkme1ilLYTU/UDZDzyM2IuqUYQyhZHoO8ELwmPeRRgyrr0XgKsXRkQDV4yCcyzYr5l3LIs/D7/fjMZz6DzZs3480338RXv/pV3HbbbZg2bRo++tGPlqKNhAFC8eJyyDNF3eylOi3VrLZpHXLCPHIOuXrIutJDnk1bOo+cYzaHXBmyziMwvS5HTZXTUJYy1MLKGuQc5QvTbL/owb0YUkh3DQnwcZRGZC1eXynJNsBLJuo2QVXWAyZC1ivtIa8llAa5Xsg6INcpN2OQhxSaAKVSWS8Gs6JukXwecrccss4j7Oyusj7RUc6/Vq4p7EhRI3HevHm4/fbb0dfXhwceeMCqNhEFUGz4ablV1s2glm9sx5B1wji837TKnvH8ZlUPeZPsIfe6HKZ3uDNC1mswf5xT59G/pmKjatRQzj9Wh6wD8nNfix5ypSe2FsdjKVHeO8C6smcAiboBssr6eJzlrShSyRzyWqPZoKgbIKuwx0yErHOD3IH8IfGVQM75Nlv2TP1iuPEdTcgq6+QhtzdN9UqDvLbX+5aMRKfTiY9//OP4y1/+YsXpiAIo1iD3udNhvPGkXIfcJl6aBpV8Y/7vWn9Aa5VAnpB12UOe27+8FjlQWP/zPNCQwkNeS/njnHye71CRlRnUyPSQW2iQpxdSVqqs240MD3kNbjiUknKJuk1Ug5x7yOMMiOaxjficSiHrxdPkNhGynv69GVG3cYWgmx0jxAove5Y/h7xayp5NdDI95LW93qeRWAMwxuQ65BZ4yHkoj10WhXyRrybqRjnk1Ym0yRJLIKWygOD5zdmltIBMD3khIUzcyA/Hatsgz+dF5nNGvtB2M/B5xOkQLBXA8mTnkNdgf2XmkNOr2QzZi7Z8+glmaCaVdQQU3u58YetRClm3jOaMkHX9Y/mhZsqehUpcg7xYuGEdschDLoWsx5OIJ+2llUSo00wGOVFNiIqR4r+LF3VLSB5yu4TycKNrLGpvlXXCOA3pjRTGgGAs10seTPe135v7Ym1VeMgLEflQChjGUuJCpBZfyvm8eVarrAPyBkrA67LU48K9GKM1XBaMcsgLx+NySOPYyvzx7PNZuXlVTTgEQTLK8wm7xShk3TKM1iEHZA96zJjtCkD2kNu1r/jGa9Rk2TNtDzmFrFcbyvm31h1wNBJrgJAiaahQr1RmHXJ75ZDLHnJlDjmJulUzdW4HXGkvllrps2BU20PeluEhN9//SlG3RA17yPNtzkmlEi3MIee1yK1+LnNyyGuwv5QLD4+z9q6v1PD7Z7VB3pgRsj5x3ze8Fnm+0md8k9NHIetF01iAynrUlIdcfAHaUWEdsD6HPCNkPclD1u157YQIhawTVQXPBfW48pc60sLeKuuZAmCxREpqY0ON75jVKoIg6OaR8zHtV/HejkcT4KN8YDiMnqGgqe/mm1bhjBzy2psK6/LmkKcNcguN21Q6VGckHMfax9813TdaSGXPwrUbsh5WbKz+/uX9lt27iUDPUFB6JwyHYpbeO+VG8IbthyZsvwTSc2S+kHU+jClkvXjCChX0B/aG0DOmrrkCFFb2LCjlkNvz/Wcmh5wxpsghz1P2LJEkD3mVwB2EAPDH1/pqev6lkVgDWBF6yicwO9Yhb8gy3JQe1UCN75jVMtn9qmRcClnP7N+u7l4s/+mz4EuOfUdDWHbHRjzU3Wv4e5WbT7WcQ5637FlMu7RcIXR19+If2w8BEPv03k17TfeNFnLZM9E4qrWQ7q7uXnztj1ulnx/q7rXs3tU6Xel7dSwYAwD0D0csu3dd3b24+tcvSz8/9e6hCdsv3EM+lsjnIRf/JlG34ujqCeErr4xIPz+yP4Jljw/hoX0h1eN5dlc+0T0lwbh9a5AD5gxypRc9X9mzmMJDTtFI9qWruxff/et26edHXu+r6fm3tlY1ExTu6Somv42H4okq6+mwH5ssenkOeTieRCKZkhbl9R6npeI9RHnRq0UuibopQkR7hoK4+eFtUDoAGIAUA9Y8vA37DO6c8hzyFAPC6b0Au2w+WYkvT9kzOWS9+AUJ7xslyRQz3TdacJV1vlFjl+gdK+D3TllNKsXMj+uJiFVzgtFzT+R+aTBYizxKHvKi6RlL4ObuUSi3PlIMSAFY8+oo9o3nbmJLHnITIevBhM0NchMh61HFuDQUsk4eclszEd+LNBJrACsW1rLXMGG7kHWlcNd4NEGCbjWCWjk7jlT2TCHq1tXdqykUJggC1hncNVXmgQYTaVG3GvSQ81KGWoTj1om6WdU3WvBFEzeOainFoNT3rpYp5b2jfskk4JJrkeshechtauRVA137wtDSxBQEYF1POOfzQsqe2d0g95rykIvHOATtvHApZD2uMMgpksOWTMT5t3ZWNROYcFy7ZrNRfAqhK7uFrHtcDqktY5GEouQZGeTVTKOBHHJlH/cdD4Mx9cUGYwx9x3MXKWo4HYJk4AXTzvl8JcKqkXKqrFvVN1pk14qtpf4q9b2rZUp576hfMmmUPOQUsl5q+oJJaAw9MCb+Phu57Jnx7wnavOwZX/dF8kRlKI+pczs1DTllDjkve0YecnsyEedfGok1gLUecvuprAOK0meRhOQ9LaQGNWEfuLE9rppDzo1F2SCfOcmnu2M6c5JP9Xdq8PHOI//skp5hJfny4q1UWbeyb9TIXjTVUn+V+t7VMqW8d9QvmQQklfV8om5plXWbel2rgZl+p66HfKY/d26Xy56Z8ZCLfWnXaAYzOeQRA+tW7nGnkHX7MxHnXxqJNYAVnq76dHhrKJaUcnHsFMarDG+mkme1gbzJopJDrlKH/IrF7bo7pqsXtxv+bq63EEwb5LXkceXkU0+3MmTdyr5RI3uRVUv9Vep7V8uU8t5Rv2TSIKmsU8h6qbmiw6frIV/dmWuMFKKyHkp7yAM2NUrNlD3jRrveRrTyfFGp7Jk9r32iMxHnXxqJNYAVC+vMkHU7esjlWuSyh5wM8mpGKnumkkOuJurW2eLH2pWL4BDEsHPl32tXLkJHi9/wd/PxHoyLi5hayknm5IuY4WkBVpQ9s7Jv1Mj2YtSSKn6p710tU8p7R/2SScCoynramUkh64XT2eDC2iWNcABwCsj4e+2SRnQEctc+3vQUGTURss71AOy6eVKIyrq+QZ72kMeTiJOH3NZMxPmXLJoagBsvxSxSuTHPmFhDGLCXQc7DmymHvHYopOzZqsXtWNIxGeu6e9F3PIyZk3xYvbjd9OTMQ+ElD3kNGXic/Aa5dR5ywLq+USNbpKfWNlBKee9qnVLeO+oXGaMq69xDTiHrxbGqox5LWjxY1xNGXzCJmX4nVnf6VI1xQBYnK8xDbs++kgxyEx5y3ZB1hYdcLntWW++SWmKizb9k0dQAViyslV6yeLpshh1D1scyVNYph7yaadDJIefeW2XIOqejxY81F88v6rslD3ktlz3LW4c8N0+/WKzoGzWya8XapQKElZTq3k0ESnnvqF9EGgyqrPM62D7ykBdNR8CFNQsbDB3rLaLsmX095FwV3UAOuYFUS4/SICcPeVUwkeZfGok1QDhWvMq6wyHkGCV2MlKUNat5zjF5yKsbKYc8mplDLiqgptVfS9TH3FiNp3jIeu0ZeHobdLFECom0J8WKkPVSkxuybp+5iSAmAg0GVNYZY3LIuk2NvFqFT/ex/M5kCduXPVPUDc8HT7WsM+QhV6isk4ecsAk0EmsASS25yIV19gLeTjuHkqhbhso6GeTVjJbKekiRBFdfImMxe6zXooGnF7IeVtTGKaY6Q7nIDVm3f5sJopYIuLmHXNs4iqUABlJZrwRc1C1qykMu9qVdy57x97IhlfW4gRxyrrIeJw85YT9oJNYAVtUTzvaw28lDrsw3lkPWySCvZrRyyPmGS53bAVeJdq+zjdBaDIHWM7RDcfEeuxQ12e2MnaN3CGIiYMRDHlIIvpGoW3mRDHJTZc+4h9ye86mpsmcmc8ijZJATNoNGYg1glThT9gLeTqE8yrJn45RDXhNwlfXRbA+5isK61UwED3m9S/v+haUa5NWxEVHLKusEUQ1IdcgTDCmNckThtHfW7RA3+4jy4ZVC1s2Lutk3ZL1UZc+SkqgblT0j7AKNxBogFOeL6+IMGKWR4nU5IAj2maSVOeSjlENeEzSmN1TGs3LIuYe8XkXQzSpyokFq0MCrUzzP2fU8rVZYLzXZBrm3BjdQCMLONKafOQbZs5oNN/DIO15+pDrkBsuexVJMyje3q0Gu9JBr1aTmyGXPdDzk6fPFEik5h5w85IRNoJFYA0Ss8pC7Mw1yO6EMb6Yc8tqAb6hE4vLLEVAorJfQQ56tt1BX4yHr2WVjwnHrFdZLSbYXgzzkBFFevA6A221aYeuRtIec8sfLDy97ZjRkPSO9wKb9xd/LKSZX/9EiKoWsG/GQpxCNk6gbYS9oJNYAPB+02PDTDA+5zRa8AcohrzkCiv5TCrsF06JupVJYB3I3r2rR46rcdAhluU2sEoIsF9mLJrttGBJErSMIgpRHriXsRh7yyuFJT4lGy57x8nUeB+C2aXqB8r3MVdS1iBjxkCveG8G0Y4c85IRdoJFoY3qGglj7+Lv41wfewNrH30XPUFD1OCn8tGiVddkAstuCt1GqQx5XeMgph7yacTsd0stzLMMg5zXIy5hDXoMe8t5jIenfdz+3P2P+kEslVsd1KxdNgkBeDYKoBA18vtbwkIfJQ14xvOlNEKNlz3aNyKlia98cQ89YQufoyqBch0Z01P0B2UOun0Mu/y6YXjfTu4SwCzQSbUpXdy+W3bER927ai8e29ePeTXux7I6NeKi7N+dYqwSafB77hqzzHPIjY1Ek0yFZlENe/ajVIpdD1ktnLGbrLdSaqFtXdy8u/dlz0s8PbRnImD9C1Sbqplg01bmcttK3IIiJAhd2G8uTQ04Gefnx8hxyAyHrXT0hfOb54fTxwL07glj2+BAe2hfS/49lRhAEaS2aT2ndSNkzt1NA9quDPOSEXaCRaEN6hoK4+eFtSDEgmWIZf695eBv2ZXnKrRJoyhR1s9dCnYen80nX6RCqxrtHaKNW+my8DCHr2aHadkvRKAbl/MFJMWTMH1UXsq5YNNViegFBVANy6TN1b6XkIaeQ9bLDQ9bz1SHvGUvg5u5RKI9KMiAFYM2ro9gfNOhiLxPcwM4fsp6/7JnSwOeQyjphF2gk2pCu7l5ND5AgCFiX5SWXPeTFGTAZom42W/QGsvLFA14XeclqgIa00a3MIS+Hhzw3ZN1e470YjMwf4SpWWa/F9AKCqAYa0s/huFbIeoIM8kphNGS9a184x0vMEQTgod6YxS0rDtlDrn9hUh3yPJvM2c4m8pATdoFGog3pOx7WLPHAGEPf8bD0cyKZkuopFptDbuuQdY8r4yVC4eq1gVrI+ngZcsizQ7VrSbXbyPwRsmgTr1xkGOQ22ywkiImC7CHPU/aMQtbLjlz2TN9D3hdMQquCGGNAX7g6PeRS2bM8a9fsta3d1rrExIVGog2ZOcmn6+GaOckn/RxS5NVYqrJuMy+UwyEgoDAeSGG9NgioecgrobJeQy9lI/MHr8xQLR5yZVhhLW2eEEQ1EXBzDzmFrNsNXvYszoCUTs3umX6nrod8ps9e70K+AWuVhzzbI04h64RdoJFoQ65Y3K7r4Vq9uF36mYeeOoTijQqlt8yOYTzKsHUyyGsD3o+jyhzyMoesuxwCXDX0UjYyf0SqLWRd0T+1tHlCENUE95CPanrIxb/r6fVcdjyKaVEvbP2KDh+0dN8YA1a1e6xtWJEYziGPm/eQOx0CnDYt+UZMPGhlY0M6W/xYu3JRxmcOQfyzduUidLT4pc9lQbfic6qVIe92XPQ2ZBjkVPKsFuCbLDxMHQBC6X/XlzRkXVHir8ZCoPn8obbO4PNHtamsezNE3aqjzQRRa3CV9XENlfUIlT2rGF5FVIKesFtngwtzG8U5VIBoBDgF8e+1Sxox22+v96HRHHIpZN1EDjmVPCPsBO1j2pRVi9vxfy/uw5sHRwEAHzujDV9ednKGMQ5YV/IMsHfIOpCZN0455LWBlEMekXPIeX1Qfwnzm5WbT7UoErZqcTvOmeoFfiJ/5nU5cOmiVgByqkuxuhPlgkLWCaLyyHXI1Y2jEIWsVwy34pbrlT47Fk1hz5g4/1/Z6cN4gmGm34nVnT50BFyIh+xV+ozP9/nKnhmpQw5kbsDbMRKUmLiQVWNjjgVlI+XShW05xjgAhC3MBc0QdbOh11DpFaeQ9dpATWU9KIm6lbIOub2jQaxgtmK+aJ/kxc5gCk+/exgfWdSmUFmvjucoo+xZjfYXQdidhrTnO5/KOom6lR9BEOBxiOHqUR3b9fGDESQZsKDZhVsXN5WvgQXCnUPcA66FlENuImSd8scJO0Gj0aYkUwyHRiPSzyPhuOpxVtYTVi7O7bjoVeaQZ5dBI6oTtTrkfEyXUtTN63JIId0TQbX74vlTAQB/2zoAQC4tVy0h65kq69XRZoKoNfLWIaeyZxXFy5XWdTzkf+sV15Ufaa8rS5uKxSuJuuWrQ24+ZN2O61xi4kKj0aYMjUeRUEyqeQ3yCRCy3qgwwhsph7wm4BsrY4occqnsWQm9t4IgSM+MHce61Vx8imiQP7PjMMajiaqrQ+5SJMTXUs14gqgmZJV1DVG3JHnIK4lci1y9fw5HknjpsFhnvFoMcp5SljeHXApZN+4hp5B1wk7QaLQp/cPhjJ+HNQxyKxfWdTYXdaMc8tpDziHPFXUrZcg6IEeVTAQP+SnTAjixxY9oIoUntx+yNLKmHAiCIC2eyENOEJUhn8o695DXkYe8InCl9ZiGM/nvfRGkAJw+2Y12f3WsoepMesjzbbArRUHdNE4JG1H7K9EqZWAkkvHzaN6Q9eIn13rKISfKjFSHPCqO71SKyaJuJd504caoHTefrEYQBHwkLej21639VaeyDgDedL7fROgvgrAjDZLKur6oG3nIKwOvRR7V8JDzcPXLqsQ7DijLnml7yOPJFJLpayYPOVGt0Gi0KTke8lBM9TieC2qFh9zuIevkIa89GrNyyMOKXfBShqwD8nifKGW0PnJ6GwBg064jOJ6eT6pF1A0AecgJosJwlfVIEoirGH2UQ15ZPDyHXKXs2UAoiVeHxI3vFTOrxyCXy55pe8iVxnr+HHKFQU6iboSNoNFoU7iHvLle9Apr5ZBbGbJud+VpqkNeewQUBjljTFJYdwilDyXn432i5CSfPL0BJ08PIJ5kkoe8WnLIAVkRdyKkGBCEHQkoPN9qeeRhqkNeUXiWV0zFmfxYn7imXDzFjbb66pn3ZQ+5tkGuNNbzq6wr6pBPkHc/UR1UdDQmk0nccsst6OzshM/nw0knnYQf/OAHYEye6Blj+Pa3v43W1lb4fD5ccMEF2LVrVwVbXR64h/yUGY0AtHPIeT1hK0JPDx4Pg79Gn3rnEHqGgkWf00p4NAAAPPx6n+3aR5iHb6wkUwyReCqjBrkglG5R1zMUxJGxKABgz5HghBlLH1nUlvHz/2zuqYpr7xkKSs//C7uPVkWbCaLWcDkEyfudrbTeM5bAaExcu/1+dwg9Y4mc/0+Ujp6xBI6ExT55sEe+/z1jCax9cwz/9c44AODcaZ6KtbEQ+AZsVEfUbdehMQDiRv7tT+zQfT8o0zGp7BlhJyo6GteuXYt77rkH//Vf/4V33nkHa9euxe233467775bOub222/Hz372M/zyl7/Eyy+/DL/fj4suugiRSETnzNVPf9pDPr+1AUDpPeRd3b244CfPgm+FPLvzCJbdsREPdfcWdV6r6OruxXf/ul36+ZHX+2zVPqIw6t1OcLt7LBJX1CAvXSh1V3cvlt2xEf3D4jO250hwwowlpyNzk+N3L+63/bXz/hpNpzW82HPU9m0miFpFTditqyeEZY8PgZtM6/ZFsOzxITy0L1SBFk48+P0fjIg9sOFgFMseH8LXXx3GsseHcO+OIIbTmyU/fydYVf3CPdoRDQ95V3cvrv71ywCAFAPu3bRX9/2g9KDbMRKUmLhUdDS+8MIL+NjHPoZLL70UHR0duPzyy7F8+XK88sorAETv+J133olvfetb+NjHPoZFixbh//7v/9Df349HH320kk0vOQNZHnItUTfZIC/cgOkZCuLmh7dBmRKWYuKfNQ9vw74Ke6N4+5hN20cUjsMhSHoAY9GEZJDXl0hhXTnW+XBimBhjqWcoiDv+sSPjsyRjtr52tbmJ0bNPEBUjIAm7iQ9lz1gCN3ePQum/TDIgBWDNq6PYN06e8lKidv9T6T9d+0RVdWVKebX1i6yynushV3s/JFP67zQPiboRNqWiij7nnXce7r33XuzcuRMnn3wytm7dis2bN+MnP/kJAKCnpweDg4O44IILpP/T1NSEpUuX4sUXX8SVV16Zc85oNIpoNCr9PDo6CgCIx+OIx9WN2nLD26HVnlgihSPj4jXMnVoPABgOxRGLxXLCeLk6tcepfb58PPjyfggQIJsoMgKAB17ej68tn1vQua3Aju3L14eEcRq8LoxFEjg+HsFoOC025naW5N7acSyVjHgcXGkhnkjgwder79qN9NeXP9wBgJ7FamZCzKeJRHo3KSX+qVJ4HvlwNIl4KoUHe0JilJOKsLcgAA/sDeFrpwWsb0gqJc9vVX5Pi0Hv/muh1y/xtOcjnkgANngeuRxBOJbImR8KeZ8r5Q2cQu3OORNiTq0SjPZBRQ3ym2++GaOjo5g/fz6cTieSySR+9KMf4eqrrwYADA4OAgCmT5+e8f+mT58u/S6bW2+9Fd/73vdyPv/HP/6B+vp6i6+gODZs2KD6+dEIwJgLToFh5+ubAbiQSDE8+re/I9txuL/PAcCB3e9ux/rjbxfUjld3OpBiAoDcnN0UY3h1+x6sT1Qub9/O7dPqQ8I4LOYEIODpTS8gmAAAJyLjw1i/fr3l32XnsWQ1zkgEH0n/+4lNm/Dqgfqqu3Yj/bUh3WZ6FqufCdGHx45VugVFEUmJa47NR44hCoZXh/I8o0PjWN8/ank7Mua3gQEk66pHOdxK9O6/Fkb6ZcNzz1nQuuJ5+6gAwInBI0dz1gSFvM93DYrnA4DDA/1Yv76vRC23BxNiTrU5oZCxFJGKGuRdXV24//778Yc//AELFizAli1bcOONN6KtrQ3XXnttQef8xje+ga985SvSz6Ojo2hvb8fy5cvR2NhoVdOLIh6PY8OGDbjwwgvhdueqhb+67zjwxqs4YVI9PvGR9+Fbrz2JeJJh6fs/jLZmX8axDx7qBo4fwzlnnYEVp7cW1J7trl3Yunkfkix3l9EhCFhy6olYUUHPmR3bl68PCeP838FXMHBgGKecfhbGInFg13a0t07DihVnWf5ddhxLJSMoh+td9IEP4M3Xj1fdtRvprws/3EHPYpUzIebTcBh4/nkgEACq2Hhcf2AEO0eiOCnQhBVt9dh+bBxbj4WgUmlLfEZb/FjRVgIPeVguDXtRayvg8+kcXLvo3X8t9PolHg5jw/HjuPD974e7ocHClhaGf+cR/GbnG/AFGrFixbkZvyvkfR5+/SAe6hGdVyd2zMKKFaeWrvEVZELMqVUCj9TOR0UN8ptuugk333yzFHq+cOFC7N+/H7feeiuuvfZazJgxAwBw6NAhtLbKxuahQ4dwxhlnqJ7T6/XC6/XmfO52u203KLXadCQohje0Nfvg8XjQ5PNgaDyKYBw5x4fTeTUNPk/B13fl0tn41eYe1d8xAFctnV3Re2fn9tlxXFUbDT7x/oUTDOF0WltDXeHjWQ87jyXLUVyH2+Wqyms302Z6Fqufmu7DeFyMFXY4xD9VSmM6pzeUANwOB67srMevdqh7gBgDrjqxHu5SXK/inO4qv6fFoHf/tdDtl3RapNvlssWz6K8T1/PRRCqnPYW80+rrZJX5Oo89rrGU1PScWiUYvf8VncFCoRAcWROC0+lEKp0L1NnZiRkzZuCpp56Sfj86OoqXX34Z556buVNWS3D157Ymcce3ySfumwyn82uVWCHq1tnix9qVi+AQRBVm5d9rVy5CR4u/4HNbgd3bRxQHL302FkkgJKmsl0bULXssCWBwCpgQY6kan6NqbDNB1DJcZX0srbLe2eDCtXNk77Q0pwJYu6QRHYGK+n1qns4GF9YuaYQDkO47//uKzjrVz6upX6SyZ4lcjQD+flAGrOd7PyiV1T1U9oywERV9Ii+77DL86Ec/wqxZs7BgwQK88cYb+MlPfoLPfOYzAABBEHDjjTfihz/8IebOnYvOzk7ccsstaGtrw8c//vFKNr2kDIyIoVitzWJYW1Pag6imtB6KiwZMsXXIVy1ux5KOyVjX3Yu+42HMnOTD6sXttlnw2r19ROFwlfXxSEIaz/4iNpjywcfSAy/vx6vb92DJqSfiqqWzJ8RYqsbnqBrbTBC1iqyyLhtIx9MltU5rdsHjjGFJix9XnVhfNUZftbOqox5LWjxY1xNGXzCJmX4nVnf60BFw4br5CdXPqwWp7JlGHfJVi9uxvX8U972wD7Mm1+PSRa267wcvqawTNqWiT+Xdd9+NW265Bddddx0OHz6MtrY2fOELX8C3v/1t6Zivf/3rCAaD+PznP4/h4WG8733vw+OPP466Ks7Bygf3kLemPeTN9WKIzXAo1yC3qg45AHS0+LHm4vlFn6dU2L19RGE01qXLnkXi0i54fQnrkAPiWPra8rlYn9iFFcvnTqiQrmp8jqqxzQRRi/CQde4hjyQZNvSLVWG+fUYDBqJHsKItUJowdUKTjoALaxbm5nxrfV4tSB7yuHodcgBwOcVNootPm5H3PcENfIA85IS9qKhB3tDQgDvvvBN33nmn5jGCIOD73/8+vv/975evYRWGe8jbsjzkI2oecgsNcoKoBJKHPJpALJlKf0bjmSAIwm7wsmfjaYN840AUwQTDCfUOnDHZhYGBSraOqDXq3GkPeULbIB+LiJF1DQY28r1uhfYAecgJG1E9cSsTiIGRTA+5lkHOGEM4vWtYbMg6QVSKBslDnkAirR9RjCYCQRAEURoaJA+5OFf/tVdcr1w6sw6CYLz0FkEYgRvk8SRDMsXgdOSOsbG09kygzoBBTjnkhE2h0WgzIvEkjgVF8ba2LIN8OMsgjyZS4NUeyIAhqpUAF3WLJqSIj0CJQ9YJgiAI8wQUom6hRApPD4jh6pfNmphlx4jSojSgoxpecslDXpc/9SwjZJ085ISNoFWvzeDe8XqPE41pdfXmenUPOTdeAMDnJg85UZ00KHLIOZSCQRAEYT+UKutPDUQRTjLM9jtxWrMLCZV60ARRDHWKtW0knkK9J/eY8fTawchGPnnICbtCBrnNGBhOK6w3yeFfUsh6KNsgF3cFPS6HahgPQVQDDQqVdUd6zJOHnCAIwn5wUbfxRAp/S4erf6Q9vV4hg5ywGKdDgNspIJ5keT3kjUZC1t2ksk7YE1r12oz+tIe8rVkO/9LKIbdSYZ0gKoWyDjnfWCq1yjpBEARhHh6yPhpjeCYdrv6R9tqtekNUHq/LiXgyoVn6bNxMDrmTQtYJe0Kj0WYoPeQcHrI+HI5lHCsprFO4OlHF8JfoeDQhRX34aZOJIAjCdvCQ9RSAWAo4qcGJ+U20gUqUDl76LKJR+sxUDrmbQtYJe0Kj0Wb0j3CDXMVDnhOyTgrrRPXToDDI+U63nzzkBEEQtuNQKNMoet80L6mrEyWFC7GpGeSpFJM95AbWDQePh6V//2nLQfQMBS1qJUEUBxnkNqN/mIesyx7yJp+oYjEWTSCZknO0wnFxEiKFdaKaUb5E40lxfPtpTBMEQdiKrp4QLnjiaMZnv9sTwkP7QhVqETER4B7yaCI3ZH08HVUHyJv7WnR19+LCnz4r/fz3Nwew7I6NeKi716KWEkThkEFuMwZ0POSMZSpRk4ecqAXq3M6c0LF6L41pgiAIu9AzlsDN3aPINolSANa8Oop94wm1/0YQRaPnIR9Ph6t7nI4MRfZseoaCuPnhbVD4tJBi4p81D2/DPvKUExWGDHKbMaDiIfe4HJJwm1LYjUTdiFpBubPtcTngptwugiAI29C1LwytyHRBANb1hNV/SRBFIueQ53rIef54PkG3ru5ezdQKQRCwjrzkRIWhVa+NGIvEMZbOhVF6yAHZSz6syCMPx8kgJ2oD5cuUSp4RBEHYi75gUrOqGWPi7wmiFHDPt1rZMx41mi9cve94GExjADPG0HecNpSIykIGuY0YSJc8a6xz5YhaqZU+4yHremE6BFENKF+mtMFEEARhL2b6nboe8pl+mreJ0uBNlyeLqnnIDQq6zZzk0/WQz5zkU/0dQZQLMshtRH+65JmyBjlH8pCrGORkwBDVjvJlSh5ygiAIe3FFh0/XQ766kwwaojRwp1NE1UPOS57prxuuWNyu6yFfvbi9yFYSRHGQQW4juIdcWYOco+YhD8dIZZ2oDZT1Q2mDiSAIwl50NriwdkkjHACcAjL+XrukER0BWocQpUEyyHVE3QJe/RrknS1+rF25CA4BcDqEjL/XrlyEjha/9Q0nCBPQDGojBtIe8lYVD3lzvTjZjKp4yH0Usk5UOQ0KrzjVICcIgrAfqzrqsaTFg3U9YfQFk5jpd2J1p4+McaKkSGXPVEXdxDVxYx4POQCsWtyOJR2Tsa67F33Hw5g5yYfVi9vJGCdsAc2iNqI/7SFv0/GQD4di0meksk7UCspwM6pBThAEYU86Ai6sWdhQ6WYQEwip7JlKyPp41JjKOqejxY81F8+3rnEEYREUsm4jeA1ytRzy5noPAHVRNzLIiWpH+TKlGuQEQRAEQQCA10DZs3w55ARhd8ggtxG8Bnl2yTMAaFQpexZK59P4yKNIVDnKHHISdSMIgiAIAgDqXHplz4zlkBOE3SGD3CYwxtAvechzQ9abVUTdIuQhJ2oEpRFOIoUEQRAEQQBKUTftHHLykBPVDhnkNmE4FJcmmxkGVdZDcXFn0EcGOVHlKF+mAQpZJwiCIAgCch1yVZX1KIWsE7UBGeQ2gXvHWwIeScBCiapBzj3kpLJOVDnKlyl5yAmCIAiCAGQPeTRBOeRE7UIGuU3Qyx8H5LJnmXXIeQ45GeREdUM55ARBEARBZFPnzu8hpxxyotohg9wmcIX1VpVwdUD2kIdiScTSu4Sksk7UChk55BSyThAEQRAE5LJnenXIyUNOVDtkkNuAnqEgHnn9IABgYCSCnqFgzjENdW4Igvhv7iWXPeQ0ERHVzXAoJv17/ZsDqs8AQRAEQRATC8lDrquyTutgorohg7zCdHX3YtkdG7GldxgA8Fb/CJbdsREPdfdmHOd0CGhITzgj4RgSyRRiSXG3kHLIiWqmq7sXV//6Zennx98aVH0GCIIgCIKYWEg55Fke8lgiJeWVN9ZRyDpR3ZBBXkH2HQ3i5oe3IcUAlv6MMSDFgDUPb8O+LC9hc70HgOghDylyaSiHnKhWeobkZ4CT0nkGCIIgCIKYOGh5yHn+OAD4KdWNqHLIIK8gf3ytHwKPQ89CEASsy/IQ8jzy4VBcCld3CHJJCIKoNrq6e009AwRBEARBTBx4Dnm2qBvPH6/3OOFy0jqYqG5oBFeQvuEwGGOqv2OMoe94OOMzZekzWdDNpWnQEITd6Ttu7hkgCIIgCGLiwD3k2WXPqOQZUUuQQV5BZjb7dL2DMydllkBrUpQ+o5JnRC0wc5K5Z4AgCIIgiImDtoecBN2I2oEM8gpy+dltut7B1YvbMz7LCFmPixMRlTwjqpkrFrebegYIgiAIgpg4eKU65KmM9YJc8owE3YjqhwzyCtIxxY+1KxfBIYgq6sq/165chI4Wf8bxzSoh6z5SWCeqmM4Wc88AQRAEQRAThzrFOlcZts5F3ShknagFaBRXmFWL27GkYzLWdfei73gYMyf5sHpxu6ohopZDTiHrRLVj5hkgCIIgCGLiUOfKNMi5gU455EQtQaPYBnS0+LHm4vl5j2tWySGnkHWiFjD6DBAEQRAEMXFwO8WouRQDovEkkHZOcQ855ZATtQCFrFcRcg55TBGyThMRQRAEQRAEUXsIgqAQdpND1kcph5yoIcggryIaM0LWSdSNIAiCIAiCqG3k0mey0vo4qawTNQQZ5FVEs88DABgJJyhknSAIgiAIgqh5eN640kNOOeRELUEGeRUh1yGPIRQnUTeCIAiCIAiitvG60qXPlB5yUlknaggyyKsIXvYsnmQ4Nh4DQB5ygiAIgiAIonaRPeSyQU51yIlaggzyKqLe44TLIQAABkYj6c9oZ5AgCIIgCIKoTbxpgzyqErJOOeRELUAGeRUhCIJU+mxgOAxA3jUkCIIgCIIgiFqjTiVknXLIiVqCDPIqgyutD45wDzkZ5ARBEARBEERt4lURdaMccqKWIIO8yuC1yMeiVPaMIAiCIAiCqG24h5yXPWOMKQxyyiEnqh8yyKsMLuzG8VHIOkEQBEEQBFGjZJc9C8WSSKYYAMohJ2oDMsirjKYsg5xE3QiCIAiCIIhaRSp7llZZ595xp0OgSFGiJiCDvMporvdk/Ex1yAmCIAiCIIhapU5SWRcNcl7yLOB1QRCEirWLIKyCDPIqozHHQ04GOUEQBEEQBFGb1Ll5DrkYsk4lz4hagwzyKiM7h5wMcoIgCIIgCKJWkXPIuYecFNaJ2oIM8iojO4ecQtYJgiAIgiCIWkXOIRc95FTyjKg1yCCvMkjUjSAIgiAIgpgoSDnkicwccip5RtQKZJBXGc31VPaMIAiCIAiCmBh4s8qeUQ45UWuQQV5lKD3kHpcDTgepSxIEQRAEQRC1iRSynqAccqI2IYO8ymhSeMhJ0I0gCIIgCIKoZbJF3XgOeYAMcqJGIIO8ylB6yOspXJ0gCIIgCIKoYepc2WXPxBzyRsohJ2oEMsirDK/LKeWNk8I6QRAEQRAEUcvUZeWQSx5yyiEnagQyyKsQ7iUnhXWCIAiCIAiiluE55FGqQ07UKGSQVyHcICcPOUEQBEEQBFHLyGXPSGWdqE3IIK9CvG6x2/YNBbH28XfRMxSscIsIgiAIgiAIwnqyRd2oDjlRa5BBXmV0dfdiW98IAODwWBT3btqLZXdsxEPdvRVuGUEQBEEQBEFYi1T2LEtlnULWiVqBDPIqomcoiJsf3pbxWTLFkGLAmoe3YR95ygmCIAiCIIgaQvKQZ4Wsk0FO1ApkkFcRXd29EARB9XeCIGAdeckJgiAIgiCIGqIunaqZTDFE4kmEYqKnnHLIiVqBDPIqou94GIwx1d8xxtB3PFzmFhEEQRAEQRBE6eAecgA4GoxJ/6YccqJWIIO8ipg5yafrIZ85yVfmFhEEQRAEQRBE6fA4ZXNlaCwKQMwr97jIjCFqAxrJVcQVi9t1PeSrF7eXuUUEQRAEQRAEUTocDkEyvofGRYOc8seJWoIM8iqis8WPtSsXwSEAToeQ8ffalYvQ0eKvdBMJgiAIgiAIwlLqcgxyClcnagfaXqoyVi1ux5KOyVjX3Yu+42HMnOTD6sXtZIwTBEEQBEEQNYnX7QQiCRxJh6yToBtRS9BorkI6WvxYc/H8SjeDIAiCIAiCIEoOV1ofGhdF3ShknaglKGSdIAiCIAiCIAjbUucSldaPjJOHnKg9yCAnCIIgCIIgCMK28NJnXGWdcsiJWoIMcoIgCIIgCIIgbIs3Lep2hFTWiRqEDHKCIAiCIAiCIGxLroecDHKidiCDnCAIgiAIgiAI28JF3UYjCQCUQ07UFmSQEwRBEARBEARhW7xpUTcO5ZATtQQZ5ARBEARBEARB2BavO9NkCVDIOlFDVNQg7+jogCAIOX+uv/56AMCHPvShnN998YtfrGSTCYIgCIIgCIIoIzyHnEM55EQtUdHR/OqrryKZTEo/v/XWW7jwwguxatUq6bPPfe5z+P73vy/9XF9fX9Y2EgRBEARBEARROeqyQ9Yph5yoISo6mqdOnZrx82233YaTTjoJH/zgB6XP6uvrMWPGjHI3jSAIgiAIgiAIG5Adsk455EQtYZvtpVgsht///vf4yle+AkEQpM/vv/9+/P73v8eMGTNw2WWX4ZZbbtH1kkejUUSjUenn0dFRAEA8Hkc8Hi/dBZiAt8Mu7SHMQ31Y/dR8H8bj4MuVeCIB1Oh11nw/TgAmRB8mEgBjQCol/qlB4unripfj+lIpeX6r4XtabuKMiX/b8J2RZY+jzslqe84oggkxp1YJRvtAYCz99FWYrq4u/NM//RMOHDiAtrY2AMC9996L2bNno62tDdu2bcOaNWtwzjnn4JFHHtE8z3e/+11873vfy/n8D3/4A4W7EwQxYXBGIvjIlVcCAP724INI1tVVuEUEQRDWQPPbxOOpgwL+ckAOW79tSQI+27gVCUKdUCiEf/qnf8LIyAgaGxs1j7ONQX7RRRfB4/Hgr3/9q+YxTz/9NJYtW4bdu3fjpJNOUj1GzUPe3t6OoaEh3RtRTuLxODZs2IALL7wQbjeF3FQj1IfVT833YTAI96RJAIB4fz/Q0lLhBpWGmu/HCcCE6MNwGHj+eSAQAGrUeIynUtgwOIgLZ8yA21FizeBwGO50emP82WcBn6+03zdBiIfD2HD8OC58//vhbmiodHMy+N1LB/D9x96Vfn73exfC6RB0/sfEZULMqVXC6OgoWlpa8hrktthb2r9/P5588kldzzcALF26FAB0DXKv1wuv15vzudvttt2gtGObCHNQH1Y/NduHimtyu1wZP9ciNduPE4ia7sN4HBAEwOEQ/9Qwboej9Aa54vzuCXBPy0Y6ZdTtctnuWaz3yu0JeF2o83oq2JrqoKbn1CrB6P23xQx23333Ydq0abj00kt1j9uyZQsAoLW1tQytIgiCIAiCIAii0ijLnlHJM6LWqPiITqVSuO+++3DttdfC5ZKbs2fPHvzhD3/AihUrMGXKFGzbtg3/9m//hg984ANYtGhRBVtMEARBEARBEES5qFOougWo5BlRY1R8RD/55JM4cOAAPvOZz2R87vF48OSTT+LOO+9EMBhEe3s7Vq5ciW9961sVailBEARBEARBEOXG6yIPOVG7VHxEL1++HGq6cu3t7Xj22Wcr0CKCIAiCIAiCIOyCsg55gGqQEzWGLXLICYIgCIIgCIIg1KAccqKWIYOcIAiCIAiCIAjbUqcMWacccqLGIIOcIAiCIAiCIAjbogxZJw85UWuQQU4QBEEQBEEQhG1RhqwHvJRDTtQWZJATBEEQBEEQBGFb6lzkISdqFzLICYIgCIIgCIKwLYOjEenfz+06gp6hYAVbQxDWQgY5QRAEQRAEQRC2pKu7F5fdvVn6+dmdR7Dsjo14qLu3gq0iCOsgg5wgCIIgCIIgCNvRMxTEzQ9vQ4rJn6WY+GfNw9uwjzzlRA1ABjlBEARBEARBELajq7sXgiCo/k4QBKwjLzlRA5BBThAEQRAEQRCE7eg7HgZjTPV3jDH0HQ+XuUUEYT1kkBMEQRAEQRAEYTtmTvLpeshnTvKVuUUEYT1kkBMEQRAEQRAEYTuuWNyu6yFfvbi9zC0iCOshg5wgCIIgCIIgCNvR2eLH2pWL4BAAp0PI+HvtykXoaPFXuokEUTSuSjeAIAiCIAiCIAhCjVWL27GkYzLWdfei73gYMyf5sHpxOxnjRM1ABjlBEARBEARBELalo8WPNRfPr3QzCKIkUMg6QRAEQRAEQRAEQVQAMsgJgiAIgiAIgiAIogKQQU4QBEEQBEEQBEEQFYAMcoIgCIIgCIIgCIKoAGSQEwRBEARBEARBEEQFIIOcIAiCIAiCIAiCICoAGeQEQRAEQRAEQRAEUQHIICcIgiAIgiAIgiCICkAGOUEQBEEQBEEQBEFUADLICYIgCIIgCIIgCKICkEFOEARBEARBEARBEBXAVekGlBrGGABgdHS0wi2RicfjCIVCGB0dhdvtrnRziAKgPqx+ar4Pg0H532NjgNdbubaUkJrvxwnAhOjDUEh8JuPx2n0WUymxH48ehdtRYn9POCz/++hRwOcr7fdNEOKRiPwsVroxRMFMiDm1SuD2J7dHtRBYviOqnL6+PrS3t1e6GQRBEARBEARBEMQEo7e3FzNnztT8fc0b5KlUCv39/WhoaIAgCJVuDgBxt6S9vR29vb1obGysdHOIAqA+rH6oD2sD6sfqh/qwNqB+rH6oD2sD6kf7wBjD2NgY2tra4NCJHKr5kHWHw6G7I1FJGhsb6UGpcqgPqx/qw9qA+rH6oT6sDagfqx/qw9qA+tEeNDU15T2GRN0IgiAIgiAIgiAIogKQQU4QBEEQBEEQBEEQFYAM8grg9Xrxne98B94aVVqdCFAfVj/Uh7UB9WP1Q31YG1A/Vj/Uh7UB9WP1UfOibgRBEARBEARBEARhR8hDThAEQRAEQRAEQRAVgAxygiAIgiAIgiAIgqgAZJATBEEQBEEQBEEQRAUgg5wgCIIgCIIgCIIgKgAZ5GXm5z//OTo6OlBXV4elS5filVdeqXSTJiS33norlixZgoaGBkybNg0f//jHsWPHjoxjIpEIrr/+ekyZMgWBQAArV67EoUOHMo45cOAALr30UtTX12PatGm46aabkEgkMo7ZuHEjzjrrLHi9XsyZMwf/+7//W+rLm7DcdtttEAQBN954o/QZ9aP9OXjwIP7f//t/mDJlCnw+HxYuXIju7m7p94wxfPvb30Zrayt8Ph8uuOAC7Nq1K+Mcx44dw9VXX43GxkY0Nzfjs5/9LMbHxzOO2bZtG97//vejrq4O7e3tuP3228tyfROBZDKJW265BZ2dnfD5fDjppJPwgx/8AErdWOpH+7Fp0yZcdtllaGtrgyAIePTRRzN+X84+e+ihhzB//nzU1dVh4cKFWL9+veXXW4vo9WE8HseaNWuwcOFC+P1+tLW14Z//+Z/R39+fcQ7qw8qT71lU8sUvfhGCIODOO+/M+Jz6sYphRNl48MEHmcfjYb/5zW/Y22+/zT73uc+x5uZmdujQoUo3bcJx0UUXsfvuu4+99dZbbMuWLWzFihVs1qxZbHx8XDrmi1/8Imtvb2dPPfUU6+7uZu95z3vYeeedJ/0+kUiw0047jV1wwQXsjTfeYOvXr2ctLS3sG9/4hnTM3r17WX19PfvKV77Ctm/fzu6++27mdDrZ448/XtbrnQi88sorrKOjgy1atIh9+ctflj6nfrQ3x44dY7Nnz2af+tSn2Msvv8z27t3LnnjiCbZ7927pmNtuu401NTWxRx99lG3dupV99KMfZZ2dnSwcDkvHXHzxxez0009nL730EnvuuefYnDlz2FVXXSX9fmRkhE2fPp1dffXV7K233mIPPPAA8/l87L//+7/Ler21yo9+9CM2ZcoU9re//Y319PSwhx56iAUCAXbXXXdJx1A/2o/169ezf//3f2ePPPIIA8D+9Kc/Zfy+XH32/PPPM6fTyW6//Xa2fft29q1vfYu53W725ptvlvweVDt6fTg8PMwuuOACtm7dOvbuu++yF198kZ1zzjns7LPPzjgH9WHlyfcsch555BF2+umns7a2NvbTn/4043fUj9ULGeRl5JxzzmHXX3+99HMymWRtbW3s1ltvrWCrCMYYO3z4MAPAnn32WcaY+BJzu93soYceko555513GAD24osvMsbEydPhcLDBwUHpmHvuuYc1NjayaDTKGGPs61//OluwYEHGd61evZpddNFFpb6kCcXY2BibO3cu27BhA/vgBz8oGeTUj/ZnzZo17H3ve5/m71OpFJsxYwb7z//8T+mz4eFh5vV62QMPPMAYY2z79u0MAHv11VelY/7+978zQRDYwYMHGWOM/eIXv2CTJk2S+pR/97x586y+pAnJpZdeyj7zmc9kfPbJT36SXX311Ywx6sdqINsIKGefXXHFFezSSy/NaM/SpUvZF77wBUuvsdbRM+Q4r7zyCgPA9u/fzxijPrQjWv3Y19fHTjjhBPbWW2+x2bNnZxjk1I/VDYWsl4lYLIbXXnsNF1xwgfSZw+HABRdcgBdffLGCLSMAYGRkBAAwefJkAMBrr72GeDye0V/z58/HrFmzpP568cUXsXDhQkyfPl065qKLLsLo6Cjefvtt6RjlOfgx1OfWcv311+PSSy/NudfUj/bnL3/5CxYvXoxVq1Zh2rRpOPPMM/GrX/1K+n1PTw8GBwcz7n9TUxOWLl2a0YfNzc1YvHixdMwFF1wAh8OBl19+WTrmAx/4ADwej3TMRRddhB07duD48eOlvsya57zzzsNTTz2FnTt3AgC2bt2KzZs345JLLgFA/ViNlLPPaI4tHyMjIxAEAc3NzQCoD6uFVCqFa665BjfddBMWLFiQ83vqx+qGDPIyMTQ0hGQymbHoB4Dp06djcHCwQq0iAHGSu/HGG/He974Xp512GgBgcHAQHo9HemFxlP01ODio2p/8d3rHjI6OIhwOl+JyJhwPPvggXn/9ddx66605v6N+tD979+7FPffcg7lz5+KJJ57Av/zLv+CGG27Ab3/7WwByH+jNnYODg5g2bVrG710uFyZPnmyqn4nCufnmm3HllVdi/vz5cLvdOPPMM3HjjTfi6quvBkD9WI2Us8+0jqE+tZZIJII1a9bgqquuQmNjIwDqw2ph7dq1cLlcuOGGG1R/T/1Y3bgq3QCCqDTXX3893nrrLWzevLnSTSFM0tvbiy9/+cvYsGED6urqKt0cogBSqRQWL16M//iP/wAAnHnmmXjrrbfwy1/+Etdee22FW0cYpaurC/fffz/+8Ic/YMGCBdiyZQtuvPFGtLW1UT8ShA2Ix+O44oorwBjDPffcU+nmECZ47bXXcNddd+H111+HIAiVbg5RAshDXiZaWlrgdDpz1J0PHTqEGTNmVKhVxJe+9CX87W9/wzPPPIOZM2dKn8+YMQOxWAzDw8MZxyv7a8aMGar9yX+nd0xjYyN8Pp/VlzPheO2113D48GGcddZZcLlccLlcePbZZ/Gzn/0MLpcL06dPp360Oa2trTj11FMzPjvllFNw4MABAHIf6M2dM2bMwOHDhzN+n0gkcOzYMVP9TBTOTTfdJHnJFy5ciGuuuQb/9m//JkWuUD9WH+XsM61jqE+tgRvj+/fvx4YNGyTvOEB9WA0899xzOHz4MGbNmiWtdfbv34+vfvWr6OjoAED9WO2QQV4mPB4Pzj77bDz11FPSZ6lUCk899RTOPffcCrZsYsIYw5e+9CX86U9/wtNPP43Ozs6M35999tlwu90Z/bVjxw4cOHBA6q9zzz0Xb775ZsYEyF903MA499xzM87Bj6E+t4Zly5bhzTffxJYtW6Q/ixcvxtVXXy39m/rR3rz3ve/NKTm4c+dOzJ49GwDQ2dmJGTNmZNz/0dFRvPzyyxl9ODw8jNdee0065umnn0YqlcLSpUulYzZt2oR4PC4ds2HDBsybNw+TJk0q2fVNFEKhEByOzCWF0+lEKpUCQP1YjZSzz2iOLR3cGN+1axeefPJJTJkyJeP31If255prrsG2bdsy1jptbW246aab8MQTTwCgfqx6Kq0qN5F48MEHmdfrZf/7v//Ltm/fzj7/+c+z5ubmDHVnojz8y7/8C2tqamIbN25kAwMD0p9QKCQd88UvfpHNmjWLPf3006y7u5ude+657Nxzz5V+z8tlLV++nG3ZsoU9/vjjbOrUqarlsm666Sb2zjvvsJ///OdULqvEKFXWGaN+tDuvvPIKc7lc7Ec/+hHbtWsXu//++1l9fT37/e9/Lx1z2223sebmZvbnP/+Zbdu2jX3sYx9TLb105plnspdffplt3ryZzZ07N6Pcy/DwMJs+fTq75ppr2FtvvcUefPBBVl9fT+WyLOLaa69lJ5xwglT27JFHHmEtLS3s61//unQM9aP9GBsbY2+88QZ74403GAD2k5/8hL3xxhuSAne5+uz5559nLpeL/fjHP2bvvPMO+853vkOllgyi14exWIx99KMfZTNnzmRbtmzJWO8olbapDytPvmcxm2yVdcaoH6sZMsjLzN13381mzZrFPB4PO+ecc9hLL71U6SZNSACo/rnvvvukY8LhMLvuuuvYpEmTWH19PfvEJz7BBgYGMs6zb98+dskllzCfz8daWlrYV7/6VRaPxzOOeeaZZ9gZZ5zBPB4PO/HEEzO+g7CebIOc+tH+/PWvf2WnnXYa83q9bP78+ezee+/N+H0qlWK33HILmz59OvN6vWzZsmVsx44dGcccPXqUXXXVVSwQCLDGxkb26U9/mo2NjWUcs3XrVva+972Peb1edsIJJ7Dbbrut5Nc2URgdHWVf/vKX2axZs1hdXR078cQT2b//+79nLPqpH+3HM888o/ouvPbaaxlj5e2zrq4udvLJJzOPx8MWLFjAHnvssZJddy2h14c9PT2a651nnnlGOgf1YeXJ9yxmo2aQUz9WLwJjjJXDE08QBEEQBEEQBEEQhAzlkBMEQRAEQRAEQRBEBSCDnCAIgiAIgiAIgiAqABnkBEEQBEEQBEEQBFEByCAnCIIgCIIgCIIgiApABjlBEARBEARBEARBVAAyyAmCIAiCIAiCIAiiApBBThAEQRAEQRAEQRAVgAxygiAIgiAIgiAIgqgAZJATBEEQBEEQBEEQRAUgg5wgCIIgbMKRI0fg8XgQDAYRj8fh9/tx4MAB3f/z3e9+F2eccYZlbfjQhz6EG2+80bLzEQRBEAShDRnkBEEQBGETXnzxRZx++unw+/14/fXXMXnyZMyaNavSzSIIgiAIokSQQU4QBEEQNuGFF17Ae9/7XgDA5s2bpX+b4VOf+hQ+/vGP48c//jFaW1sxZcoUXH/99YjH49Ixv/jFLzB37lzU1dVh+vTpuPzyy6X/++yzz+Kuu+6CIAgQBAH79u1DMpnEZz/7WXR2dsLn82HevHm46667TH9vNBrFmjVr0N7eDq/Xizlz5uB//ud/pN+/9dZbuOSSSxAIBDB9+nRcc801GBoakn7/xz/+EQsXLoTP58OUKVNwwQUXIBgMmr5HBEEQBGEXXJVuAEEQBEFMZA4cOIBFixYBAEKhEJxOJ/73f/8X4XAYgiCgubkZ//RP/4Rf/OIXhs/5zDPPoLW1Fc888wx2796N1atX44wzzsDnPvc5dHd344YbbsDvfvc7nHfeeTh27Biee+45AMBdd92FnTt34rTTTsP3v/99AMDUqVORSqUwc+ZMPPTQQ5gyZQpeeOEFfP7zn0drayuuuOIKQ98LAP/8z/+MF198ET/72c9w+umno6enRzK4h4eHcf755+P/+//+P/z0pz9FOBzGmjVrcMUVV+Dpp5/GwMAArrrqKtx+++34xCc+gbGxMTz33HNgjFnSDwRBEARRCQRGbzKCIAiCqBiJRAJ9fX0YHR3F4sWL0d3dDb/fjzPOOAOPPfYYZs2ahUAggJaWFtX//93vfhePPvootmzZAkD0VG/cuBF79uyB0+kEAFxxxRVwOBx48MEH8cgjj+DTn/40+vr60NDQkHO+D33oQzjjjDNw55136rb7S1/6EgYHB/HHP/7R0Pfu3LkT8+bNw4YNG3DBBRfknO+HP/whnnvuOTzxxBPSZ319fWhvb8eOHTswPj6Os88+G/v27cPs2bPz3leCIAiCqAYoZJ0gCIIgKojL5UJHRwfeffddLFmyBIsWLcLg4CCmT5+OD3zgA+jo6NA0xrVYsGCBZBQDQGtrKw4fPgwAuPDCCzF79myceOKJuOaaa3D//fcjFArlPefPf/5znH322Zg6dSoCgQDuvffeHME5ve/dsmULnE4nPvjBD6qef+vWrXjmmWcQCASkP/PnzwcA7NmzB6effjqWLVuGhQsXYtWqVfjVr36F48ePm7ovBEEQBGE3yCAnCIIgiAqyYMECBAIBXHPNNXjllVcQCASwbNky7Nu3D4FAAAsWLDB9TrfbnfGzIAhIpVIAgIaGBrz++ut44IEH0Nraim9/+9s4/fTTMTw8rHm+Bx98EF/72tfw2c9+Fv/4xz+wZcsWfPrTn0YsFjP8vT6fT7fN4+PjuOyyy7Bly5aMP7t27cIHPvABOJ1ObNiwAX//+99x6qmn4u6778a8efPQ09Nj9LYQBEEQhO0gg5wgCIIgKsj69euxZcsWzJgxA7///e+xZcsWnHbaabjzzjuxZcsWrF+/3vLvdLlcuOCCC3D77bdj27Zt2LdvH55++mkAgMfjQTKZzDj++eefx3nnnYfrrrsOZ555JubMmYM9e/aY+s6FCxcilUrh2WefVf39WWedhbfffhsdHR2YM2dOxh+/3w9ANPDf+9734nvf+x7eeOMNeDwe/OlPfyrgDhAEQRCEPSCDnCAIgiAqyOzZsxEIBHDo0CF87GMfQ3t7O95++22sXLkSc+bMsTxf+m9/+xt+9rOfYcuWLdi/fz/+7//+D6lUCvPmzQMAdHR04OWXX8a+ffswNDSEVCqFuXPnoru7G0888QR27tyJW265Ba+++qqp7+3o6MC1116Lz3zmM3j00UfR09ODjRs3oqurCwBw/fXX49ixY7jqqqvw6quvYs+ePXjiiSfw6U9/GslkEi+//DL+4z/+A93d3Thw4AAeeeQRHDlyBKeccoql94cgCIIgygkZ5ARBEARRYTZu3IglS5agrq4Or7zyCmbOnInW1taSfFdzczMeeeQRnH/++TjllFPwy1/+Eg888IAUGv+1r30NTqcTp556KqZOnYoDBw7gC1/4Aj75yU9i9erVWLp0KY4ePYrrrrvO9Hffc889uPzyy3Hddddh/vz5+NznPieVLWtra8Pzzz+PZDKJ5cuXY+HChbjxxhvR3NwMh8OBxsZGbNq0CStWrMDJJ5+Mb33rW7jjjjtwySWXWHp/CIIgCKKckMo6QRAEQRAEQRAEQVQA8pATBEEQBEEQBEEQRAUgg5wgCIIgCIIgCIIgKgAZ5ARBEARBEARBEARRAcggJwiCIAiCIAiCIIgKQAY5QRAEQRAEQRAEQVQAMsgJgiAIgiAIgiAIogKQQU4QBEEQBEEQBEEQFYAMcoIgCIIgCIIgCIKoAGSQEwRBEARBEARBEEQFIIOcIAiCIAiCIAiCICoAGeQEQRAEQRAEQRAEUQH+f4XpMa0Dim8AAAAAAElFTkSuQmCC", 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" ] @@ -2128,8 +1080,9 @@ } ], "source": [ - "from capymoa.learner.classifier import OnlineBagging\n", - "from capymoa.stream.stream import DriftStream, Drift, SEA, AbruptDrift, GradualDrift\n", + "from capymoa.classifier import OnlineBagging\n", + "from capymoa.stream.generator import SEA \n", + "from capymoa.stream.drift import Drift, AbruptDrift, GradualDrift, DriftStream\n", "\n", "# Generating a synthetic stream with 1 abrupt drift and 1 gradual drift. \n", "stream_sea2drift = DriftStream(stream=[SEA(function=1), \n", @@ -2140,72 +1093,20 @@ "\n", "OB = OnlineBagging(schema=stream_sea2drift.get_schema(), ensemble_size=10)\n", "\n", + "# Since this is a synthetic stream, max_instances is needed to determine the amount of instances to be generated. \n", "results_sea2drift_OB = prequential_evaluation(stream=stream_sea2drift, learner=OB, window_size=100, max_instances=15000)\n", "\n", - "print(stream_sea2drift.drifts)\n", - "plot_windowed_results(results_sea2drift_OB)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2dddf15c-7677-4c3f-8b8f-b1821bf2f019", - "metadata": {}, - "source": [ - "## Creating a synthetic stream with concept drifts from MOA\n", - "\n", - "* Demonstrates the flexibility of the API, these level of manipulation of the API is expected from experienced MOA users.\n", - "* Notice that the meta information about the drifts is available even though the drift was not created using the DriftStream builder APIs. \n", - "\n", - "EvaluatePrequential -l trees.HoeffdingAdaptiveTree **-s (ConceptDriftStream -s generators.AgrawalGenerator -d (generators.AgrawalGenerator -f 2) -p 5000)** -e (WindowClassificationPerformanceEvaluator **-w 100**) **-i 10000 -f 100**" + "# print(stream_sea2drift.drifts)\n", + "plot_windowed_results(results_sea2drift_OB, ylabel='Accuracy')" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "5f3964f3-74bd-499b-b470-732b7d29c05c", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:39:51.848736Z", - "iopub.status.busy": "2024-03-21T04:39:51.848602Z", - "iopub.status.idle": "2024-03-21T04:39:53.603570Z", - "shell.execute_reply": "2024-03-21T04:39:53.603159Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from capymoa.stream.stream import Stream\n", - "from moa.classifiers.trees import HoeffdingAdaptiveTree, HoeffdingTree\n", - "from moa.classifiers.bayes import NaiveBayes\n", - "from moa.streams import ConceptDriftStream\n", - "\n", - "stream_sea2drift = DriftStream(moa_stream=ConceptDriftStream(), \n", - " CLI='-s (ConceptDriftStream -s generators.SEAGenerator -d (generators.SEAGenerator -f 3) -p 5000 -w 1) \\\n", - " -d generators.SEAGenerator -w 200 -p 10000 -r 1 -a 0.0')\n", - "\n", - "\n", - "hat = MOAClassifier(schema=stream_sea2drift.get_schema(), moa_learner=HoeffdingAdaptiveTree())\n", - "ht = MOAClassifier(schema=stream_sea2drift.get_schema(), moa_learner=HoeffdingTree())\n", - "nb = MOAClassifier(schema=stream_sea2drift.get_schema(), moa_learner=NaiveBayes())\n", - "\n", - "results_sea2drift_hat = prequential_evaluation(stream=stream_sea2drift, learner=hat, window_size=100, max_instances=12000)\n", - "results_sea2drift_ht = prequential_evaluation(stream=stream_sea2drift, learner=ht, window_size=100, max_instances=12000)\n", - "results_sea2drift_nb = prequential_evaluation(stream=stream_sea2drift, learner=nb, window_size=100, max_instances=12000)\n", - "\n", - "# The drift location is based on the number of windows (50 i.e. 50x100) and the gradual_drift width as well (10 i.e. 10x100)\n", - "plot_windowed_results(results_sea2drift_hat,results_sea2drift_ht, results_sea2drift_nb)" - ] + "execution_count": null, + "id": "c7fdefe7-9b15-4341-a6ad-1bd40e19071d", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -2224,7 +1125,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.9.19" } }, "nbformat": 4, diff --git a/notebooks/01_evaluation.ipynb b/notebooks/01_evaluation.ipynb new file mode 100644 index 00000000..4ee0147e --- /dev/null +++ b/notebooks/01_evaluation.ipynb @@ -0,0 +1,1421 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "223810fd-84a1-40f9-a303-2a64403b49fe", + "metadata": {}, + "source": [ + "# Evaluation\n", + "\n", + "This notebook further explores **high-level evaluation functions**, **Data Abstraction** and **Classifiers**\n", + "\n", + "* **High-level evaluation functions**\n", + " * We demonstrate how to use ```prequential_evaluation()```, ```test_then_train_evaluation()``` and ```windowed_evaluation()```, and how to further encapsulate prequential evaluation using ```prequential_evaluation_multiple_learners```\n", + " * We also discuss particularities about how these evaluation functions relate to how research has developed in the field, and how evaluation is commonly performed and presented.\n", + "\n", + "* **Supervised Learning**\n", + " * We clarify important information concerning the usage of **Classifiers** and their predictions\n", + " * We added some examples using **Regressors**, which highlight the fact that the evaluation is identical to **Classifiers** (i.e. same high-level evaluation functions)\n", + " \n", + "Complete documentation about the examples discussed in here can be found in the documentation: LINK_CAPYMOA_ORG\n", + "\n", + "**last update on 18/04/2024**" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "d5886daf-0881-4697-8788-5abdc3b30276", + "metadata": {}, + "outputs": [], + "source": [ + "# Classification data\n", + "arff_elec_path = '../data/electricity.arff'\n", + "csv_elec_path = '../data/electricity.csv'\n", + "# Regression\n", + "csv_fried_path = '../data/fried.csv'" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "df79e7d7-bde3-4b59-8479-a44247b3c26f", + "metadata": {}, + "source": [ + "## 1. The difference between Evaluators\n", + "\n", + "* The following example implements an **while loop** that updates a ```ClassificationWindowedEvaluator``` and a ```ClassificationEvaluator``` for the same learner. \n", + "* The ```ClassificationWindowedEvaluator``` update the metrics according to tumbling windows which 'forgets' old correct and incorrect predictions. This allows us to observe how well the learner performs on shorter windows. \n", + "* The ```ClassificationEvaluator``` updates the metrics taking into account all the correct and incorrect predictions made. It is useful to observe the overall performance after processing hundreds of thousands of instances.\n", + "\n", + "* **Two important points**:\n", + " 1. Regarding **window_size** in ```ClassificationEvaluator```: A ```ClassificationEvaluator``` also allow us to specify a window size, but it only controls the frequency at which cumulative metrics are calculated.\n", + " 2. If we access metrics directly (not through ```metrics_per_window()```) in ```ClassificationWindowedEvaluator``` we will be looking at the metrics corresponding to the last window.\n", + " \n", + "For further insight into the specifics of the Evaluators, please refer to the documentation: LINK_CAPYMOA_ORG" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "9b29ce7d-7e76-4741-a728-2bd2e795eb79", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ClassificationWindowedEvaluator] Windowed accuracy reported for every window_size windows\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " classified instances classifications correct (percent)\n", + "0 4500.0 89.200000\n", + "1 9000.0 89.266667\n", + "2 13500.0 89.288889\n", + "3 18000.0 87.888889\n", + "4 22500.0 87.711111\n", + "5 27000.0 85.066667\n", + "6 31500.0 85.133333\n", + "7 36000.0 86.222222\n", + "8 40500.0 87.111111\n", + "9 45000.0 88.644444" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ClassificationEvaluator] Cumulative accuracy: 87.5684145480226\n" + ] + } + ], + "source": [ + "from capymoa.stream import stream_from_file\n", + "from capymoa.evaluation import ClassificationWindowedEvaluator, ClassificationEvaluator\n", + "from capymoa.classifier import AdaptiveRandomForest\n", + "\n", + "stream = stream_from_file(path_to_csv_or_arff=csv_elec_path)\n", + "\n", + "ARF = AdaptiveRandomForest(schema=stream.get_schema(), ensemble_size=10)\n", + "\n", + "# The window_size in ClassificationWindowedEvaluator specifies the amount of instances used per evaluation\n", + "windowedEvaluatorARF = ClassificationWindowedEvaluator(schema=stream.get_schema(), window_size=4500)\n", + "# The window_size ClassificationEvaluator just specifies the frequency at which the cumulative metrics are stored\n", + "classificationEvaluatorARF = ClassificationEvaluator(schema=stream.get_schema(), window_size=4500)\n", + "\n", + "while stream.has_more_instances():\n", + " instance = stream.next_instance()\n", + " prediction = ARF.predict(instance)\n", + " windowedEvaluatorARF.update(instance.y_index, prediction)\n", + " classificationEvaluatorARF.update(instance.y_index, prediction)\n", + " ARF.train(instance)\n", + "\n", + "# Showing only the 'classifications correct (percent)' (i.e. accuracy)\n", + "print(f'[ClassificationWindowedEvaluator] Windowed accuracy reported for every window_size windows')\n", + "display(windowedEvaluatorARF.metrics_per_window()[['classified instances','classifications correct (percent)']])\n", + "\n", + "print(f'[ClassificationEvaluator] Cumulative accuracy: {classificationEvaluatorARF.accuracy()}')\n", + "# We could report the cumulative accuracy every window_size instances with the following code, but that is normally not very insightful. \n", + "# display(classificationEvaluatorARF.metrics_per_window()[['classified instances','classifications correct (percent)']])" + ] + }, + { + "cell_type": "markdown", + "id": "c61735c4-c41a-443c-be3c-e6cc3b526395", + "metadata": {}, + "source": [ + "## 2. High-level evaluation functions\n", + "\n", + "In CapyMOA, for supervised learning, there are three primary evaluation functions designed to handle the manipulation of Evaluators. They streamline the process, ensuring users need not directly update them. Essentially, these functions execute the evaluation loop and update the relevant Evaluators:\n", + "\n", + "1. ```test_then_train_evaluation()``` -> ```ClassificationEvaluator```\n", + "2. ```windowed_evaluation()``` -> ```ClassificationWindowedEvaluator```\n", + "3. ```prequential_evaluation()``` -> ```ClassificationEvaluator``` and ```ClassificationWindowedEvaluator```\n", + "\n", + "In simple terms, ```prequential_evaluation()``` combines the functionality of ```test_then_train_evaluation()``` and ```windowed_evaluation()```. In most cases, you are better off using ```prequential_evaluation()``` instead of worrying about the two other functions. We use the same functions for Regression, the functionality and interpretation are the same, but the metrics are different. \n", + "\n", + "**Result of a high-level function**\n", + "* The return from these functions is a dictionary that includes the Evaluators and some other metrics (like wall-clock and cpu time).\n", + "* The choice of a dictionary is to keep it simple and flexible.\n", + "\n", + "**Common characteristics for all high-level evaluation functions**\n", + "* Each one of these functions specify a ```max_instances``` parameter, which by default is None. Depending on the source of the data (e.g. a real stream or a synthetic stream) the evaluator will never stop executing! The notion is that Streams in CapyMOA are infinite, we process them as such. Therefore, it is a good idea to specify ```max_instances``` \n", + "\n", + "**Evaluation practices in the literature (and practice)**\n", + "\n", + "Interested readers might want to peruse section *6.1.1 Error Estimation* from [Machine Learning for Data Streams](https://moa.cms.waikato.ac.nz/book-html/) book. We further expand the relationships between the literature and our evaluation functions in the documentation: LINK_CAPYMOA_ORG" + ] + }, + { + "cell_type": "markdown", + "id": "165bf940-8fb0-43fb-b81b-9aafbbb242a9", + "metadata": {}, + "source": [ + "### 2.1 test_then_train_evaluation()\n", + "\n", + "Test then train evaluation yields cumulative results, which means the metrics are calculated based on all instances processed so far. \n", + "Frequently in benchmark analyses, when summarizing performance metrics within a table, the figures provided typically represent cumulative results.\n", + "\n", + "* The Evaluator object present in the results dictionary (key: 'cumulative') returned from ```test_then_train_evaluation()``` is a ```ClassificationEvaluator```\n", + "* The example below show how we can access the metrics from the results\n", + "* One can inspect the ```metrics_per_window()``` function of a ```ClassificationEvaluator``` returned from ```test_then_train_evaluation()```, however, it will not contain any value\n", + "* To avoid confusion, ```window_size``` is not a parameter for ```test_then_train_evaluation()```, even though technically someone might want to investigate the cumulative results several times. If that is desirable, we suggest using the ```ClassificationEvaluator``` directly (as shown earlier in this tutorial) while specifying the ```window_size``` of the ```ClassificationEvaluator```" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1e10c4e1-8c64-4ac9-9864-1d10a9dadb92", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The content of the results dictionary (wallclock = 6.35 seconds): \n", + "{'cpu_time': 7.032704999999993,\n", + " 'cumulative': ,\n", + " 'learner': 'AdaptiveRandomForest',\n", + " 'max_instances': None,\n", + " 'stream': ,\n", + " 'wallclock': 6.345656156539917}\n", + "\n", + "~~ Cumulative kappa statistic: 74.43350770290526 and Accuracy: 87.5684145480226 ~~\n", + "\n", + "All the metrics accessible through the Evaluator: \n", + "\n", + "{'F1 Score (percent)': 87.23622588506547,\n", + " 'F1 Score for class 0 (percent)': 89.34375059117308,\n", + " 'F1 Score for class 1 (percent)': 85.08328257818499,\n", + " 'Kappa M Statistic (percent)': 70.71179743149794,\n", + " 'Kappa Statistic (percent)': 74.43350770290526,\n", + " 'Kappa Temporal Statistic (percent)': 15.26774969915763,\n", + " 'Precision (percent)': 87.43697986542071,\n", + " 'Precision for class 0 (percent)': 88.1579929814082,\n", + " 'Precision for class 1 (percent)': 86.71596674943322,\n", + " 'Recall (percent)': 87.03639164918238,\n", + " 'Recall for class 0 (percent)': 90.56184084372005,\n", + " 'Recall for class 1 (percent)': 83.51094245464469,\n", + " 'classifications correct (percent)': 87.5684145480226,\n", + " 'classified instances': 45312.0}\n" + ] + } + ], + "source": [ + "from pprint import pprint\n", + "from capymoa.evaluation import test_then_train_evaluation\n", + "\n", + "ARF = AdaptiveRandomForest(schema=stream.get_schema(), ensemble_size=10)\n", + "\n", + "results = test_then_train_evaluation(stream=stream, learner=ARF)\n", + "\n", + "# We can see below the content of the results. Some metrics can be directly obtained, \n", + "# such as wallclock, while others are accessible through the Evaluator (i.e. key 'cumulative')\n", + "print(f\"The content of the results dictionary (wallclock = {results['wallclock']:.{2}f} seconds): \")\n", + "pprint(results)\n", + "\n", + "# Common metrics, such as kappa() or accuracy(), can be accessed directly through the Evaluator object\n", + "print(f\"\\n~~ Cumulative kappa statistic: {results['cumulative'].kappa()} and Accuracy: {results['cumulative'].accuracy()} ~~\\n\")\n", + "\n", + "# All other metrics can be accessed through metrics_dict() via the Evaluator: \n", + "print('All the metrics accessible through the Evaluator: \\n')\n", + "pprint(results['cumulative'].metrics_dict())" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f7b6994b-b90a-42cd-a37a-0d81f60faba3", + "metadata": {}, + "source": [ + "### 2.2 windowed_evaluation()\n", + "\n", + "A ```windowed_evaluation()``` update metrics per windows, which means metrics are reported based on subsets (windows) of instances. Reporting metrics per window allow us to observe how the model was performing in a particular point in time, instead of overall performance. \n", + "\n", + "* The Evaluator object present in the results dictionary (key: 'windowed') returned from ```windowed_evaluation()``` is a ```WindowedClassificationEvaluator```\n", + "* The relevant results are accessible through ```metrics_per_window()``` function. \n", + "* If the ```window_size``` does not perfectly divide ```max_instances``` (leaving no remainder in the division), the remaining instances are utilized to update a final window. This practical consideration is relevant when conducting benchmarking.\n", + "* One can access metrics directly from the ```WindowedClassificationEvaluator``` object, like ```accuracy()```, however, these contain only the results for the last window. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "4efa9f75-6cb9-44ad-b751-2c14be06d466", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The content of the results dictionary (wallclock = 6.26 seconds): \n", + "{'cpu_time': 6.744022000000001,\n", + " 'learner': 'AdaptiveRandomForest',\n", + " 'max_instances': None,\n", + " 'stream': ,\n", + " 'wallclock': 6.260118246078491,\n", + " 'windowed': }\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " classified instances classifications correct (percent) \\\n", + "0 4500.0 89.200000 \n", + "1 9000.0 89.266667 \n", + "2 13500.0 89.288889 \n", + "3 18000.0 87.888889 \n", + "4 22500.0 87.711111 \n", + "5 27000.0 85.066667 \n", + "6 31500.0 85.133333 \n", + "7 36000.0 86.222222 \n", + "8 40500.0 87.111111 \n", + "9 45000.0 88.644444 \n", + "10 45312.0 88.377778 \n", + "\n", + " Kappa Statistic (percent) Kappa Temporal Statistic (percent) \\\n", + "0 77.405998 33.149931 \n", + "1 78.394297 36.026490 \n", + "2 78.556735 27.080182 \n", + "3 74.962727 15.895062 \n", + "4 73.664002 22.980501 \n", + "5 68.714954 -17.482517 \n", + "6 68.246645 -22.080292 \n", + "7 71.097829 -4.201681 \n", + "8 73.837466 13.043478 \n", + "9 77.057053 27.000000 \n", + "10 76.467391 24.092888 \n", + "\n", + " Kappa M Statistic (percent) F1 Score (percent) \\\n", + "0 72.480181 88.704076 \n", + "1 76.644101 89.197184 \n", + "2 77.581395 89.283982 \n", + "3 71.118177 87.514314 \n", + "4 68.633012 87.014155 \n", + "5 64.064171 84.547494 \n", + "6 61.836851 84.212374 \n", + "7 65.536409 85.569113 \n", + "8 70.271656 86.928148 \n", + "9 77.023381 88.544966 \n", + "10 76.270417 88.253253 \n", + "\n", + " F1 Score for class 0 (percent) F1 Score for class 1 (percent) \\\n", + "0 91.072741 86.332958 \n", + "1 90.063773 88.330515 \n", + "2 89.625484 88.929720 \n", + "3 89.749859 85.202281 \n", + "4 90.265798 83.338355 \n", + "5 87.746171 80.887372 \n", + "6 88.148804 80.059613 \n", + "7 88.673730 82.416336 \n", + "8 88.510301 85.323887 \n", + "9 89.691346 87.360871 \n", + "10 89.546272 86.915186 \n", + "\n", + " Precision (percent) Precision for class 0 (percent) \\\n", + "0 88.643721 91.341194 \n", + "1 89.192964 90.119391 \n", + "2 89.262437 90.364583 \n", + "3 87.800223 88.239645 \n", + "4 87.987070 87.092391 \n", + "5 85.346992 84.302733 \n", + "6 84.842511 85.852312 \n", + "7 85.828438 87.522539 \n", + "8 86.826815 89.395758 \n", + "9 88.659401 88.530466 \n", + "10 88.390135 88.293260 \n", + "\n", + " Precision for class 1 (percent) Recall (percent) \\\n", + "0 85.946249 88.764512 \n", + "1 88.266538 89.201404 \n", + "2 88.160291 89.305537 \n", + "3 87.360802 87.230261 \n", + "4 88.881748 86.062521 \n", + "5 86.391252 83.762835 \n", + "6 83.832709 83.591528 \n", + "7 84.134337 85.311351 \n", + "8 84.257871 87.029717 \n", + "9 88.788336 88.430826 \n", + "10 88.487010 88.116795 \n", + "\n", + " Recall for class 0 (percent) Recall for class 1 (percent) \n", + "0 90.805861 86.723164 \n", + "1 90.008224 88.394584 \n", + "2 88.898377 89.712697 \n", + "3 91.312667 83.147854 \n", + "4 93.679211 78.445831 \n", + "5 91.482890 76.042781 \n", + "6 90.571533 76.611523 \n", + "7 89.855609 80.767093 \n", + "8 87.642213 86.417222 \n", + "9 90.883074 85.978578 \n", + "10 90.835361 85.398230 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pprint import pprint\n", + "from capymoa.evaluation import windowed_evaluation\n", + "from capymoa.evaluation.visualization import plot_windowed_results\n", + "\n", + "ARF = AdaptiveRandomForest(schema=stream.get_schema(), ensemble_size=10)\n", + "\n", + "# Notice the addition of a window_size\n", + "results = windowed_evaluation(stream=stream, learner=ARF, window_size=4500)\n", + "\n", + "# We can see below the content of the results. \n", + "# Note that some metrics can be directly obtained, such as wallclock, while others are accessible through the Evaluator\n", + "print(f\"The content of the results dictionary (wallclock = {results['wallclock']:.{2}f} seconds): \")\n", + "pprint(results)\n", + "\n", + "# Metric values per window are accessible as a Pandas DataFrame through metrics_per_window()\n", + "display(results['windowed'].metrics_per_window())\n", + "\n", + "# When we plot the results, the plotting function uses the metrics_per_window()\n", + "plot_windowed_results(results)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "99802e5d-c85c-448a-86ca-93910a245666", + "metadata": {}, + "source": [ + "### 2.3 prequential_evaluation()\n", + "\n", + "A ```prequential_evaluation()``` combines ```windowed_evaluation()``` and ```test_then_train_evaluation```. Internally, it maintains a ```ClassificationWindowedEvaluator``` and ```ClassificationEvaluator```. This allows us to have access to the **cumulative** and **windowed** results without running two separate evaluation functions. \n", + "\n", + "* The results dictionary returned from ```prequential_evaluation()``` contains Evaluators objects ```ClassificationWindowedEvaluator``` (key: 'windowed') and ```ClassificationEvaluator``` (key: 'cumulative').\n", + "\n", + "* ```cumulative``` results can be accessed via ```metrics_dictionary()```\n", + " \n", + "* Notice that the computational overhead of training and assessing the same model twice outweighs the minimum overhead of updating the two Evaluators within the function. Thus, in most cases it is advisable to use the ```prequential_evaluation()``` function instead of ```windowed_evaluation()``` and ```test_then_train_evaluation``` separately." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "d3e613fc-0e1c-422e-9731-a7ed7637e0a7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The content of the results dictionary (wallclock = 6.59 seconds): \n", + "{'cpu_time': 7.510519999999985,\n", + " 'cumulative': ,\n", + " 'ground_truth_y': None,\n", + " 'learner': 'AdaptiveRandomForest',\n", + " 'max_instances': None,\n", + " 'predictions': None,\n", + " 'stream': ,\n", + " 'wallclock': 6.5862720012664795,\n", + " 'windowed': }\n", + "All the metrics accessible through the Evaluator: \n", + "\n", + "{'F1 Score (percent)': 87.23622588506547,\n", + " 'F1 Score for class 0 (percent)': 89.34375059117308,\n", + " 'F1 Score for class 1 (percent)': 85.08328257818499,\n", + " 'Kappa M Statistic (percent)': 70.71179743149794,\n", + " 'Kappa Statistic (percent)': 74.43350770290526,\n", + " 'Kappa Temporal Statistic (percent)': 15.26774969915763,\n", + " 'Precision (percent)': 87.43697986542071,\n", + " 'Precision for class 0 (percent)': 88.1579929814082,\n", + " 'Precision for class 1 (percent)': 86.71596674943322,\n", + " 'Recall (percent)': 87.03639164918238,\n", + " 'Recall for class 0 (percent)': 90.56184084372005,\n", + " 'Recall for class 1 (percent)': 83.51094245464469,\n", + " 'classifications correct (percent)': 87.5684145480226,\n", + " 'classified instances': 45312.0}\n", + "\n", + "~~ [cumulative] kappa statistic: 74.43350770290526 and accuracy: 87.5684145480226 ~~\n", + "\n", + "[cumulative] Metrics: \n", + "\n", + "{'F1 Score (percent)': 87.23622588506547,\n", + " 'F1 Score for class 0 (percent)': 89.34375059117308,\n", + " 'F1 Score for class 1 (percent)': 85.08328257818499,\n", + " 'Kappa M Statistic (percent)': 70.71179743149794,\n", + " 'Kappa Statistic (percent)': 74.43350770290526,\n", + " 'Kappa Temporal Statistic (percent)': 15.26774969915763,\n", + " 'Precision (percent)': 87.43697986542071,\n", + " 'Precision for class 0 (percent)': 88.1579929814082,\n", + " 'Precision for class 1 (percent)': 86.71596674943322,\n", + " 'Recall (percent)': 87.03639164918238,\n", + " 'Recall for class 0 (percent)': 90.56184084372005,\n", + " 'Recall for class 1 (percent)': 83.51094245464469,\n", + " 'classifications correct (percent)': 87.5684145480226,\n", + " 'classified instances': 45312.0}\n", + "\n", + "\n", + "[windowed] Metrics per window:\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " classified instances classifications correct (percent) \\\n", + "0 4500.0 89.200000 \n", + "1 9000.0 89.266667 \n", + "2 13500.0 89.288889 \n", + "3 18000.0 87.888889 \n", + "4 22500.0 87.711111 \n", + "5 27000.0 85.066667 \n", + "6 31500.0 85.133333 \n", + "7 36000.0 86.222222 \n", + "8 40500.0 87.111111 \n", + "9 45000.0 88.644444 \n", + "10 45312.0 88.377778 \n", + "\n", + " Kappa Statistic (percent) Kappa Temporal Statistic (percent) \\\n", + "0 77.405998 33.149931 \n", + "1 78.394297 36.026490 \n", + "2 78.556735 27.080182 \n", + "3 74.962727 15.895062 \n", + "4 73.664002 22.980501 \n", + "5 68.714954 -17.482517 \n", + "6 68.246645 -22.080292 \n", + "7 71.097829 -4.201681 \n", + "8 73.837466 13.043478 \n", + "9 77.057053 27.000000 \n", + "10 76.467391 24.092888 \n", + "\n", + " Kappa M Statistic (percent) F1 Score (percent) \\\n", + "0 72.480181 88.704076 \n", + "1 76.644101 89.197184 \n", + "2 77.581395 89.283982 \n", + "3 71.118177 87.514314 \n", + "4 68.633012 87.014155 \n", + "5 64.064171 84.547494 \n", + "6 61.836851 84.212374 \n", + "7 65.536409 85.569113 \n", + "8 70.271656 86.928148 \n", + "9 77.023381 88.544966 \n", + "10 76.270417 88.253253 \n", + "\n", + " F1 Score for class 0 (percent) F1 Score for class 1 (percent) \\\n", + "0 91.072741 86.332958 \n", + "1 90.063773 88.330515 \n", + "2 89.625484 88.929720 \n", + "3 89.749859 85.202281 \n", + "4 90.265798 83.338355 \n", + "5 87.746171 80.887372 \n", + "6 88.148804 80.059613 \n", + "7 88.673730 82.416336 \n", + "8 88.510301 85.323887 \n", + "9 89.691346 87.360871 \n", + "10 89.546272 86.915186 \n", + "\n", + " Precision (percent) Precision for class 0 (percent) \\\n", + "0 88.643721 91.341194 \n", + "1 89.192964 90.119391 \n", + "2 89.262437 90.364583 \n", + "3 87.800223 88.239645 \n", + "4 87.987070 87.092391 \n", + "5 85.346992 84.302733 \n", + "6 84.842511 85.852312 \n", + "7 85.828438 87.522539 \n", + "8 86.826815 89.395758 \n", + "9 88.659401 88.530466 \n", + "10 88.390135 88.293260 \n", + "\n", + " Precision for class 1 (percent) Recall (percent) \\\n", + "0 85.946249 88.764512 \n", + "1 88.266538 89.201404 \n", + "2 88.160291 89.305537 \n", + "3 87.360802 87.230261 \n", + "4 88.881748 86.062521 \n", + "5 86.391252 83.762835 \n", + "6 83.832709 83.591528 \n", + "7 84.134337 85.311351 \n", + "8 84.257871 87.029717 \n", + "9 88.788336 88.430826 \n", + "10 88.487010 88.116795 \n", + "\n", + " Recall for class 0 (percent) Recall for class 1 (percent) \n", + "0 90.805861 86.723164 \n", + "1 90.008224 88.394584 \n", + "2 88.898377 89.712697 \n", + "3 91.312667 83.147854 \n", + "4 93.679211 78.445831 \n", + "5 91.482890 76.042781 \n", + "6 90.571533 76.611523 \n", + "7 89.855609 80.767093 \n", + "8 87.642213 86.417222 \n", + "9 90.883074 85.978578 \n", + "10 90.835361 85.398230 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from capymoa.evaluation import prequential_evaluation\n", + "from capymoa.stream import stream_from_file\n", + "from capymoa.classifier import OnlineBagging\n", + "\n", + "ARF = AdaptiveRandomForest(schema=stream.get_schema(), ensemble_size=10)\n", + "\n", + "results = prequential_evaluation(stream=stream, learner=ARF, window_size=4500)\n", + "\n", + "# We can see below the content of the results. \n", + "# Note that some metrics can be directly obtained, such as wallclock, while others are accessible through the Evaluator\n", + "print(f\"The content of the results dictionary (wallclock = {results['wallclock']:.{2}f} seconds): \")\n", + "pprint(results)\n", + "\n", + "# All other cumulative metrics can be accessed through metrics_dict() via the Evaluator: \n", + "print('All the metrics accessible through the Evaluator: \\n')\n", + "pprint(results['cumulative'].metrics_dict())\n", + "\n", + "# We can observe the cumulative results directly (as we did with test_then_train_evaluation results)... \n", + "print(f\"\\n~~ [cumulative] kappa statistic: {results['cumulative'].kappa()} and accuracy: {results['cumulative'].accuracy()} ~~\\n\")\n", + "\n", + "# ... we can also observe the other cumulative metrics through metrics_dict()\n", + "print('[cumulative] Metrics: \\n')\n", + "pprint(results['cumulative'].metrics_dict())\n", + "print('\\n')\n", + "\n", + "\n", + "# We can also inspect the windowed results... \n", + "print('[windowed] Metrics per window:')\n", + "display(results['windowed'].metrics_per_window())\n", + "\n", + "# ...and plot them\n", + "plot_windowed_results(results)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "309212f3-8986-482b-88ac-85f862325bfe", + "metadata": {}, + "source": [ + "### 2.4 Evaluating a single stream using multiple learners\n", + "\n", + "```prequential_evaluation_multiple_learners()``` further encapsulates experiments by executing multiple learners on a single stream. \n", + "\n", + "* This function behaves as if we invoked ```prequential_evaluation()``` multiple times, but internally it only iterates through the Stream once. This is useful if we are faced with a situation where accessing each Instance of the Stream is costly this function will be more convenient than just invoking ```prequential_evaluation()``` multiple times. \n", + "\n", + "* This method does not calculate ```wallclock``` or ```cpu_time``` because the training and testing of each learner is interleaved, thus timing estimations are unreliable. Thus, the results dictionaries do not contain the keys ```wallclock``` and ```cpu_time```. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "960419e2-1cd5-4d93-ba6e-50a74f9b8999", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OB final accuracy = 79.15342514124293 and ARF final accuracy = 87.5684145480226\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from capymoa.evaluation import prequential_evaluation_multiple_learners\n", + "\n", + "stream = stream_from_file(path_to_csv_or_arff=arff_elec_path)\n", + "\n", + "# Define the learners + an alias (dictionary key)\n", + "learners = {\n", + " 'OB': OnlineBagging(schema=stream.get_schema(), ensemble_size=10),\n", + " 'ARF': AdaptiveRandomForest(schema=stream.get_schema(), ensemble_size=10)\n", + "}\n", + "\n", + "results = prequential_evaluation_multiple_learners(stream, learners, window_size=4500)\n", + "\n", + "print(f\"OB final accuracy = {results['OB']['cumulative'].accuracy()} and ARF final accuracy = {results['ARF']['cumulative'].accuracy()}\")\n", + "plot_windowed_results(results['OB'], results['ARF'], metric=\"classifications correct (percent)\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "120f118c-d8a5-4d10-aad1-a75a2ac122d8", + "metadata": {}, + "source": [ + "## 3. Regression\n", + "\n", + "* We introduce a simple example using regression just to show how similar it is to assess regressors using the **high-level evaluation functions**\n", + "* In the example below, we just use ```prequential_evaluation()``` but it would work with ```test_then_train_evaluation()``` and ```windowed_evaluation()``` as well.\n", + "* One difference between Classification and Regression evaluation in CapyMOA is that the Evaluators are different. Instead of ```ClassificationEvaluator``` and ```ClassificationWindowedEvaluator``` functions use ```RegressionEvaluator``` and ```RegressionWindowedEvaluator```" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3f9b6eeb-f5ce-403d-bf95-5d880be1a278", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "kNNRegressor [cumulative] RMSE = 2.7394543131282583 and AdaptiveRandomForestRegressor [cumulative] RMSE = 3.303185689295369\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from capymoa.evaluation.visualization import plot_windowed_results\n", + "from capymoa.evaluation import prequential_evaluation\n", + "from capymoa.stream import stream_from_file\n", + "from capymoa.regressor import KNNRegressor, AdaptiveRandomForestRegressor\n", + "\n", + "stream = stream_from_file(path_to_csv_or_arff=csv_fried_path, enforce_regression=True)\n", + "kNN_learner = KNNRegressor(schema=stream.get_schema(), k=5)\n", + "ARF_learner = AdaptiveRandomForestRegressor(schema=stream.get_schema(), ensemble_size=10)\n", + "\n", + "kNN_results = prequential_evaluation(stream=stream, learner=kNN_learner, window_size=5000)\n", + "ARF_results = prequential_evaluation(stream=stream, learner=ARF_learner, window_size=5000)\n", + "\n", + "print(f\"{kNN_results['learner']} [cumulative] RMSE = {kNN_results['cumulative'].RMSE()} and \\\n", + " {ARF_results['learner']} [cumulative] RMSE = {ARF_results['cumulative'].RMSE()}\")\n", + "\n", + "plot_windowed_results(kNN_results, ARF_results, metric='adjusted coefficient of determination')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "410145fa-5203-4c79-b86f-8ce891691751", + "metadata": {}, + "source": [ + "### 3.1 Evaluating a single stream using multiple learners (Regression)\n", + "\n", + "* ```prequential_evaluation_multiple_learners``` also works for multiple regressors, the example below shows how it can be used. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "121e54e2-4ba2-41be-8397-408ad40db23d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cumulative results (RMSE) for each learner:\n", + "kNNReg_k5, RMSE: 2.74, adjusted R2: 0.70 \n", + "kNNReg_k2, RMSE: 3.10, adjusted R2: 0.62 \n", + "kNNReg_k5_median, RMSE: 2.96, adjusted R2: 0.65 \n", + "ARFReg_s5, RMSE: 3.74, adjusted R2: 0.44 \n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from capymoa.evaluation import prequential_evaluation_multiple_learners\n", + "\n", + "# Define the learners + an alias (dictionary key)\n", + "learners = {\n", + " 'kNNReg_k5': KNNRegressor(schema=stream.get_schema(), k=5),\n", + " 'kNNReg_k2': KNNRegressor(schema=stream.get_schema(), k=2),\n", + " 'kNNReg_k5_median': KNNRegressor(schema=stream.get_schema(), CLI='-k 5 -m'),\n", + " 'ARFReg_s5': AdaptiveRandomForestRegressor(schema=stream.get_schema(), ensemble_size=5)\n", + "}\n", + "\n", + "results = prequential_evaluation_multiple_learners(stream, learners)\n", + "\n", + "print('Cumulative results (RMSE) for each learner:')\n", + "for learner_id in results.keys():\n", + " print(f\"{learner_id}, RMSE: {results[learner_id]['cumulative'].RMSE():.2f}, adjusted R2: {results[learner_id]['cumulative'].adjusted_R2():.2f} \")\n", + "\n", + "# Tip: invoking metrics_header() from an Evaluator will show us all the metrics available, \n", + "# e.g. results['kNNReg_k5']['cumulative'].metrics_header()\n", + "plot_windowed_results(results['kNNReg_k5'], results['kNNReg_k2'], results['kNNReg_k5_median'], \n", + " results['ARFReg_s5'], metric=\"root mean squared error\")\n", + "\n", + "plot_windowed_results(results['kNNReg_k5'], results['kNNReg_k2'], results['kNNReg_k5_median'], \n", + " results['ARFReg_s5'], metric=\"adjusted coefficient of determination\")" + ] + }, + { + "cell_type": "markdown", + "id": "f1fffd6b-7421-4c07-9cca-dde220164387", + "metadata": {}, + "source": [ + "### 3.2 Plotting predictions vs. ground truth over time (Regression)\n", + "\n", + "* In Regression it is sometimes desirable to plot **predictions vs. ground truth** to observe what is happening with the Stream. If we create a custom loop and use the Evaluators directly it is trivial to store the ground truth and predictions, and then proceed to plot them. However, to make people's life easier ```plot_predictions_vs_ground_truth``` function can be used.\n", + "\n", + "* For massive streams with millions of instances it can be unbearable to plot all at once, thus we can specify a ```plot_interval``` (that we want to investigate) to ```plot_predictions_vs_ground_truth```. By default, the plot function will attempt to plot everything, i.e. if ```plot_interval=None```, which is seldom a good idea. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f71c8f1b-776d-4204-b485-284aed60af2c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from capymoa.evaluation import prequential_evaluation\n", + "from capymoa.evaluation.visualization import plot_predictions_vs_ground_truth\n", + "from capymoa.regressor import KNNRegressor, AdaptiveRandomForestRegressor\n", + "\n", + "stream = stream_from_file(path_to_csv_or_arff=csv_fried_path, enforce_regression=True)\n", + "kNN_learner = KNNRegressor(schema=stream.get_schema(), k=5)\n", + "ARF_learner = AdaptiveRandomForestRegressor(schema=stream.get_schema(), ensemble_size=10)\n", + "\n", + "# When we specify store_predictions and store_y, the results will also include all the predictions and all the ground truth y. \n", + "# It is useful for debugging and outputting the predictions elsewhere. \n", + "kNN_results = prequential_evaluation(stream=stream, learner=kNN_learner, window_size=5000, store_predictions=True, store_y=True)\n", + "# We don't need to store the ground-truth for every experiment, since it is always the same for the same stream\n", + "ARF_results = prequential_evaluation(stream=stream, learner=ARF_learner, window_size=5000, store_predictions=True)\n", + "\n", + "\n", + "# Plot only 200 predictions (see plot_interval)\n", + "plot_predictions_vs_ground_truth(kNN_results, ARF_results, ground_truth=kNN_results['ground_truth_y'], plot_interval=(0, 200))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f1c9048-811c-4c74-b32b-fe3f07de4050", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/01_evaluation_and_data_reading.ipynb b/notebooks/01_evaluation_and_data_reading.ipynb deleted file mode 100644 index db3e812d..00000000 --- a/notebooks/01_evaluation_and_data_reading.ipynb +++ /dev/null @@ -1,1304 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "223810fd-84a1-40f9-a303-2a64403b49fe", - "metadata": {}, - "source": [ - "# Evaluation methods and Data reading\n", - "\n", - "* Using prequential, test-then-train and windowed evaluation. \n", - "* We show how using either CSV or ARFF the API works.\n", - "* The CSV reader infer the task (classification or regression), but one can force the task to be interpreted as regression.\n", - "\n", - "**Notebook last update: 08/12/2023**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "98fa6c2c-2dd9-403e-9558-3d5e2ba9f1a4", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:38:40.242075Z", - "iopub.status.busy": "2024-03-21T04:38:40.241473Z", - "iopub.status.idle": "2024-03-21T04:38:43.714854Z", - "shell.execute_reply": "2024-03-21T04:38:43.714334Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "capymoa_root: /home/antonlee/github.com/tachyonicClock/MOABridge/src/capymoa\n", - "MOA jar path location (config.ini): /home/antonlee/github.com/tachyonicClock/MOABridge/src/capymoa/jar/moa.jar\n", - "JVM Location (system): \n", - "JAVA_HOME: /usr/lib/jvm/java-17-openjdk\n", - "JVM args: ['-Xmx8g', '-Xss10M']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sucessfully started the JVM and added MOA jar to the class path\n" - ] - } - ], - "source": [ - "from capymoa.evaluation import windowed_evaluation, prequential_evaluation, test_then_train_evaluation, prequential_evaluation_multiple_learners" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "30fe25b3-25c3-42b4-a09e-7ecd655d7109", - "metadata": {}, - "source": [ - "## File paths" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d5886daf-0881-4697-8788-5abdc3b30276", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:38:43.717192Z", - "iopub.status.busy": "2024-03-21T04:38:43.716903Z", - "iopub.status.idle": "2024-03-21T04:38:43.721392Z", - "shell.execute_reply": "2024-03-21T04:38:43.720875Z" - } - }, - "outputs": [], - "source": [ - "# Classification\n", - "arff_elec_path = '../data/electricity.arff'\n", - "csv_elec_path = '../data/electricity.csv'\n", - "# Stream with 100k instances\n", - "rbf_path = '../data/RBFm_100k.arff'\n", - "# Stream with 580k instances and around 100 features (csv)\n", - "covtfd_csv_file_path = '../data/covtFD.csv'\n", - "# Regression\n", - "csv_fried_path = '../data/fried.csv'" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "db159a24-78b9-472b-b3f4-78ea04522abe", - "metadata": {}, - "source": [ - "## Classification evaluation" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "df79e7d7-bde3-4b59-8479-a44247b3c26f", - "metadata": {}, - "source": [ - "### Reading a stream from a CSV file and using one learner\n", - "* Uses the ClassificationWindowedEvaluator directly" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9b29ce7d-7e76-4741-a728-2bd2e795eb79", - 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classified instancesclassifications correct (percent)Kappa Statistic (percent)Kappa Temporal Statistic (percent)Kappa M Statistic (percent)F1 Score (percent)F1 Score for class 0 (percent)F1 Score for class 1 (percent)Precision (percent)Precision for class 0 (percent)Precision for class 1 (percent)Recall (percent)Recall for class 0 (percent)Recall for class 1 (percent)
04500.088.35555675.50333527.92297170.32842687.75831590.47272785.02857187.91552889.81949586.01156187.60166491.13553184.067797
19000.086.31111172.44789218.41059670.21276686.22407687.31988585.12795886.21634387.42786585.00482286.23181187.21217185.251451
213500.088.35555676.70264420.72617275.62790788.37098988.63833588.05834188.34041290.04405386.63677188.40158687.27583389.527340
318000.086.35555671.7991415.24691467.46157985.92908788.44561583.34237786.19279087.00481385.38076785.66699389.93494181.399046
422500.086.84444471.73967117.54874766.42087486.09056289.62131882.03883587.17035586.14762488.19308585.03719293.38692076.687465
527000.084.95555668.435024-18.35664363.79679184.44131587.69761980.64054985.32571583.98886286.66256983.57505991.74904975.401070
631500.084.75555667.490884-25.18248260.86708583.81660587.81527579.64391784.36860785.74401782.99319783.27177989.98907976.554478
736000.084.20000067.055826-19.49579860.47804383.52809186.85524180.20050183.55006386.74298480.35714383.50612986.96779080.044469
840500.085.44444470.4667721.79910066.42747385.24505787.00654683.45541885.13626188.00160582.27091685.35413286.03373984.674526
945000.087.91111175.53043422.28571475.53956887.81331689.12869786.38638688.02746487.17748288.87744687.60020791.16925684.031159
\n", - "
" - ], - "text/plain": [ - " classified instances classifications correct (percent) \\\n", - "0 4500.0 88.355556 \n", - "1 9000.0 86.311111 \n", - "2 13500.0 88.355556 \n", - "3 18000.0 86.355556 \n", - "4 22500.0 86.844444 \n", - "5 27000.0 84.955556 \n", - "6 31500.0 84.755556 \n", - "7 36000.0 84.200000 \n", - "8 40500.0 85.444444 \n", - "9 45000.0 87.911111 \n", - "\n", - " Kappa Statistic (percent) Kappa Temporal Statistic (percent) \\\n", - "0 75.503335 27.922971 \n", - "1 72.447892 18.410596 \n", - "2 76.702644 20.726172 \n", - "3 71.799141 5.246914 \n", - "4 71.739671 17.548747 \n", - "5 68.435024 -18.356643 \n", - "6 67.490884 -25.182482 \n", - "7 67.055826 -19.495798 \n", - "8 70.466772 1.799100 \n", - "9 75.530434 22.285714 \n", - "\n", - " Kappa M Statistic (percent) F1 Score (percent) \\\n", - "0 70.328426 87.758315 \n", - "1 70.212766 86.224076 \n", - "2 75.627907 88.370989 \n", - "3 67.461579 85.929087 \n", - "4 66.420874 86.090562 \n", - "5 63.796791 84.441315 \n", - "6 60.867085 83.816605 \n", - "7 60.478043 83.528091 \n", - "8 66.427473 85.245057 \n", - "9 75.539568 87.813316 \n", - "\n", - " F1 Score for class 0 (percent) F1 Score for class 1 (percent) \\\n", - "0 90.472727 85.028571 \n", - "1 87.319885 85.127958 \n", - "2 88.638335 88.058341 \n", - "3 88.445615 83.342377 \n", - "4 89.621318 82.038835 \n", - "5 87.697619 80.640549 \n", - "6 87.815275 79.643917 \n", - "7 86.855241 80.200501 \n", - "8 87.006546 83.455418 \n", - "9 89.128697 86.386386 \n", - "\n", - " Precision (percent) Precision for class 0 (percent) \\\n", - "0 87.915528 89.819495 \n", - "1 86.216343 87.427865 \n", - "2 88.340412 90.044053 \n", - "3 86.192790 87.004813 \n", - "4 87.170355 86.147624 \n", - "5 85.325715 83.988862 \n", - "6 84.368607 85.744017 \n", - "7 83.550063 86.742984 \n", - "8 85.136261 88.001605 \n", - "9 88.027464 87.177482 \n", - "\n", - " Precision for class 1 (percent) Recall (percent) \\\n", - "0 86.011561 87.601664 \n", - "1 85.004822 86.231811 \n", - "2 86.636771 88.401586 \n", - "3 85.380767 85.666993 \n", - "4 88.193085 85.037192 \n", - "5 86.662569 83.575059 \n", - "6 82.993197 83.271779 \n", - "7 80.357143 83.506129 \n", - "8 82.270916 85.354132 \n", - "9 88.877446 87.600207 \n", - "\n", - " Recall for class 0 (percent) Recall for class 1 (percent) \n", - "0 91.135531 84.067797 \n", - "1 87.212171 85.251451 \n", - "2 87.275833 89.527340 \n", - "3 89.934941 81.399046 \n", - "4 93.386920 76.687465 \n", - "5 91.749049 75.401070 \n", - "6 89.989079 76.554478 \n", - "7 86.967790 80.044469 \n", - "8 86.033739 84.674526 \n", - "9 91.169256 84.031159 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from capymoa.evaluation import ClassificationWindowedEvaluator\n", - "from capymoa.stream import stream_from_file\n", - "\n", - "from capymoa.learner.classifier import AdaptiveRandomForest\n", - "\n", - "stream = stream_from_file(path_to_csv_or_arff=csv_elec_path)\n", - "ARF = AdaptiveRandomForest(schema=stream.get_schema(), CLI=\"-s 5 -x (ADWINChangeDetector -a 0.001) -p (ADWINChangeDetector -a 0.01)\")\n", - "evaluator = ClassificationWindowedEvaluator(schema=stream.get_schema(), window_size=4500)\n", - "\n", - "while stream.has_more_instances():\n", - " instance = stream.next_instance()\n", - " prediction = ARF.predict(instance)\n", - " evaluator.update(instance.y_index, prediction)\n", - " ARF.train(instance)\n", - "\n", - "display(evaluator.metrics_per_window())" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f7b6994b-b90a-42cd-a37a-0d81f60faba3", - "metadata": {}, - "source": [ - "### Reading from an ARFF file and using 2 learners\n", - "* Uses the ClassificationEvaluator, thus it uses a cumulative approach for calculating the metrics (not windowed)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "4efa9f75-6cb9-44ad-b751-2c14be06d466", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:38:57.281474Z", - "iopub.status.busy": "2024-03-21T04:38:57.279936Z", - "iopub.status.idle": "2024-03-21T04:39:04.294765Z", - "shell.execute_reply": "2024-03-21T04:39:04.294177Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OB final accuracy = 79.05190677966102 and ARF final accuracy = 86.36564265536724\n" - ] - } - ], - "source": [ - "from capymoa.evaluation import ClassificationEvaluator\n", - "from capymoa.stream import stream_from_file\n", - "\n", - "from capymoa.learner.classifier import OnlineBagging, AdaptiveRandomForest\n", - "\n", - "stream = stream_from_file(path_to_csv_or_arff=arff_elec_path)\n", - "OB_learner = OnlineBagging(schema=stream.get_schema(), ensemble_size=5)\n", - "ARF_learner = AdaptiveRandomForest(schema=stream.get_schema(), ensemble_size=5)\n", - "\n", - "# Not a windowed evaluator!\n", - "OB_evaluator = ClassificationEvaluator(schema=stream.get_schema())\n", - "ARF_evaluator = ClassificationEvaluator(schema=stream.get_schema())\n", - "\n", - "while stream.has_more_instances():\n", - " instance = stream.next_instance()\n", - " \n", - " OB_prediction = OB_learner.predict(instance)\n", - " ARF_prediction = ARF_learner.predict(instance)\n", - " \n", - " OB_evaluator.update(instance.y_index, OB_prediction)\n", - " ARF_evaluator.update(instance.y_index, ARF_prediction)\n", - "\n", - " OB_learner.train(instance)\n", - " ARF_learner.train(instance)\n", - "\n", - "print(f\"OB final accuracy = {OB_evaluator.accuracy()} and ARF final accuracy = {ARF_evaluator.accuracy()}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "99802e5d-c85c-448a-86ca-93910a245666", - "metadata": {}, - "source": [ - "### Reading the data from a CSV, then evaluating it using two learners. \n", - "* **Using the ```prequential_evaluation``` which internally executes both ```ClassificationWindowedEvaluator``` and ```ClassificationEvaluator```**\n", - "* ```prequential_evaluation``` allow us to have the windowed and cumulative results. So we can inspect the last accuracy and over time too. \n", - "* We also plot the final results using ```plot_windowed_results```" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d3e613fc-0e1c-422e-9731-a7ed7637e0a7", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:39:04.296700Z", - "iopub.status.busy": "2024-03-21T04:39:04.296459Z", - "iopub.status.idle": "2024-03-21T04:39:11.175265Z", - "shell.execute_reply": "2024-03-21T04:39:11.174826Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OnlineBagging final accuracy = 78.65686793785311 and AdaptiveRandomForest final accuracy = 85.24231991525424\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from capymoa.evaluation.visualization import plot_windowed_results\n", - "\n", - "from capymoa.evaluation import ClassificationWindowedEvaluator\n", - "from capymoa.stream import stream_from_file\n", - "\n", - "from capymoa.learner.classifier import OnlineBagging, AdaptiveRandomForest\n", - "\n", - "stream = stream_from_file(path_to_csv_or_arff=csv_elec_path)\n", - "OB_learner = OnlineBagging(schema=stream.get_schema(), ensemble_size=2)\n", - "ARF_learner = AdaptiveRandomForest(schema=stream.get_schema(), ensemble_size=2)\n", - "\n", - "OB_results = prequential_evaluation(stream=stream, learner=OB_learner, window_size=1000)\n", - "stream.restart()\n", - "ARF_results = prequential_evaluation(stream=stream, learner=ARF_learner, window_size=1000)\n", - "\n", - "print(f\"{OB_results['learner']} final accuracy = {OB_results['cumulative'].accuracy()} and \\\n", - " {ARF_results['learner']} final accuracy = {ARF_results['cumulative'].accuracy()}\")\n", - "\n", - "plot_windowed_results(OB_results, ARF_results)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "041d84a3-aae9-4ad9-b440-a13d6808aeb7", - "metadata": {}, - "source": [ - "### Simple test-then-train evaluation (cumulative). " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "be7d5006-9f53-4c8b-85d9-f256fe9e1047", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:39:11.177068Z", - "iopub.status.busy": "2024-03-21T04:39:11.176845Z", - "iopub.status.idle": "2024-03-21T04:39:11.641257Z", - "shell.execute_reply": "2024-03-21T04:39:11.637595Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "78.65686793785311\n", - "{'learner': 'OnlineBagging', 'cumulative': , 'wallclock': 0.4540889263153076, 'cpu_time': 0.4009213349999996, 'max_instances': -1, 'stream': }\n" - ] - } - ], - "source": [ - "stream = stream_from_file(path_to_csv_or_arff=arff_elec_path)\n", - "\n", - "l = OnlineBagging(schema=stream.get_schema(), ensemble_size=2)\n", - "\n", - "res = test_then_train_evaluation(stream, l)\n", - "print(res['cumulative'].accuracy())\n", - "print(res)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "73eba126-2880-464c-a1ec-cdb16dc516da", - "metadata": {}, - "source": [ - "### Simple windowed evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ed29a1d2-6539-47ff-a3da-db17a4315a18", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:39:11.645084Z", - "iopub.status.busy": "2024-03-21T04:39:11.644834Z", - "iopub.status.idle": "2024-03-21T04:39:12.509179Z", - "shell.execute_reply": "2024-03-21T04:39:12.508805Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "stream = stream_from_file(path_to_csv_or_arff=arff_elec_path)\n", - "\n", - "l = OnlineBagging(schema=stream.get_schema(), ensemble_size=2)\n", - "\n", - "res = windowed_evaluation(stream, l)\n", - "plot_windowed_results(res)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "309212f3-8986-482b-88ac-85f862325bfe", - "metadata": {}, - "source": [ - "### Prequential evaluation, single stream, multiple learners. \n", - "* Important: the ```prequential_evaluation_multiple_learners``` iterate through the stream testing and training with each learner\n", - "* This method does not calculate ```wallclock``` or ```cpu_time``` because the training and testing of each learner is interleaved" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "960419e2-1cd5-4d93-ba6e-50a74f9b8999", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:39:12.512742Z", - "iopub.status.busy": "2024-03-21T04:39:12.512466Z", - "iopub.status.idle": "2024-03-21T04:39:19.684271Z", - "shell.execute_reply": "2024-03-21T04:39:19.683895Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OB final accuracy = 78.65686793785311 and ARF final accuracy = 85.24231991525424\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "stream = stream_from_file(path_to_csv_or_arff=arff_elec_path)\n", - "\n", - "# Define the learners + an alias (dictionary key)\n", - "learners = {\n", - " 'OB': OnlineBagging(schema=stream.get_schema(), ensemble_size=2),\n", - " 'ARF': AdaptiveRandomForest(schema=stream.get_schema(), ensemble_size=2)\n", - "}\n", - "\n", - "results = prequential_evaluation_multiple_learners(stream, learners)\n", - "\n", - "print(f\"OB final accuracy = {results['OB']['cumulative'].accuracy()} and ARF final accuracy = {results['ARF']['cumulative'].accuracy()}\")\n", - "plot_windowed_results(results['OB'], results['ARF'], metric=\"classifications correct (percent)\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e43b65ec-4a28-48e9-9020-5b6d0334ce86", - "metadata": {}, - "source": [ - "### prequential_evaluation with a larger stream (100k instances)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "83c12719-ba61-4a0d-90c4-4addc7ed8b20", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:39:19.686060Z", - "iopub.status.busy": "2024-03-21T04:39:19.685883Z", - "iopub.status.idle": "2024-03-21T04:39:28.030719Z", - "shell.execute_reply": "2024-03-21T04:39:28.030163Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'learner': 'OnlineBagging',\n", - " 'cumulative': ,\n", - " 'windowed': ,\n", - " 'wallclock': 7.812121868133545,\n", - " 'cpu_time': 4.39874992,\n", - " 'max_instances': -1,\n", - " 'stream': }" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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gfP311+3ahl24cAG7d+/GjBkzjH4tlZWVHW4z5L0y5D2YPHkyxGIxPvzww3bvw3//+1/U1NTgzjvvNHq8nICAAEycOBEbNmxAUVFRh/vLysr4/z937lykpaXhl19+6bAdNy7uQsat7dh0GTVqFABoPcYB1Rp7zTXgf/31F06dOoU77rjD6PHrMnXqVLi7u2PNmjUd/ia41zV06FDExMTg3//+N+rr6016Hm1cXV31vldnzpzRW6WeEEJI19EMOSGEEK3eeust7N69GxMmTMCyZcvQt29fFBUVYcuWLTh69ChfSExTXFwcYmJisGLFChQUFMDDwwNbt27tsG712rVrmDRpEubPn49+/frByckJv/zyC0pKSnDfffcBAL7++mt88sknmDNnDmJiYlBXV4fPP/8cHh4eJgWtt4qNjcVLL72EN954A+PGjcPdd98NiUSClJQUhISEYM2aNRg9ejS8vb2xaNEiLF++HAzD4Ntvv9V6QWLo0KHYtGkTnn32WQwbNgxubm6YNWuW1ud+9913cccdd2DUqFFYsmQJ3/bM09OT76lujNWrV+Pw4cO48847ERERgdLSUnzyyScICwvD2LFju/Qe+Pv744UXXsDrr7+O6dOn429/+xuuXr2KTz75BMOGDePX05vq448/xtixYxEfH4+lS5ciOjoaJSUlOHHiBPLz85GWlgYAeP755/Hzzz9j3rx5ePjhhzF06FBUVlbi999/x/r16zFw4EDExMTAy8sL69evh7u7O1xdXTFixIgO67Y50dHRGDBgAPbu3duhvz33/owdOxaPPvooWlpasG7dOvj6+uKf//yn0ePXxcPDA++//z4eeeQRDBs2DAsWLIC3tzfS0tLQ2NiIr7/+GgKBAF988QXuuOMO9O/fHw899BBCQ0NRUFCAAwcOwMPDA3/88YfR7/3QoUOxd+9evPfeewgJCUFUVBRGjBgBADh9+jQqKysxe/Zso/dLCCHECN1e150QQkiPkZOTwy5cuJD19/dnJRIJGx0dzT7++ONsS0sLy7La255dunSJnTx5Muvm5sb6+fmxS5cuZdPS0tq1oyovL2cff/xxNi4ujnV1dWU9PT3ZESNGsJs3b+b3c+bMGfb+++9nw8PDWYlEwgYEBLAzZ85kU1NT240RJrY943z55Zfs4MGDWYlEwnp7e7MTJkxg9+zZw99/7NgxduTIkaxUKmVDQkLYf/7zn+yuXbs6vO76+np2wYIFrJeXFwuAb4Gmre0Zy7Ls3r172TFjxrBSqZT18PBgZ82axV66dEnrmG9tZ8a10MrKymJZlmX37dvHzp49mw0JCWHFYjEbEhLC3n///ey1a9c6vF5tOnsPWFbV5iwuLo4ViURsYGAg++ijj7JVVVXttpkwYQLbv3//Dvvn3gNdbe4yMjLYhQsXskFBQaxIJGJDQ0PZmTNnsj///HO77SoqKtgnnniCDQ0NZcViMRsWFsYuWrSILS8v57f57bff2H79+rFOTk4GtUB77733WDc3t3bt7TTHu3btWlYmk7ESiYQdN24cm5aWZtL4ud+ZrjZ+v//+Ozt69Gj+eBg+fDj7448/ttvm7Nmz7N133836+vqyEomEjYiIYOfPn8/u27eP38bQY4ZlWfbKlSvs+PHjWalUygJo1wItKSmJDQ8Pb9dmjRBCiPkxLGtEdQ9CCCGEEDtSU1OD6OhovPPOO1iyZAkAIDs7G1FRUXj33XexYsUKK4+w+7W0tCAyMhIrV67EU089Ze3hEEKIXaM15IQQQghxWJ6envjnP/+Jd999t9uq5tu6jRs3QiQSdehlTgghxPxohpwQQgghRIOjz5ATQgjpPjRDTgghhBBCCCGEWAHNkBNCCCGEEEIIIVZAM+SEEEIIIYQQQogVUEBOCCGEEEIIIYRYgZO1B2BpSqUShYWFcHd3B8Mw1h4OIYQQQgghhBA7x7Is6urqEBISAoFA9zy43QfkhYWFkMlk1h4GIYQQQgghhBAHk5eXh7CwMJ33231A7u7uDkD1Rnh4eFhtHHK5HLt378bUqVMhEomsNg5C9KHjlPQUdKySnoKOVdJT0LFKeoqecqzW1tZCJpPx8agudh+Qc2nqHh4eVg/IXVxc4OHhYdMHDnFsdJySnoKOVdJT0LFKego6VklP0dOO1c6WTVNRN0IIIYQQQgghxAooICeEEEIIIYQQQqyAAnJCCCGEEEIIIcQK7H4NuSFYlkVbWxsUCoXFnkMul8PJyQnNzc0WfR5iG4RCIZycnKjVHiGEEEIIIUQnhw/IW1tbUVRUhMbGRos+D8uyCAoKQl5eHgVpDsLFxQXBwcEQi8XWHgohhBBCCCHEBjl0QK5UKpGVlQWhUIiQkBCIxWKLBctKpRL19fVwc3PT2xie9Hwsy6K1tRVlZWXIyspCr1696HdOCCGEEEII6cChA/LW1lYolUrIZDK4uLhY9LmUSiVaW1vh7OxMwZkDkEqlEIlEyMnJ4X/vhBBCCCGEEKKJIkOAAmRiEXRcEUIIIYQQQvShiIEQQgghhBBCCLECqwbkkZGRYBimw3+PP/44AGDixIkd7vvHP/5hzSETQgghhBBCCCFmYdWAPCUlBUVFRfx/e/bsAQDMmzeP32bp0qXttnnnnXesNVy7lJ2dDYZhcO7cOQDAwYMHwTAMqqurrTouS7r1NRNCCCGEEEIsK6u8Ack7r+DJH88ieecVZJU3WHtINsGqRd38/f3b/fz2228jJiYGEyZM4G9zcXFBUFBQdw/NKFnlDdicmof8qiaEeUsxP1GGKD9Xiz9vXl4eXn31VezcuRPl5eUIDg7GXXfdhVWrVsHX19ekfY4ePRpFRUXw9PQ061g1q9dzVe3vuecerFmzBhKJxKzP1RmZTIaioiL4+fl16/MSQgghhBDiiDan5mHl1nQwDAOWZcEwDDYcykDy3ATMS5RZe3hWZTNV1ltbW/Hdd9/h2WefbRe8ff/99/juu+8QFBSEWbNm4ZVXXtFbEb2lpQUtLS38z7W1tQAAuVwOuVzeblu5XA6WZaFUKqFUKk0a95bT+Xhh2/kOB9fbd8fjnqFh/HYsy/L/mvpcmjIzMzFmzBj07t0b33//PaKionDx4kUkJSVhx44dOH78OHx8fDrdDzcW7j1wcnJCQEAAWJblx2wu//3vfzF9+nTI5XKkpaVhyZIlcHFxwerVq836PJ1hGAYBAQEAYJbfhS5KpRIsy0Iul0MoFFrsecyJ+xu59W+FEFtDxyrpKehYJT0FHavEUrIrGrByazqULAAuvlD/m7Q1HYPDPBDha3jHq55yrBo6PoY1d9Rlos2bN2PBggXIzc1FSEgIAOCzzz5DREQEQkJCkJ6ejqSkJAwfPhzbtm3TuZ/XXnsNr7/+eofbf/jhhw6BvJOTE4KCgiCTySAWi8GyLJrlhgdoeVVNuO+rNNXBdQsBA/y0eBBk3oa1u3IWCYzqgX7PPffg8uXLSE1NhVQq5W8vKSnBkCFDcO+99+K9995DQkICFi1ahKysLPz222/w9PTEihUrsHjxYgBAbm4uBg4ciMOHDyM+Ph5Hjx7FrFmzkJ2dDU9PT/zwww944YUX8OWXX+LFF19EQUEBRo4ciY8++qhd5sI333yDjz/+GDk5OQgPD8eyZcvwyCOP8Pd7e3vju+++w5133snftnz5chQXF2Pz5s0AgKysLLz00ktITU1FY2MjevfujVWrVmHixIn8Y4qLi7F8+XIcOXIEAQEBePnll/HGG2/g0UcfxaOPPgoAuHbtGpYvX45z584hMjISb7/9NubMmcM/v67X/Ouvv+K1117D1atXMWDAAHz88cfo1asX/9z//ve/sWHDBjQ3N2POnDnw8fHBvn37cOTIEa2/o9bWVuTl5aG4uBhtbW0G/24JIYQQQgixF3/kCLC/kIESHWMdAVjcHsJiVoTlJsmspbGxEQsWLEBNTQ08PDx0bmczM+T//e9/cccdd/DBOAAsW7aM///x8fEIDg7GpEmTkJGRgZiYGK37eeGFF/Dss8/yP9fW1kImk2Hq1Kkd3ojm5mbk5eXBzc0Nzs7OaGxtw+DkPWZ5PUoWmL/xnMHbX3htClzEhv06KisrsX//frz55psIDAxsd5+HhwcWLFiArVu34vPPP4dAIMAnn3yC1atXY9WqVdi6dSuee+45TJs2DX369IGbmxsAwNXVFR4eHvxFC3d3d3h4eMDZ2RlNTU349NNP8e2330IgEGDhwoVYvXo1vvvuOwCqLIa3334bH374IQYPHoyzZ8/i//7v/+Dr64tFixbxY5NKpfzv4Nq1azh69CgWLVrU7vcya9YsvP3225BIJPj2229x//334/LlywgPDweguhBRUVGB/fv3QyQSYcWKFSgvL4ezszM8PDygUCiwcOFCyGQynDhxAnV1dXj++efbPb+u17xmzRq899578Pf3x2OPPYann36aD7a///57rF27Fh999BHGjBmDTZs24b333kNUVJTOP7Dm5mZIpVKMHz++x/Qhl8vl2LNnD6ZMmQKRSGTt4RCiEx2rpKegY5X0FHSsEkvZvTkdKCoGtE0DMwwkvsGYMSPB4P31lGOVy9TujE0E5Dk5Odi7d6/emW8AGDFiBADgxo0bOgNyiUSidU2ySCTq8AtTKBRgGAYCgYD/z1qMef6MjAywLIt+/fppfUy/fv3wxRdfoKKiAgAwY8YMvnL9ypUrsW7dOhw6dAh9+/blH3/re6D5s1wux4YNG/j3/IknnsDq1av5bV9//XWsXbsW99xzDwAgJiYGV65cweeff46HHnqIH9cDDzwAoVCItrY2tLS0YObMmXjxxRf5/QwePBiDBw/mt3/zzTfx66+/4n//+x+eeOIJXLlyBfv27UNKSgoSExMBAF988QV69erF/x53796NjIwMHDx4kJ/B/9e//oUpU6bofY3cdrfddhv/Pt15551obW2Fs7MzPv74YyxZsgRLliwBALz66qvYs2cP6uvrdf7eBAJV1oO2Y8/W9cQxE8dExyrpKehYJT0FHavE3MJ9XaErJ5thGIT7upp0zNn6sWro2GwiIN+4cSMCAgLapTNrw1XFDg4Otsg4pCIhLq2eZvD27+25ho1Hs6HQcoQJGQYPjY3Es1N6A1CtJ66rrYO7h7vWAE4qMn6NsaGrDRISbl5xYhgGQUFBKC0tNfh5XFxc2l0ACQ4O5h/f0NCAjIwMLFmyBEuXLuW3aWtr61AY7v3338fkyZOhUChw48YNPPvss/j73/+On376CQBQX1+P1157Ddu3b0dRURHa2trQ1NSE3NxcAMDVq1fh5OSEIUOG8PuMjY2Ft7c3//PVq1chk8napdMPHz7coNep+T5xx1hpaSnCw8Nx9epVPPbYY+22Hz58OPbv32/QvgkhhBBCCHFE/m5irZPjgCqeuZeKulmXUqnExo0bsWjRIjg53RxORkYGfvjhB8yYMQO+vr5IT0/HM888g/Hjx7cLnMyJYRiD08YB4IEREfjyaJbW+1iweHBEBL8/pVKJNrEQLmKnLs/Ex8bGgmEYXL58GXPmzOlw/+XLl+Ht7c1Xsb/16gzDMEYVM9P2eO5iQH19PQDg888/5zMYOLcWMgsKCkJsbCwAoE+fPqirq8P999+PN998E7GxsVixYgX27NmDf//734iNjYVUKsU999yD1tZWg8faFZqvk1vPb8mib4QQQgghhNizopomfLDvBgCAAcAw4OtvCRggeW4CIruhO5Uts2ofcgDYu3cvcnNz8fDDD7e7XSwWY+/evZg6dSri4uLw3HPPYe7cufjjjz+sNNKOovxckTw3AQIGEAqYdv9a8uDy9fXFlClT8Mknn6CpqandfcXFxfj+++9x7733GlUkzlSBgYEICQlBZmYmYmNj2/0XFRWl97FcwM69hmPHjmHx4sWYM2cO4uPjERQUhOzsbH77Pn36oK2tDWfPnuVvu3HjBqqqqtptk5eXh5KSEv62lJSULr/OPn36dNiPOfZLCCGEEEKIPVIqWTy3OQ01TXIMDPPEnmfHY9n4aDiLVCHoS3f2c/iWZ4ANzJBPnTpVa+q1TCbDoUOHrDAi48xLlGFYpA82afQhvzdRZvErPR999BFGjx6NadOm4c033+Tbnj3//PMIDQ3Fv/71L4s+v6bXX38dy5cvh6enJ6ZPn46WlhakpqaiqqqqXYG96upqFBcXQ6lU4vr161i9ejV69+6Nvn37AgB69eqFbdu2YdasWWAYBq+88kq7Geq4uDhMnjwZy5Ytw6effgqRSITnnnsOUqmUv/gwZcoUxMTEYNGiRXjnnXdQV1eHl19+GQC6dIHiySefxNKlS5GYmIjRo0dj06ZNSE9PR3R0tMn7JIQQQgghxF7992gWjmdUQCoS4v17ByHa3w0r7+gLsZMQH+67jv1XSrBkrP4JPEdg9RlyexDp54qk6XH4z/2DkTQ9rlvSLnr16oXU1FRER0dj/vz5iImJwbJly3DbbbfhxIkTBvUgN5dHHnkEX3zxBTZu3Ij4+HhMmDABX331VYcZ8oceegjBwcEICwvD/fffj/79+2PHjh38UoX33nsP3t7eGD16NGbNmoVp06a1Wy8OqNqrBQYGYvz48ZgzZw6WLl0Kd3d3voq5UCjEr7/+ivr6egwbNgyPPPIIXnrpJQDoUqXzBx54AC+88AJWrFiBIUOGICsrC4sXL+4x1dMJIYQQQgjpLpcKa/HurqsAgFWz+iHa342/b35iGBgGOHajArkVjdYaos2wmT7kllJbWwtPT0+t/d+am5uRlZWFqKgoiwdWSqUStbW18PDwsGo1d3uTn58PmUyGvXv3YtKkSVq3OXbsGMaOHau3Or8ppkyZgqCgIHz77bda7+/O48tc5HI5/vzzT8yYMcOmq1YSQscq6SnoWCU9BR2rxFya5Qr87aOjuFZSjyn9AvHZ34d2yFRd+OVfOHytDI/fFoPnp8UZtf+ecqzqi0M1WT1lnRBj7N+/H/X19YiPj0dRURH++c9/IjIyEuPHj+e3+eWXX+Dm5oZevXrhxo0beOqppzBmzJguBeONjY1Yv349pk2bBqFQiB9//BF79+7Fnj3m6VtPCCGEEEJIT5VV3oDN6iW82eUNuFZSDz83Cd6+O17rstH7hslw+FoZtqTm45nJveEkdNwJSwrISY8il8vx4osvIjMzE+7u7hg9ejS+//77dlfH6urqkJSUhNzcXPj5+WHy5MlYu3Ztl56XYRj8+eef+Ne//oXm5mb06dMHW7duxeTJk7v6kgghhBBCCOmxNqfmYeXWdFUnJ5ble47PHhQCXzeJ1sdM7hsIX1cxSutacPBqGSb3C+zGEdsWCshJjzJt2jRMm6a/V/zChQuxcOFCsz6vVCrF3r17zbpPQgghhBBCerKs8gas3JquamV2y0rojcey8PeREVrra4mdBJg7NAyfHc7ETym5Dh2QO25uACGEEEIIIYQQk21OzdPZyYhhGGxKzdP52Pnqlmf7r5SiuKbZIuPrCSggB7S2XSOkq+i4IoQQQggh9iy/qknnOS/LssivatL52NgANwyL9IaSBbaeybfUEG2eQwfk3LrjxkYqt0/MjzuubLn6IyGEEEIIIaYK85bqnSEP85bqffy9w8IBAJtS8qBUOuZklkOvIRcKhfDy8kJpaSkAwMXFRecB1VVKpRKtra1obm6mtmd2jmVZNDY2orS0FF5eXhAKhdYeErEQzYqiYd5SzE+UIUrLOilCCCGEEHs0P1GGDYcytN7HsizuVael63JnfDBe//0icisbcSKzAmNi/SwxTJvm0AE5AAQFBQEAH5RbCsuyaGpqglSq+yoSsS9eXl788UXsj2ZFUZZlwTAMNhzKQPLcBMzr5MuHEEIIIcQeRPm5InluAp7/OR0AwDCAQH1ulDw3QWtBN01SsRCzB4fgu5O5+CkljwJyR8QwDIKDgxEQEAC5XG6x55HL5Th8+DDGjx9PKcwOQCQS0cy4HdNaUVT9b9LWdAyL9On0C4gQQgghxB6MVgfRDIDp/YMQ6eeKexNlBp8L3TcsHN+dzMWuC8WoamiFt6vYgqO1PQ4fkHOEQqFFAyihUIi2tjY4OztTQE5ID8dXFNVSxISrKJo0Pc4KIyOEEEII6V4nMyoAAANlXvj0waFGP35AqCcGhHrgQkEttp0twJKxUeYeok2jgJwQQozUlYqihBBCCCH25ESmKiAfGe1r8j7uHRaOCwUX8NH+6ziXW4UwHxeHqc1D1cUIIcRI+iqGGlJRlBBCCCHEXpxUB+SjYkwPyLmJjqpGOf53vgifHc7EpLUHsUVPH3N7QQE5IYQYaVLfAOjqzGFIRVFCCCGEEHuQV9mI/KomOAkYJEZ4m7SPrPIGvPb7Rf5nlgUUShZKVlWbJ7u8wVzDtUkUkBNCiBEaWtrw+u+X+J+FDAOBRuOEpyf3poJuhBBCCHEI3Ox4QpgnXCWmrYbma/NowdXmsWe0hpwQQgzUplDi8R/O4HxBDXxcxfjwvkE4llGB/KomXCqsQUZZA87kVll7mIQQQggh3cIc68cdvTYPBeSEEGIAlmXx8q8XcPBqGZxFAvx3USIGh3tjbC9/AEB2eQMmv3cIB6+W4XhGOUbHOF4fTUIIIYQ4DpZlcSqzEkDX1o+HeUv1dq+x99o8lLJOCCE6ZJU3IHnnFTz541ncu+EEfkrJg4ABPrxvMAaHt18nFennigUjwgEAb++4ovNKLyGEEEKIPcirbEJBdRNEQgZDTVw/DgDzE2V6Z8jtvTYPBeSEEKLF5tQ8TFp7EJ8dzsT/0grxV7YqFX32oBBM7R+k9THLJ/WCq1iI9PwabD9f1J3DJYQQQgjpVtz68YFhXnARm554HeXniuS5CRAwgFDAQHM5+Wuz+tt9bR4KyAkh5BZZ5Q1YuTUdSnWVT81rtr+dK9RZ7dPPTYKl46MBAO/uuorWNmU3jJYQQgghpPudNMP6cc68RBn2PzcRy8ZH4874YPi4igEAZfUtXd63raOAnBBCbtGVap9Lx0XDz02CnIpG/PhXrqWGSAghhBBiNSzL8gXdurJ+XFOknyuSpsfhowVD8NacAQCAjceyUd3Yapb92yoKyAkh5BZdqfbpKnHCU5N7AQA+3Hcddc1yi4yREEIIIcRacisbUVTTDJGQwZBw09eP6zK1XxD6BnugvqUNnx/JNPv+bQkF5IQQcgt91TwNqfZ53zAZov1cUdHQis8P2/eXCCGEEEIcz4kM1ez4YJk3pGKh2fcvEDB4Wj3B8dWxbFQ22O8sOQXkhBByi9vjAqDUUSTdkGqfIqEAz0/rAwDYcDgTr/5+AU/+eBbJO68gS8f6c300q72bug9CCCGEEHO5uX7cx2LPMbVfIPqHeKChVWHXs+QUkBNCyC2+0PjQFwoYvuqngAGS5yYYVO1z+oAgyLylaGlT4pvjOdieXojPDmdi0tqD2KJnDfqtNKu9m7oPQgghhBBz0Vw/PtJM68e1YRgGT0/uDQD4+ng2Kuy0wJvp9ekJIcQO7bxQhF0XS+AkYLDh70ORmlOF/KomhHlLcW+izODWG9kVjcivVq01ZwGw/P8ASVvTMSzSp9N9aVZ75x5r7D4IIYQQQswpu6IRJbUtEAsFFlk/rmly3wDEh3rifEENPjuSiRfu6GvR57MGmiEnhBC1miY5XvntIgDgHxNiMKlvIJKmx+E/9w9G0vQ4o4Lfzal5EJhYqV1zH6ZWeyeEEEIIsQRu/figcC84i8y/flyTapZctZb8m+M5djlLTgE5IYSovb3jMsrqWhDt54onbo/t0r66UqndnPsghBBCiP2yRp0Zbv34KDP0HzfE7XEBGBjmiSa5Ap/ZYbFcSlknhBCorvb++JdqxnnN3fFdvuIb5i1VzW5rCagNqdTO7wMMVEnvpu2DEEIIIfZpc2oeVm5NB8MwYFkWDMNgw6EMJM9NwLxOCtCaqt368W4KyLm15A99lYKvjmejqVWOC9cFuOR0HfeNiEBUD1++RzPkhBCH1yxX4IVt6QCAB0aEY4QZvmDmJ8r0zm53Vqmd24eyi/sghBBCiP3RrDOjULLt/k3amo5sC82UZ5Y3oKyuBWInAQaHe1nkObSZ2MefL5b77ck8nK1g8MXRbLsodEsBOSHEIWmmeN3/+UlkVzQi0EOCpDvizLL/KD9XJM9N4Cu0a64EX3N3vEHr0Z3Uld01MYBR1d4JIYQQYn+sVWeGWz8+pBvWj2vqUCwXDBSs5S9AdAdKWSeEOBzNFC8ly/JZ5XcMCIKHs8hszzMvUYZhkT7YlJqHrPIGHLhSipY2pcGPX7v7KhQsMDTcG2E+Uvx2rhCuEiH+9+Q4CsYJIYQQB2atOjM314/7WWT/unDFchU6lgJuSs1D0nTzTKp0NwrICSEORWsrMbVvTuRg8egoswa7kX6u/BfEF0cy8eb2y3hvzzX8bWAopGLdV5YvFNTg13OFAIDXZ/dHpJ8rfk8rRH2LAq4S+ugmhBBCHJm+WjUsC4R5ORu8r6zyBmxOzePbvM5PlGldl82yLE5mVgIARkb7mD54E9hzoVtKWSeEOBRrthL7+6gIhHpJUVLbgi+PZendNnnnFQDA7EEhGBDqCTeJE2L93QAA6fnVFhsjIYQQQmyf3lo1AK6V1qNZruh0P5tT8zBp7UF8djgT29ML8dnhTJ3rsjPK6lFe3wKJkwCDunH9OKBxAUKLnl7olgJyQohDseYVVomTECum9QYArD+YgcqGVq3bHblehiPXyyEWCrBiah/+9oQwLwBAWn6NxcZICCGEENsX5eeKN+8awP+sWbNGwAD7LpfiwS9O4Wxulc62aMYWhjuhnh0fGuENiVP3rR8HzFMs11ZRQE4IcSjWvsI6e2Ao+gV7oK6lDR/tv9HhfqWSxds7VLPjD46MgMzHhb9voMwTAM2QE0IIIQToHegOAJCKhbgzPhjLxkfjwIqJ+HbJCLg7OyE1pwpzPjmODYcy2s1+b0rJw5ncKqzYkqZawqcFA/BZg1wh3E8PqM5b4tTP2506FstlIWTso9AtLUQkhDiU+YkybDiUofW+7rjCKhAwWHlHHBZ++Re+PZmNh8ZEtgu6f08rxMXCWrhLnPDE7bHtHsvNkKfn1/D9RgkhhBDimM7lVQMAxsb64T8LhvC3R/q54j/3D8bijSkAcDPoVs8wJ21N73TfChbYcb4IrmIh3ttzDQzDQKHe0cYT2egb4mGxXue6cMVyfzyVg5RLGRjWLxr3j4jo0cE4QDPkhBAHw11h1Yxlher2Yt11hXV8b3+MjfWDXMHi37uv8re3tCn4n/8xMQY+ruJ2j+sb7A6RkEFlQ2uPLl5CCCGEkK7jAvJBMq8O953KqoRQz4V7sZBBjL9rh/aqmrIrGvHv3df4VHYOa8VWY5F+rlgxtRcW9VZixdRePT4YByggJ4Q4oHmJMjxxm2r2OcjDGcvGR2P/cxO79UrvSnW/89/OFeJCgWpN+LcncpBf1YRADwkeHhPV4TESJyHigjwAAGmUtk4IIYQ4NH0BeX5VE1hoz0cXMMDU/kH4YtEwnftmGCBUT6V2SxfCdSQUkBNCbA63VklbARJzqW2SAwBmDw5B0vS4br/COiDUE7MHhQAA/vHdafzju9N4d5dqdvyZyb11tkRLCOPWkVNhN0IIIcRRlde3IL+qCQwDxKvPDTR1VjNH5uPSYV225r/vzE3AkAgfnTPoPb3VmC2hNeSEEJuyOTUPK7emg2EYfp30hkMZSJ6bYNYZ7KyKRgBAlK/1Up36Bnvgt3OFyK9qavelpm9p+MAwL3x/Khdp6qvihBBCCHE83HlAjL8bPJxFHe43tGYOty57k0Yf8nsTZYj0c0VmeYPOXuc9vdWYLaEZckKIzTC2/UZXcPuy1tqjrPIGvKPuNX6rF7ad1/laB6rT0i4U1LRbz0UIIYQQx6EvXR3oWJVc899ba+ZE+rkiaXoc/nP/4HZZg/bcasyWUEBOCLEZm1Pz9KZXmWutUmubEvlV6hlyKwXkpr7W2AA3uIiFaGhVILOs3pJDJIQQQoiN6iwgB1Sz3/ufm4hl46NxZ0KI0TVzjAnqiekoZZ0QYjPyq5r0Xok111qlvKpGKFnARSxEgLvELPs0lqmvVShgMCDEE39lVyItvwa9zNgLNKu8AZs1UtbmJ8qsdsGCEEIIIdoplSyfsq4vIAduzn6bSl9KOzEPCsgJITaDL0Bi4bVKXDp4hK+r1Xp5d+W1JoSpAvL0/GrcMzTMLOPprrX7hBBCCOmarIoG1Da3QeIkQJ8g812Y16WrQT3Rj1LWCSE2o7vWKmWrC7pF+rqYZX+m6MprTVBfDU8zU6X17ly7TwghhJCuOZdbDQCID/WESEjhXE9Hv0FCiM2I8nPFW3PiO9zOwLxrlaxd0A3o2rqsger2JpcLa9HapuzyWLpr7T4hhBBCui4tvxpA5+nqpGeglHVCiE1JjPQBAIgEDPqFeCAtvwY+bmL8Td2z2xyyK1QBuTVbngGmr8sK93GBl4sI1Y1yXCmuRUKYV5fG0V1r9wkhhBDSdVxBt4EUkNsFmiEnhNgUrnJ4n2B3bP7HKAR5OKOivhW/ni0w23Nk2cAMOUdXqxF9GIZBfKhqltwcaev8enYdz0V9RgkhhBDb0CxX4HJRLQCaIbcXVg3IIyMjwTBMh/8ef/xxAEBzczMef/xx+Pr6ws3NDXPnzkVJSYk1h0wIsTAuWI7yc4PESYhHxkUBANYfyjRL3+2WNgUKq1UzvpF+1ltD3lUD1bPi6eqr5F1BfUYJIYSQnuFiYS3kChZ+bmK6YG4nrBqQp6SkoKioiP9vz549AIB58+YBAJ555hn88ccf2LJlCw4dOoTCwkLcfffd1hwyIcTCuIA8Wj1TfN/wcHhKRcgqb8Cui8Vd3n9eparlmatYCH8367Q8M4cE9TrydDPMkEf5uWLBiPAOt1OfUUIIIcS2cO3OBoZ5Wa1TDDEvqwbk/v7+CAoK4v/73//+h5iYGEyYMAE1NTX473//i/feew+33347hg4dio0bN+L48eM4efKkNYdNCLGgzDJ1QO6vCgLdJE5YNDoSAPDpwQydM7mGyi5XVVi3Zsszc+DWjV0vrUNja1uX9+cqVpUUiQtyh5NA9b58eP8ganlGCCGE2JBzBvYfJz2HzRR1a21txXfffYdnn30WDMPg9OnTkMvlmDx5Mr9NXFwcwsPDceLECYwcOVLrflpaWtDS0sL/XFurWmMhl8shl8st+yL04J7bmmMgpDO2cJxmlqvWkMu8JPw4HhgWis8OZ+B8QQ0OXinB2Fhfk/efUar6TIjwkfbov0cfqRCBHhKU1LbgXE4lhkV6d2l/6eqKrX8fIcOprCr8nl6EszlVmNY3wAyjNT9bOFYJMQQdq6SnoGO1ZzibWwUAGBDi7rC/q55yrBo6PpsJyH/99VdUV1dj8eLFAIDi4mKIxWJ4eXm12y4wMBDFxbrTVtesWYPXX3+9w+27d++Gi4v114tyafmE2DJrHadNbUB5vepj6frpY8hLu3nfcF8BDhcL8NYvKXiiv+mtvg5nCgAI0FZdhD//NF+hOGsIEApQAgE27z2JshDTMwdYFjiXIwTAoDorHX7NDAAhfk3NRrwiA7acSECfqaSnoGOV9BR0rNquejmQV6U6Tyq+dAp/XrPygKzM1o/VxsZGg7azmYD8v//9L+644w6EhHSttdELL7yAZ599lv+5trYWMpkMU6dOhYeHR1eHaTK5XI49e/ZgypQpEIlEVhsHIfpY+zhNz68BUk7B302Mu/82td19g6qbMOn9o7heK0Bowii+F7exNm1MBVCJScPjMWNIqBlGbT05rpk4v/cG5B6hmDEjwfT9VDSi6eRRiJ0EeOju6ZArlPjh7YOoaFEiashY9Au23menLtY+VgkxFB2rpKegY9X2HbxWBqSeRbSfC+7521hrD8dqesqxymVqd8YmAvKcnBzs3bsX27Zt428LCgpCa2srqqur282Sl5SUICgoSOe+JBIJJJKOhZpEIpFN/MJsZRyE6GOt4zSvWrXcJNrfrcPzR/iLMHtQKLaeycfnR7Ox4e+JJj1HTqWqwnpsoEeP/1scHKHq2X6hsLZLr+VSiWrdft9gD7g4qz4/J/T2x66LJdh3pRwDw01fImBp9JlKego6VklPQceq7TpfqFrWNyjcm35HsP1j1dCx2UQf8o0bNyIgIAB33nknf9vQoUMhEomwb98+/rarV68iNzcXo0aNssYwCSEWxvUg5wq63erRidEAgF0XS3CjtM7o/TfLFSis4Vqe9fzK4QmhXgBUM9zVja0m7+dCQY16fzezDqYPUF343GmGyvaEEEII6TquwvpgKuhmV6w+Q65UKrFx40YsWrQITk43h+Pp6YklS5bg2WefhY+PDzw8PPDkk09i1KhROgu6EUJ6tky+B7n2YDk2wB1T+wVi96USPP7DWfQOdEeYtxTzE2U6H6Mpr7IRLKuq3O7rKjbr2K3B00WESF8XZFc0Ij2/BuN7+5u0H66gW7xGQH57XCCcBAyuldQjo6weMf5u5hgyIYQQQkzAsizS1N/XAykgtytWnyHfu3cvcnNz8fDDD3e47/3338fMmTMxd+5cjB8/HkFBQe3S2gkh9oVveeanO/jrHai672pxHbanF+Kzw5mYtPYgtqTmdbp/rsd5pJ9Lj255pikhzAvAzavmxlIqWVwoUK1xitdYl+8pFWF0rB8AmKX/OyGEGCKrvAHJO6/gyR/PInnnFf5zmxBHl13RiOpGOcROAsQF2V5tF2I6q8+QT506VWdfYWdnZ3z88cf4+OOPu3lUhJDuxrIsf+IVpSNlPau8AZ8czOB/VrJQlQgHkLQ1HcMiffSmomdXqANy356frs5JCPPE72mFSMuvMenx2RUNqG9pg8RJgF4B7S+ETO8fhMPXyrDrQjEemxhrjuHalazyBmxOzUN+VZNRmRqEEO02p+Zh5dZ0MAwDlmXBMAw2HMpA8twEzEuUWXt4hFjVuTyu3ZkHxE5Wn1MlZkS/TUKITSiubUaTXAGhgEG4j/YWhZtT83TObDMMg02dzJJnlavaT9hT0MSlrXFp58Y6r14/3i/EA07C9l8JU/oFgmGAtPwaFFQ3dWWYdmdzah4mrT2Izw5nGp2pQQjpKKu8ASu3pkPJAgol2+7fpK3pyKaZcuLg0vJU39eUrm5/jJ4hz8rKwpEjR5CTk4PGxkb4+/tj8ODBGDVqFJydnS0xRkKIA8hSp6uH+7hAJNR+rTC/qklnRg3Lssiv0h80cid09jRD3j/EAwIGKK1rQXFNM4I8jfscPq+eWddcP87xd5dgWIQP/squxO6LxXhoTJRZxtzTaQYOXIaGMZkatopm/Ik18RdctXzGcxdck6bHWWFkhNiGs+qlaYMoILc7Bgfk33//PT744AOkpqYiMDAQISEhkEqlqKysREZGBpydnfHAAw8gKSkJERERlhwzIcQOcQXdovUEAGHeUr0nbGHeUr3Pwaes21GQ4SJ2Qu9Ad1wprkNafjWCPHW3hdQmvUB3QA4A0wYE4a/sSuy8QAE5xx4DB0oVJtbW1QuuhNizljYFLheq6r0MlnlbeTTE3AxKWR88eDA+/PBDLF68GDk5OSgqKsLp06dx9OhRXLp0CbW1tfjtt9+gVCqRmJiILVu2WHrchBA7wxV00zcjNz9RpveE7V49gUOzXIGimuZOn6MnGqgu7GZs2rpSyeIi1/JMvY9bTesfCABIya5EeX2LqUO0K/YWOFCqMLEF/AVXLQy54EqIPbtcVIdWhRI+rmLIfOhvwd4YFJC//fbbOHXqFB577DHIZB1PeCUSCSZOnIj169fjypUriI6ONvtACSH2Latc1YNcV0E3QBVIJ89NgIABhBonbgIGSJ6boHfmO6dCtX7c3dkJ3i4iM43aNoR4q9LUN6fkGVWVOLO8AQ2tCjiLBIjR8b6HebsgPtQTShbYe6nEbGPuyewtcOhqbQZCzKErF1wJsXfnclUF3QaGedpNlxhyk0EB+bRp0wzeoa+vL4YOHWrygAghjulmyrr+ftfzEmXY/9xELJsQDS91YP38tD6dptVmafQ4t6cvs82peVi39zoAoKy+1ajiYucLqgEA/UM8OxR00zR9gCoNfie1PwNgf4GDvc34k56Ju+B6K0MuuBJi787x68cpXd0eGV1lXSgUorS0tMPtFRUVEAqFZhkUIcSxtLYpkVepmsGO1jNDzon0c0XS9Dg8MCIcAHCtpL7Tx9hjyzMu1VgzljIm1fh8vrr/uI7145xp/VUB+bEb5ahtlndt0HZAM1NDE4OeGTjom/FXsoBURA1ZSPeYlyjD8Eifdrd9sWgY1TEgDo9rbToo3Mu6AyEWYfS3rK6r6C0tLRCLxV0eECHE8eRWNkLJAq5iIQLcJQY/bkyMHwBVoKjrs4nDV1jvYcGSPl1NNeZmyDsLyGMD3NArwA1yBYsDVzpekHVE8xJleOvu+Ha3JUb69MjAYX6iDEo9fz+/nCnAF0cykVFaj+SdV/Dkj2eNWhpBiDG4WhUezqq6wzdK66w5HEKsKqu8Aav/uMh/3npJjW6QRXoAg3+rH374IQDVSd4XX3wBN7ebaaUKhQKHDx9GXFzPqipLCLENmWU3148bk04+JMIbEicBSutakFHWgNgA3enuN1PWtfc474m6kmqsULK4qK7YmhCmPyAHVGnr1/ffwM4LxZg9KFTndo7UOquhRQEA6BPojqsldTiXV4W6ZjncnXtWjYIoP1fMjA/GH+lFAFQpwly19bggd1wqqsOb2y/jze2XIWQYsKAq7MQylMqbn1v3DJXhy2NZOHStDMvGx1h5ZIR0P677haY5nxynz107ZHBA/v777wNQneStX7++XXq6WCxGZGQk1q9fb/4REkLs3s1gWf/68Vs5i4RIjPTGsRsVOJ5Rrjcgt8eU9a60gcssq0djqwIuYiGi/Tt/36f1D8J/9t/AnksleOz704jwde0QbDta6yzuQtKkvgGQK5TILG/A4WvluDMh2MojM159SxsAYFS0D/zcnRHmLcW9iTJE+Lrgw/3X8f4eVZ0ChR31XSe2p6SuGa0KJZwEDO4frgrIU7Kq0NDSBlcJzQwSx6HZ/UITtySNPnfti8Ep61lZWcjKysKECROQlpbG/5yVlYWrV69i165dGDFihCXHSgixU1zLM309yHUZrZG2rktjaxtKalVpkPYUkHeluNh5dbuz/iEeEN66GFqLS4Wq7duULHacL+5QPM4RW2fxx62/G6b0U7WH23Op5xW+kyuU+CurEgDw0p398J/7ByNpehwi1QUQm+XKDuvlOVSFnZhTXqVqdjzES4rYADfIfKRoVShxIqPCyiMjpHtR9wvHYvQa8gMHDsDbmyr8EULMh5shN6Sg261Gx/gCAE5kVEBx66VkNa7lmadUBG9X+6l10a4NnICB5nf3mrvj9V49T1cXiBnQyfpxQB1sbzvP/8ziZrD9z5/T8eK281jyVUqHK/kcez15yFDPkMf4u2KyOiDff6UUcoXSmsMyWlpeNRpaFfB2EaFfsEeH+/UtfaAq7MScuOKe4T4uYBgGE3r7AwAOXSuz5rAI6XbU/cKxGJ3/o1Ao8NVXX2Hfvn0oLS2FUtn+xGP//v1mGxwhxDEY2vJMm/hQT7hLnFDb3IaLhTVICPPqsI09FnTjzEuUYVikDzal5iG3ogH7rpSiWa6Ep1T/OuYL6hlyQ9aP81fqtZwcsAB++CtX7+Pt8eShrlmO0jpV1kW0vxvcJE7wcRWjsqEVKdmVfOZGT3Dshmr2cVSMLwRapsK7sjSCEGPkqgNymY/qmJrQOwDfnczFwWul/DIYQhwBfe46FqNnyJ966ik89dRTUCgUGDBgAAYOHNjuP0IIMUZts5yvqhtpQsE1J6EAI6JVs+RcYHGrLPX68Shf+ynopolrA/fxA0Px8JgoAMC3J3N0bt+mUPIF3TqrsA7ov1IPAJG+LhgT46s3rdneTh64rA4/NzE8pSIIBQxujwsAAOy91LMq0R/LUC330HURwd76rhPblVelCsjDvFWf1aNifCESMsirbEK2OtOJEEdAn7uOxeiA/KeffsLmzZuxadMmrFu3Du+//367/wghxBhZ6nW4/u4Sk6tTj4lVBeTHM7SvI7fnGfJbLRgRDgGjujhxo1R7f/aMsgY0yRVwFQsNKqSnr0+1UMDgjvhgvDknXuv9gH2ePNyse3Dz/ePXkV8u7rQNn61oalXgbG4VAGBMrPaA/NalEZye2ned2C7NlHUAcJM4YZi6L/nBqz3rQhchXRHl54pXZ/Xnf+Y+fwUMfe7aI6MDcrFYjNjYWEuMhRDigDLLVUGjKQXdOFwgkZJdiZY2RYf7s8tVJ3n22n5LU5i3C26PUwWG35/SPkvOF3QL9TSooJshV+o1gzaOgIHdnjzw68cDbr6ucb38IHYSIK+yCddKtF8MsTUp2ZWQK1iEeDojUk8GybxEGfY/NxHLxkfzf0d3JgTbZfV8Yj1cUTeZz81jkdaRE0clcVKFaX5uYtyZEIJl46Ox/7mJ9Llrh4wOyJ977jl88MEHPebqPyHEtmWVmV7QjdMrwA3+7hI0y5U4m1vd4X6u5VmEHVVY1+fvoyIAAD+fzkdja1uH+8/nVwMwLF0d6DhDqutKPRe0DZJ5AQDigjzs9uRB2wy5i9gJY9UXh/ZeLrHKuIzFp6vH+nW6PpdbGrFqVj8AwNncajoXIGbTLFeguLYZwM0ZcgCY0EcVkJ/MrECzvOMFV0Ls1fbzRQCAh8dGtet+QeyP0UXdjh49igMHDmDHjh3o378/RKL2Kabbtm0z2+AIIfavKwXdOAzDYHSML347V4jjN8oxUr2mHAAaWtr44ltRDhKQj4v1Q4SvC3IqGvHbuULcPzy83f3njSjoxtEsHpdf1cT3qb715CDSzxVLx0Xj8R/OwEnI2O3JAzdDfuuFpCn9ArH/Sil2XyrB47fZfjbZcXXdBa5bgSFGRvlC7CRAQXUTbpTWo1egu6WGRxxIQbVqdtxVLIS3y81zyz6B7gjycEZxbTP+yqrEePWMOSH2rKK+BcfV7f7ujA+28miIpRkdkHt5eWHOnDmWGAshxAFxM41dTSfnAvJjGRV4VuN2bnbc20UETxfT1qj3NAIBgwdHROBff17GtydycN8wGT/72aZQ4lKRqqCbIS3PNHEzpJ3hZt6vFNWhtU0JsZPRyVgWl1XegM0aFxfmq9PuDaFUsvxxFe3f/kLSJHVht7S8apTWNiPAw9m8Azej6sZWXFD3l9e1flwbqViIkdG+OHytDIeulVFATswij6+w7tIuW4Nrf7YpNQ+HrpVRQE4cwq6LJVAoWcSHejpMdp8jMzog37hxoyXGQQhxQCzLdqkHuSauQnRaXjXqW9rgJlF9vHHrx+11plaXe4aG4d+7r+JSUS3O5FZjaIQ3AOBGWT2a5Uq4SZwsljEg85HCUypCTZMc10rqjA78LW1zah5Wbk0HwzB8K6UNhzKQPDfBoPT6wpomNMuVEAkZyG6pHh/g4YxBMi+cy6vG3sulWDAiXMderO9kZgVYVtVHPdDICwcTevvzAfkj46ItNELiSDQD8ltN6KMKyA9eLcUrM/t199AI6XbbzxcCUNXqIPbPpGmLtrY27N27Fxs2bEBdXR0AoLCwEPX1PaOIDSHENhTXNqNJroBQwGg9CTOGzMcF4T4uaFOy+CvrZvuzbL7lmWMF5N6uYswaGAIA+E6jBVp6vmpGdECoh9ae0+bAMAyfDs89n63IKm/Ayq3pULKAQsm2+zdpazpfkV+fjLKbNQmchB2/Rrlq67a+jpxrE2jM7DiHK7R1KrNSa50CQoyVV6Uu6Obd8btgTKwfhAIGGWUNfOBOiL0qr2/BCUpXdyhGB+Q5OTmIj4/H7Nmz8fjjj6OsTFX1Mjk5GStWrDD7AAkh9osr6Bbu4wKRlsDGWFz7M81+5FkO1PLsVgvVxd22pxehQt3r/bw6QDa0oJupuFnx8wXVFn0eY21OzdNZvIxhGGxKzet0H5ll+jsDcAH50RvlaGix3WC1s/7j+sT4uyLUS4pWhRKnMivNPTTigHIruJZn0g73eUpFGKwuFnn4OlVbJ/Zt54ViKFlgYJhnlycrSM9g9BnwU089hcTERFRVVUEqvfmhOWfOHOzbt8+sgyOE2LcMvqCbeYJlLrA4duNmP/IcvsK6432pJYR5YWCYJ1oVSj7Q5Aq6xYd5Wfa5+YDctmbI86ua9LZwy1fP0unDV1j3116IsFeAG8J9XNDapsSR6+Vat7G24ppmZJY1QMAAo6INL+jGYRiGr35N/aGJOeRV6U5ZBzTan12lgJzYt+3pqurqlK7uOIwOyI8cOYKXX34ZYrG43e2RkZEoKCgw28AIIfYvy0wF3ThcpegrxXUoV88IZzlQD3JtHhypmiX//mQuWtoUfEG37pohv1pcp7U3vLWEeXecfeMwDKP3fk5muboHuY66BwzD8LPkey7ZZtr6cfXs+IBQT5OLHU6k/tDEjHIruRly7QH5xD6qgonHMyrQ2qbstnER0p1K65pxSr3s7o4BFJA7CqMDcqVSCYWi48lVfn4+3N2p0iohxHBZ5VzrKNNbnmnydZMgLkj1OXQyswJ1zXI+MHfElHUAmDUwBJ5SEQqqm/D54Uy0tinh7uyECAunwYV5S+HtIoJcweJqcZ1Fn8sYU/oFQqmjdTbLsrjXgKJuGaX6Z8gBYHJfVUC+/4qqUq6tOca3OzM+XZ0zOtYPTgIG2RWNBq29J0SXmkY56ppVyzvCtKwhB4D+IR7wdRWjvqUNp3OqunN4hHSbXVy6usyL0tUdiNEB+dSpU7Fu3Tr+Z4ZhUF9fj1dffRUzZsww59gIIXaO60Fuztnrm2nrFchRr0n0dRXDw9kxWp7dylkkxPzEMADA+3uuAQA8nJ2QY+HCSAzD8LPktlLYjWVZfHIgg/9ZKNBsrQQkz03o9MJNQ0sbimubAeieIQeAYZHe8JSKUNUox5lc2woeWJblZ8iN6T9+KzeJExIjVdX7aV0v6QouXd3fXQKpWKh1G4GA4VueUVYGsVf/U6erz6Ribg7F6IB87dq1OHbsGPr164fm5mYsWLCAT1dPTk62xBgJIXaotU3JV8vtasszTVxht+MZ5Q5d0E2Tj4tqiZFCPVFbWN2MSWsPYosBBcy6gqu0ft5GAvIf/8rD3sslEAsF+GJhIpaNj0aolypFffHoSINannHHlI+rGF4uYp3bOQkFuF3dk9zW0tazyhtQVNMMsVCAYZE+XdrXhN6q10jreklXcOnqt7YRvNUECsiJHSutbcZf2aoimXfEB1l5NKQ7Gd2HPCwsDGlpadi0aRPS0tJQX1+PJUuW4IEHHmhX5I0QQvTJrWyAkgVcxUIEuEvMtt/hUT4QChjkVDTyxd0iHazlmaas8ga8u/tqu9tYAKy6zdewSB+LXbCID/UCYBuF3TLL6vHG/y4BAJ6f1geT+wVicr9ABHk449XfL/KF2jqTUaZ//bimKf0C8cvZAmxOyUNRTTPCvKWYnyizej2DY+p2OoPDvXTORhpqQm9/JO+8guMZFWiWK+As6tr+iGPS14Nc07hefmAY4HJRLUpqmxHo4dwdwyOkW+y4UAyWVX0261q6QeyTSX2GnJyc8MADD+Cdd97BJ598gkceeYSCcUKIUbgAKMrfVWcbKlO4O4swUD0z+3taIQAg0gErrHPM0ebLVPHq38O1kjo0y61X2E2uUOLpTefQJFdgdIwvloyN4u8bGqFKuT6TWwWlAWu9uR7k0X6d1z2oaGgFAFQ3ybE9vRCfHc7slsyEzhxXX6gypf/4rfoGuyPAXYImuQKp2baVmk96js4KunF83SR8B4fDNEtO7AxfXZ3S1R2O0QH5mjVr8OWXX3a4/csvv6SUdUKIwbLKDQ9sjMUFGo2tqiDQkVPWzdHmy1Qhns7wdRWjTcniihULu32w9zrS82vgKRVh7fyBEGisHY8LcodUJERdcxtuqGe/9eF7kHcyQ55V3oBXf7vA/6xkAYWShVKdmWCtImhKJYsTmaoZcm55R1cwDKORRkztz4hp8tSfQzIDZgW5C67v77mG5J1X+O8SQnqy4ppmpOSo0tVnUEDucIwOyDds2IC4uLgOt/fv3x/r1683y6AIIfYv08wtzzTdWjn60LUyhz1pC/OW6p0hN6TNl6k0C7udz6+22PNok1XegOSdV/DAFyfx0YEbAIC35sQj2LP963USCjBI5gUABs3wdtaDnGPNzAR9LhXVorpRDlexEAlm6kXP9SOndb3EVIamrG9OzcO3p3IBAIU1zfjskG1knRDSVTsuFIFlVVlbIV6UdexojA7Ii4uLERzc8cqNv78/ioqKzDIoQoj942fIzVjQ7ea+2890/nKmwGFP2uYnyvTOkBvS5qsruMJu3VlpfXNqHiatPYjPDmXy7b0AoLG1Tev2XKXwzlopKZUsf9x2toZcX2aCkmX5AIS7cPDkj2d1zvYZso2huOrqI6J9IRKatGqtg7GxfhAwwLWSehRWWy7jgtgnhZJFATdD7qM7EMkqb8DKrenQ/LNSsNbPOiHEHP48T+nqjszob2OZTIZjx451uP3YsWMICQkxy6AIIfYvk+tBbuaU9azyBrz864V2tznySVuUnyuS5yZAwKjafGn+a0ibr66K52bIu6mwG3fSrmRVv3dNun7/QzTWketTVNuMJrkCTgKm05k8fZkJLKta//rED2dUFw4OZ+pcY85fXNCzjTFu9h/vero6x8tFjMHhqveQZsmJsUpqm9GqUMJJwHTIYNFkq1knhHRFVnkDVv12ASnqDC0uq4w4FqMD8qVLl+Lpp5/Gxo0bkZOTg5ycHHz55Zd45plnsHTpUkuMkRBiZ2qa5CivVxW8ivQzb8E1OmnraF6iDPufm4hl46NxZ0IIlo2Pxv7nJhrU5quruMJu10vru6Wwmym//yEyVTCZVd6AivoWnfvm1o+H+7p0OrusLzMBAGqb2/C/9KJ2a8s115ifyanC6ZyqmxcXtGxjzMWlrPIGvPXnZRy9rpohjzBz5wF+HTm1PyNG4rJFQr2lEAp0F/i0Zj0MQiyBu+D67ckc/rb7PjvhkNl8js7otmfPP/88Kioq8Nhjj6G1VXVC7ezsjKSkJLzwwgtmHyAhxP5wgUSAuwTuziKz7ptO2rSL9HNF0vSO9T8sLcjDGX5uEpTXt+BSUS2GqGdSLcWU37+niwi9AtxwvbQeZ3KrMaVfoNbHZxpRYZ3LTEjamg6GYcCyLP/vm3cNwK6LJTpnk5UscPenx/Xun7u4YMjvdHNqHlZuTef3DQD/920qkucmmO2izITe/nhvzzUcu1EOuUJptnR4Yv8MLejGZ51o+ftmWVi0HgYh5qaZzaWJu+BqyZakxPYY9Y2pUChw5MgRrFy5EmVlZTh58iTS0tJQWVmJVatWWWqMhBA7w6WrW6KgmzWLmJGOGIZBfKgHAOB8N6wjN/X3z60jT1VXudWGmyGPCTDsuNWVmbBgRAQ8pCLomQzslKEXlzRP+jRP/My9hCM+1BM+rmLUtbThbG61WfZJHEOugQXd9NbDAGC+5pmEWB5l8xFNRgXkQqEQU6dORXV1Ndzc3DBs2DAMGDAAEonEUuMjhNiZrPIGfKdOz6ptlpu9+rm1i5iRjuLV1by7Yx35/EQZlCb8/rmZ+zN6CrtxPchjjKh7wGUm/Of+wUiaHsfPeOi7cCAUMHh0YgwenRijM4XX0ItL3XXSJxAwGNdL1d2A2p8RY+TzAbn+41lXPQzu6P70UAZfGIsQW0fZfEST0TllAwYMQGZmpiXGQgixc9x6qTM51QCAK8V1Zq9+bu0iZqSjBL71meUD8ig/V4yI8gGgmjEz9Pc/VF3YLS2/Bq1tSq3bGNqD3BCGXDgyx8Wl7jzpm6huf7YpJc8sFeGJY+BmyMM7mSEHdGWdTMDCURFgWeDpTeeQkq07y4UQW6Hvgipl8zkeo9eQv/nmm1ixYgXeeOMNDB06FK6u7U9MPDw8zDY4Qoj90LZeimVVqYbmXi81L1GGYZE+2JSah/yqJoR5S3FvooyCcSu5WditDo2tbXARG/3VY7A2hRLXS1SB850JwfyJTWe//yg/V3i7iFDVKMfFwhq+ajinsbUNhTXNADrvQW4IfWvMNS8caG6jZFl++ezz0/oYdDyHeUuhmkPsGJSb+6SvqkEOACivb8X29EIwDIMNhzLMulad2J+8KvUMeSdryDna6mG8Oqs/imqasedSCR75OhUf3DcIp7Iq+c//+YkyiyyRIsRU8xNl+PRghtb7KJvP8Rh9VjRjxgwAwN/+9rd2aXDcyYRCYfkquoSQnodPndUyW2dMgSpDWauIGeko0MMZAe4SlNa14HJRLYZG+FjsuU5lVaKioRXeLiK8f+8gg4uLMQyDoRHe2Hu5FKdzqjoE5NxMr7eLCD6uYrOM1ZALR7duk5ZXhdzKJlwoqDXoOeYnyrC+G076ssob8Ob2S/zPShb83zoVKCK6NMsVKKlVdTYwZIZcF6GAwYf3DcaCL07ibG41Fm9MgZBhwIKlC0PEJml29OBWJmm7KEscg9EB+YEDBywxDkKInaP1Uo4tPtQT+66UIj2/xqIB+f/SVWtIpw8IMrrS99AIHz4gf2Rc+/u49ePmmB3XZMiFI81trhTX4o4PjmD7+SIszavGIJmX3se2tCnAXQez5Elfd19wI/aB+9x3kzjBy6VrHTekYiFWzeyHOZ+oOhQouGORLgwRG8OyLNbsuAIAuDM+COG+rpTN5+CMDsgnTJhgiXEQQuycvpY1tF7K/sWHqQJySxZ2a1MosfOCKiC/Mz7E6Mdz68hTc6r4rC8Ov37cyidKcUEemDskDD+fzseaPy/jp2UjdRZtUyhZJG09DyULjI31RXyoJ/Krmy1y0kcX3IgpuHR1fUUOjbH7UgkEDDq0kgLowhCxHbsvleB0ThWcRQKsmtUfgR7O1h4SsTKTFvIdOXIEGzZsQGZmJrZs2YLQ0FB8++23iIqKwtixY809RkKIHZifKMOGQ7ReylHFd0NhtxOZFahqlMPHVYyR0cbPwieEecJJwKCsrgX5VU3t2jBlWmiG3BTPTumN39MKcSqrEgeuluL2OO19078+no20vGq4S5zw73mDEORpuZM+uuBGTJFnREE3Q+i78KNUsu3a/GWVN2CzxnIRWmdOukObQol3dqpmx5eMjaJgnAAwocr61q1bMW3aNEilUpw5cwYtLao1EDU1NXjrrbfMPkBCiH3gilhxGKp+7lC4gPxGWT0aWtos8hzbNdLVnYxMVwcAZ5EQ/dXjPJPbvv1ZZrm6B7kZKqx3VYiXFA+NjgQAJO+4CoWW6cC8ykb8e/dVAMDKGXEWDcYBajdITJNnYA9yQ+mbaWcB7Ltcgtd+v4hPDtzApLUH8dnhTGxPL8RnhzPN3vGDEG02p+Yjo6wB3i4i/N+EGGsPh9gIo89Y3nzzTaxfvx6ff/45RKKb633GjBmDM2fOmHVwhBD7MjMhhF/HOikuQN2yZiIV2nEAAR7OCPJwBssCl4oMK0hmDLlCiZ0XiwEAM+ODTd5PIpe2nn0zIGdZ1qZmyAHgsYmx8JSKcLWkDlvP5Le7j2VZvPTrBTS2KjA8ygf3Dwu3+Hg02w1yBAzoghvRy5iWZ4bQd2EIAFoVLL46no13dl2FklUt69D8N2lrertZdELMqbG1Dev2XgMAPHl7L3g4d61uArEfRgfkV69exfjx4zvc7unpierqanOMiRBipy4U1kDJAgHuEny+MBFJ0+PoRN2BDFDPPqdbIG39eEYFqhvl8HMTY3iU6UXjuHXkp3NuBuTFtc1obFVAKGDMFjh0laeLCI/fpppdeX/PNTTLb3Y4+e1cIQ5fK4PYSYA1d8dDIOj62lxDcD2i+wa5AwAGh3vTBTeiV16lKsVc5mOeJQ2aF4a4DCzu33fmJuDrh4cjQs/fMLfOnBBL+PJoFkrrWiDzkeKBkZa/UEp6DqMD8qCgINy4caPD7UePHkV0dLRZBkUIsU/ncqsBAINkXmYp4EN6lgR1P/ILFijstj29EIDp6eocLiC/UlyLenVqPTc7Hu7jArGT6fs2t4WjIhHqJUVRTTM2HssGoGql8/ofFwEAT03qhZhuntGP9HPF3KFhAIBADwldcCM6sSxr9jXkwM0LQ8vGR+POhBA+E2v+MBkm9PZHgswLuq5RUQFCYikV9S1YfygTALBiah9InIRWHhGxJUYXdVu6dCmeeuopfPnll2AYBoWFhThx4gRWrFiBV155xRJjJITYiXP51QCAgZ20aiL2KZ6fIa82635b25TYdbEEgGnV1TUFejgj1EuKguompOVVY0ysH19h3RbWj2tyFgnx7JTeeG5LGj7afx1ldc3YfbEEVY1yRPu5Ytl461wk7xWomiG/XlJvlecnPUNNkxx16oteYd7mzTzR106QChASa/jP/huob2nDgFAPzEro2vcUsT9GB+QrV66EUqnEpEmT0NjYiPHjx0MikWDFihV48sknLTFGQoid4GbIB1NA7pC4lPXM8gbUt7TBTWJSo48OjmWUo6ZJDj83SZfS1TlDI7xRUN2E1OwqjIn1s1gPcnO4a3Ao3t11FcW1zfhSPUsOAFkVDfj1bIFV0sV7Bajep6zyBsgVSqP7wRPHwKWr+7tL4CzqvtlC6vhBugtXyf9acR0OXC0FAKyc3rfblhGRnsPob0mGYfDSSy+hsrISFy5cwMmTJ1FWVoY33njDpAEUFBTgwQcfhK+vL6RSKeLj45Gamsrfv3jxYjAM0+6/6dOnm/RchBDrKatrQUF1ExhG1ZOaOB5/dwlCPFWF3S6aMW39T3V19RnxQRCa4UQnMVK9jlxdaT3DRnqQa5Nb2YiSuuYOt7NWLFAV7OkMV7EQbUoWORVUIItoZ+6CbobSts6cc+8wGS2zIGaxOTWPr+S/70opuGYYRTW0JIJ0ZPL0hFgshru7O9zd3eHmZtqsQVVVFcaMGYPbbrsNO3bsgL+/P65fvw5vb+92202fPh0bN27kf5ZIJKYOmxBiJWl51QCAWH83uFNlUYcV5eeKwppmvP6/S5jQ219r719j+gOr0tVV1dXv7EJ1dU1DwlXfQWdzqqBU2l6FdU2bU/MgYBgodKTfbkrN05m6aykMwyA20B1pedW4XlKP2AD3bn1+0jPkValbnlkhRXxeogzDIn2wSf05U1zThJTsKvwvrQiPTYw1Wxs24piyyhuwcmu6Kgi/5bM5aWs6hkX60IUf0o7RAXlbWxtef/11fPjhh6ivV80auLm54cknn8Srr77arhVaZ5KTkyGTydoF21FRUR22k0gkCAoKMnaohBAbck4dkA+idHWHtTk1D8czKgAAlwprcbW4DhsOZSB5bgKfWr05NQ8rt6aDYRiwLAuGYTpso+l4ZgVqm9sQ4C5BYmTX09UBIC7IHS5iIepa2pBeUINC9YyGra0hB4D8qia9/b+tVaAq1t9NFZCX1uMOq4yA2DprzZBzNNeZtymUuPezkzidU4XnNqfhx2UjzZJtQxzT5tQ8vXUKrHGhlNg2owPyJ598Etu2bcM777yDUaNGAQBOnDiB1157DRUVFfj0008N3tfvv/+OadOmYd68eTh06BBCQ0Px2GOPYenSpe22O3jwIAICAuDt7Y3bb78db775Jnx9fbXus6WlBS0tLfzPtbWqfrdyuRxyudzYl2s23HNbcwyEdMaSx+mZ3EoAQHyoO/0dOKDsCtWMgebpiUKdw5e0NR2DwzzAgu04q8C23ybCV3Xyzh1D29Xp6tP6B0KpaINSAbMYGOaJE5mV2JKSC5YFPKVOcBczNnfshnhIwIABoOXET32/NcYc7aea9bxaXGtz71l3o+9/7XLVyxmCPa1zjN7qnbv7428fn8Bf2ZX4ZP81/GOC43UOomPVPHIrGvReKM2taKD3uIt6yrFq6PgYVtcRo4Onpyd++ukn3HFH+2vef/75J+6//37U1Bi+LtDZ2RkA8Oyzz2LevHlISUnBU089hfXr12PRokUAgJ9++gkuLi6IiopCRkYGXnzxRbi5ueHEiRMQCjsWAXnttdfw+uuvd7j9hx9+gIsLpSARYg1KFngxRYgmBYPnE9oQZnsTjcTC/sgRYH8hAyW0zTqx8FWvRKpoAaBlGwFY3B7CYlaEkr+tTQm8nKo6rpb3b0OMh/nG+2euALsKBJAKWTQpGES6sXgm3kzRvhmVNgFvnROqw3HN940FA+ClQQr4W6Fo9IUqBp9fESLEhUXSQNt734j1vXFWiPJmBk/2a0OsjZQVOVXK4IcMIQQMi2cGKBBue6tUSA+g7/tO23cZsV+NjY1YsGABampq4OGh+yTF6IA8ICAAhw4dQt++fdvdfvnyZYwfPx5lZWUG70ssFiMxMRHHjx/nb1u+fDlSUlJw4sQJrY/JzMxETEwM9u7di0mTJnW4X9sMuUwmQ3l5ud43wtLkcjn27NmDKVOmGJXWT0h3stRxmlHWgOkfHoOzSICzL93epT7RpGd6enM6dlwo5gvbGEvAAHcMCMK6+QkAVMfq+5v34vMrQgS4S3BkxXizVq49fL0cS745w/88Z3AI3rl7gNn2b05bzxTgxV8v3kzzh2q+/K27+mPukFCrjCm3shGT3j8KsZMAaS879t88ff93pFCyiF+9F3IFi0PPjUOIl220GmNZFss3pWPnxRKEeTljar9AlNS1IMxLinuGhiDS176vJtOxah7ZFQ2Y9sExrd93AgbY/dRYPtuLmKanHKu1tbXw8/PrNCA3OmX9iSeewBtvvIGNGzfyxdVaWlrwr3/9C0888YRR+woODka/fv3a3da3b19s3bpV52Oio6Ph5+eHGzduaA3IJRKJ1qJvIpHIJn5htjIOQvQx93F6oUhVbyI+1BNSZyrK6IjCfV11rqkTMMDEPgEAgINXS7WexChZwN1ZdVxmlTfgp1M52JqpCvLGxvpBIhGbdbyJUX7tfi6sbkZ+TavO4nLWdN+ISIyM8ecLVIV5S3FvonWrRUf6e8BZJECzXIni+jabfN+6G33/31RW3QS5goVIyCDM192m1muvuTsBxzMOIL+6GRuP54BhVOt+Pz+apbOWhb2hY7VregV5IXluAv65NZ3/yhMKVBdMk+cmIDbIRlJC7ICtH6uGjs3ogPzs2bPYt28fwsLCMHDgQABAWloaWltbMWnSJNx99938ttu2bdO7rzFjxuDq1avtbrt27RoiIiJ0PiY/Px8VFRUIDjZPNV1CiOWdy1O1j6KCbo5LX+9fAFg1sx9YqAJyXTan5iG3shEnMyvAgIFCfaLzy7kCjIrxNeuJMle5nZOSXYlJaw/a7Am5ZoEqWyAUMIjxd8PFwlpcL6mjgJy0wxV0C/WS2lQwDgDVTXLUtbQBUGWasBo1LahCNjHUvEQZWuQKvPzbRXhJRbh/RLjVL5QS22V0DpmXlxfmzp2LmTNnQiaTQSaTYebMmbj77rvh6enZ7r/OPPPMMzh58iTeeust3LhxAz/88AM+++wzPP744wCA+vp6PP/88zh58iSys7Oxb98+zJ49G7GxsZg2bZrxr5YQYhVpearaEgMpIHdY2nr/cv8mz01ApJ+r3m36h3hAyQLHMyqgZKFu86U6kTd3z22uZY0mJav6z1q9vXuiXgGqBbjXS+utPBJia/LUAbktthfjWglqw1XIJsQQ3CrxEdE+SJoeR8E40cnoGXLNFmVdNWzYMPzyyy944YUXsHr1akRFRWHdunV44IEHAABCoRDp6en4+uuvUV1djZCQEEydOhVvvPEG9SInpIdolitwuUjV7YBmyB3brb1/taVW69tm+Y9n8HtakdZ9m7OVDLWsMY9egar+4zcoIHcYWeUN2Kzxtzs/UaY1O8KWA3JbbSVIep7immYAQJCHs5VHQmyd0QG5uc2cORMzZ87Uep9UKsWuXbu6eUSEEHO6WFiDNiULPzcJQm2kcA+xHkNSq3Vtw0I1W65tjbk5T5TphNw8YvkZ8jorj4R0h82peVi5Nf1mcUGGwYZDGVqXeeSp/4Zk3rYXkId5S3VfkAODMG/6HiOG4QLyQE8KyIl+BqWsT58+HSdPnux0u7q6OiQnJ+Pjjz/u8sAIIfbhnDpdfZDMU3WSQ4iJ+BNlLRjGfCfK3fU89o5LWb9RWg+lqeX1SY/ALfNQsqoK6pr/alvmwa0hD7fBGfL5iTKdF+QULIt5Q8O6eUSkpyquVQXkwRSQk04YFJDPmzcPc+fORb9+/ZCUlIQtW7bg2LFjOH36NPbu3YsPP/wQ8+fPR3BwMM6cOYNZs2ZZetyEkB7iXF41AEpXJ12n70SZZVnca6Zia931PPYu3McFYqGq0npBNWUV2DN+mYcW2tZd30xZt72LW9pqWWjWnfvuZK71Bkd6FC4gD6SUddIJg1LWlyxZggcffBBbtmzBpk2b8Nlnn6GmRjXrxTAM+vXrh2nTpiElJaVDf3JCiGO7WWHd28ojIT0dd6KcpE6LVSqVEDAMWNwsDGeJ5+HSb7mWNVSYxzBOQgGi/V1xpbgO10vrbHK9MDEPY5Z5NMsVKK1rAWCbM+SA9loWvq5ivLn9Mr48loUQL2c8Mi7a2sMkNoxlWVpDTgxm8BpyiUSCBx98EA8++CAAoKamBk1NTfD19bXp/m+EEOupqG9BXqXqRCxBRn03SddxJ8o/nspByqUMDOsXjftHRJg9SDakAB3pXGyAmyogL6nH7XGB1h4OsRB9666VLNDY2sb/nF+lmh13lzjBU2q754/aalm0KVm8veMK/vXnZYR4STEjnlrwEu3qWtrQ2KoAAARRyjrphMlF3QxtbUYIcVxp+dUAgBh/V3g42+6JF+lZIv1csWJqL/zZdh0zpvay2EVhW+vt3RP1CnAHUEStz+zc/EQZNhzK0Hn/vsulWP7jWTwyLgrr1duJnQTIrmjsUT3q/298NAqrm/DNiRw8vekc2hRKXC6u67SqPHE8JerZcQ9nJ7iIrV5Dm9g4o/uQE0KIoc7lVgOgdHVCHFWvQOpF7gii/Fzx6qz+/M+a66+n9Q+EUMDg97RC/O2jY9hxvhgAUNnQiklrD2JLD+rrzTAMXp3VH1P6BaK1TYnlP53DhkMZ2J5eiM8OZ/a410Msp4hLV6fZcWIACsgJIRZzLl9dYT3cy7oDIYRYBV9pvaRO5xpjYh+4mWEPZyfcmRCCZeOjsf+5idjw90R8cN8gfjtW419dVdhtmVDA4NkpvfmflSw6rSpPHA9X0C3I0/YKFxLbQwE5IcQiWJZFGldhPczLqmMhhFhHhK8rnAQMGloV/IwRsU/p6iVKE/oE4D/3D0bS9Di+5sLFwloIjajCbut+Tyu0q9dDzK+EL+gmsfJISE9AATkhxCKyyhtQ0ySHxEmAuGB3aw+HEGIFYicBH5RR2rp9S1NnRA0M61hfKL+qCSwMq8LeE9jb6yHmV1RLFdaJ4YwOyKOjo1FRUdHh9urqakRHUwsIQogKV9BtQKgnREK69keIo+LS1q+X1Fl5JMSSuBnygTKvDvfxVdi1YBgGYd49K63X3l4PMT9+hpxS1okBjD5Lzs7OhkKh6HB7S0sLCgoKzDIoQkjPxxV0G0jp6oQ4NH4dOc2Q262S2maU1LZAwAD9Qzw63D8/Uaa3T/m9iTJLD9Gs7O31EPO7uYacUtZJ5wyuw//777/z/3/Xrl3tWp4pFArs27cPkZGRZh0cIaTnOsetH6eCboQ4tNhA1ZIVSlm3X1y9kN6B7lpbPEX5uSJ5bgKStqaDYRiwLMv/mzw3gV/W0FNovh6lOi4XqCfMe+LrIeZXzK8hpxly0jmDA/K77roLgCoVZ9GiRe3uE4lEiIyMxNq1a806OEJIz9QsV+BSUS0AYLCW9EVCiOPQTFnnAjFiX9LV68cTtKwf58xLlGFYpA82pebxfbvvTZT12OCVez1P/XQWafk16BfsgY8WDLHK68kqb8BmjfeV+qFbV0ubAhUNrQCo7RkxjMEBuVKpBABERUUhJSUFfn5+FhsUIaT7ZZU34KdTOUi5JsAlp+u4b0SEyV/ol4tqIVew8HUV01o6QhxclJ8rBAxQ29yGsvoWBLjTCaq94WqGJHSyRCnSzxVJ0+MsP6BuEunnisVjIvHMpjQ4i4RWCcY3p+Zh5S2ZBxsOZSB5bgLmUeq8VZTWtgBQFbX0dhFZeTSkJzB6DXlWVhYF44TYmc2peZi09iC+OJqNsxUMvjiajUlrD2KLia1buHT1gTIvmg0jxME5i4SI8FUFKjdKKG3d3rAsy8+QO2LNkP4hqqyAy0W1UCq1ryu3lKzyBqxUp81zfdCpH7r1FWtUWKdzIGIIowPy5cuX48MPP+xw+0cffYSnn37aHGMihHSjdl/oLAsWDBRs177Q+fXjlK5OCAEQy6Wt0zpyu5NT0YiaJjnEQgH6BDlei8toP1dInARoaFUgp7KxW597c2qe3mrv1A/dOm6uH6dsIGIYowPyrVu3YsyYMR1uHz16NH7++WezDIoQ0n3M/YWeVd6Ag1dKAQDXSuqQRVfoCXF4/DryUmp9Zm+4dPW+IR4QOzlei0snoQBxwarK8hcLa7r1ufOrmvRWe6d+6NZRop4hD6T148RARn9yVlRUtKuwzvHw8EB5eblZBkUI6T7m/ELnUt9rmtsAADvOF3cp9Z0QYh96BXKF3WiG3N7cTFfXXdDN3nGt3i4W1nbr81I/dNtUpJ4hD6aAnBjI6IA8NjYWO3fu7HD7jh07EB0dbZZBEUK6T5i3FAy6/oWumfrO6WrqOyHEPvQKUKUyUy/yjrLKG5C88wqe/PEsknde6XFZRekGFnSzZ/2CrROQUz9028StIQ+klHViIIOrrHOeffZZPPHEEygrK8Ptt98OANi3bx/Wrl2LdevWmXt8hBALm58ow6cHM7TeZ8wXOp/6ruXkgEt9t6fquoQQw8X4u4FhgIqGVlTUt8DXTWLtIdmEnl4hu02hxIUCVRBKM+TApcKabm3tF+XnigdGRODbkzntbhcw1A/dmmgNOTGW0QH5ww8/jJaWFvzrX//CG2+8AQCIjIzEp59+ioULF5p9gIQQyzqdU8X/fwEDKFkWAAPGyC90WstGCNFFKhYizFuKvMom3Citp4Act2QVcZ+d6n+TtqZjWKSPzQdUN8rq0SRXwE3ihGh/N2sPx2rigjwgYIDy+laU1bUgoBsDscZWBQAg0tcF2RWNEDDA9uVj0TfYcS+QWBsfkFPKOjGQSdU3Hn30UeTn56OkpAS1tbXIzMykYJyQHuhCQQ1e+uU8AGDRqAgsHRsFL7HqvofHRBo1Q2Ou1HdCiH3i0tap0rqKPVTITs9TrR8fEOoBocBx2ztJxULEqC9IdGfaulLJ4tA1VRHVt+bEI9RLCiULFNe0dNsYSHtKJYvSOgrIiXFMCsjb2tqwd+9ebNu2jZ8RKywsRH09fckS0lNUN7bi0e9Po6VNidv6+OPVWf2xYmovTApRAgBulBq3jnF+ogxK0Fo2Qoh2XKV1WkeuYg9ZRVyFdUfsP36rm4Xduq/SenpBDcrrW+EmcUJipA/G9/YDABy5TkWWraWioRVyBQuGAQLcKROIGMbogDwnJwfx8fGYPXs2Hn/8cZSVlQEAkpOTsWLFCrMPkBBifkoli6c3nUNeZRPCfVyw7t7BEKhnN6I9VCeIZ3KqoFBqP1nUJsrPFZPiAgEADFTp70IBQ2vZCCEANHuRU+szwD4qZKdRQTdePytUWt+vbjE6rpcfxE4CjI31BwAcvVHWbWMg7XEtz/zcJBAJHa8NIDGN0WvIn3rqKSQmJiItLQ2+vr787XPmzMHSpUvNOjhCiPlklTdgc2oe8quaUFTdhNScKkicBPj0wSHwdBHx24W4AK4SIepa2nCluBb9Qwxfh1bbLAcAjO/tDw+pCGHeUtybKKNgnBCCXoHqlHVqfQZAlVW04VDXC2paS7NcgStFqosrCQ5c0I3DfVd2b0BeAgC4PS4AADA6xhcMA1wrqUdxTTOlTOuheU4U5i3F/EQZosxwrkIF3YgpjA7Ijxw5guPHj0MsFre7PTIyEgUFBWYbGCHEfDQr+SpZlq8fNGdwaIeAW8AAQ2ReOHKjAqnZVQYH5M1yBc7lVgMAXv9bfwrCCSHtcDPkpXUtqGmUt7sQ6Iii/FyRPDcBz/+cDgDQbFLh7y6Bt6tYz6Ot73JRLdqULHxcxT1iNt/SuJT13MpG1DbL4eFs2eO7tLaZr3A/sY8qIPd2FSMh1BNp+TU4eqMc9wwNs+gYeipLdjcoqqX148R4RudSKJVKKBSKDrfn5+fD3d3dLIMihJiPZiVfhZJt15Vsc2qe1v7gQyO8AQAp2ZUGP8+ZnCq0KpQI8nBGhK9Ll8dNCLEvbhInhKhPUm+UUdo6oLooytVCmxwXiAdHRsDHVYyS2hb849vTaGnreL5lK9LzVWulE8I8u63Nly3zchEj1Et1YeJyN8ySH7iqSlcfGOYJf421ymN7cevIKW1dm1vPiTT/TdqarvWcyBglNENOTGB0QD516tR2/cYZhkF9fT1effVVzJgxw5xjI4SYgSmVfBMjvACoAnJdRYdudSKzAgAwKsaXTs4IIVrFUtp6O6V1LVCygJOAwfq/D8Wbdw3At0uGw1UsxInMCiT9nA6lEbU8uhOtH++oO9eRc+vHb1Onq3PG9VKtIz92o9xmjx1rsnR3g2KaIScmMDpl/d///jemT5+Ofv36obm5GQsWLMD169fh5+eHH3/80RJjJIR0gSmVfBNCPSESMiipbUF+VRNkPp3PeJ9UB+Qjo326NmBCiN3qFeCGw9fKqPWZWkG16vM32MuZbxvWP8QTnz44FA9/lYJfzxVCKhbCy0Vs9rWuXcXNkA+k9eO8fsEe2HOpxOIBeUubAkfVldRvvyUgHxLuDRexEOX1rbhsZB0YR5Bf1aTzQoU5uhvQGnJiCqMDcplMhrS0NGzatAlpaWmor6/HkiVL8MADD0AqpTVEhNgavpKvlqBcVyVfqViIAaGeOJtbjdScyk4D8qZWBc7lVQMARkX7mWXchBD7461eN/57WiHETgKbCS6tpVAdkHOpzpzxvf2x5u54PP9zOn78Kw8Mo+peYc61rl1R39KGjDLVRRWaIb+pu1qf/ZVViYZWBfzdJRhwS8AtdhJgZLQv9l8pxdHr5RSQa2BZFiU1TToatJqnuwHNkBNTGJWyLpfLERMTg+vXr+OBBx7AO++8g08++QSPPPIIBeOE2Kj5iTK9M+S6KvkOi1TNdP+VVdXpc5zOqYJcwSLE0xkyH/osIIR0tDk1D2v3XAMAlNW14LPDmZi09iC2dDFFtCfjZuNCvDp+biZG+oBLrGVZmH2ta1ecz68BywIhns7t1i87uv6hquD3Rmm9wev/s8obkLzzCp788SySd15BlgG/Vz5dvY8/37JU09hY1YXxozeoHzmnpU2B5zan4a9s3ec05uhuwK0hD6QZcmIEowJykUiE5uZmS42FEGIBXCVfza9sQ/qDJ6oLu6UaUNjtRKbqS38krR8nhGjBFVLSvDZoK8GlNXEp62FaAvLNqXlagy0AYND1ta5dkU7rx7UK8XSGl4sIbUrWoDoJm1PzMGntQXx2OBPb0wsNvkh1QB2Q35quzhmnLux2KqsSzXLbLQzYXSobWvH3L/7CtrMFEAoYzB0SBs0/LQGDTs+JDFHf0oa6ljYANENOjGN0yvrjjz+O5ORkfPHFF3ByMvrhhBArmJcoQ3FNM9buuYZADwnuHhLWaX9wrtL69dJ6VDW06m3BczJTFbSPjPY178AJIXaBL6SkY+nMptQ8JE2Ps8LIrItLWdc2Q66v/oeCZXHsRjmaWhWQioUW66msC19hXUbp0JoYhkH/EA8cu1GBi4U1GBCq+/3RrPbN/12o/03amo5hkT5av6Mzy+qRXdEIkZDBWHUBt1vFBrghyMMZxbXNSMmu5Au9OQrNvwc3iRCHrpWhsLoZ7s5O+OSBIRjXyx9P3h6Luz89jsqGVkwfEIR/TovrcrtWbv24u8QJbhKKkYjhjD5aUlJSsG/fPuzevRvx8fFwdW1/8G7bts1sgyOEmI/YSZUQMybWz6ATX183CWL8XZFR1oDTOVWY3C9Q63YNLW1I49ePU0BOCOnIlOKSjqBA/bpDtaxb1Vf/A1AFxSPX7MMgmReOXC+zSE9lXbiaIYNohryDfsFcQK6/sJupF6m4dPXhUT46gz6GYTC2lx9+Pp2PI9fLHSog1+wxrmRvtnr1dhFh8/+NQi91p4dIP1eMiPLBjgvFSIzQfvHDWCXq9eOBNDtOjGR02zMvLy/MnTsX06ZNQ0hICDw9Pdv9RwixTXXNqjQqD2eRwY/h1pGn5OhOWz+dU4U2JYtQL6lB1dgJIY6HDy713O9oWJbVWdQN0F//gwEQ5ClBTZMch66VWaynsjYV9S18qv0AqrDeAVdErbOA3NSLVPv5dHXtF8k54/h+5I6zjvzWHuOab29NkxwiYfuwhwvCDVm3b4gi9Qx5MAXkxEhGzZC3tbXhtttuw9SpUxEUFGSpMRFCLKCuWQ4AcHc2/M9+WKQPfkrJQ6qeIign+HZnNDtOCNFufqIMGw5laL1Pyd6sTu1IaprkaGhVre/VlrLO1f9IUs/2cbPfLMsieW4C7h4Shid/OIM/LxRr3b+llgJw6erR/q5GXeB1FNyxfLmoFgoly7ezu1WYtxQMGEBLzW9d1b7rmuX4K0t1gVzX+nHOGHVht8tFtSira3GI4nvGZh1wyzqyK8wTkPMz5FTQjRjJqBlyJycn/OMf/0BLS4ulxkMIsRBuhtzYgBxQFfDRVRiG6z8+KoYCckKIdlxwKWBuFpUUasyYJ/2cblABSXvCzYD6uYnhLBJq3WZeogz7n5uIZeOjcWdCCJaNj8b+5yZiXqIMQgEDoVAAHfGexZYCpKkLug2kdHWtov3d4CwSoLFVgRw9gd78RBmURnZAOXq9HG1KFlF+rp3WCPBzk/AXB445SLV1Y7MOuPcws8w8ATn1ICemMjplffjw4Th79qwlxkIIsaBadUDuJjF8RkPmI0WAuwRyBcuvE9dU39LGz5aMjPYxyzgJIfapQ3A5IRo7nhqLUdG+aGhVYNGXfyHFgYJyfQXdNEX6uSJpehz+c/9gJE1vX3hK31IAc/RU1oYv6Ebp6loJBQzigrh+5LrT1v3cxJCoa7twVb45t8cFal3TfLPdmf7Zcc5YB0tbN/bvgQvIC2uazFKNnktZpwrrxFhGF3V77LHH8NxzzyE/Px9Dhw7tUNQtISHBbIMjhJiPKSnrDMNgWKQPtp8vQmpOFUbckpaeml0JhZKFzEeKMG9aP04I0Y8LLjV9uXgYlnydguMZFVj05V9Yc3c8rhTX6awY3t0VxS2lQM/6cUPpWwpgjp7K2vZJLc861y/EA+fyqnGxsBazBoZo3eaHU7loblMi3EeKGfHBKKhuRmNrG/ZdLsXeyyXYd7kEk/reXCeuVLI4cFV/u7Nbje/ljw2HMnHkehm/5MGeGfv34OsqhruzE+qa25Bb2Yje6oJvpuJS1mmGnBjL6ID8vvvuAwAsX76cv01zbZNCQf0OCbFFpqSsA0BipDe2ny/SOnPFrx+PonR1QohppGIh/rtoGJZ+k4qjN8rx1E/n+NnCWyuGa1ZQ7q6K4pair6CboW5dZ65UsvyK5Lfv7lpP5VtllTfgv0ezUF7fCgaAq0R7mj25uY78YmGN1vub5Qp8cTQLAPDk7b3aHburfruAb07k4OlN5/DHE2P53+H5ghqU17fCVSzE8CjDMtKGRnhD4iRAaV0LrpfWdzngtHXc38M/f04HC1XxQ4HgZt2FW/8eGIZBlJ8r0vNrkFnW0OX3h2bIiamMDsizsrIsMQ5CiIXVtXAz5MYV4eHWkZ/OqepQoIbrP07rxwkhXSEVC/HyzL6Yvu4IAFWhNwB8caZ//pyOU1kV2Hq6QBVwGtG32VYVGJiy3pl5iTIMi/TBptQ8ZJU1YP+VErQqWLiasQ8ydyGEwwKY8cGRHnkhpDtwldYvFdZqnZnedqYAZXUtCPF0xuxBoe3ue/nOfrhQUIMzudX4x3en8ctjYyAVC/l09XG9/Pk2pp1xFqmC9yPXy3H4WpndB+SA6u9h58Vi7Ltcirhgd0zsE4B7E2U6Pxu4gLyrhd3kCiUqGlQ1tiggJ8Yyeg15RESE3v8IIbbpZtsz407S4oLc4SZRpXRdK6nT2J8cFwq49eMUkBNCuua3c4XtCr1pYgH8zAXjWnAVlHsSfT3IjcUtBVj/96F4dGIsAOCDfdegVOp6xwyn2UpKc3eWbK3W08UFuUMoYFDR0IrSuvaFkBVKFhsOq9KqHxkX3SG4FjsJ8MkDQ+HnJsaV4jo8+eMZJO+4jK+OZwMA4kONW7s/Xt2D/KiDFHYDgMqGVgDA8tt7dai7cCtuuUtWFwu7lda1gGUBkZCBj4u4S/sijsfogBwAMjIy8OSTT2Ly5MmYPHkyli9fjowM7Ws2CCHWx7KsRsq6cTPkTkIBBod7AUC7Ksip2aoZ8whfly7P8BBCSH5VE1gdITcDwEWsO0XaUhXFLamgWpXe2pWUdW0eHhsFd2cnXCupx/bzRV3eH99KSoueeCGkOziLhIjxVwV6t6at/3m+CDkVjfB2EeG+4dqzC4I8nfHRgiFgGGDv5VKsP5SJmiZVltvaPVexxYj3nCvsdjKzAi1tjrGsNLeiEQAQ7tt5bZsoM/UiL65Rff4EejhDoKv1ASE6GB2Q79q1C/369cNff/2FhIQEJCQk4NSpU+jfvz/27NljiTESQrqoSa6AQj21YewacuBm2vpfGv3Iaf04IcSc9FVIFggY9FHPOmpjqYriltIsV6C8XjVzau6A3FMqwiNjowEAH+y7zn/2m8rYVlJEpV+weh15wc1K6yzL4tODqgmsxaOj4CLW/X0c6OHMtyjXfPeNzUyIC3KHn5sEzXIlTudUdf6AHq6+pQ0V6hlymY8RAXkXU9aLa9Tp6lTQjZjA6IB85cqVeOaZZ3Dq1Cm89957eO+993Dq1Ck8/fTTSEpKssQYCSFdVK+eHRcKGL2zTLokRnoDAFKyKvkTM+o/Tggxp/mJMr2B33NTeuu939wVxS2JK/7kIhbCy8W4rCVDPDQ2Eh7OTrhRWo//pRd2aV/WaK1mD7h15Jqtzw5fL8elolq4iIVYOEr/Ms/NqXk6Z1qNyUxgGAaDZV4AgFW/XUTyzitdng22ZXmVqtlxbxcRPAzICOTS2cvqWvhuNKYoVldYD6T148QERgfkly9fxpIlSzrc/vDDD+PSpUtmGRQhxLxu9iB3MqntyWCZN5wEDIprm1FQ3YRaWj9OCDEzrkKygFFdPNT8N3luAsb28ufv14xTuPt7VEG3qpsF3SzRisrDWYSl41Sz5B92cZa8swslPelCSHfiK60X3UxZ/+TADQDA/cPD4e2qf52xuTITNqfmYe/lEgDAjdJ6fHY4E5PWHjQq7b0nyVUH5OEGzI4Dqr8VPzfV7yJHnepuCi5lPZhmyIkJjA7I/f39ce7cuQ63nzt3DgEBhvVFJIR0L1N6kGuSioUYoC4kk5pdhZSsSihZ1Qk0VRMlhJjLvEQZ9j83EcvGR+POhBAsGx+N/c9N5Ct5c/cvHRcNLoz9adnIHlfpu6BadeJv7nR1TYvHRMLLRYSMsgb8kWb6LHmUnytWzx7A/3zrhZKedCGkO/VTB+R5lU2oaZLjdE4VTmVVQiRk8Mi4qE4fb47MBK4gn2ZYr1Cydl2Qj5shNyRdncOlrWd24f0orqUK68R0Rp+dL126FMuWLUNmZiZGjx4NADh27BiSk5Px7LPPmn2AhJCuM7Wgm6Zhkd44l1eNlOxKSEWqtPeR0Yb1QiWEEENxFcP13f/CjL7Yc6kEmeUNaFN0vZJ4d+MKulmyIKa7epb83V1X8eG+65iZEAwnoUm1fOEpFfH/ju/lhzAfF72tpAjg5SJGqJcUBdVNuFxUi/+q+47PGRyKYM/Of+/zE2XYcEh7wWRDMxP4gnxaZtq5tHd9f2s9kbEz5AAQ6euKlOyqLlVaL1EvQwmkGXJiAqMD8ldeeQXu7u5Yu3YtXnjhBQBASEgIXnvtNSxfvtzsAySEdN3NgNz0vrSJkT74/EgWUrOrIHJSXbWndHVCiLVE+rkis7wBWRUNGB3rZ+3hGIVLWbf0+utFoyPxxZFMZJY3YNm3p+EqcUKYtxTzE2X8rKAhdl4sBgAsGBFudwGcJUX4uqCgugn//DmdDxT/b0KMQY/llnAkbU0HwzB8P3OWZQ3OTHDEgnymBORR6or4XelFXlSrTlmnGXJiAqPPzhmGwTPPPINnnnkGdXWqnsTu7u5mHxghxHz4lHVJFwLyCFVht6sldeCy6CggJ4RYi7n6B1tDYbW6B7mFW0a6SZwwMtoXOy4UY/+VUggY1XnchkMZSJ6bYFCqf7NcgQNXSgEA0/sHWXS89mRzah5OZKiKn3JBIgCcyalCjL+bQfuYlyjDsEgfbErNQ35VE8K8pUZlJvBp7zpmyO2xIJ9JAblv11LWWZZFiTplnWbIiSmMzl3KysrC9evXAagCcS4Yv379OrKzs40eQEFBAR588EH4+vpCKpUiPj4eqamp/P0sy2LVqlUIDg6GVCrF5MmT+ecnhBjGHDPkvm4SyNRf3iwLeElFaGx1jJ6mhBDbE2mm/sHWUFB9s6ibJWWVN2CXenYbULXMMnYN8dHr5WhsVSDY0xkJYZ6WHK7d0LZ2m2Ps2m1uCcd/7h+MpOlxRi0TcLSCfEoli/xK1d+WUWvI/bmLe/U63y99qhrlaG1TAqCAnJjG6IB88eLFOH78eIfbT506hcWLFxu1r6qqKowZMwYikQg7duzApUuXsHbtWnh7e/PbvPPOO/jwww+xfv16nDp1Cq6urpg2bRqam5uNHTohDutmUTfT15BvTs1DnkZ6W02T3K4rtRJCbFu0mfoHdzelkkWRuiJzqIVnKPk1xFoY2jqLS1ef1j/IIhXh7ZE53ndz0Oxc0NM7ExiipK4ZrQolnASMUanjkeoZ8trmNlQ1Gt/6jPt79nMTQ+xkWp0G4tiMni47e/YsxowZ0+H2kSNH4oknnjBqX8nJyZDJZNi4cSN/W1TUzcqTLMti3bp1ePnllzF79mwAwDfffIPAwED8+uuvuO+++4wdPiEOqbaLM+Tc1X5NLFQz5Ulb0zEs0sfuvtgJIbaNS1nPrWhEm0JpcsGy7lZW3wK5goVQwCDQXWLR5+rqGmK5Qok9l1Qts6YPoHR1Q9nS2m0u7f3bkzl8YbnfnhiL+FD7y3bIVbctC/WWGvV54CwSIsTTGYU1zcgqr4ePq3EFa0tqqaAb6RqT1pBza8c11dTUQKEwLn31999/x7Rp0zBv3jwcOnQIoaGheOyxx7B06VIAqvT44uJiTJ48mX+Mp6cnRowYgRMnTmgNyFtaWtDS0sL/XFtbCwCQy+WQy42/6mUu3HNbcwzEcdU0tQIAXEQCvcegruP0p1M5YMAAWhLwGAA/nsrBiqm9zDZeQjpDn6nEVyqExEmAljYlssvrEGFEimp3uvVYzSlTnUMFukvAKhWQKy239CfEQ6L3szvEQ6L3b+hYRgVqmuTwcRVhUKg7/b0ZqKvvu7mFeoqxclov/JFWiNK6FrS2aj8n7umfq1nqv60wL6nRryHS1wWFNc24UVKLhBDjamMVqNetB7iLe+x719P0lGPV0PEZHZCPHz8ea9aswY8//gihUNX6SKFQYM2aNRg7dqxR+8rMzMSnn36KZ599Fi+++CJSUlKwfPlyiMViLFq0CMXFqjSpwMDAdo8LDAzk77vVmjVr8Prrr3e4fffu3XBxsf6X9Z49e6w9BOKAbmQLAAiQc+MK/qy/3On2tx6nKdcEULIMgI4peEqWRcqlDPzZRrUdSPejz1TH5iMWoqiNwZYdh9DP27bbn3HH6plyBoAQUmUT/vzzT4s+p18ToGSF6p80P79V68j9667jzz91f3ZvzlR9d/RxbcGunTssOVS70tX33VLcIUQpGPy67zjy/XX/vfTUz9UDuarjla0vM/pvi2lQPXbvqfNwLkoz6rFH1c/bWl1q8b9p0p6tH6uNjY2dbwQTAvLk5GSMHz8effr0wbhx4wAAR44cQW1tLfbv32/UvpRKJRITE/HWW28BAAYPHowLFy5g/fr1WLRokbFDAwC88MIL7fqh19bWQiaTYerUqfDw8DBpn+Ygl8uxZ88eTJkyBSKR6et4CTHFj8UpQFUVRiUOwoyEYJ3b6TpOLzldR9rRbCi0pOAJGAbD+kVjBs2Qk25En6kEALbXnEPRpVL4RffDjFER1h6OVrceq/lHsoDr19E/KgQzZsRb/Pndowrw4q8XwTAMlEpWPWfL4M3Z/TA/MUzn45RKFm++ewhAK5ZMG4oJvf0tPlZ7ovm+syyrni9n8NZd/TF3SKhVxnSs9SIyThfAS9YLM26P7XB/T/9c3bflPFBQhDED+2DGuKjOH6Ch5HgOju24CievYMyYMdCoxx755SJQUIBhA3pjxsRoox5LTNNTjlUuU7szRgfk/fr1Q3p6Oj766COkpaVBKpVi4cKFeOKJJ+DjY9yai+DgYPTr16/dbX379sXWrVsBAEFBqvVKJSUlCA6+GUSUlJRg0KBBWvcpkUggkXRckyUSiWziF2Yr4yCOpaFVVf3T29XZoOPv1uP0vhER+Fy99uxWLID7R0TQcU2sgj5THVtMgDtwqRR5Vc02fxxwx2pxrWoJkczXpVvGfN+ISIyM8cem1DzkVDTgwJUyNMkVEAqFep//dE4lyupb4S5xwrg+gRA5CXVuSzrSfN9NaVlmCdEBqlTsnEr9fy899XM1X929IMrf3ejxxwaq3pvsyiajH1tar/qbDvXunr9pcpOtH6uGjs2kCk8hISH8rHZXjBkzBlevXm1327Vr1xARobrKHRUVhaCgIOzbt48PwGtra3Hq1Ck8+uijXX5+QhzFzSrrphV14yq1Jm1Nv3m1X/2vPVZqJYT0DFE9sPVZYTe1PNPEtc4CgC+OZOLN7Zfxn/03cPeQMJ1VoXdeUC0NvL1vACQUjJtE8323BdzfS3YP60xgqFx1yzNjepBzuErr2eUN/DmOoYrVVdaDjKjsTogm05sSm8EzzzyD0aNH46233sL8+fPx119/4bPPPsNnn30GQFVA7umnn8abb76JXr16ISoqCq+88gpCQkJw1113WXPohPQoN/uQm34VkavUaktX+wkhjq0nBuRcD/LQbgzINT04MgIbDmeioLoJP5/Ox4IR4R22YVmWb3c2vT9VV7cXfKvAMuODTlvX0NKG8npVUedwX+MDcpmPC4QCBk1yBUpqW4wKrotrVFXWg6jKOjGRVQPyYcOG4ZdffsELL7yA1atXIyoqCuvWrcMDDzzAb/PPf/4TDQ0NWLZsGaqrqzF27Fjs3LkTzs500BNiqLoutj3j2NrVfkKIY+MC8oLqJjTLFXAW2f5MLheQh1m4B7kuziIhHp0Qg9X/u4SPD9zAPUM7zpJfKqpFXmUTJE4CTOhDa8fthczHBQwD1LW0oaKhFX5ulm27153yqlTFs7xcRPAwYfJBJBQg3McFWeUNyCyvNzggb2xt41vL0gw5MZXVm3bOnDkT58+fR3NzMy5fvsy3POMwDIPVq1ejuLgYzc3N2Lt3L3r37m2l0RLS8zTLFWhVqNaQdzUgJ4QQW+LrKoa7sxNYFsitNKyarTXVNsv5C6TdmbJ+qwUjwhHgLkFBdRM2p+Z1uH+XOl19Qm9/uIjpe8NeOIuEfGZGT8oqMQTXg9yUdHVOpHpm3Zj3hpsddxULu5SFSByb1QNyQohlcSd/DAO40okVIcSOMAzTo9LWC6pUs+PeLiKrBrrOIiEemxgDAPj4wA20tLXvhc6lq98RT+nq9iZKI23dnnAX5GRdCMij/NwAqNaRG6q4VhWQB9LsOOkCowPypqamdj3VcnJysG7dOuzevdusAyOEmAdX0M1N7ASBwH7WixFCCNCz1pFbo6CbLvcND0eQhzOKapqxOeXmLHlGWT2uldTDScDg9rhAK46QWAL/92Jnhd3yKrs+Qx7lb/xnCTdDHkwBOekCowPy2bNn45tvvgEAVFdXY8SIEVi7di1mz56NTz/91OwDJIR0jbnWjxNCiC3SrI5s66xd0E2Ts0iIx27jZskz0CxXzZLvUs+Oj471g6eUUnDtjb3PkHcpIPc1ISDnZsipoBvpAqMD8jNnzmDcuHEAgJ9//hmBgYHIycnBN998gw8//NDsAySEdI05KqwTQoitilbPamX2pIDcSgXdbnXvMBmCPZ1RXNuMTepZcm79OFVXt0+RPSijxBhmCcjVnyW5lY1oU9fe6UwJVVgnZmB0QN7Y2Ah3d3cAwO7du3H33XdDIBBg5MiRyMnJMfsACSFd09Ue5IQQYst6Uso6t4bcFmbIAUDiJMRjt8UCAD7cdw0vbEtHWn4NAKBPkLs1h0YsJFqjF7lSyVp5NOahVLLIqzK9Bzkn2MMZEicB5AqWv3jWmSJKWSdmYHRAHhsbi19//RV5eXnYtWsXpk6dCgAoLS2Fh4eH2QdICOkaSlknhNgzbsavrK4F9S1tVh6NfraUss6ZnxgGL6kTKhrk+PGvm2vJ560/ji1aKrCTni3USwqRkEFLmxJF6nTrnq60rgWtbUoIBUyXAmOBgOGXwBh6ga+EUtaJGRgdkK9atQorVqxAZGQkRowYgVGjRgFQzZYPHjzY7AMkhHRNLT9DTinrhBD74+Esgp+bGIDtryO3paJunMLqZtQ0dbyQoWSBpK3pNv+eEuM4CQV8JXJ7+d1y6eqhXlI4CbvWQMrYjBtuDTn1ICddYfRRe8899yA3NxepqanYuXMnf/ukSZPw/vvvm3VwhJCuoxlyQoi9M3ZWyxpa25QorWsBYDtryAFgc2oeBIz2DhwMw2ATzZLbHS5tvSfUXTCEOdaPc7iMG0MuVtwoqUNJrepveuuZfJv+/CG2zaTLSEFBQRg8eDAEgpsPHz58OOLi4sw2MEKIeXApnDRDTgixVz1hHXlxbTNYFpA4CeDrKrb2cHj5VU1goX0tMcuyyK8ybC0t6Tn4C1h2UmndHD3IOYZerNicmoep6w7zP393MheT1h6kZR7EJEZPmTU0NODtt9/Gvn37UFpaCqWyfRXCzMxMsw2OENJ1VNSNEGLvTOkf3N0Kq1WpraFeUjA6ZqStIcxbPR62Y1DOMAzCbGg2n5gH9/eSbSe9yM3Rg5xjyGdJVnkDVm5Nh2ZNPIX6h6St6RgW6cPPtBNiCKPP0B955BEcOnQIf//73xEcHGxTXyqEkI64lHUPCsgJIXbKlP7B3c3WWp5x5ifKsOFQhtb7WJbFvYmybh4RsbSekFFiDLOmrKs/Swqqm9DSpoDESdhhm82peXovYm1KzUPSdMoaJoYz+gx9x44d2L59O8aMGWOJ8RBCzIwLyN0oICeE2KkeMUOubo8U4mlbAXmUnyuS5yYgaWs6GIYBy7L8v8lzE2imzw5xAXleZSPkCiVEXSyEZm3mDMj93MRwlzihrqUNuRWN6BXYsf1fflUTWC3BOEDLPIhpjD5D9/b2ho+PjyXGQgixAD5lXUJryAkh9inCRxVg1DTJUdXQCm8bWqPN4VPWbWyGHADmJcowLNIHm1LzkF/VhDBvKe5NlFEwbqcC3Z0hFQnRJFcgv6qJD9B7osbWNpSpiyWG+3Y9IGcYBpF+rjhfUIOs8gatAXmYt1RH1QVa5kFMY/QlsTfeeAOrVq1CY2OjJcZDCDEzqrJOCLF3UrEQIeq2Q7ZaObqwxvZ6kGuK9HNF0vQ4/Of+wUiaHkfBuB0T/H979x0X1ZX2Afx3Z+i9N+mioihqRI0lxljSfFNZNcbNGpNXU0zRbIzJm2p2N8bsZmOSTd1N2d0Ug2WzKcYEe+yiInYFQTpIr8Iwc94/hrmCwDADU+H3/Xz4IHPvzByGA95nnnOeRyEhqjV4zS6rs/JoeievQvt75e3qCG9X0yQeulvSf11cQGer1QFwmwf1jNFX6G+++SaysrIQHByM6OhoODq2n/xHjhwx2eCIqPdqLrPKOhH1fTGB7iisvozssnqMifK19nA60GXIbakHOfVfsYHuOFNci+wy+06wmXK5uk53Afm/91+U/61UcJsH9Z7RAfmdd95phmEQkbmwyjoR9QfR/u7Yk1luUP9gS9OIK3vIuZyVbMGVoNO+M+SWDsj3ZJbhpxPFUCok/P13Y3Aop5LbPKjXjL5Cf/nll80xDiIyg+YWDZpatK0JvZghJ6I+zJYrR9eptH+PJQkIaV1aT2RN0XbQmcAQeSbsQa7T1d8SlVqDV747CQC479ooTIsPxrT4YJM9L/VfPU6ZHT58GKdPnwYAJCQkYPTo0SYbFBGZhi47DrDKOhH1bbGtldZtcQ95pbbmFII9Xey+ojX1DbrflxwuWe9Al+UurW1CfVML3J2110//2ncR50vr4OfuhGUzBpvs+YiMvkIvLS3FPffcgx07dsDHxwcAUFVVhRtuuAFr165FYGCgqcdIRD2kK+jm7qSEUiFZeTREROajy/jllNXLezptRWWzdiy2WGGd+qe2/bYvq9RwcezYb9semCMg93Z1hL+7E8rrm5FdVo/hA7xRVteENannAADLbxoCbzeuOiTTMfpt2scffxy1tbU4efIkKioqUFFRgRMnTqCmpgZPPPGEOcZIRD1Uy4JuRNRPRPi5QamQ0KhSo6SmydrDaaeidTgs6Ea2ws/dCV6tK+culttnllyjEfKSdVMG5EDHZet/3nwWtU0tGD7AC3NYRZ1MzOgM+ebNm7FlyxYMHTpUvm3YsGF47733cOONN5p0cETUO7VNLOhGRP2Do1KBCF9X5JQ3ILusXu9e7eyyeqS06bk9JynCrL2YK5taM+QMyMlGSJKEmEAPHMurQnZZHYaEdOy3besu1TWhqUUDpUJCqI9pazNEB7gj7WIlcsrqcSyvCimH8wAAK29P4IpDMjmjr9I1Gk2HVmcA4OjoCI1GY5JBEZFpsAc5EfUnMQHuckA+YaB/p+ekpOXh2Q0ZcpsiSZLw0c4srE5OxGwzZb50e8i5ZJ1sSYy/G47lVdlk3QVD6Jarh/mYvjaDT2tP828O5eHf+y9CCODu0QMwJsrPpM9DBPRgyfq0adPw5JNPorCwUL6toKAAy5Ytw/Tp0006OCLqHS5ZJ6L+JCbAA0DXrZyyy+rx7IYMaASg1oh2n1dsyDBby7QKOUPOCutkO3S/L7bYKtAQueXmWa6ekpaHT3ZnAwDyqxpRWqt9R234AC+TPg+RjtEB+d/+9jfU1NQgOjoaAwcOxMCBAxETE4Oamhq8++675hgjEfWQrso6K6wTUX8QE6C9MM/uonJ0Slpel8XeJEnCN2l5ZhlXZbP28wAf0wYORL0RE2jfrc/MUdBN96ad6OTYH388bbdvXpBtM/oqPSIiAkeOHMGWLVtw5swZAMDQoUMxY8YMkw+OiHpHlyH3YkBORP1Adxny/MpGCNHZpTYghEB+ZaPJx1Tf1IKGFu2bAGHMkJMNiZF7kdtnUTdz9CCX37Tr5O+E7k27FTfHm+z5iIAe9iGXJAkzZ87EzJkzTT0eIjIhXYacS9aJqD+Ibs2Q51Y0QK0RHYovhfu66r3YDjfDHu/C6ssAtG+M8m8x2RLd70tZXRNqLqvgamedz8yRIbfGm3ZEBgXk77zzDhYvXgwXFxe88847es9l6zMi2yHvIXdmhpyI+r4wb1c4OSjQ3KJBQWUjIv3bX6jPSYrAhzuzOr2vEAJzzVDUrbBKewHPlmdkazxdHBHg4YyyuibklNVjaLD5Og2YgzkCcmu8aUdk0FX6W2+9hfnz58PFxQVvvfVWl+dJksSAnMiGsMo6EfUnCoWEGH93nC2pxYWyug4BebS/G6L93JFdXg8JkPeJSgBWJyci2gytzwqqtBnyMD1t2IisJTbAHWV1Tci2s4C8sVktF1szZUA+JykCH1n4TTsig67Ss7OzO/03Edm2Gi5ZJ6J+JjrADWdLarXFl4a0P/bdsUJkl9fD2UGBOUkR2JtVhqxL9bhxWIjZWp4VtS5ZZ4V1skUxAe44mFNhd4Xd8iq12XEvFwf4uDmZ7HFjAtyxOjkRK65qjSiEMNubdkRGV1l/9dVX0dDQsfhDY2MjXn31VZMMiohMgxlyIupvrhR2ax9g1De14LVNpwEAj0+Lwx/uHI6Xb0sAABwvqOpy32hvZJfVY8vpUgDAudI6uwt6qO/TBZj2Njfllmf+pu9cMDspAtt+PxWLp8RiVmIYFk+JxbbfTzXbm3ZERgfkK1euRF1dx+qlDQ0NWLlypUkGRUSmwaJuRNTfyK3PytsnD97bnomSmiZE+rnhf6+LBQCMjfaDk1KBwurLyCk3baXplLQ8TH9zBzIvaQOdQzmVmP7mDqwzU2s1op6IaQ3I7a2dlzn2j7cVHeCOFTfH4915o7Hi5nhmxsmsjA7IdUs3rnbs2DH4+fmZZFBEZBrMkBNRf9NZ67Ocsnr841ftlrsX/2cYXBy15aRdnZQYHekDANiTWWayMeh6GWvaJN01QvuxYkOG3QU/1HfpAvILZfVmWSViLrlmaHlGZC0GB+S+vr7w8/ODJEkYPHgw/Pz85A9vb2/MnDkTc+bMMedYichIV/qQM0NORP2DLsAoqGxEU4saAPCHH06hWa3BlMGBmDE0qN35k+ICAAB7s0wXkMu9jDuh62VMZAui/N0gSdrrhYoGlbWHY7A8M2fIiSzJ4LTZmjVrIITAAw88gJUrV8Lb21s+5uTkhOjoaEyYMMEsgyQi47WoNWhUaS9GmSEnov4iwMMJHs4OqGtqQV5FA/IqGrH1TCkcFBJevm1Yh0B5Upw//poK7Msqh0YjoFB0Hkgbg72MyV64OCoR5u2KgqpGu1q5Ye4l60SWZPBV+oIFCwAAMTExmDhxIhwdmXEjsmV1TS3yvz0YkBNRPyFJEmIC3HG8oBpni+vwl1/OAgAemByDgYEeHc5PDPeBu5MSlQ0qnCqqwfAB3h3OMRZ7GZM9iQlwR0FVI7LLG2AP4a0QggE59SlG7yG//vrr5WD88uXLqKmpafdBRLZBt1zdxVEBR6XRv+pERHYr0NMZALB8/TFkl9XD190Rj0+L6/RcR6UC42K0NXD2ZZWb5PnnJEXozZCzlzHZEt02j4smLmxoLocvVqKpRQMA+OpArt1ViCe6mtFX6Q0NDXjssccQFBQEd3d3+Pr6tvsgItvAHuRE1B+lpOVh+xltq7GGZu22nap6FTafKO7yPrp95HtMtI88JsAdc9oE3RIElBKgkMBexmRz7Kn1WUpaHmZ/tE/++h+7s9m9gOye0QH58uXLsW3bNnzwwQdwdnbGP/7xD6xcuRJhYWH417/+ZY4xElEPsMI6EfU3uurmV+emBfRXN584UBuQH8yuQHNr5q23LtU2AQDGRPpgtL/A/06OYS9jskmxdpIhl3+/2/yCqzWC3QvI7hkdkH///fd4//33kZycDAcHB1x33XV44YUX8Nprr+HLL780xxiJqAeuBOTMkBNR/9DT6ubxIZ7wc3dCQ7Max/Krej2O6kYVdp2/BAD4wx3DsGCwBk/fOIiZcbJJci/yioZ2rfpsDbsXUF9ldEBeUVGB2NhYAICXlxcqKioAAJMnT8auXbtMOzoi6rHa1iXrXsyQE1E/0dPq5gqFhAkD/QGYph956qkSqNQCQ4I9MSioYyE5IlsS7usKB4WEyyoNqputPZqu5Vc2QtPFOwbsXkD2zOiAPDY2FtnZ2QCA+Ph4pKSkANBmzn18fEw6OCLqOS5ZJ6L+Rq5u3onuqptPal22vjez94XdfswoBADMSgzt9WMRmZuDUiFXK790ufdt/8xFqUCH7Sg67F5A9szogHzhwoU4duwYAODZZ5/Fe++9BxcXFyxbtgzLly83+QCJqGd0GXJPZy5ZJ6L+oTfVzSfFaTPkR/Mq0dDc0uV53aluUOHX89os+60jGJCTfQjy0nYm+O6ihL/8ct7mCryV1lzGznOXujzO7gVkz4xOnS1btkz+94wZM3DmzBkcPnwYcXFxSExMNOngiKjnmCEnov4mJsAdq5MTsWJDBiRJghBC/txddfNIPzcM8HFFQVUjDuVU4vrBgT0aw8+nitGiEYgP8URckAdUKlVPvx0ii0hJy8OBC9otqHn1Ev6xOwd/352N1cmJNlGEUKXWYMlXR1BZr0KwlzMu1TYZ/ftNZMt6faUeFRWFqKgoU4yFiEyohkXdiKgfmp0UgbHRfvgmLQ/5lY0I93XF3KSIbi/WJUnCxIH+WHc4H3szy3ockP+YUQQA+B8uVyc70LEzgQR16yqTFRsyMDbaz+qB7mubTuNQTiU8nR3w9aJroWgt4GbM7zeRLTM6IH/iiScQFxeHJ554ot3tf/vb35CZmYk1a9aYamxE1AvyknVmyImon4kOcMeKm+ONvt+kuACsO5zf437klfXNclE4LlcneyBXLu9kq4eucnlPfpdM5b/pBfhsTw4A4M05IxEbqC2SaM0xEZma0XvIN2zYgEmTJnW4feLEiVi/fr1JBkVEvVfXxCXrRETGmNhaaf1kYQ2qGowvN/1L63L1YaFecuBAZMt62pnAEs4U1+DZDccBAEtuGIgbE0KsNhYiczL6Sr28vBze3t4dbvfy8kJZWe9bhRCRabAPORGRcYK8XDAoyAPnS+uwL6sctxiZ5f6hdbk6q6uTvZA7E3SRIbd05fLssnqkpOUhu6we+zLL0KhS47pBAXhq5hCLjoPIkozOkMfFxWHz5s0dbv/pp5/k/uREZH1csk5EZLxJcdr2Z8YuW6+ob8beLG3LNC5XJ3vRm84EppaSlofpb+7Ax7suYPOJYlS3JhamDw2GUmG77diIesvoK/WnnnoKjz32GC5duoRp06YBALZu3Yo333yT+8eJbAirrBMRGW/iQH98vjfH6H7kP58shlojkBDmhRgWmCI70bYzAQBoWmNzhQSLVi7XFZfTCHTI1r/6/UlMHRzIwm3UZxmdIX/ggQfw5ptv4pNPPsENN9yAG264AV988QU++OADLFq0yKjHeuWVVyBJUruP+PgrRRqmTp3a4fjDDz9s7JCJ+iUuWSciMt74WH8oJOBCWT2Kqg3fP/sjl6uTnZqdFIFtv5+K/50cDSeFNhheeXuCRVueycXlOqErLkfUV/UodfbII4/gkUcewaVLl+Dq6goPj54XLklISMCWLVuuDMih/ZAWLVqEV199Vf7azc2tx89F1F+oNYJF3YiIesDb1REjBnjjWH419maWI3lMeLf3Ka9rwt7WJe6zuFyd7FB0gDuW3zgYZ85fwK5iCScLayz6/LZcXI7I3Hp1pR4Y2LMene0G4OCAkJCuqya6ubnpPU5EHemCcYABORGRsSbGBeBYfjX2ZJUZFJBvPlkMjQBGDPBGlD+X1ZL9SvAV2FUMbDtTCiFEl1lrU7O14nJElmTQlfo111yDrVu3wtfXF6NHj9b7y3nkyBGjBnD+/HmEhYXBxcUFEyZMwKpVqxAZGSkf//LLL/HFF18gJCQEt912G1588UW9WfKmpiY0NTXJX9fUaN/hU6lUUKlURo3NlHTPbc0xUP9RWad9J9nJQQGF0ECl0hh0P85Tshecq2RO46N98AGAPZllaG5u7jYo+eFYIQDg5oSgDnOSc5XshUqlQpyXgKujEqW1TTiWW4GEMC+LPPfdo0Lw0c6sTo8JIZA8KpS/QySzl7+rho7PoID8jjvugLOzMwDgzjvv7PGgrjZ+/Hh8/vnnGDJkCIqKirBy5Upcd911OHHiBDw9PXHvvfciKioKYWFhyMjIwIoVK3D27Fls3Lixy8dctWoVVq5c2eH2X375xSaWu6emplp7CNQPFNQDgAOcJDU2bdpk9P05T8lecK6SOTSrASWUKKlpwp1//RlhbsD4IA2COknS1TQD+y8oAUhwvXQamzad7vQxOVfJHjgogDgPFY5XKvDxD3twU3jny8jN4Z5YCV9lKQBIkCAgARAA7onV4OSBHThpsZGQvbD1v6sNDQ0GnSeJrjZstPHOO+9g8eLFcHFxQW5uLsLDw6FQGF0PrltVVVWIiorCX//6Vzz44IMdjm/btg3Tp09HZmYmBg4c2OljdJYhj4iIQFlZGby8LPMuX2dUKhVSU1Mxc+ZMODqyyBaZ16GcStz7ySFE+7shdelkg+/HeUr2gnOVzGn9kQI89x/t5b8EQCFJEBB47c4EJF8zAACQU16P9YcLsSerHCcKazAk2B0/PDapw2NxrpK90M3Vav8EvPTDWYwM98b6h8Zb7PmbWjQY+eoWqAUwbUggBgV5YPaYAYjyt35CjWyLvfxdrampQUBAAKqrq/XGoQZlyJ966incc889cHFxQUxMDIqKihAUFGSywer4+Phg8ODByMzM7PT4+PHaPwr6AnJnZ2c5m9+Wo6OjTfzAbGUc1Lc1tmjfZ/Ny7dl84zwle8G5SqaWXVaP57+9kosTANStuYv/+/Ykrh0YiIM5FXh2QwYkSYK6tU/UuZJ6fHusuMvK1JyrZC+mDQ3GSz+cRUZBNaqbNAjw6HhdbQ5nSqqhFoCPmyM+uX+sxfavk/2y9b+rho7NoDR3WFgYNmzYgIsXL2orHebnIzc3t9OP3qirq0NWVhZCQzuvUJqeng4AXR4nIi32ICci6hl97Zc0Anj432lYsV7bL1kXjAPawH3FhgzklNVbaKRE5hHs5YKEMC8IAew4e8liz3uqqBoAkBDmxWCc+hWDAvIXXngBS5cuRWxsLCRJwtixYxETE9PuIzo6GjExMUY9+dNPP42dO3ciJycHe/fuxV133QWlUol58+YhKysLf/jDH3D48GHk5OTgu+++w+9+9ztMmTIFiYmJPfpmifqL2svaIhKezrb7riERkS3S134JAM6U1KGro+yXTH3FtHjtStjtZ0ot9py6VmvDQq23xZTIGgxKny1evBjz5s3DxYsXkZiYiC1btsDf37/XT56fn4958+ahvLwcgYGBmDx5Mvbv34/AwEBcvnwZW7ZswZo1a1BfX4+IiAgkJyfjhRde6PXzEvV1texBTkTUI/rbLwFeLo6obuy8ci77JVNfcUN8EN7dlold5y5BpdbAUWn62lFX0wXkCWHeZn8uIlti8NW6p6cnhg8fjs8++wyTJk3qdJ+2sdauXdvlsYiICOzcubPXz0HUH+mWrHswICciMsqcpIgu2y9JAGaNCMU3aXntlqvLx9kvmfqIkeE+8Hd3Qnl9M9JyKjFhYO8TcfqoNQKni3QBOTPk1L8Y/XbXggULTBKME5H5yEvWXbhknYjIGDEB7lidnAiFBCgVUrvPq5MTsWhKbJdL2oUQmNtFUTcie6JUSLh+SCAAYPtZ8y9bzymvR0OzGi6OCsQGepj9+YhsiUHpMz8/P5w7dw4BAQHw9fXVW2ihoqLCZIMjop7RZci9mCEnIjLa7KQIjI32wzdpecivbES4ryvmJkUgOsAdgDYwX9FaZV0IIX9enZwon0Nk76bFB2HjkQJsO1OK/7t1qFmf61TrcvX4EC8oFSzoRv2LQVfrb731Fjw9PeV/s/IhkW1jlXUiot6JDnDHipvjOz3WXcBO1BdcNygQSoWEzNI65JY3INKM/cDlgm5crk79kEFX6wsWLJD/ff/995trLERkIlyyTkRkXvoCdqK+wNvVEWOjfbH/QgW2nSnB/ZOM66ZkjJOFV1qeEfU3Ru8hP3LkCI4fPy5//d///hd33nkn/u///g/Nzc0mHRwR9Qwz5ERERNRbuvZn28zYj1wIIS9ZZ4V16o+MDsgfeughnDt3DgBw4cIFzJ07F25ubli3bh2eeeYZkw+QiIx3JSBnhpyIiIh6RheQ779QjobmFrM8R0lNE8rrm6FUSIgP8TTLcxDZMqMD8nPnzmHUqFEAgHXr1uH666/HV199hc8//xwbNmww9fiIqAdq5CXrzJATERFRzwwM9ECEnyuaWzTYk1luluc4VVTd+lzucHFUmuU5iGyZ0QG5EAIajQYAsGXLFtx6660AtH3Dy8rKTDs6IjKaRiNQ18Ql60RERNQ7kiRh2pDWZetnSszyHCcLuFyd+jejA/KkpCT88Y9/xL///W/s3LkTs2bNAgBkZ2cjODjY5AMkIuPUN7dA1yLXi0vWiYiIqBemDdVe328/cwlCd4FhQnKF9VAWdKP+yej02Zo1azB//nx8++23eP755xEXFwcAWL9+PSZOnGjyARL1VHZZPVLatKSZkxSBmH7Qkka3f9xRKcHZwej33IiIiIhk42P84OqoRHHNZZwqqjF5JvtkESusU/9mdECemJjYrsq6zp///Gcoldz3QZbRXbCdkpaHZzdkQJIkCCEgSRI+2pmF1cmJmJ0UYcWRm9+V5eqOkCTJyqMhIiIie+biqMToSB/szSrH71OO4Yb4IJMlOaobVciraATAHuTUfxkdkOfl5UGSJISHhwMADh48iK+++grDhg3D4sWLTT5Aoqt1F2yfLa7BsxsyoBGAvHa79fOKDRkYG+2H6D6cKdf1IPdw5v5xIiIi6p2UtDzsy9IWdDtTXIvzpXUmS3KcLtIuVx/g4wofN6dej5XIHhm9nvXee+/F9u3bAQDFxcWYOXMmDh48iOeffx6vvvqqyQdI1FZ2Wb0cbKs1ot3n5eszMP5PW3DTml+1wXgnJEnCN2l5lh20hdWwBzkRERGZgO66q+1lle66a8WGDOSU1ffq8U/K/ceZHaf+y+iA/MSJExg3bhwAICUlBcOHD8fevXvx5Zdf4vPPPzf1+IjaSUnL07sMu6S2Se/9hRDIr2w09bBsSi0DciIiIjIBfdddpkhynCzU7h/ncnXqz4wOyFUqFZydnQFo257dfvvtAID4+HgUFRWZdnREV8mvbOyywqcEYFKcPxZMjIZS0fV/HuG+rmYcofXVyj3IWWGdiIiIek7fdZcpkhynCtnyjMjogDwhIQEffvghfv31V6SmpuLmm28GABQWFsLf39/kAyRqK9zXtct3ahUKCYnhPrh/YrTe/zzm9vGibsyQExERkSnou+7qbZLjskqN86V1ALhknfo3owPy1atX46OPPsLUqVMxb948jBw5EgDw3XffyUvZicxlTlJEt8F2TIA7VicnQiFps+aA9rNCAlYnJ/bpgm7AlQw5e5ATERFRbxhy3dVT50vqoNYI+Lo5ItTbpcePQ2TvjE6hTZ06FWVlZaipqYGvr698++LFi+Hm5mbSwRFdTRdsP7P+SoERpUJbbb1tsD07KQJjo/3w+k9nsPlkMbzdHPHto5P6fDAOMENOREREpqG77lrR2t1G3Vo1V0Lvkxy6/eMJYd5s00r9Wo+u2JVKZbtgHACio6NNMR6ibs1OisCezDJ8m16IuCAPzBwWjLlJER3+U4gOcMequ0dg88liVDWo4NFPAlQG5ERERGQquiTHN2l52JtZhmP51Rga6tnrlme6Cuss6Eb9XY+u2NevX4+UlBTk5uaiubm53bEjR46YZGBE+tQ1aYPOhZOiMX98VJfn+bo7YUiwJ86W1CItpwI3Dw+11BCthkXdiIiIyJSiA9yx4uZ4FFY1YuLr23CmuBZVDc296h1+JUPOgJz6N6P3kL/zzjtYuHAhgoODcfToUYwbNw7+/v64cOECbrnlFnOMkagDXVXPAT7dFxMZH+sHANh/ocKsY7IV7ENORERE5hDm44rBwR7QCGB3ZlmPH0etEThdVAuAATmR0QH5+++/j48//hjvvvsunJyc8MwzzyA1NRVPPPEEqqurzTFGsiHZZfVYvfkMHv/6KFZvPoPssnqrjKOgyvCAfFyMNiA/mN0/AvIrS9aZISciIiLTmjokCACw8+ylHj9Gdlk9GlVquDoqERPgYaqhEdklowPy3NxcTJw4EQDg6uqK2lrtu1v33Xcfvv76a9OOjmxKSloepr+5Ax/vuoAfMwrx8a4LmP7mDqxLy7PoOGouq+SgM8yQgDxaG5CfLq5BdaPKrGOzBVeWrDNDTkRERKZ1/eBAAMDOc5e6rMDenVNF2v3j8aGeUCpY0I36N6MD8pCQEFRUaDONkZGR2L9/PwAgOzu7x7+UZPuyy+rx7IYMaIR2mVHbzys2ZCDHgpnywtbsuI+bI9yduw86g7xcEBPgDiGAwxf7fpZct7/e04DXhoiIiMgYSdG+cHVUorS2SV52bizd/vFhoVyuTmR0QD5t2jR89913AICFCxdi2bJlmDlzJubOnYu77rrL5AMk25CSltdlSwpJkvCNBbPkhUYsV9cZ37ps/UAf30cuhOCSdSIiIjIbZwclJg70B6DNkvfEqdYK6wlh3iYbF5G9MjqF9vHHH0Oj0QAAlixZAn9/f+zduxe33347HnroIZMPkGxDfmVjlysghBBykTVLKGh9LkOWq+uMi/HD2kN5ONDH95E3qtRyj1AuWSciIiJzuH5IILaeKcXOc6V4ZOpAo+4rhJBbnrGgG1EPAnKFQgGF4kpi/Z577sE999xj0kGR7Qn3ddVmyDsJyiVJQriv4cFxb+X3IEOuK+x2oqAa9U0tBi11t0e67LhSIcHNSWnl0RAREVFfNHVwEICTSMupRF1TCzyMuK4qrrmMivpmKBUShoR4mm+QRHbCoN+ejIwMgx8wMTGxx4Mh2zUnKQIf7czq9JgQAnOTIiw2lsKqywBg1JsA4b5uGODjioKqRhzNrcLkQQHmGp5V6Qq6eTg7dLnFgIiIiKg3Iv3dEBPgjuyyeuzJLMNNCSEG31e3XD0u0AMujkweEBkUkI8aNQqSJHVbtE2SJKjVapMMjGxLTIA7VicnYvn6jm/OrE5ORHSAu8XGUlDZAMC4JeuAdh/5xqMFOJBd3mcDcvYgJyIiIku4fnAgssvqsfPcJaMCct1y9WFcrk4EwMCAPDs729zjIDswOykCG47kY/+FCsQGuONCa2X18TH+Fh2HLkNuzJJ1QLtsXRuQ99195CzoRkRERJZw/eBAfL43BzvPatufGboyT1dhnfvHibQMCsijoqLMPQ6yE9WN2oDvpduG4dM9Odh17hI+25uNl29LsMjzN7doUFKrDciNzZDr9pGn51XhskrdJ5dJsQc5ERERWcK1sf5wclCgoKoRWZfqERfkoff87LJ6pKTlYde5MgCAr7uTJYZJZPOMbnu2atUqfPrppx1u//TTT7F69WqTDIpsV3G1tqBaqLcrHpwcAwBIOZSHmtZA0PzPfxlCAM4OCgR4GPeHPCbAHQEezmhu0SAjv9pMI7QuXYbciwE5ERERmZGrk1JuK7vjbKnec1PS8jD9zR34eOcFNKq021uXrzuGdRZsm0tkq4wOyD/66CPEx8d3uD0hIQEffvihSQZFtqmxWY3KBm3gHeLtgimDAjAoyAP1zWqkHLLMH9SCNhXWjS1aJkkSxsfq+pGXm3xstuBKhpxL1omIiMi8rh8cCEB/P/Lssno8uyEDGgGo29Sj0ghgxYYM5LRugSTqr4wOyIuLixEaGtrh9sDAQBQVFZlkUGSbimu0S8XdnJTwctFW8X6gNUv+2Z4ctKg1Zh+DLiA3drm6ju6d3IM5fXMfeS2LuhEREZGFTB2iDcgPZFegsbnzws4paXldJlEkScI3zJJTP2d0QB4REYE9e/Z0uH3Pnj0ICwszyaDINhW1LlcP8XaR/7DeNXoAfN0cUVDViF9OlZh9DIU96EHelm4f+eGLlVBZ4A0ES2NATkRERJYyMNADA3xc0dyiwf7szlcf5lc2dtmpSQiB/MpGcw6RyOYZHZAvWrQIS5cuxWeffYaLFy/i4sWL+PTTT7Fs2TIsWrTIHGMkG1Fc3VpMzftKMOziqMT88dqif5/sNn81/oLK3mXIBwd5wtvVEQ3NarntRl+iC8g9nLlknYiIiMxLkiRc35ol33m282Xr4b6ukNB1hjzct2fXdER9hdEB+fLly/Hggw/i0UcfRWxsLGJjY/H444/jiSeewHPPPWeOMZKNKGoNyEO8Xdrd/rsJUXBUSjh8sRLpeVVmHYO8h7yHf7wVCgljo/vuPnJWWSciIiJL6m4f+R2jwtrtHW9LCIG5SRFmGxuRPTA6IJckCatXr8alS5ewf/9+HDt2DBUVFXjppZfMMT6yIUVyhfX2AXmQlwtuS9RuV/jUzFnyQnkPuUs3Z3bt2tbCbgf7YD9yLlknIiIiS5o40B8OCgnZZfW4WN6+QJtGI/Du1kz5a4Wk/VAqJCgkYHVyIqID3C09ZCKb0uOrdg8PD4wdO9aUYyEbV9xFhhwAHpgcg41HC7DpeBGeuzUeod6mX34khJAz5OE+bj1+nHFtCrupNQJKhXHV2m1ZbZM2Q+7FKutERERkAZ4ujhgT5YsD2RXYde4S7ptwJcB+bdNp/Hi8CI5KCauTE3G+tA75lY0I93XF3KQIBuNE6EVATv2Pbsn61RlyABg+wBvjY/xwILsC/9x7Ec/e0rE1Xm+V1zejqUUDSer8TQFDDQv1gruTErWXW3C2uBbDwrxMOErrYoaciIiILO36IYE4kF2Bnecu4b4J0QC0qyb/0bpy8i+zR+KOUQOsOEIi28WrdjJYsRyQd579fmByDA5kV+CzPdnIrahHlL875iRFIMZE737qCroFeTrDycHo3RYyB6UCY6L9sOvcJRzILu+jATkz5ERERGQZUwcH4Y3NZ7Hj7CUs+fIwWtQCP7d233n2lngG40R69DyqoX7lskqN8vpmAJ1nyAGgqkF7vKlFg5+OF+PjXRcw/c0dWGei/pIFvWx51pbcj7wP7SMXQrCoGxEREVnciYIqAECLRmDT8WI5GJ8Y64+HpsRacWREto8BORmkpEabHXdxVMDbtWP2NbusHs9tPC5/LQCoNQIaAazYkIGcsvoO9zHWlYJupg3Iu+qNaW+aWjRQqbXfCwNyIiIisoTssno8e9U1oM7+7HJcLG+w/KCI7AgDcjJIUZvl6pLUsQhaSlpep7cD2sr835ggS55f2buWZ22NCPeGs4MC5fXNyLrU9ZsF2WX1WL35DB7/+ihWbz6DbBO8sWAuNa3ZcUkC3J0YkBMREZH5WeIakKgv41W7ncguq0dKWp5cmdKUe7MNIVdY9+p8uXp+ZWOXmWYhhBxM90ahXGG99wG5s4MS8aGeOJZXjadS0jEpLqDDa5qSlodnN2RAkiQIISBJEj7amYXVyYmYbYM9M3X7xz2cHaDoQ5XjiYiIyHZZ4hqQqC9jQG4HbCEwlDPkXfT/DvdtzZx38Qc53ARZ7QITLllPSctDRl41ACAjvxonC2vk1/Q3Y8Jx+GIlVmzI0H47uu+p9fOKDRkYG+1nc606dAE5W54RERGRpei7BpQkySTXgER9GZes27jssno8uyEDGnFlT7ap92YborhaGwx3VdBtTlJEl++OagRwU0JIr8egy5D3dsm67jVtO1rda7p8fQaGvrQZv/lwX1fvLdjs8isWdCMiIiJL03cNKITAXBtcVUhkSxiQ2zhb2ZdTqFuy3kXLs5gAd6xOToRCApQKSfu5zbBXfn8Sjc3qHj9/Q3MLKhu0AWdvM+T6XlMAuKzS6L2/NZZfGbKXve2SdSIiIiJL6PQasPXz6uREm1tRSGRreOVu4/IrGqDRWH9fjtyDvIs95AAwOykCY6P98E2bve7Xxvjh8a+P4mhuFZ5cexQf/HYMlD3Y36zrQe7p4tDrJdn69jpJEjB1cCAGBXvik93ZUHfy2lt6+ZWhWxbq5B7k/LUmIiIiy+nsGnBuUgSDcSID8MrdhgkhcLGiAV015bJkYFgkZ8i7DsgBIDrAHStujm932z8WjMVv/3EAv5wqwavfn8QrtyfozVB3xpQ9yPXtdVJIEuJDvTAnKQL/+PVCp/e35PKrtlsWutvLXiMvWececiIiIrKszq4Biah7Vl2y/sorr0CSpHYf8fFXfpEvX76MJUuWwN/fHx4eHkhOTkZJSYkVR2w5Qgi8+sMpZORX6z3HEoFhc4sGZXVNAHq2XHxcjB/+OnckAOCf+y5i9eYzRrcSM2VAbshep6uXX7V9++Dl2xIs9o6voVsWssvqsflEMQAgs7TOptuzERERERGRltX3kCckJKCoqEj+2L17t3xs2bJl+P7777Fu3Trs3LkThYWFuPvuu604WssQQmDl96fw2Z4cAEDyNeFQSNrl1DqW3JdTUqPNjjs5KODr1rPs6/8khuGFWUMBAB/uvICPdmbhx4xCfLzrAqa/uQPrutkLb6qCboDhe51mJ0Vg2++nYvGUWMxKDIWfuxOAK28OWEJ+ZSM0Xbx5oNYI7Mksw8e7sjD9zR04fLESAHC6qMag15SIiIiIiKzL6kvWHRwcEBLSsQJ3dXU1PvnkE3z11VeYNm0aAOCzzz7D0KFDsX//flx77bWWHqrZtOsx7uOCourL+Da9EADw+t0jcM+4SDw+LQ5/256J9Yfz4aSU8NPSKRgY6GGR8cktz7xdjF5q3ta0+CD86cfTENBWXgdgcCsx3R5yU7Q8Awzf69R2+dX2s6VY+NkhfL43BwsmRpskW6/PZZUa2Zfquqz2Dmhbtl29ikK3ut1W27MREREREZGW1QPy8+fPIywsDC4uLpgwYQJWrVqFyMhIHD58GCqVCjNmzJDPjY+PR2RkJPbt29dlQN7U1ISmpib565qaGgCASqWCSqUy7zejh+65rx7D+iMFeP7bk5AgQUBACMh7xl+7MwHJo0OhUqkwwNsJf7x9KH46UYT6JjVqG5qgUjkbNYac8nqsP1yI/KpGhPu44jdjwhDt332wll9RBwAI9nTu1Wv4zcFcKCQJ6s76VAL4+sBFPH3joM7HUNkAAAjxdDLZz3GAtxOemj6w3W36HntSjA+ujfHF/uxK/GXzabyRPMIk4+js59LYrMFT6zKQeanrpecSAD93J5TXN3d5XN9r2pmu5imRreFcJXvBuUr2gnOV7IW9zFVDxyeJrjbTWsBPP/2Euro6DBkyBEVFRVi5ciUKCgpw4sQJfP/991i4cGG74BoAxo0bhxtuuAGrV6/u9DFfeeUVrFy5ssPtX331Fdzc3MzyffRUaSPwWroSAldnnbU/khdGqRF4VRL2w9MKnK5S4K5oNaaGGv6j218qYW2WAlLro+s+zxuowfgg/Y+ztUDCd7lKjAnQ4HeD9LcE0+ef5xQ4Wi518v0CEgRG+wssGNz5479yWInKZglLh7cgxrPHQ+i1i3XAX487QILA8kQ1BvQy+dzZz0UD3c9HgpejwDUBAjuLpE5/dmeqpB6/pkREREREZB4NDQ249957UV1dDS8vry7Ps2qG/JZbbpH/nZiYiPHjxyMqKgopKSlwde3ZcuDnnnsOTz31lPx1TU0NIiIicOONN+p9IcxNpVIhNTUVM2fOhKOjdh/2X345D4WU00nGWIJSAi55DsKCq7KbeR7ZOJ16HvVuobj11lEGPXdOeT2Wvb1Hu5S59Tbd57UXlHjgtsmI8u/6zYrDP54BcnORNHQgbjUi23q1Uw7ncWx3Z9+vtrr52GGxnT5+i1qDpw5sBSDwm1umIVhP6zVLOCOOYdOJEuxvDMYns8f0+HH0/VwEgAmxfnhrTiL83Z1wsbwB6w4XyFn02WMGIMrfDX/5pWevaVc6m6dEtohzlewF5yrZC85Vshf2Mld1K7W7Y/Ul6235+Phg8ODByMzMxMyZM9Hc3Iyqqir4+PjI55SUlHS651zH2dkZzs4dl3I7OjraxA+s7TgKa5ogumhqJlqPXz3mCXGBQOp5pF2sgoODg0F7ujemF3fZ5kuSJGxIL9LbpqKkVrtKYYCfW69ew3vGR+Hvu7M7PSYAzBsf1enjl9Y3Qq0RcFRKCPP1gKIHfcxN6Zmbh+KXU6XYdb4chy5WY2JcgN7z29UI8HXFnNYq7vp/LsCoCB+E+GhT8HEh3nhulneH83r6mnbHVn5fiLrDuUr2gnOV7AXnKtkLW5+rho7N6lXW26qrq0NWVhZCQ0MxZswYODo6YuvWrfLxs2fPIjc3FxMmTLDiKE1H7ofdia56jI8Y4A0XRwUq6puRWVpn0PPkVzbqbfOVX6m/anixrgd5LzPT7aqbG1ExXlfQLdTb1erBOKAt9DZ/fCQA4PXNZ6DRdL3kPyUtD9Pf3IGPd11oV1X+pf+ewI8ZRVB3cV8JQH7V5W7HYmjFeCIiIiIisj1WzZA//fTTuO222xAVFYXCwkK8/PLLUCqVmDdvHry9vfHggw/iqaeegp+fH7y8vPD4449jwoQJfabC+pykCHy0M6vTY131GHdyUOCaSF/szSrHgewKDArufkO1HPh3kSHvLPBv60qV9d5XFW9b3fxfe3NQ36zGytsTMFtPP/WCKm1BN3NXNTfG49MHYf3hfGTkV+Ohfx+Gi5OyXfYb0GbGn92Qoa0or3vtWz//a99FvY9vyM9Fx9CK8UREREREZFusGpDn5+dj3rx5KC8vR2BgICZPnoz9+/cjMDAQAPDWW29BoVAgOTkZTU1NuOmmm/D+++9bc8gmpcturtiQAUmSIISQP+vLbo6L8cPerHIczK7Ab6+N6vZ5ehL466jUGlyq0y5ZD/E2zd5tXSux+qYW/GvfRZwtqdV7fmFrpthULc9MIcDDGZPiAvDLqRKkni5p7RMv4aOdWVidnIgbh4XgtU2n9bYsGxzsgfOlnbc16+7ncrW27dmIiIiIiMg+WDUgX7t2rd7jLi4ueO+99/Dee+9ZaESW15Ps5vgYfwDncTC7Qg7i9YkJcMe8cZH48kCufJskaZdFd7esubS2CUIATkoF/N2djP329LpuUCD+te8idp8v03uebkn9AAMzxpaQXVaPLadL5K/bZsGXr8/AM8joojqAlkIChoR4YdF1sUa/IUNERERERH2DTRV166+MzW6OjvSBo1JCcc1l5FY0IMqAXuLlddpe1U5KCc1qgUFBHvj4vqRug76iKm0wHOztbPL929fG+kGpkJBT3oC8igZE+HVe6b2wdQzhNpQhT0nL63IbAKAtqObr5oiqBlWngbluSTqXmxMRERER9V82VdSNDOPiqMTIcB8AwIHsim7Pr2tqwfazpQCAx6dpW2BJkAwK+uT9416mD4Y9XRxxTaQPAOBXPVnygtaA3JaWrOsrlCcBmD40CBsfnYSuFi+0XZKue0Pm3XmjseLmeAbjRERERET9BANyOzUuxg8AcNCAgHzr6RI0tWgQG+CO3ySFAwAyL9Xhskrd7X3lCusm2j9+tclx2noBv56/1OlxIYRcZd2Wlqzrq5CvUEgYHOzJCuhERERERKQXA3I7NT7WH4BhAfmPGUUAgFmJoQjxcoGfuxPUGoGzxfqLqQFtK6ybKSAfpO3hvTervNMWYFUNKjS2vnFgrjH0xJykCL2t5HTZ79lJEdj2+6lYPCUWsxLDsHhKLLb9fqreqvJERERERNQ/MCC3U2OifKGQgNyKBhRVd91HvPayCjvOabPPsxJDIUkSEsK8AAAnC2u6fZ7iGl0PcPMEwyPDveHp4oDqRhWOF1R3OK5brh7g4QwXR6VZxtATxmS/uSSdiIiIiIg6w4DcTnk4O2D4AG8A+rPkW0+XorlFg4GB7hjS2rN8WKguIO8YAF9N13IsxAQ9yDvjoFRg4kBttv/Xcx2XresC8gE+tpMd12H2m4iIiIiIeoMBuR0bF63dR66vsNsP8nL1MHnP87DWDPmpIgMy5GZesg5o258BwK+ZHQu72eL+8baY/SYiIiIiop5iQG7HuivsVnNZhV2tWef/SQyVb08I02bWzxTVdrpvW6dFrUFprSUCcu0+8iMXK1HX1NLuWKGcIbfNgJyIiIiIiKinGJDbMV1Anllah7K6pg7Ht5wqQbNag0FBHhjculwd0O5/dnVUolGlRnZZXZePf6muCRoBOCgk+Hs4m/4baBXl745IPze0aAQOXChvd8wWW54RERERERGZAgNyO+bj5oT4EG2gfaiTLHnb6uptKRUShoZq76evsJtu/3iwlwuUii4aapuIrtr61f3IC5ghJyIiIiKiPooBuZ3TZcmv3kde3ajCrtbe3rNGhHa4n7yPXE9Abon94zrXxekC8vaF3QqZISciIiIioj6KAbmd62ofeeqpEqjUAkOCPTGozXJ1Hd0+cn0Zcl07tRALBOQTBwZAIQFZl+rlIPyySo2yumYAQLiNFnUjIiIiIiLqKQbkdk4XkJ8urkF1o0q+/ceMQgAdl6vrXOlFXg0hOi/sZskMubebIxLDfQAAu1uXresCc3cnJbxdHc0+BiIiIiIiIktiQG7ngjxdEBvgDiGAtBxtlry6QSXvxb61k+XqADA42BNKhYTKBhWKWgPvqxXVmLcH+dWm6PaRt7Y/a1vQTdeyjYiIiIiIqK9gQN4HXL1s/edTxWjRCMSHeCIuyKPT+7g4KjGo9VhXy9aLdAGxBTLkADC5tR/5nswyaDTC5nuQExERERER9QYD8j7g6sJuuurq/9PFcnWdYaH6C7vplqxbYg85AIyO9IG7kxIV9c04VVTDgm5ERERERNSnMSDvA8bH+gMAThRUo6CqEXsy9S9X1xnWZh/51dQagZJabW/zUAstWXdUKjBhoPZ72XX+EvLZ8oyIiIiIiPowBuR9wAAfVwzwcUWLRmDVptNo0QgMC/VCbGDny9V19FVaL6trglojoFRICPR0Nsu4OzO5tf3Z7vNlcoacATkREREREfVFDMj7iITW5ec/tC5Xn9iaadZHlyEvqGpEVUNzu2O6Qm9Bns5QKixXUO26wdp95Gk5lbhwqR4A95ATEREREVHfxIC8D0hJy0Pq6ZJ2t326Jxvr0vL03s/b1RERftpg91RR+yy5rqCbJVqetRUb4I4wbxc0qzUobV0yzww5ERERERH1RQzI7Vx2WT2e3ZCBqzuJawSwYkMGcsrq9d6/q8JuRXIPcssGw5IkYVSEz5WvATQ0tVh0DERERERERJbAgNzOpaTlddmjW5IkfNNNlryrfeTFNZatsK6TkpaHn04Uy18LADeu2dVttp+IiIiIiMjeMCC3c/mVjRDi6vy4lhAC+a29vLuS0EWl9SsZcssF5L3N9hMREREREdkTBuR2LtzXVW+GPLybgmi6DHnWpXpcVqnl24urtYG8JTPkvc32ExERERER2RMG5HZuTlKE3gz53KQIvfcP9nKGv7sT1BqBs8W18u2FVZbfQ97bbD8REREREZE9YUBu52IC3LE6OREKCVAqpHafVycnIjrAXe/9JUmS25/p9pFrNAIlNZZfst7bbD8REREREZE9cbD2AKj3ZidFYGy0H75Jy0N+ZSPCfV0xNymi22BcZ1iYF349XybvIy+rb0KLRkAhAYGezuYcejtzkiLw0c6sTo8Zku0nIiIiIiKyJwzI+4joAHesuDm+R/e9utJ6cWtBt0BPZzgqLbeIQpftX7EhA5IkQQghfzYk209ERERERGRPGJCTXGn9THEN1BohV1gPsXAPcqD32X4iIiIiIiJ7wYCcEO3vDjcnJRqa1cguq0NRlbZ4WpiFe5DL4+lFtp+IiIiIiMhesKgbQamQEB/iCUC7bL2oRpcht05ATkRERERE1B8wICcA7feR6/aQW7LCOhERERERUX/DJesE4Mo+8lOFNWhWawBYZw85ERERERFRf8GAnAC0zZBXw8NFOy2YISciIiIiIjIfBuQEABgU7AEHhYTKBhUqG1QAGJATERERERGZE/eQEwDAxVGJuCAP+WtJAoI8GZATERERERGZCwNykg1r3UcOAAEeznBy4PQgIiIiIiIyF0ZcJAvxap8Rzy6rt9JIiIiIiIiI+j4G5AQASEnLwwc7s+Svy2qbMP3NHViXlmfFUREREREREfVdDMgJ2WX1eHZDBoS4cpsAoBHAig0ZyGGmnIiIiIiIyOQYkBNS0vIgSVKnxyRJwjfMkhMREREREZkcA3JCfmUjRNv0eBtCCORXNlp4RERERERERH0fA3JCuK+r3gx5uK+rhUdERERERETU9zEgJ8xJitCbIZ+bFGHhEREREREREfV9DMgJMQHuWJ2cCIUEKBVSu8+rkxMRHeBu7SESERERERH1OQ7WHgDZhtlJERgb7Ydv0vKQX9mIcF9XzE2KYDBORERERERkJgzISRYd4I4VN8dbexhERERERET9ApesExEREREREVkBA3IiIiIiIiIiK7CZgPz111+HJElYunSpfNvUqVMhSVK7j4cffth6gyQiIiIiIiIyEZvYQ37o0CF89NFHSExM7HBs0aJFePXVV+Wv3dzcLDk0IiIiIiIiIrOweoa8rq4O8+fPx9///nf4+vp2OO7m5oaQkBD5w8vLywqjJCIiIiIiIjItq2fIlyxZglmzZmHGjBn44x//2OH4l19+iS+++AIhISG47bbb8OKLL+rNkjc1NaGpqUn+uqamBgCgUqmgUqlM/w0YSPfc1hwDUXc4T8lecK6SveBcJXvBuUr2wl7mqqHjs2pAvnbtWhw5cgSHDh3q9Pi9996LqKgohIWFISMjAytWrMDZs2excePGLh9z1apVWLlyZYfbf/nlF5tY7p6ammrtIRB1i/OU7AXnKtkLzlWyF5yrZC9sfa42NDQYdJ4khBBmHkun8vLykJSUhNTUVHnv+NSpUzFq1CisWbOm0/ts27YN06dPR2ZmJgYOHNjpOZ1lyCMiIlBWVmbV5e4qlQqpqamYOXMmHB0drTYOIn04T8lecK6SveBcJXvBuUr2wl7mak1NDQICAlBdXa03DrVahvzw4cMoLS3FNddcI9+mVquxa9cu/O1vf0NTUxOUSmW7+4wfPx4A9Abkzs7OcHZ27nC7o6OjTfzAbGUcRPpwnpK94Fwle8G5SvaCc5Xsha3PVUPHZrWAfPr06Th+/Hi72xYuXIj4+HisWLGiQzAOAOnp6QCA0NBQSwyRiIiIiIiIyGysFpB7enpi+PDh7W5zd3eHv78/hg8fjqysLHz11Ve49dZb4e/vj4yMDCxbtgxTpkzptD0aERERERERkT2xepX1rjg5OWHLli1Ys2YN6uvrERERgeTkZLzwwgtGPY5ui7yu2rq1qFQqNDQ0oKamxqaXVlD/xnlK9oJzlewF5yrZC85Vshf2Mld18Wd3JdusVtTNUvLz8xEREWHtYRAREREREVE/k5eXh/Dw8C6P9/mAXKPRoLCwEJ6enpAkyWrj0FV7z8vLs2q1dyJ9OE/JXnCukr3gXCV7wblK9sJe5qoQArW1tQgLC4NCoejyPJtdsm4qCoVC7zsSlubl5WXTE4cI4Dwl+8G5SvaCc5XsBecq2Qt7mKve3t7dntN1qE5EREREREREZsOAnIiIiIiIiMgKGJBbiLOzM15++WU4OztbeyhEXeI8JXvBuUr2gnOV7AXnKtmLvjZX+3xRNyIiIiIiIiJbxAw5ERERERERkRUwICciIiIiIiKyAgbkRERERERERFbAgJyIiIiIiIjIChiQW8B7772H6OhouLi4YPz48Th48KC1h0R9yKpVqzB27Fh4enoiKCgId955J86ePdvunMuXL2PJkiXw9/eHh4cHkpOTUVJS0u6c3NxczJo1C25ubggKCsLy5cvR0tLS7pwdO3bgmmuugbOzM+Li4vD55593GA/nOxni9ddfhyRJWLp0qXwb5ynZioKCAvz2t7+Fv78/XF1dMWLECKSlpcnHhRB46aWXEBoaCldXV8yYMQPnz59v9xgVFRWYP38+vLy84OPjgwcffBB1dXXtzsnIyMB1110HFxcXRERE4I033ugwlnXr1iE+Ph4uLi4YMWIENm3aZJ5vmuyOWq3Giy++iJiYGLi6umLgwIH4wx/+gLb1mjlXyRp27dqF2267DWFhYZAkCd9++22747Y0Lw0Zi9kJMqu1a9cKJycn8emnn4qTJ0+KRYsWCR8fH1FSUmLtoVEfcdNNN4nPPvtMnDhxQqSnp4tbb71VREZGirq6Ovmchx9+WERERIitW7eKtLQ0ce2114qJEyfKx1taWsTw4cPFjBkzxNGjR8WmTZtEQECAeO655+RzLly4INzc3MRTTz0lTp06Jd59912hVCrF5s2b5XM438kQBw8eFNHR0SIxMVE8+eST8u2cp2QLKioqRFRUlLj//vvFgQMHxIULF8TPP/8sMjMz5XNef/114e3tLb799ltx7Ngxcfvtt4uYmBjR2Ngon3PzzTeLkSNHiv3794tff/1VxMXFiXnz5snHq6urRXBwsJg/f744ceKE+Prrr4Wrq6v46KOP5HP27NkjlEqleOONN8SpU6fECy+8IBwdHcXx48ct82KQTfvTn/4k/P39xQ8//CCys7PFunXrhIeHh3j77bflczhXyRo2bdoknn/+ebFx40YBQPznP/9pd9yW5qUhYzE3BuRmNm7cOLFkyRL5a7VaLcLCwsSqVausOCrqy0pLSwUAsXPnTiGEEFVVVcLR0VGsW7dOPuf06dMCgNi3b58QQvuHU6FQiOLiYvmcDz74QHh5eYmmpiYhhBDPPPOMSEhIaPdcc+fOFTfddJP8Nec7dae2tlYMGjRIpKamiuuvv14OyDlPyVasWLFCTJ48ucvjGo1GhISEiD//+c/ybVVVVcLZ2Vl8/fXXQgghTp06JQCIQ4cOyef89NNPQpIkUVBQIIQQ4v333xe+vr7y3NU995AhQ+Sv58yZI2bNmtXu+cePHy8eeuih3n2T1CfMmjVLPPDAA+1uu/vuu8X8+fOFEJyrZBuuDshtaV4aMhZL4JJ1M2pubsbhw4cxY8YM+TaFQoEZM2Zg3759VhwZ9WXV1dUAAD8/PwDA4cOHoVKp2s3D+Ph4REZGyvNw3759GDFiBIKDg+VzbrrpJtTU1ODkyZPyOW0fQ3eO7jE438kQS5YswaxZszrMJc5TshXfffcdkpKSMHv2bAQFBWH06NH4+9//Lh/Pzs5GcXFxuznk7e2N8ePHt5urPj4+SEpKks+ZMWMGFAoFDhw4IJ8zZcoUODk5yefcdNNNOHv2LCorK+Vz9M1n6t8mTpyIrVu34ty5cwCAY8eOYffu3bjlllsAcK6SbbKleWnIWCyBAbkZlZWVQa1Wt7t4BIDg4GAUFxdbaVTUl2k0GixduhSTJk3C8OHDAQDFxcVwcnKCj49Pu3PbzsPi4uJO56numL5zampq0NjYyPlO3Vq7di2OHDmCVatWdTjGeUq24sKFC/jggw8waNAg/Pzzz3jkkUfwxBNP4J///CeAK3NN3xwqLi5GUFBQu+MODg7w8/MzyXzmXCUAePbZZ3HPPfcgPj4ejo6OGD16NJYuXYr58+cD4Fwl22RL89KQsViCg8WeiYjMbsmSJThx4gR2795t7aEQtZOXl4cnn3wSqampcHFxsfZwiLqk0WiQlJSE1157DQAwevRonDhxAh9++CEWLFhg5dERXZGSkoIvv/wSX331FRISEpCeno6lS5ciLCyMc5XIjjBDbkYBAQFQKpUdqgSXlJQgJCTESqOivuqxxx7DDz/8gO3btyM8PFy+PSQkBM3Nzaiqqmp3ftt5GBIS0uk81R3Td46XlxdcXV0530mvw4cPo7S0FNdccw0cHBzg4OCAnTt34p133oGDgwOCg4M5T8kmhIaGYtiwYe1uGzp0KHJzcwFcmWv65lBISAhKS0vbHW9paUFFRYVJ5jPnKgHA8uXL5Sz5iBEjcN9992HZsmXyKiTOVbJFtjQvDRmLJTAgNyMnJyeMGTMGW7dulW/TaDTYunUrJkyYYMWRUV8ihMBjjz2G//znP9i2bRtiYmLaHR8zZgwcHR3bzcOzZ88iNzdXnocTJkzA8ePH2/3xS01NhZeXl3xhOmHChHaPoTtH9xic76TP9OnTcfz4caSnp8sfSUlJmD9/vvxvzlOyBZMmTerQOvLcuXOIiooCAMTExCAkJKTdHKqpqcGBAwfazdWqqiocPnxYPmfbtm3QaDQYP368fM6uXbugUqnkc1JTUzFkyBD4+vrK5+ibz9S/NTQ0QKFofymvVCqh0WgAcK6SbbKleWnIWCzCYuXj+qm1a9cKZ2dn8fnnn4tTp06JxYsXCx8fn3ZVgol645FHHhHe3t5ix44doqioSP5oaGiQz3n44YdFZGSk2LZtm0hLSxMTJkwQEyZMkI/r2kndeOONIj09XWzevFkEBgZ22k5q+fLl4vTp0+K9997rtJ0U5zsZqm2VdSE4T8k2HDx4UDg4OIg//el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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from capymoa.datasets import RBFm_100k\n", - "\n", - "stream = RBFm_100k()\n", - "l = OnlineBagging(schema=stream.get_schema(), ensemble_size=10)\n", - "\n", - "res = prequential_evaluation(stream, l, window_size=1000)\n", - "display(res)\n", - "plot_windowed_results(res)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ab1acb93-fb94-437f-ae63-7bc1c732071d", - "metadata": {}, - "source": [ - "### Reading the data from a large CSV\n", - "* Using the covtFD.csv file with more than 580k instances and 100 features\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe8f9288-d58e-48df-bd2a-18a9981d23e7", - "metadata": { - "execution": { - "iopub.execute_input": "2024-01-10T21:56:45.931524Z", - "iopub.status.busy": "2024-01-10T21:56:45.931391Z", - "iopub.status.idle": "2024-01-10T21:58:43.883687Z", - "shell.execute_reply": "2024-01-10T21:58:43.883279Z" - }, - "tags": [ - "skip-execution" - ] - }, - "outputs": [], - "source": [ - "%%time\n", - "# TODO: This csv file is no longer downloaded by default. This should be fixed\n", - "# in the future.\n", - "from capymoa.stream import stream_from_file\n", - "\n", - "# Loads the csv data to memory, it should take from 1 minute to 2 minutes. \n", - "stream = stream_from_file(path_to_csv_or_arff=covtfd_csv_file_path, class_index=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c8b2c123-75b8-40c0-b909-11202203b095", - "metadata": { - "execution": { - "iopub.execute_input": "2024-01-10T21:58:43.885460Z", - "iopub.status.busy": "2024-01-10T21:58:43.885329Z", - "iopub.status.idle": "2024-01-10T21:59:03.002245Z", - "shell.execute_reply": "2024-01-10T21:59:03.001242Z" - }, - "tags": [ - "skip-execution" - ] - }, - "outputs": [], - "source": [ - "# TODO: See above\n", - "from capymoa.learner import MOAClassifier\n", - "from capymoa.evaluation import test_then_train_evaluation\n", - "from moa.classifiers.bayes import NaiveBayes\n", - "\n", - "learner = MOAClassifier(moa_learner=NaiveBayes())\n", - "\n", - "stream.restart()\n", - "\n", - "results = test_then_train_evaluation(stream=stream, learner=learner, max_instances=None, sample_frequency=None)\n", - "\n", - "display(results)\n", - "display(results['cumulative'].metrics_per_window())\n", - "print(results['cumulative'].accuracy())" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1abf71a5-39a7-4262-bea1-cc7ad0cd4391", - "metadata": {}, - "source": [ - "## Regression evaluation" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "120f118c-d8a5-4d10-aad1-a75a2ac122d8", - "metadata": {}, - "source": [ - "### Reading a stream from a CSV file and using one learner\n", - "* Uses the ```RegressionWindowedEvaluator``` directly" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a538089c-1f97-4b03-a980-450ee75e3c78", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:39:28.037377Z", - "iopub.status.busy": "2024-03-21T04:39:28.037209Z", - "iopub.status.idle": "2024-03-21T04:39:34.738258Z", - "shell.execute_reply": "2024-03-21T04:39:34.737665Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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75000.02.7080923.5377540.6649400.7076930.4991700.498066
\n", - "
" - ], - "text/plain": [ - " classified instances mean absolute error root mean squared error \\\n", - "0 5000.0 3.301312 4.212785 \n", - "1 5000.0 2.981176 3.867996 \n", - "2 5000.0 2.828599 3.704090 \n", - "3 5000.0 2.812868 3.685687 \n", - "4 5000.0 2.802291 3.656377 \n", - "5 5000.0 2.742057 3.593869 \n", - "6 5000.0 2.766988 3.633832 \n", - "7 5000.0 2.708092 3.537754 \n", - "\n", - " relative mean absolute error relative root mean squared error \\\n", - "0 0.826254 0.855881 \n", - "1 0.727015 0.767106 \n", - "2 0.696853 0.740711 \n", - "3 0.690389 0.733612 \n", - "4 0.695275 0.734827 \n", - "5 0.683031 0.726970 \n", - "6 0.662587 0.709115 \n", - "7 0.664940 0.707693 \n", - "\n", - " coefficient of determination adjusted coefficient of determination \n", - "0 0.267467 0.265852 \n", - "1 0.411548 0.410250 \n", - "2 0.451348 0.450138 \n", - "3 0.461814 0.460627 \n", - "4 0.460029 0.458838 \n", - "5 0.471515 0.470349 \n", - "6 0.497156 0.496047 \n", - "7 0.499170 0.498066 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from capymoa.evaluation import RegressionWindowedEvaluator\n", - "from capymoa.stream import stream_from_file\n", - "\n", - "from capymoa.learner.regressor import AdaptiveRandomForestRegressor\n", - "\n", - "stream = stream_from_file(path_to_csv_or_arff=csv_fried_path, enforce_regression=True)\n", - "ARF = AdaptiveRandomForestRegressor(schema=stream.get_schema(), ensemble_size=5)\n", - "\n", - "\n", - "evaluator = RegressionWindowedEvaluator(schema=stream.get_schema(), window_size=5000)\n", - "\n", - "while stream.has_more_instances():\n", - " instance = stream.next_instance()\n", - " prediction = ARF.predict(instance)\n", - " evaluator.update(instance.y_value, prediction)\n", - " ARF.train(instance)\n", - "\n", - "display(evaluator.metrics_per_window())" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "5906e14c-db58-4fe9-90fe-a1765137509f", - "metadata": {}, - "source": [ - "### Reading from an ARFF file and using 2 learners\n", - "* Uses the RegressionEvaluator, thus it uses a cumulative approach for calculating the metrics (not windowed)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d7efbb7c-abc0-49f4-8a7b-99c740947119", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:39:34.745559Z", - "iopub.status.busy": "2024-03-21T04:39:34.745098Z", - "iopub.status.idle": "2024-03-21T04:39:55.447223Z", - "shell.execute_reply": "2024-03-21T04:39:55.446789Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OB final MAE = 2.171382783882719 and ARF final MAE = 2.864632534308498\n" - ] - } - ], - "source": [ - "from capymoa.evaluation import RegressionEvaluator\n", - "from capymoa.stream import stream_from_file\n", - "\n", - "from capymoa.learner.regressor import KNNRegressor, AdaptiveRandomForestRegressor\n", - "\n", - "stream = stream_from_file(path_to_csv_or_arff=csv_fried_path, enforce_regression=True)\n", - "kNN_learner = KNNRegressor(schema=stream.get_schema(), k=5)\n", - "ARF_learner = AdaptiveRandomForestRegressor(schema=stream.get_schema(), ensemble_size=5)\n", - "\n", - "# Not a windowed evaluator!\n", - "kNN_evaluator = RegressionEvaluator(schema=stream.get_schema())\n", - "ARF_evaluator = RegressionEvaluator(schema=stream.get_schema())\n", - "\n", - "while stream.has_more_instances():\n", - " instance = stream.next_instance()\n", - " \n", - " kNN_prediction = kNN_learner.predict(instance)\n", - " ARF_prediction = ARF_learner.predict(instance)\n", - " \n", - " kNN_evaluator.update(instance.y_value, kNN_prediction)\n", - " ARF_evaluator.update(instance.y_value, ARF_prediction)\n", - "\n", - " kNN_learner.train(instance)\n", - " ARF_learner.train(instance)\n", - "\n", - "print(f\"OB final MAE = {kNN_evaluator.MAE()} and ARF final MAE = {ARF_evaluator.MAE()}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "5dd4be58-65bc-45f6-9be5-4ad3effc3ab1", - "metadata": {}, - "source": [ - "### Reading the data from a CSV, then evaluating it using two learners. \n", - "* **Using the ```prequential_evaluation``` which internally executes both ```RegressionWindowedEvaluator``` and ```RegressionEvaluator```**\n", - "* ```prequential_evaluation``` allow us to have the windowed and cumulative results. So we can inspect the last accuracy and over time too. \n", - "* We also plot the final results using ```plot_windowed_results```" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "3f9b6eeb-f5ce-403d-bf95-5d880be1a278", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:39:55.449227Z", - "iopub.status.busy": "2024-03-21T04:39:55.449071Z", - "iopub.status.idle": "2024-03-21T04:40:20.790234Z", - "shell.execute_reply": "2024-03-21T04:40:20.789858Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "kNNRegressor final RMSE = 2.7394543131282583 and AdaptiveRandomForestRegressor final accuracy = 3.303185689295369\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from capymoa.evaluation.visualization import plot_windowed_results\n", - "\n", - "from capymoa.evaluation import prequential_evaluation\n", - "from capymoa.stream import stream_from_file\n", - "\n", - "from capymoa.learner.regressor import KNNRegressor, AdaptiveRandomForestRegressor\n", - "\n", - "stream = stream_from_file(path_to_csv_or_arff=csv_fried_path, enforce_regression=True)\n", - "kNN_learner = KNNRegressor(schema=stream.get_schema(), k=5)\n", - "ARF_learner = AdaptiveRandomForestRegressor(schema=stream.get_schema(), ensemble_size=10)\n", - "\n", - "kNN_results = prequential_evaluation(stream=stream, learner=kNN_learner, window_size=5000)\n", - "stream.restart()\n", - "ARF_results = prequential_evaluation(stream=stream, learner=ARF_learner, window_size=5000)\n", - "\n", - "print(f\"{kNN_results['learner']} final RMSE = {kNN_results['cumulative'].RMSE()} and \\\n", - " {ARF_results['learner']} final accuracy = {ARF_results['cumulative'].RMSE()}\")\n", - "\n", - "plot_windowed_results(kNN_results, ARF_results, metric='adjusted coefficient of determination')" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "410145fa-5203-4c79-b86f-8ce891691751", - "metadata": {}, - "source": [ - "### Simple test-then-train evaluation (cumulative). " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b65c0845-f5dd-4272-9782-bacf5517c362", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:40:20.793968Z", - "iopub.status.busy": "2024-03-21T04:40:20.793763Z", - "iopub.status.idle": "2024-03-21T04:40:32.478883Z", - "shell.execute_reply": "2024-03-21T04:40:32.478509Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.171382783882719\n", - "{'learner': 'kNNRegressor', 'cumulative': , 'wallclock': 10.1120023727417, 'cpu_time': 5.936483706000004, 'max_instances': None, 'stream': }\n" - ] - } - ], - "source": [ - "stream = stream_from_file(path_to_csv_or_arff=csv_fried_path, enforce_regression=True)\n", - "\n", - "kNN_learner = KNNRegressor(schema=stream.get_schema(), k=5)\n", - "\n", - "res = test_then_train_evaluation(stream, kNN_learner)\n", - "print(res['cumulative'].MAE())\n", - "print(res)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "96ffc568-8502-42e7-9768-a368ddc3c3f8", - "metadata": {}, - "source": [ - "### Prequential evaluation, single stream, multiple learners. \n", - "* Important: the ```prequential_evaluation_multiple_learners``` iterate through the stream testing and training with each learner\n", - "* This method does not calculate ```wallclock``` or ```cpu_time``` because the training and testing of each learner is interleaved" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "121e54e2-4ba2-41be-8397-408ad40db23d", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-21T04:40:32.481302Z", - "iopub.status.busy": "2024-03-21T04:40:32.480587Z", - "iopub.status.idle": "2024-03-21T04:41:15.973861Z", - "shell.execute_reply": "2024-03-21T04:41:15.973458Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "kNNReg_k5: {'classified instances': 40768.0, 'mean absolute error': 2.171382783882719, 'root mean squared error': 2.7394543131282583, 'relative mean absolute error': 0.5343076205436615, 'relative root mean squared error': 0.5474215436522585, 'coefficient of determination': 0.7003296535453785, 'adjusted coefficient of determination': 0.7002487728453343} \n", - "\n", - "kNNReg_k2: {'classified instances': 40768.0, 'mean absolute error': 2.455209968602826, 'root mean squared error': 3.101880727919028, 'relative mean absolute error': 0.6041483823103337, 'relative root mean squared error': 0.6198447362911451, 'coefficient of determination': 0.6157925028921607, 'adjusted coefficient of determination': 0.6156888057072509} \n", - "\n", - "kNNReg_k5_median: {'classified instances': 40768.0, 'mean absolute error': 2.3265919348508635, 'root mean squared error': 2.95901097128398, 'relative mean absolute error': 0.5724996117282386, 'relative root mean squared error': 0.5912952611845242, 'coefficient of determination': 0.6503699141007252, 'adjusted coefficient of determination': 0.6502755493214316} \n", - "\n", - "ARFReg_s5: {'classified instances': 40768.0, 'mean absolute error': 2.864632534308498, 'root mean squared error': 3.7390289654917566, 'relative mean absolute error': 0.70489413681425, 'relative root mean squared error': 0.7471652285789276, 'coefficient of determination': 0.44174412120259876, 'adjusted coefficient of determination': 0.441593448549081} \n", - "\n" - ] - }, - { - "data": { - "image/png": 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clnWbR44cIS0trYg3/fH9FJw3AwODEo9f03FVxjmsShITEzly5Ajz58/n888/Vy1X9z4oy7WtjOsqIyMj86wi56TLyMjIPEXo6ekV80QtXbq0mJd7wIABnD9/ngsXLqiWxcbGsn79+hL3UaCknDx5UrUsPz+/WFh6SkoKeXl5RZY1b94chUJRpD2amZlZMYVfT0+P4cOHs23bNrWKaOFq5wMGDCAqKoqtW7eqlmVkZGgMky+MQqFgyJAh7N69m0uXLhX7vOBcDhgwgAsXLnDu3DnVZ+np6axYsYK6devSpEkTQPJQRkZGsnLlymLbyszM1FmPbHt7e7p3787q1asJCwtTO2Z19O/fH0tLSxYsWEBubm6xz9VVkS+JgmrlmtqdPU5pz2VZGDBgAHl5eSxbtky1LD8/n6VLlxZZz8HBgZ49e7J8+XLu379fbDuFj9/MzAwoflyVcQ6rkgIP9uP3yZIlS4qtq+kcqKMyrquMjIzMs4rsSZeRkZF5ihg4cCDr1q3DysqKJk2acO7cOQ4fPoytrW2R9WbPns26det4/vnnmTFjhqoFW506dfD19dW6j6ZNm9KxY0fmzp1LQkICNjY2bNy4sZhCfvToUaZOncqIESNo0KABeXl5rFu3TqWAF9C2bVsOHz7MokWLcHZ2pl69enh7e/Ptt99y7NgxvL29mThxIk2aNCEhIYErV65w+PBhEhISAJg4cSK//PIL48aN4/Llyzg5ObFu3boira60sWDBAg4ePEiPHj1UbdPu37/Pli1bOH36NNbW1syZM4cNGzbwwgsvMH36dGxsbPjzzz8JDg5m27ZtqnDmsWPHsnnzZt555x2OHTtGly5dyM/P586dO2zevJn//vtPbYG68vDzzz/TtWtX2rRpw6RJk6hXrx4hISHs3buXa9euqf2OpaUly5YtY+zYsbRp04bRo0djb29PWFgYe/fupUuXLvzyyy9lGoeJiQlNmjRh06ZNNGjQABsbG5o1a6Yxr7y057IsDBo0iC5dujBnzhxCQkJo0qQJ27dvV5tf/euvv9K1a1eaN2/OxIkT8fDwICYmhnPnzhEREcH169cBaNWqFXp6enz33XckJydjZGRE7969cXBw0Pk5rEosLS3p3r0733//Pbm5ubi4uHDw4EGCg4OLrdu2bVsAPvnkE0aPHo2BgQGDBg1SKe+FqYzrKiMjI/PMUj1F5WVkZGRkKoPExETx9ddfF+3s7ERzc3Oxf//+4p07d4q1SxJFUfT19RV79OghGhsbiy4uLuJXX30lrlq1qsQWbKIoioGBgWLfvn1FIyMjsXbt2uLHH38sHjp0qEjLqqCgIPGNN94QPT09RWNjY9HGxkbs1auXePjw4SLbunPnjti9e3fRxMREBIqMMyYmRnz33XdFNzc30cDAQHR0dBT79Okjrlixosg2QkNDxcGDB4umpqainZ2dOGPGDPHAgQOlasFW8P1x48aJ9vb2opGRkejh4SG+++67RVpEBQYGii+//LJobW0tGhsbix06dBD37NlTbFs5OTnid999JzZt2lQ0MjISa9WqJbZt21acP3++mJycXOS8VqQFmyiK4s2bN8WhQ4eqxtSwYUPxs88+U32urV1e//79RSsrK9HY2Fj09PQUJ0yYIF66dEm1zvjx40UzM7Ni4/viiy/Ex8WHs2fPim3bthUNDQ1L1Y6ttOeSUrZgE0VRjI+PF8eOHStaWlqKVlZW4tixY1VtAB8/b4GBgeK4ceNER0dH0cDAQHRxcREHDhwobt26tch6K1euFD08PEQ9Pb1i91JFzqGmYyu4zgsXLiyyvKBtWeF2hppasD3+3YJ9Fb4mERERqvvGyspKHDFihBgVFaX22n311Veii4uLqFAoitxL6t4ppbmu6o6l8Pgfv1YyMjIyzyKCKMoVOmRkZGRkNNOtWzeMjIw4fPhwdQ9FRkZGRkZGRuapR449kpGRkZHRyv3797Gzs6vuYcjIyMjIyMjIPBPISrqMjIyMjFrOnj3LrFmzCAwMpE+fPtU9HBkZGRkZGRmZZwI53F1GRkZGRi2vv/46+/fv55VXXmHhwoXF+j/LyMjIyMjIyMjoHllJl5GRkZGRkZGRkZGRkZGpIcjh7jIyMjIyMjIyMjIyMjIyNQRZSZeRkZGRkZGRkZGRkZGRqSE8cwmGSqWSqKgoLCwsEAShuocjIyMjIyMjIyMjIyMj85QjiiKpqak4OzujUGj3lT9zSnpUVBRubm7VPQwZGRkZGRkZGRkZGRmZZ4zw8HBcXV21rvPMKekWFhaAdHIsLS2reTTayc3N5eDBgzz33HMYGBhU93Bk1CBfoycD+To9GcjXqeYjX6MnA/k6PRnI16nmI1+jJ4Mn5TqlpKTg5uam0ke18cwp6QUh7paWlk+Ekm5qaoqlpWWNvuGeZeRr9GQgX6cnA/k61Xzka/RkIF+nJwP5OtV85Gv0ZPCkXafSpFzLheNkZGRkZGRkZGRkZGRkZGoIspIuIyMjIyMjIyMjIyMjI1NDkJV0GRkZGRkZGRkZGRkZGZkagqyky8jIyMjIyMjIyMjIyMjUEGQlXUZGRkZGRkZGRkZGRkamhiAr6TIyMjIyMjIyMjIyMjIyNQRZSZeRkZGRkZGRkZGRkZGRqSHISrqMjIyMjIyMjIyMjIyMTA1BVtJlZGRkZGRkZGRkZGRkZGoIspIuIyMjIyMjIyMjIyMjI1ND0K/uAcjIyMjIyMjIyMjIyNQkguPS2egTysW7Cm7pBzDauw717Myqe1gyzwiyki4jIyMjIyMjIyMjI/OQzZfCmbPNFwEBpShw/XQIK08H893wFoxo51bdw5N5BpDD3WVkZGRkZGRkZGRkZJA86HO2+aIUIV8UERHIF0WUIny0zZeQuPTqHqLMM4CspMvIyMjIyMiUSHBcOj8cDODPuwp+OBhAsCyoysjIPIVsvhSOIAhqPxMEgU2Xwqt4RDLPInK4u4xMDSI4Lp3Nl8KJSMzEtZYJI9u5yflPMjIy1Y4c+ikjI/OsEJ6QgVIpqv1MFEUiEjOreEQyzyKyki4jU0NQCcGCgCiKCILA8hOBshAsIyNTrRQO/QQRHoZ+ghT62b6uDXVlY6KMjMwTjiiKHPN/wOl7cahX0UFAwLWWSZWOS+bZRFbSZWRqAEWE4IfCL7IQLCMjU07KE5WTlJFDWEIGofEZhCVkEPbw943IZDQ4lVShnx8936gSjqJmIUc6yTyLPCv3vV9UMgv23ebMvXit6+WLIiPaulbRqGSeZWQlXUamBqDKfxKLS8LPkhD8LPOsCEIylY+mqJxvhjWns6ddEUU8PCGD0IR0wuIzSMnKK/O+npXQTznSSeZZ5Fm47+8nZ/LDf3fZfjUCUQRDPQWvd62Li7UJ83b5IQgCSqVS+v1QRNt8KYI5L8gymUzlIivpMjI1gLvRqeTL+U/PLM+CICRTNWiPyrlR4vcdLIyoY2uKm40p7jam1LE15eTdWHZdu68KcX+cpz30U450knkWedrv+7TsPJafCGTlqSCycpUADG7pzIf9G+JmYwpA9/r2bPAJ5eKtQNo38cDWwogF++7w+4lA6tia8koH9+o8BJmnHFlJl5GpRpRKkXXnQzlxN1brek+7EPws87QLQjJVy+ZL4QgIoCGjUiFAPTuzhwq4GW42ptSxMcXd1hS3WqaYGOoV+04rt1rsvBaldntKEZ5v6qjLQ6hxyJFOcqTPk4KurlNWbj7LTwRq/PxJvu/z8pVsuhTO4kN3iUvLAaB93Vp88mITWrlZF1m3rp0Zs56rz768AAY8Vx8DAwMycvJZcjiAT3fcxNnahB4N7KvhKGSeBWQlXUammgiOS+ejrb5cCEnQup5ShEEtnKpoVDJVjTal6kkWhGSqhzv3UzR6vBUCDGjuxC+vtinTNuvZmfHd8BZ89DDaQ6lUohAE8h/u5vv/7vDn6x3Q13s6u7qGxqc/05FOcqTPk0FZrlNqVi6RSZlEJGQSmST9RCRmEJko/b9AedVEvlJkr+996tiY0srdmvoOFugp1LcsqymIoshx/1gW7LtNwIM0AOramjLnhcb0b1pbY8u1x5nRpz5hCRlsvxLJu+uvsOWdTjR2sqzMocs8o8hKuoxMFZOvFFl9OpgfDvqTnafE1FCPuS80wkhfjznbC02woBKCV5wMYvGoVqWeRKoC2bNScVKzcjl+54FGpepZUABkdIMoimy5HMGpgDiN6wiCoArjLCsj2rnRvq5NkdBPb09bpv5zlTP34vl6723mDW5a3uHXWO7GpHI+ULMhVRCe7krPcqTPk4G26zR7qy/nAuNJzc4jMlFSxktTf8JATyA3X/3cBBCWkMGc7VIKjZmhHs1drWjlVotWbtJvRytjreOtSvnh8aJw1qYGzOhTnzHedTDUL5txURAEvh3WgqikTM4HJfDG2ovseLcLtS01H69M5RIcl85Gn1Au3lVwSz+A0d51ngp5VFbSZWSqkICYVD7c6su18CQAunrZ8c2w5irBuUM9GzYVmrgaO1rw3ubr7LgWRbu6NrzWsU41jv4RsmelYiSm57DmbAhrzwRrFZaUIpgYPJ3eSRndkZqVy6c7bmoMSS9AFEVGVeD5VBf6uWhkK975+zJrz4bQxMmSke2fjudfFEU2XQxn3m4/Vb6qOpTKip3Tms7mi2EaP5MjfWoOa8+GaPxMBLZfjSy23NrUANdaJrhYm+BibYpLLRPV3661TEhIz6HvohNqOzsIwCsd3AmOS8c3Ion0nHzOByVwPuiRQcvR0phWbta0cremlZs1zV2sMDPSrzT5QZ3ib2Kgxw8H/dl2pVBRuC51mdLLCysTg3Lvy1BfwfLX2jFs2RkCY9N5Y+1FNr/dCTOj6lOrnlXHiep+QkApClw/HcLK08FPhTwqK+kyMlVAbr6SFSeD+OlwADn5SiyM9Pl0YGNGtnMr4h2va2dWTOCJTsliwb47fLn7Fi1drWnualXVwy+C7FkpPw9Ss/jjVDB/nw8lIycfALdaJkQkZmrsybrjaiRt3Gsxqr1bjYqkkKkZ+EYkMW3DVULjM9BTCLzfrwF25kbM3V5UCBZFke+Gt9D5s/l8M0fe69uAxYfv8smOG3g6mNG2jo1O91HVpGTl8vH2G+zxvQ9At/p29GrowNd7b6nOpSg+SlC5E5361L3zsvPy2XP9Pv9cCNPYfk+O9Kk+8pUi18ITOe4fyzH/B9yMTNG6vqe9GRM6132oiJvibG2CeQkKpbWpYZE0l8ffJQUKUL5S5N6DNK6FJ3ItPImrYUncjUklOiWLA37RHPCLBqR0m7q2ZgTFpUs70KH8UEzxR+D344Ho6QnkPYwGGNTSmdmFisJVFCtTA9ZM6MDQ387gF5XC9A1XWTGuXbWE/T+rjpMi8igiIKgiE58GeVRW0mVkKplbUSl8uPU6flHSJNqroT0LhjXHyap0IZITu3lwKSSRg7dimLz+MnundcPKtPwW4IqitYgSsmdFHRGJGSw/EcSmS+Hk5EleucZOlkzt5cXzzRzZfiVCrSBU38EC/5hU5my/gU9wAl8PaVatlnqZmoMoiqw6Hcx3B+6Qmy/iYm3Cz6+0UinI3o9F5Yxq51Zpwsq03l7ciU5h/81o3l53hV1Tu+Bs/WSGgF8LT2LahiuEJ2SirxCY1b8hk7p5oFAI9G7koDqnLtbGhMZlsN8vmukbrrJ6Qnu61rer7uFXmNjUbNb7hPL3+TDi0rJLXP9pDvWvacSmZnPyrqSUnwqIIzkzt1Tf01MIPNfUkbGd6pZ5nwVpLtreJXoKgYaOFjR0tGBUe6naeXp2Hjcik7kWnsT18CSuhSdxPznrkYKuBqUIQ387g7utGWaGepga6mFqqK/6bWakh4mhHmaFlpka6ZGckcNH23wlkUQll0i/8/JFmjlb8tWQZrR2r1Xm4y8Jd1tT/hjfjtErznPkzgO+3O3HvMFNq9Sg/iw7Tp72mj6ytPcM8ayGwlQXOXlKfjl2j9+O3SNPKWJlYsC8wU0Y0sqlTC9wQRBYOKIld5aeJiwhg/c3X2PluHYoqqlIS0RiJkoNOdT5osjGC2Fk5uTT3MWKFq5WeNib1/iCMpVFYGway44HsuNqJHkP3VFt3K2Z2tuLXg0dVPeBJkHI3caU308G8uPBu/x7NZLrEUn8NqYNjRyrr0iN/B6pfuLTsvlwqy9H7zwAoH/T2nw/vGUR4526qJzKQqEQ+GFES4Lj0rkTncrb6y6z5Z1OGBsUrxRfU1EqRf44HcT3B/zJU4q41jLh51da06aQYP/4Oc3LVzJtw1X234xm0rpL/P2Wd5H1nyRuRiaz5kwIu69HkZMvGRJrWxoxqIUzq88Eq/WmK0Xo5GFbxSPVzpP0fiopj7awt/y4fyw3IpOLfN/SWJ/uDezp2dCBunamjPz9nNrrpIs0l7K+S8yM9OnoYUvHQvdHTEoW766/wuXQRI2RY4kZuSRmJJV7rI+jEKRImMpQ0Ato7V6LJaNaMeWfK/x5LhR3WzPe7Fqv0vb3OM9i9wlRFLkQnMDu61FPdU2fGqGk//rrryxcuJDo6GhatmzJ0qVL6dChg9p1e/bsyYkTJ4otHzBgAHv37q3soT6xPKuhMNWFb0QSH27xxT8mFZBaFH05pCkOFuUrLGJlYsBvY9owbNlZjtx5wPKTQUzu6anLIZcafYWgbi5QkZiRWyQ/ztRQj2bOVjR3lZT25i5W1LU1U2tkqAwBqzoKityKSuHX4/fYd+O+6lx18bLl3V5edPKwVWuk0SQITenpRfu6Nkz75ypBsem89MsZvnypabFUiargSXuPPEkCe2k5FxjPzE1XiUnJxlBfwWcDm/Cat3u1p0KYGemzclw7Bv9ymhuRycze6stPo2tWsUtNxKVl88Hm66pWmAOaO/LNsBYl5qzq6ylYMroVaX9e4lRAHBNWX2DT209Oped8pcihW9GsPhPCheBHucSt3Kx5o2s9XmjmiIGegoaOFkUjfXhU1HTONl+2TO6MSw2InHiS3k+a8mg/G9gES2MDjt+N5VRALEkZRb3lzVws6dnAgZ4N7WnlZl2ko4K20PSa4EmtbWlM+3o2XA1PUtstQSHAwBZODG7pQnpOHpk5+aTn5JORnUdGrvQ7PSf/4fI8MrLzycjNIzg2nfSH6WPqiEjKqszDAuCF5k58/EJj/rfvNl/vvYVrLRP6V1FrytD4dJTPSPeJ6OQstl2JYMulcELiM7Su+zQU9ax2JX3Tpk28//77/P7773h7e7NkyRL69++Pv78/Dg4Oxdbfvn07OTmPWkPEx8fTsmVLRowYUZXDfqJ4lkNhCqgqYT0rV+qfueJkIEoRbM0M+fKlZgxo7lhhYbWZixXzBzdl7vYbLPzvDq3crOnkWbVejDP34th/877GzxUCfDygMfeTs7gRkczNqGQycvK5EJJQpNWchZE+zR562lu4WtPC1YpzgXHM2X5DpwJWZRUU0XQ/XQlL5Nej9zjy0MMJ0Ldxbd7t5VkhS377ujbsm9GN9zZd48TdWD7adoPzQVUb/v6kvUeeJIG9NOTlK/n56D2WHg1AFKUc019ebVOjFEI3G1N+G9OWsat82HU9isZOltVmTCwtZ+7FMXPTNWJTszHSV/DFoKa80qH0BjAjfT2Wj23L2FUXuByayNhVF9j6Tqca9Sw8TnJmLpsvhvPnuRCVAK+vEHihuROvd6lbLBpAXaRP/ya1eX/LdYJi0xn7hw+b3+mEnblRdRwO8GS9n7Tl0c7ffavIupbG+nRrYE/PBvb0aGiv1dBfmtD06mZkOzet/dff79ewzOP97sAdVpwMUqv4V6Wi9la3eoQmpPP3+TBmbLzKpkmdaPlY33VdIooiu65HccI/VmNkAjz5KSk5eUoO345h86VwTt6NVUWLmBnq0bOhPftuRqt1HFU0gqQmUO1K+qJFi5g4cSKvv/46AL///jt79+5l9erVzJkzp9j6NjZFC9Js3LgRU1NTWUnXwrMYClOYyqwkWthD28zVisWHAwiKlXKuBrd05otBTbDVoeAyur0bF0MS2H4lkmkbrrJvelccqqjtx+FbMUz55wo5eUoa1Dbn3oM0rcVkQPLUBMWm4RuRzI3IZHwjkvCLSiE1O49zQfGcC4ovviM17WPyRRF3G1OMDfQw0leo/W3wWI/myiooou5++v1EIJ52Ztx7eO0VArzYwpkpPT11pkTZmBmyZkL7agt/f5LeI0+SwF4aopIymbnxmsrQNbKdK/MGN8XUsNqn8GJ08rTli0FN+GynH9//d4eGjub0blS7uodVjLx8JUsOB/Dr8XuIItR3MOeXV9vQ0NGizNsyNdRn9YT2jF5xntv3Uxjzhw9bJ3cqdd0RXaMpeigoNo21Z0PYejlCVbiylqkBr3q7M7ZjXa0ts9RF+qx705sRy84SFJfO+NUX2DCpI5bG1VMv5Ul6P2kbK4CduSGj2rvRs6EDrR/zlpdEVaa5lId6dmY69/hrU/yrUlETBIF5g5oSmZjJMf9Y3vzzIv9O6aKzQnWFuR6exJd7bnE5NFHrekoRbkel8CAlq8pkRV1xJzqFzRcj2HEtkoT0R87ZDvVsGNnOjQHNHTE11GfLpXDV/aRUKlEIAiLUmAiSiiCIorbA1colJycHU1NTtm7dypAhQ1TLx48fT1JSEjt37ixxG82bN6dTp06sWLFC7efZ2dlkZz8qfpKSkoKbmxtxcXFYWtYcD4Q6cnNzOXToEP369cPAoPwT38zNvuy/Ga2xjUbvRvb8PqZ1+QdagwmJT6f/T2c0Hvt3w5pSz84MU0OpIImpoT6mBnoYGyi0elK2Xonkkx1+CPAwP1tQWTIdLIyYP6gxfRsXjwTRBRk5eYxYfoG7D9LoULcWf05oW6ZJvDzs9r3Ph9tukq8U6dfYgcUjWxCdnMWWy5FEJGXiam3CiLYu1LEteTLKzVcSGJvOjchkbkSmcDMqBb+oFI0VhEuLnkLASF+h+snMySdZQ3szAWjibElnDxuMDSQl39hAD2P9gv8X+q3/6O/Y1GxeXXVR41j1BBja2oW3u9elrm3lTQ4XQxJ5b4svMSkPvX8DG/Fym7LVOigLmTn5TFh7iSvhyWo/VwjwQjNHloxsUaH96Oqd98PBAFaeVp9HC1DP1pR+TRyoa2uKu40pdW1NcbAwKtX5C4lPZ+vlKNV9/3Jb50q91kduP2DOv34kZeZiZqTHl4OaMLilU6XtryRKc41EUeSzXbfZdCkCcyN9tkzqgJeDeRWPVDNRSZm8v+UGl8OSABjVzoVPXmiEiWHFcujj0rJ55Y+LhMRn4GFnxj9vtcfWzFAHIy49j89NCkFACTRwMMc/Jk21Xn0HMyZ0qsPglk4Vqh0QHJfO6D8ukJCeS7s61qwe17bC57E8zNzsq9Gjpqv3k66YudmXfTei1Xo/a9pYK4vQ+IxyyQ+a2HYlko93+BVJyRCBBUOaMryNS7m3G5YSxvZ727kaeJXWnq0Z5jUMd0t3rd9Jy87j1T8ucjs6FU97MzZP7IBlBdq9FeZBajY/Hgpg+1Wp3aapoR7vdK+HjakBn+++XeT4lZJoiiiChbE+s/rVZ3Q712qrZ1SAtjk0JTOXPTei2XolkhuFOhbUtjBiaGtnhrdRP9+Gxmew8WIYV+6E0KZRXUa3d6/Q/VSZpKSkYGdnR3Jycol6aLUq6VFRUbi4uHD27Fk6deqkWj579mxOnDiBj4+P1u9fuHABb29vfHx8NOawz5s3j/nz5xdb/s8//2BqWjMvoK7ZFargSJSApJoUR0Ckm6NIXxclVlUrT1QaShHC02FHiIKgVM3HrgkBEUMFGOqh+m2kAEM9qe/O3RRN2xT5oHk+7pUsj8Zkwo++emQrBfo6KxlUR3Mf34pyNkZgc5ACEYF2dkpe9VKip+N3/Jq7Cq7HC4gazqmJHlgaQp4Scgv95Ik1Kd9VpEttkZEelXctCpOWC3/fU3A7STLQtLNTMtJDiZGO5ON8JfgnC1yJE/BNEMhWajvXIp0cREZ7Vs2xa0MUYdENBWHpZXvuDRUidsZgZyxibwz2xtLf9sYiloaS8Hz+gcDGQIVKACz4/YqnEm8H3U6leUrYGargZLR0fd3MRMbXz8f+CYlczFPCb7f0CEwVsDcWeb95PqY1wPF/PV5gQ6CCzHwBYz2RUR5K2tjp7tolZMNPN/VIyhFwNROZ2iQfkyo67geZsOCanob3KIBI01oiPZxEGliK6MqmF5EOS/30yMoXaGKt5K2GSirZblyMFbcV+CVpnpd7OYkMqVv97yelCItv6D18PxVHgUhvZ7FS5/SnldhMOP9AQUI22BhBRwdlhd6Xl7MvsyNzR7HlQ02G0saojdbvJmXDopt6JOcI1LdU8k5jJfoVeCbylHD8vsDBCIVqLm5vr2SQ+yO5Xd3xZythU+Cj+62ehchIj3ycq0n90TSH9nIWSc4B33iB3IdynZ4g0qyWiLeDSCNrUedyZ3WRkZHBq6++WiolvQZMmeVn1apVNG/eXKOCDjB37lzef/991d8FnvTnnnvumfGkR5wK5khUgMbPRQRORgv4xOkzur0rk7rVw8Gi+nLLyktsajan7sVxKiCeM4HxJGaU3J7E3EgPaxMDqRhJbj5ZudLEKCKQrYTsYvOk9reEniCQZl2fAc/VL+dRlJ7aDaKZsdmXw1EKRvRuS++G9jrfxx+nQ9h07i4Ar3Zw5YsXG1eKFfaWfgA3ToeordKpJwiM61yPWWrOqVIpkpOvJDtPSVZuPtl5SrJzpb/XnAthj6/mCJJWbla0drOWrnuekuxc6R7IzlWS9XB7WQ/viaw86XeGluI0CkHA2sGJAQOqzgPyslJk5ekQFh+5x6U4BQlYsHR0CxrULnvILkjn80p4Ert977P/ZkyRZ6i2hSEPUnM05L4JXI5X0KddY8Z6u5f7HqnoO+9+chaf7PAjLF1NGsVDFAJ0rGeDp70ZIfEZhCZkEJmURY4SojIgKqP42I0NFDhZGhP8sFBN0SY/sDFIjzcGdS235f5xz0LHerVYeCiAW9FS4ck3Otfhg371MayIlKcjynKNuvTMZtjvPkQlZ7EvsTYrx7aptk4P2bn5fHPgLuvvhgPQwtWSxSNa4F4Joaiduqbz6qoLRKTnsi3WntXj2lRJpfsfDgagEIJVRd0KIwCj2rnx1UtNKmXfrdsn8vqfl7mVBEcznPnh5eZVcq1z8pR8+99d/JLCtKwlcF+0wLt7W52mn5WVzJx8Zm27QVj6A80rCQJzRnarsV7Ams54HW0nLCWMz/d8jqhmxtuRuYNxfcfhbqHdo966Yyqv/HGBgBQ4k+PKt0PL3ppNFEWO3InlxwP+hCVI9SNauFry2YBGtFKT767u+N9Uiqy/EM6iQwEEp+bz4w0D3upal3d7elRpB46Q+HTe++kMIsXn0KNRj85LfQczRrR1ZXBLpzJFIulKZ6psUlJSSl7pIdWqpNvZ2aGnp0dMTEyR5TExMTg6aq+KmJ6ezsaNG/nyyy+1rmdkZISRUfGXsoGBQY2+iIWpyFjj0rJZeToEkCZphaJ4DpCTlQmLD9/lcmgif54LY+PFCMZ41+Gdnh7lrkZeEUpb5C0nT8nl0ERO3I3lxN1Ybt8veuNbGOljZ2EkVb5UI7ToKQTGdqpbJH9LqRTJzM1XVRbNUP3kkfGwqujqM8HciEhWq6yIQFRKdpXcWy+1ceNqRAprz4bw4dYb7J3eTWe5T6IosvjQXX4+eg+Ad3p48tHzDSstnHq0dx1Wng5WPxbgFe86Gs+pEaBOJbU2N2aPb7Ta7wgCLB7VuuwFavbfZsXJYLXGBEEQcLc1q/L3ytQ+DejgYcf0DVcJiktn+HIfvhzcjBHtXEt1vURR5Pb9VHZej2TP9ftEJj2qBGtnbsjAFs4MaulMG3drtl5W38/dy8GcuzFpfL3PnyN34lg4ogWutcp/L5b1nSeKIpsvhfP1ntukZudhoCeQl69OvJJYMKxorlpuvpKIxExC4tMJiUsnND6D4Lh0QuPTCU/MJCtXqVLQ1aEUYcQKH5q7WuNkaYyjlTFOVgW/TXC0MsbSWF/t9Xi8xgHA8lPSs2BjZsgPI1rUyJzu0lwjx1oGrBzfjuHLznLqXjw/Hr7HJy9WjpL4OIXnETNDPS4EJ6h6NL/d3YMPnmtYaUaPRs7W/PWGN6+sOM/FkESmb/Jl+dh2lWpkyczJ51RgvFoFHaR3XnqustLeT53rO/D7a22Z+Ncl9tyIxtrMkK9ealap1f1jUrKY8rClF0CfRg4c839Q5P2kFEWMDfS4E53GyJUXWft6ezzsqz71IjY1m7f+usz18CQM9RS83NaFjRfD1ebRejlaVfn4ZIqyO2T3w7oBxT8TBIHdwbuZ2Xam1m20cLfhlzFteOvPS2y/GkU9O3Om9Sm9A8c/OpUv9/hx5p5kdHawMGLOC40Y0sqlTIZwA+DNbp4MaOHMFzv9OHgrht9PBrPfL4b/DWlO1/p2pd5WRdh+LVqrm6uJkyXfDGtOC1erCr03arp+V5axVauSbmhoSNu2bTly5IgqJ12pVHLkyBGmTp2q9btbtmwhOzub1157rQpG+uQyb5cfSRm5NHW25KfRrdh2JVJt1c8uXracvhfH4kN3uRKWxOozwaz3CeW1jnV4u0fVKeslFXkLi8/gxN0HnLgbx7nAuGJtN5q7WNHjYRXUVm7WRCRm0ufH42r3pa6giEIhYGakr7Vitn9MKn5RKdVeSRSkSurXwpO4Fp7ElPVX2Dq5E0b6FbOMKpUiX+29xZozIQB82L8h7/by0sFoNVMZxWQe36YuCoqMbO/O8pNBaj+rzkqiHerZsHd6V95/2EZq9jZfzgfF81a3euz2va/W4BUan86ua1Hsuh5FwINHuarmRvr0b+rIS62c6expW6TegabqwXVsTfnbJ4wFe29zLiie55ec4vNBTRjRtnSGgopwPzmTOdtuqNpntXa35ocRLbkSmljq+8lAT0E9OzPp3DQsuv3cfCWRiZl8tM2XC8EJWvv7nnw4BnWYGuo9Ut4tTXCyMkZfT+CnI1K1dnXJtMtfa0v7ejbFN/YE0dTZih9GtGTqP1dZeSqYRo6WDG/rWqn7LDyPKJWPjDVmhnr8OqYNPRtWTr2QwjRzsWLVhPaMW+3DMf9YPthynSWjWuncu5yVm88/PmEsOxFIbGq2xvWqYm7q1ciBRaNaMWPjVf4+H4aViQEf9q+cImbng+KZ+s9V4tKysTDWZ/HIVvRtUpuQuPRi76d8UWTCmguEJWQwbNlZVo5rR/u6Vfdc3XuQyoQ1F4lIzMTa1IAVY9vRoZ4Nk7p7ssEnlIu3AmnfxINXvOs88YWunhai0qLUetEBRESi0qJKtZ1eDR2YP7gpn+64yY+H7uJua8pLrbTnyCem57D48F3+Ph+KUgRDfQUTu9VjSk+vCnVzcbIyYcW4dvznF80XO/0Ijc/gtVU+DG3twqcvNq6UKJMCJ8ChWzH84xOq0YioEMDTwbxSq+E/iVRrTjpILdjGjx/P8uXL6dChA0uWLGHz5s3cuXOH2rVrM27cOFxcXPjmm2+KfK9bt264uLiwcePGMu0vJSUFKyurUuUCVDe5ubns27ePAQMGlMsqdPhWDG/9dQk9hcDOd7vQzKVk66woipwKiGPx4btcfVhUx9hAwWvedXi7hyf2lRgGHxyXTp8fj2sMT3a2NibysX6XduaGdK9vT/cG9nStb6e2BUzhyo/aKpHrYpwKAY5+0LNKJ9rIpEwG/nyKxIxcXuvoztdDmpd7W/lKkbnbfdl8KQKAL19qyrhOdXU00pJRJ2BV9FyGxKUXEoQ8KywI6fp+0iVKpaiq/l5gRCrQCQrGObCFM2EJGVwLT1J9z1BfQe+GDrzUyplejRzKHQIXEpfOB1uuqzxbfRvX5pthzUv93ijLO08URbZcjuCrPbdIzcrDUF/BrOca8GZXD5UipMv7SVubH4UALzZ3onsDe6KTs4hKziI6OZP7yVlEp2QV63VcGvQEgUk9PGpcpebyzks//OfPL8fuYaivYPPbndSGauqCmvZ+Pu7/gIl/XSI3X+RVb3f+N0Q33uXsvHw2XQzn12P3iEmRlPPalkY8SMnWWJCsqo59vU8on/x7E4CPBzRiUnfdteETRZE/TgXz7YE75CtFGjla8PtrbUs8rri0bN7885LkydZXsGhkSwa2cNbZuDRxLjCet9ddIiUrjzq2pqyZUNSTX1E5T6ZyWHJ5CatvrlarqOsJekxoOqFET3phFuy7zYqTQRjqKfj7LW86qDG+5uUrWe8TxqJDd0nOlOaM55s68vGAxrjrOP0hNSuXHw/e5c9zIYgiWJsa8PGAxjoxrOfmK7kQnMChWzEcuhVTJDpPE3oKgUndKzbfPSnPUln00GrPSR81ahSxsbF8/vnnREdH06pVKw4cOEDt2lJ4X1hYGApF0RAxf39/Tp8+zcGDB6tjyE8EKVm5fLpDmiTf6lavVAo6SIJ89wb2dKtvx8kAybN+LTyJP04H87dPKGM71mFS90fKuq76j4uiyNoz6sOdQYo4ikzKQl8h0KZOLclb3sCeJk6WJYb96Lp3aGV4aCuCi7UJi0e14vW1F/n7fBjt6tgwpHXZq5nm5Cl5b9M19t64j0KA719uycuV7PF6nMpoH1PXzoxZz9VnX14AA56rX+GXd03uRatQCEzp6YWLtQkzNl4DeKSsPLTH7roueQAUAnTxsmNwS2f6N3PUSeukunZmbH67EytOBrH40F0O347hypJE/jekGS80111F8ujkLOZu9+WYv+S5buVmzQ8jWuDlUDTxQZf3U0n9fT94TnN/38ycfKJTsrifnEl0cpakvCdncfBWtErBehwRUdXD+mng/X4NuBOdyuHbMUz66xK7p3Wlto5bAgXGpjF3u6/Gqv7V0YqrZ0MHFo9qxbQNV/nHJwxLYwPmvFD+/efkKdl8SVLO7ydLRmtnK2Om9q7Py21d2XktstrnpjHedUjOzOX7A/4s2HcHS2MDRnfQnr9bGtKy85i99Tr7bkhpTENbu7BgaPNSVZO3Mzdi48SOzNh4lYO3Ypj6z1UiEzOZ1N2j0qJ9/r0aweytvuTmi7Rxt2bluHbVmhMvU3rqWNbR6ElXikqG1R9Wpu3Neb4R4QkZ7L8ZzZt/XmRgCyfSsvNVcnNkYiZf7vHj7sMODI0cLfh8UBM6e1ZOKLqFsQHzBjdlSGsX5m6/we37Kcze6su2yxEsGNYczzKmhKRm5XLibiyHbsVw7M4DUgp11jE2UNDVy57Wbtb8eMhf7fv5aehpXhlUuye9qnlWPOmf/HuD9T5h1LU15cDM7uX2jImiyIm7sSw5HKDyvBkbKBjXqS6OlsZ8vfdWqT2KGTl5hCdkEpaQQVhCBuEPf8ISMghPzFAVbVOHALSrW4vVE9pjUU19WB9H1x7airLo0F1+PhKAiYEeu6Z2oX4Ziodl5uQzef1ljvvHYqAn8PPo1jpVqqqbJ8XCqiu+O3CHFSeC1ObOg5TesnhUq0pNY7kTncJ7m66rakUMaeXM/MHNsDLVfP5Luk6iKLLtSiTzd/upvOfv92vAW13rVXobQtB9FIU277wuPAuVQUWepdSsXIb9dpaAB2m0dLNm06SOFS5cdDcmlX037rP/RjT+Mala11UI8GILZ5a+UvUtRzdeCGPO9hsAzH6+IVN6li2FKDdfydbLEfxy9J7KM+Voacy7vb0Y2c61SJpTTZibRFHk2/13WH4yCIUAv7zahgEVmFPuPUjl7XWXCYxNx0BP4LOBTRjbsU6ZFex8pchXe26x9mwIAGM71uGLQU10+v4QRZGfj9xj8WGp6OqLzZ34cWRLtff6szY3PQmEp4Yzes9oUnKkuUtP0EMpKlVKe2ObxmwetLnM283KzaffohOEPzS+FviXCr/+a5ka8MFzDRnd3q1K5jSQ3i2rTwez+PBdsnKVGOopmNLLk8k9PYlKytLoiItOzuLQbclbfi4wjtxCsey2Zob0aexAvyaOdPWyUxnSKjMS8Ul5lp4oT7qM7vEJime9j1Tp9JthLSokBAmCQM+GDvRoYM/xh8r69fAkVhTOyy1QBB7+nr3Nl9jUbDJz84so5HFpOeUeh0Ih0K6uTY1R0EH3HtqKMqNPfa6EJnL6XhyT119h57tdSpW/lJqVy5t/XuJCcALGBgqWj21Hjwa6rxQvU3VEJGZq9AIoBLAxM6r0OhONHC3Z+W4Xfj4SwG/H77HjWhTngxL47uUW5bq/YlKy+Hj7DY7ckSojt3SVcp3LYoyqKLqOotDmnX8aPQsWxgasHNeOl349w/XwJD7+9wY/jmhZJkVLFEXuRKey/8Z99t2M5l6hegr6CgEnK2MikzLVp01Vcc2Qwozu4E5qVh7/23eb7w/4Y2lswGsd65T4vbx8JduvRrL0aADhD6s7O1gY8W4vL0a1d1M7v9eEuUkQBOa80IjkzFw2XgxnxsarmBvp070cz/5e3/vM3nqd9Jx8alsa8duYtrStU6tc49JTCMwb3BQ3G1O+3nuLdedDiUrKZOmrrTE1rLhInJOnZO72G2y7IqWMvdPDk9n9G1Z7b2qZ0pGRm8GMYzNIyUmhuV1zvuzyJbsCdnEp4BJe7l7sDNzJ7YTb+Nz3wdvJu0zbvp+cVST0+/F31PA2Lnw+sKlWQ3ZlYKCn4O0engxo7sRnO29y3F+S9df7hBKXliNF4zxUqH8/EchzTWpzPzkL34jkItvxsDOjX5Pa9GtSm9butdTW36jJkYg1EVlJf8rIys1n7kNr/Ssd3OjkaauT7QqCQK+GDvRsYM9x/1g+2ubLAw1FakQRvv/PX+1nViYGuNuY4m5jiquNier/7jam5OQp6b/kpBwKU070FAJLRrfixZ9Pce9BGnO33+Cn0a20CsCJ6TmMX3MB34hkLIz0Wf16+yotqCNTObjWMnlYmbZ6ixsa6iuY1b8hfRo78MHm6wTFpTN+9QXGeLvz8YDGpTIiiaLIv1cjmbfLj5SsPAz1FMzsV59J3TyqzNNQGF2G0FdGwcSaTl07M355tTXjV19g+5VImjhZ8lY3D63fEUURv6gUyWN+M5rgh1XaAQz1FHSrb8cLzZ3o17g2CRk5ZSoWWpVM7O5BcmYuvxy7x2c7b2JhrK+xiFRevpKd16L4+WgAoQ87C9iZGzG5pydjvN2rtHVSeREEgf8NbU5qVh57b9zn7XWX+futDrStU7o5Ji9fyXcH7rDyYbeDjh42LH2ljU5q47zZtR4u1sbM2HiNI3ceMGr5eVZNaFch42VyZi7vrLvMuaB49BQCX73UjFe9Kx7mL1M1iKLIp2c+JSAxAFtjWxb3XExts9pMazWNfVH7GOA9AGMDYzbc2cB3F79jy8At6ClK/xxuvhSucV5WCOBgaVzlCnph3Gykmgl7fO/z+c6bxKZKjrX8xxxx//lJHbkEAVq7WdOviSP9mtTGy6F0IfKVkdb4tCIr6U8ZPx8JICgu/WGrhsY6374gCPRq5IB3PRv23Liv7l0DgL25Ic81dcTdxhS3h0q4Wy3TEl9Az5rAqmvszI349dU2jFpxnl3Xo2hfz4axGrw1D1KyeG2VD3dj0qhlasC6N71LXbtApmZT0zy0rd1rsXd6N747cIe1Z0NY7xPG6Xtx/DiiJe20GIUepGTx8b83OXxbEgqau1jx48iW5e4DXxN5Fj0L3erb88mLTfhqzy3+t/c2V8OTUDw0HhWEU4qiiG9EMvtu3GffzfsqLzJIxp+eDewZ0NyJ3o0ditRTsDI1qNHzyAfPNSAlK5e/zoXy/ubrpGfnEZ6Yqbr2w9u4cjMymZ+OBKiMEbZmhrzTw5PXOtYpVf51TUJPIbB4VCtSs/M4eTeW19dcZNPbnWjspD3M80FqFtP+uYpPcAIgtc37sH9DnRrmnm/mxD8TjZn41yVuRCYz9Nez/PlG+2K1LUpDeEIGr6+9yL0HaVXaRUBGd6y6uYpDoYfQV+izuJekoD/OlJZT2Bu0l4DEALYFbGNkw5Gl3n5EYibaMowrWoMkNCWUfwP+JSotCmdzZ4bWH0ody5KjdQojCAKDWjpzNSyJNWeCNXYz6dnAnoUjWlZqMWkZWUl/qvCLSla1h/pqSDOsTCrPIudqY4pCENTmvOopBF5u51YuS9mzKLDqmnZ1bZj7QiO+3nubr3bfooWLVbG2FuEJUuuN0PgMalsa8feb3lUaNixTudRED62JoR7zBjelX5PafLjlOqHxGYxYfo5J3T14v18DopKy2OgTysW7Cvz072JvYcIvx+6RnJmLgZ7AzL4NeLt79XjPK5vK8CzoQmCrTN7oUpf9N+5zKTSRvb5SscqCcMpuXnYExqYXCQ01NlDQq6EDLzR3oncjB8y1RGHU5HlEEATmDWpKalYe/16N5ON/b1I4KnTZ8UfGtVqmBrzdw5NxneroJBS7ujDUV/D7a20Yu+oCl0MTGbvqAlvf6aTxelwKSWDK+is8SM3GzFCPH0a0rLQaKW3r1GL75M68vvYiwXHpDPvtLCvGtaOjR+mjEK+HJ/HmnxeJS8vB0dKY1RPa08S5Ztc8kinKyYiT/HzlZwA+9v6Y1g7q61ZYG1szpdUUvr3wLb9c/YXn6z2PpWHprnVlRrj9G/Av887NQ0BARERAYI3fGuZ3ns8QryFl3l5sWjYahopCAAsTA1lBrwKe3Le+TBHy8pV8tM2XfKXIgOaO9G/qWKn7q0xPnRwKU3He7FqPiyEJ/OcXw5T1V/htTGsO+MUQkZiJmaEeR27HEJuWg5uNCevf7Kjz9h4y1U9NVVS6eNlx4L3ufLn7FlsvR7D8RBA7r0YRk5qFAgGlKHD1VIjKgt/MxZIfRrSkkaMs9JYWXQtslUFIfAZXwhJVfysL9Yo/GRAHSL3lezdyYEBzJ3o2tC+TolqT5xGpC4Mn/16NBIrnpgK81bUeM/s10GqMeJIwNdRn9fj2jFpxjjvRqYz5w4fFo1pxzP+B6v00oq0rJ+/G8vXe2+QpRbwczPn9tbalDqMtL3XtzNg2uTMT/7r00Ijgww8jWpbYzxrgoF800zdeJStXSWMnS1ZPaIeTVfXUPZApHyHJIcw5OQcRkRENRjCiwQit649sOJJN/psITg5mxfUVzGo/q1T7qSy5OTQllHnn5qEUixdf/uLsF7RxaIO7ZdnSLmpKytyzztPx9pdh1elgbkamYGUitVWobGqip07mEYIgsHBES+5EnyY0PoOXfj2LnkJAKYqqd66DhRFb3u6Mo1XlFhCTqT5qqqJiaWzADyNa8lyT2ny0zZfoFKmVVD4iUi8HCQFYMqpVucJPn1UqQ2CrDLTlZwpA/6aOLBnd6onIvS4P269GoqcpGk0AA33FU6OgF2D1MK1qxO9nCYnPYOTyc+gJjwxJhaMIBrZw4rvhLUpVt0IX2JgZsv4tb97ffI19N6KZsfEaEYmZTOnpqbGuy+rTwXy19xaiCD0a2PPrmDZP3TV72knLSWPGsRmk5qbS2qE1czvMLfE7BgoDPmz3IVOOTGH9nfWMaDiiVFFKlSU3/xvwLwLq71EBge0B28vU0x1qXsrcs4r8NnkKCIlLZ9EhqdXHJy82rvSqzQXUVE+djISlsQGfvNiYSX9dBijW5ikuLZus3PzqGJqMDADPNXXkbGA8f54NUZv7plAIbLsSWSMNDTUVbQKbKIosvLiQOd5zcDZzrrT+0KVBW36m8FBJfVoVdNDegUGk4vmpNRV7CyMWDG3Oq3/4AIWKUhU6F+/28mLWcw2q/P40NtDjl1fa8G0tqTXiwv/8iUjM4KuXmhGemKlqReVibcyDlGy2P4yEGOPtzvzBTWtEKk5NT3OpSShFJXNPzyUoOQgHUwcW9VyEgV7p0kS7uXajq0tXTkee5odLP7C099JSfa8y5OaotCi1RlkAEZGotKgyb1N2xNUMZCX9CUcUReZuv0F2npKuXnaMaOtapfuvqZ66JxVdT7BXw5JQCOrDKQVBYNOlcPn61RCeVeEqPj1HY+6bKIpPrbJSWZQksB2POM7xiOPYm9jT0r4lrRxa0dK+JU1sm2CoZ1hl43zWwymf5eM/dS9O47wkLRerzYCkUAh8PKAxLtYmzN/tx4YL4VwJTSLgQapKSRHFRyaFuS80YlJ3j2o1eBXwJKS51CR+v/47x8OPY6gw5KdeP2FnYlem73/Y7kPORZ3jePhxzkWdo5Nzp1J9T9dys4m+iUaDn4CAs7lzubZbWY64Z1XWKQ+ykv6Es+liOOeC4jEx0GPB0OY1YqKQKR+VMcFqU3BkBajm8CwLV8+yslIZiA//qUNAwM7EjsSsRGIzYzkcdpjDYYcBKYSziW0TWtm3Uinu9qZFe1qHpoSyzX8bl9IvEXgtkOENh5dbuHrWwymf5eMvad6pCfPS+M51cbY24d1/LuMfkyotfOwdVZCWURPkriclzaWmcCTsCMuuLwPgs06f0cyuWZm34WHtwehGo1l/ez3fX/yeLYO2oK+oWrUqNSeV8/fPa/xcFEWG1R9W7u3r2qDwLMs65UFW0p9gYlKy+N++24DU1qWk4l+y9armUlkTbGUqQPL9pBuedeHqWVZWdM3x8OMcDj2s8XNBEFj7/FocTB3wi/fjeux1rj24xvXY6yRkJXA99jrXY6/z560/AXAxd1F52xOzEll+fTmCIKAUldy6fYs/b/9ZbuHqWQ+nfJaP/0mZl/o1qc2gFs5suxKp9nOFouZEo23x34IG21y585KfVgKTAvn41McAjGk8pkLK4eSWk9kTtId7SffYdncboxqN0tEoS0YpKvnk9CdEpUdhaWhJWk6a6h2iRJInurp2rTHyQ2XKOro0INckZCX9CebznTdJzcqjpasVr3epp3Vd2XpVs6mMwh9QeQqQfD/pjn8D/tX42bMgXD2urCiVShSCgAhPvbKiSw4EH2DuqbnkiXk0tmmMf4K/JLA9fD5FROZ3nq8SgtrWbkvb2m0B6V0QnhquUtqvxV4jIDGAyLRIItMi2Re879GOHioC+aJUz6IiwtWzXtfkWT3+J2leyskXNYbmV3c0WlxmHCfCT3A8/DgnI0+qFLPHyRfz2R20G2dzZzo7d8bVomrTItVRXUb+lJwUph+dTkZeBu0d2/NBuw8qtD0rIyumtJzCNxe+4ZdrUks2KyMrHY1WO6turOJY+DEMFAYs77ccS0NLtgdsJyotimxlNkfDjnIq4hSXYy6r3vXVSUn1Uv7n8z9GNRyFg6kDdiZ22JrYYqAouUZA4edeFwbkmoSspD+h7L9xn//8YtBXCHw7vAV6Cs3hVs+6p+5JIDItUueFP6ByvDXy/VRxIlIj8Lnvg0+0D0dCj1TKtX+SKFBWNviEcvFWIO2bePCKdx05962UFAgpSlHJix4v8nWXr4mKOM/2i0uIyozF2cSeYe1n4u7eVe33BUHA3dIdd0t3BnkOAqSqxzfibnAt9ho77+0kMk29N7GihqQnqa5JZdxPT9Lx64onaV6qSek4oigSmBTI8YjjHAs/xo3YGxpTWx7nQcYDvjr/FQB1LOvQyakTXVy60MGxA6YGVduCtbqM/PnKfD46+RFhqWE4mTnxQ48fSqUElsTIhiPZ7L+ZwORAlvsuZ3b72ToYrXbORp5l6VWpWN0n3p+owvULv4c/O/MZO+7t4ONTH7N18FYsDKu3S0pUWpTGYqEiImejznI26qxqmYBALeNaOJg6YG9ij72pvfS70P+z87OZd3ZeEQOVLgzINQVZSX8CSc7I5fNdfgBM7ulJYyft/YMry0sroxsux1zm6oOrlVL4A3TvrZHvp7ITmxHLhegL+Nz34UL0BY0KTzFEKnTtnyTq2pkx67n67MsLYMBz9TEwqJjw9KxEe/xz+x++ufANAMPrD+ezjp+hd30D7rumMRMBCtra3TwCg3+B1mNKtV1zQ3M6OXeik3MngpOCuZ9+X60CJIrPhiHpWbmfqoonZV6q7nScPGUeVx9c5WjYUY6HHyciLaLI581sm9HLvRcNajVgxtEZar3pAgJjm4yVUlweXCc0JZTQlFA2+m9EX6FPa4fWdHbuTBfnLjS0aYhCKFqlXpfGqeo08v9y7RdOR57GWM+Yn3r9hI2xjU62q6/QZ3b72bx9+G023N7AiAYjqGelPbq1IkSmRTL71GxERIbXH87wBsPVrjenwxwuRV8iIi2CBT4L+KbbN5U2ptLgbO6sVc51s3DDysiKBxkPiM+MJ0/MIyErgYSsBO5wp8z7exrkUVlJfwL5375bxKZm42lvxtTeXiWuH5UWpaXVy7MhYNVE/OL8WHp1KWeizmhdTykqK1T4A3TrrdFWPTpfzOdG3A2VZ+RppSShJTk7mUvRl/CJ9uHC/QsEJhcV8vQFfZrbN8fbyZs6FnX45PQnaoUrJUqsja0r+3CeOp6VaI9VN1ax5MoSAF5r/Bqz289GSAiCXdNA3TO6ayq4dwRbzzLtx9ncWaMCpESpE29UTeZZuZ+0UdOjCEry0tWkaLQCNOXRpuWkcTrqNMfDj3Mq4hQpOSmq7xgqDOno3JGebj3p4doDB1MH1Wfzu8zni7NfFDEkFaS5FBiS0nLSuBB9gbNRZzkTeYaItAguRl/kYvRFfroiKa6dnTurfk5GnCyzcSozL5P4zHjis+JJyEwgPite9ff5++c1yg+VqVQdCDnAHzf+AGBe53k0tm2s0+13dulMd9funIw4yQ+XfuDXPr/qdPsFZOVl8d6x90jOTqaZbTM+9v5Y47pmBmZ80+0bxh8Yz56gPXR37c4L9V6olHGVhvTcdM1KuiCwrO8y1XtUKSpJzEokLjOOBxkPVL9jM2OJzYiVfmfGEpMe81TrN7KS/oRx5l4cmy9JltRvh7fASL/kPrLaPHEV9dLKlJ17iff45dovHAk7AkgK25D6Q6hrWZdFlxepJkNRlKo0G+sb1ygh2MzQTGuI3YXoC4zYPYIxjcfwQr0XMNY3rsLRVT5qPWo31/Bak9fQU+jhc9+H2/G3i5wjAYFGNo3wdvKmg2MH2tZuWyTEME/MKyZcFYRs/XjpR5Siktebvv5UGz50ydMe7SGKIr9c+4UVvisAmNRiElNbTZXuj6vrQMOxw8PP+84r0/6G1h/KGr81Gj8/EHKAfnX70dOtZ5m2+6TwtN9PJfEkRBE4mjlqzMmuaERSZdQOeDyP1u+2H2tvrcXT2pOQlBDylHmqdWsZ1aK7a3d6ufWik3MnjeHpQ7yG0MahjSov2dncmWH1hxUxIJkbmtPbvTe93XsDEJYSxpmoM5yNPItPtA8JWQnsCdrDnqA9Wsf/+ZnP8Yvzk7ydjynimXnly9PPF/O5EH2B9Nx0zAx0V5fBP8Gfz898DsCEphN40eNFnW27MLPazeJs5FlORpzkTOQZurh00en2RVHk6/NfczvhNrWMarG41+IS22a2cmjFpBaT+P3673x1/itaO7TG0cxRp+MqDTvv7WSj/0ZAemcqBIXGeikACkGBrYkttia2NLRpqHG7iy8vZq3fWrWGn6dBv5GV9CeIzJx85m6/AcDYjnVoX7d0oTqDPAex6uYqtZ/li/n0r9tfZ2OU0UxYShi/Xf+NfUH7VC+ngR4DmdxyMm6WUshcL7deqgm2tlltTkee5l7SPWadmMXa59dioFe9ynp6bjo+UT5a1zHSM8I/0Z/Pz37OosuLGNFgBKMajqK2We0qGmXloc2j9tetv4r8Xc+qHh0cO9DRqSPtarfT6hFXJ1wN8RrClrtb+OvWXyy+vJjo9Gg+av8ReoqSDXPPOkHJQSojx+Pki/lcjL5IbEZssRZjTwKiKLLw0kLW3VoHwMw2M3mz+ZuPVkgKQ2OZZ8SHn5eNOpZ1mN/5kZdOKSolIUsU8bD24F7SPWYcm8HHHT6u0urGVUFydjLHw49rvJ+eBm+NNkJTQovlfBZQU6IIlKKS4JRgzZ+jpJVDqwrtQ5def3XzSEEUwL2ke9L+LOvSy60Xvdx70cKuRanf++6W7mUyGBXUonil0Svk5udyLfYaZyLPcDbqLLcTbmv8noioUrrUYaRnhK2xLTbGNiply9bYlhtxN7hw/4JGg8qNuBv02tyLvu59ecnrJdo7ti8Wfl8WkrKSmHFsBpl5mXRy6sSMNjPKtoH4QBSX/6RtsA+KY5eh7XiNkUj1rOoxutFo/r79NwsvLsTbyVunLdm23N3CzsCdKAQFC3ssLLWyPanFJM5EnuFG3A0+Of0JK59bWaFzWlYu3L/AvHPzAHir+VsM9Rqq1ZBUFobVH8Zav7VqPxOpWPu5moCspD9BLDrkT1hCBk5Wxsx+XrNl6XFORZxS/V/1YIqoXpIfnfqIpb2XPpUFlQqozgJS0enR/H79d3bc26ES9PrV6ce7rd7F07roy/7xCXZ0o9GM3D0S3zhffrz8I3M6zKmSMatDKSr5+NTHhKeFY2FgQXpuutrq0QWGhg13NnA//T4rb6xkzc019K3TlzGNx9DSvuUT6xHe4r9FY0glQH3r+rze7HW8nbyLhCGWBnXC1YftP6S2aW1+uPQDG+5s4EHGA77t9u1TF52gK1JzUllzc02Rd546fON86b2lNw1qNVCFdbap3QYjPaMqGmn5yFfm89X5r9gWsA2AuR3m8mrjV4uuZK1F2BGVYFE+L0qBIWmr/1YuBVyiXf12vNzwZZzMnfj6/NdsD9jO1z5fcz/9PtPbTK9SIbAySM1J5e9bf/PXrb9Iy03TuJ5SVNa491lZ2xGJokhCVgLhqeGEp4YTlhom/T8lHP9Ef40KVU2IIhBFkW8vfMvx8OOqaIfCXrqCOffr81/TxLZJmd/LlUFJkRnD6g9jXud5VTsowEDPgPaO7Wnv2J6ZbWcy4+gMjoUf0xg552bhxkCPgdga26oUcRtjG2yNbTEzMFP7XISmhDJ4x2C1dsQCz2dkWiS7g3azO2g3TmZODPIcxEueL5VZkctT5jHr5Cwi0yJxNXdlYY+FZVOar/4Nu6ahQMBZVCKcuwjnlmqt7fFOy3fYE7SHwORAttzdwiuNXinTmDVxPfa6qvbIjDYz8HbyLvV3DRQGfNPtG0bsHsGF6Av85fcXE5pN0Mm4SiIoOYiZx2eSp8yjf93+TGs9DYWg0Nk7Q6MBWY13/klEELVJnE8hKSkpWFlZkZycjKWl9oJr1U1ubi779u1jwIAB3IpOZ+hvZ1CKsHpCO3o3Kp1XMiI1gqE7h5KVn8WM1jNIy01TKarN7ZuzwGcBDzIeYGloyY89f6SjU8dKPqqqR12o3uN5WuWl8DV6vNBVXGYcq26sYrP/ZnKUOQB0denK1NZTaWrbtNT7OBF+gqlHpwKwsMdCnq/7fIXGXF6WXV/Gb9d+w0BhwJrn11DLqJZWa2ieMo/j4cf5+/bfXI65rFrezLYZrzZ+lefrPl9lkQHarlNpCE4OZrP/Zjb6bywShlgYhaCgf53+fN/j+4oOtxgHQg7w8amPyVXm0tqhNT/3+rnactUr0+BV3uuUnZ/NxjsbWXljJcnZyVrXFRDwtPZUeawKMNYzpq1jWzo7daaLSxc8rDw0CpnVYfDLU+bx6ZlP2Ru0F4WgYF6neQytP7T4ikEn4K/BmjdUuymM3wOm5SuapO4aiaLIct/l/HpNysMcUG8AX3X5qsRQzJpIWk4a62+v589bf5KakwpIgmBYSpjWNJ/BnoN5v+372JrYVtVQ1fJ4GHWBwDqv0zw6OXeSlPCUsEeK+MOf9Nz0Mu+rMt95peWXq7+w3Hc5AgILui2ghV2LIvNSvzr9mHt6LsHJwTS2acza59dWeTXzx3n/2PscCjuk9rMKn9P4QCmlJSlMMti1HlvmGhQFLLm8hLV+a9VGkegJekxoOqFcytaOezs05s6/5PkSvnG+7Ly3kwPBB0jNTVV9r41DG17yeonn6jyHuaF5iftZeHEhf936CxN9E/4e8DcNajUo/SDjA+GXduprewgKmHpJ43nddGcTX/t8jZWRFXuH7q1wS7a4zDhG7R7Fg8wH9KvTjx97/Fguw+C2u9uYd24e+gp9Nr64UWsYuS6Iz4xnzL4xRKZF0tK+JX8890elORjCUsKKGZBrqoJeFj1UVtJrMAXCUL/+zzPsdx/uRKcyuKUzP7/SulTfF0WRyYcncybqDO0d27PquVXFHuzYjFhmHpuJb5wveoIes9vP5pVGr9Q4z0B5KbDaqgtPVqBg99Dd5X6QVd6Khy+FAm9FcnYya/3Wsv72elVuVrva7ZjWehptarcp176WXF7CqpurMNU3ZePAjZVaOVQdx8KOMf3YdAC+7PyleuVAC3cS7rD+9nr2Be1TGSzsTOwY2XAkIxqMwM7EDqg8Bag8yl+uMpfj4cfZ5L8Jn/vaQ/yhYkJLabgYfZEZx2aQmpNKXcu6/N7vd1zMXSplX5qoTIMXlP065Snz2B24m1+v/UpMRgwAHlYeTG8zndTsVL44p7mIUkJWAuejzqvavsRmxhbZdm3T2iove0enjlgbW1f68WsiJz+H2SdncyTsCPqCPt90+4bn66kx1omipKAHn5T+FvRQVXdHBH1jyM0AhyYw9t9yedW1XaOd93Yy7+w88sQ8Ojh2YHGvxVga1ux5toCM3Az+ufMPa/3Wqgw9nlaeTG41mX51+rErcFdxxUIUaVu7LZdiLiEiYmFowcw2Mxlef3i1pKVom+9KQkDA0cwRNws33CzccLd0x83CjVMRp9gVuEtjuP+4JuP4sP2HFR16ufjL7y8WXloISG2oRjcarXa98NRwXtv3GglZCfRw7cFPvX6qtrShlJwUhuwYUux9U0CF5pGHnl/V817wuwxdHQqjVX4SFOweUn75KSwlrMSQ56y8LI6HH2dH4A7ORZ1TjcNYz5g+dfrwkudLdHDsoLqWheWHjLwMTkScAGBRz0X0q9OvbAM8PA/O/Azq7ntBD7pM11jbI0+Zx4jdI7iXdI8xjcdUKAIyT5nHxIMTuRRziXpW9djw4oZy5+uLosiMY1J0hJe1Fxte3FBpSnNWXhZvHnwT31hfXM1dWf/iep1V09dERZ0xVYWspGvhSVTSQ0wbsfjIPWqZGnD4/R7YmpcuJHNv0F7mnJqDocKQbYO3Udeqrtr1svOzmX92PruDdgPwcoOX+bjDx9We/6wLvj7/NZv9N2v0gJgbmNPEtgmuFq64mrviauGKi7kLrhau1DKqpdFYodZbIYr0cuvFhegLKutvM9tmTGszjU5OnSpk+Cj8ovay9uKfF//BRL9q+rMGJQfx6t5XSc9NZ3TD0XzS8ZNybyshK4Et/lvY5L9JJaQYKAx4od4LOJs7s8J3RZVHPDxOTHoM2wK2se3uNh5kPgAkAbaHaw96uvXky3Nfqg3/rKjQUhruJd7jncPvEJMRg52JHcv6LqORTdX0WK5Mga2A0l4nURQ5Gn6Un6/8TFByECAp1e+2epdBnoNUIY2lEQQLtncv6Z5KYb8cc5ns/GzV5wICXtZe3Eu6p/ZdUpnXPjMvk/eOv8eZyDMYKAxY1HOR5gJtl1bDnvdA3wRe2QDBJ4p61PJz4K8hkBYNterCuJ3S7zJQ0jU6G3WW94+/T3puOl7WXizru6xaChWVlsy8TDbd2cTqm6tJzE4EpHzgyS0n079u/yLKnKb7yTfWV1XQCaT3/qedPi1TxJQu+PzM5+y4t6PENkdulm64W0hKuLuFO26WbriYu6hN9yhJ8few8uCP5/6o8voO/wb8y+dnpWJg01pPY1KLSVrXvx57nTf/e5Ps/GxebfQqc73nVsUwi5CcncykQ5O4FX9L4zrlfpdUwPOrDW1e76osGhiTHsOeoD3sDNxJcPKj+gO1TWsz2HMwpvqmLL22VCWTFTwDPVx78EufX8q2M1GEPwdDyEn1nwsKaDoUXl6tcRNno87y9qG30RP02D54Ox7WHmUbw0MKogHMDMz458V/8LAq33YKSMhKYNjOYcRnxVfYgKAJpajkwxMfcjD0IJaGlvw94O8qcSzJSvpTwJOipAfHpbPRJ5RTNwK5k6xAKcLiUS0Z2tq1VN9Pzk5m8I7BJGQlMLXVVN5u+bbW9UVRZK3fWhZfXoyISLva7VjUcxG1jGvp4nCqlFxlLifDT7I1YCunI0+Xezum+qZFlPeC/ysEBVMOT9FcSRaoX6s+U1tNpZdbL51FJcRlxjFi9wjiMuMY7DmYr7t8XekRD6k5qby691VCUkJoW7stK59bqZNK87n5uRwKPcT62+vxjfPVuq4uFKCSXt6iKOIT7cOmO5s4Fn5M5TWyMbZheP3hvNzgZVWV0OoWWqLTo5lyZAoBiQGY6puyuNdiOjt3rvT9agt9VAgKXm/6eoWjCEozyV6KvsSSK0u4HnsdACsjKyY2n8iohqN05hXIysviSswVqepx1NliofGPU1lRFOm56Uw9MpVLMZcw0Tfhp14/0cm5k/qVk8Lht06Qkwr9F0Cnd9WvlxAM64ZAYghYOMHYHeBQekNPaa6Rf4I/Uw5P4UHmAxxMHPit72+VHlpZVrLysthydwurbqwiPisekHJsJ7eczAv1Xihzwad8ZT6b/Dex9OpS0nLTUAgKRjUcxdTWUys1miAjN4MDIQfYdneb1nepAgX96vbjhx4/lHkf6t55SlGJqb4p6XnpOJs5s6zfsgorEKXlUOghZp2YhVJUMr7JeD5o90Gp5sKDIQf54MQHAHzU/iNea/JaZQ9VRWJWIhMPTsQ/0Z9aRrV4tfGrLLu+TG0ebbnmEW2eXwRo8Dz0+Qxs64N+2dJQSmvsrApEUeRm3E12Bu5kX/A+VUqKJsoUMZmRANf+gctrIT5A83oleNILmHZkGscjjtPVpSvL+i4ref+PcSD4AB+elKJUFvdcTN86fcu8DXWcijjFlCNTAPi97+86r0K/+PJiVt9cjb5CnxX9VtDesb1Ot68JWUl/CngSlPTNl8KZs833YdETJQXtdBa+3IIR7dxKtY3Pz3zOv/f+xdPKky2DtpTaK34y4iSzT84mPTcdF3MXfu79c9nyeKqR8JRwtgVsY2fgTuIy40pcXyEoGFBvAJ2dOxORFkFE6sOftAgeZDwo9zh6uvbkp94/VUrhpIvRF3nr4FsoRSXzOs1jeIPhOt9HAUpRybSj0zgZcZLaprXZNHBT0XxLHeW++cb68tmZz1Qe0ccREOju2p13W71LXau6ZY4g0JSWAJIxa1fgLjb7byYkJUT1nTYObRjdaDR93fuqfXaqW2hJzUll5rGZXIi+gL6gz5ddvmSQ56BK258oirx18C0uRF/QuI6loSXD6g/D28mbNg5typX3qW2S9U/w56crP3EqUioKZ6xnzNgmY5nQbEKlh1THpMfw3vH3uBF3Q+M6LuYuTG89HW8nb53kJSdnJzPl8BR843wxNzDn1z6/ak6XEUX4ezgEHgHXDvDGAdAWzptyH9YNhdjbYGIDr20Dl9Kl4pRWELqfdp8pR6ZwL+keZgZmLOq5qEqMSSWRnZ/N1rtbWXVjlSqax8XchXdavsNAj4EVrsYclxnHwosL2Re8DwBbY1tmtZ/Fi/Ve1KlR9Vb8Lbbe3cq+4H2qfPKCYmTqPOkVNSSpe+cJCEw+MpnQlFCsjKxY2nsprR1Kl45XXs5GnWXqkankKnOlAmud5hU9ryXMS2turlG1Ol3Sa4mqFVllEp8Zz8RDEwlIDMDW2JY/nvsDr1peusujzc2CP3pDjF/J6yoMwL6hVJuidrNHv80dQNP9qcM8d11uMzs/m+Phx1lyeQkRaRFq1ynxvhdFCDsHl9bArZ1QEEFlYAq5majvlCHAtMsljjc0JZQhO4eQp8zjtz6/0c21W2kPjXuJ93h136tk5mXyRrM3eK/te6X+bmlY4LOADXc2YGdix/bB23XmkNt6dyvzz82X9tF1QaXKJY8jK+lPATVdSQ+OS6fPj8dRqrkqCgGOftCzxN6cF+5f4M2DUkuev174q8yTZmBSINOOTiM8NRxTfVO+7fYtvdx7lWkbVUVOfg5Hw46yNWBrkbxhG2MbhngNwdvJm8mHJ5c5RDc7P5vItEgiUyOLKfCBSYEa8/OqopDOHzf+4KcrP2GoMOTvAX/T2LZxpexn6dWlrPBdgZGeEX++8GfR0E0d577NPjGb/0L+0xqdAJIQ6mLugpe1Fx7WHnhae+Jp5Uk9q3pqlUJNRZQmNZ9ETEYM+4P3k5WfBYCZgRkDPQYyquEo6teqX+ZjqDBlFFpy8nP49Myn7A/eD0gVX99s9qZOFYGUnBR23dvFJv9NRYwYJaEv6NPcvjkdHDvg7eRNS/uWpSoipm6SjUiN4Ndrv7I3aC8iInqCHsPrD+edlu9UaYittkiCx2lk04iOTh3p5NSJNrXblNnDH58Zz9uH3sY/0R8rIyuW911OUzstodNX/4ad74KeEbxzGuxLYVjNSJAU+6grYGgBr26Eul1L/FpZBKGUnBRmHpvJxeiL6Av6zOs8j5e8Xip5bDrg8foWAz0GcjnmMiturFAZYZ3MnHi7xdsM9hqskwihwvjc9+Hr81+rnpsOjh34xPuTcoe9glTUbl/wPrbe3VqkPZa7hTvDGwynjUMbxh8YX6kpKY+TkJXAtCPT8I3zxUjPiO+6fUefOn10uo8Crj24xqRDk8jMy6RfnX4s7L6waG55KeYlURT58vyXbL27FRN9E9b0X6P92aogcZlxvPXfWwQmB2JvYs8f/f8oEnFQIcVCFCXF8tBnJbRWFMDSCbLTIDtF/SqmdkUVd8dmYNcQbm7V6VwPVKn8oFEmy0gA302Sch7n/2i5Ywto9zo0HwG3dsGuqYgIiKISARAQQaEPbx0B51Ylju2Hiz/w560/qWdVj22Dt5XqPZOak8ore18hNCUUbydvfu/7e+mMh2WQITLzMhm9ZzRByUH0de/Lop6LKiw7nI06y5TDU8gX85nccjJTWk2p0PbKiqykPwXUdCX9uwN3WHEyiHw1WrqeQmBSdw+tvTqz87MZvms4oSmhjGwwks86fVaucSRlJfHBiQ+4EH0BAYHpbabrXAGoCMHJwWy7u41dgbtUeYQCAp2dOzO8wXB6uvZUeUB1HZ685PIS1vitUSsIVXbxMJA83NOPTudExAncLNzYOHCjzj2Jh0IP8f7x9wE11tBKyH3TpgAJCDiYOpCVn6W1crezmbOkuFt54mntiZmBGR+e+LBExb9BrQaMajiKFz1eLHdBlgpTTqFFKSpV9yPAqIajmNthboWLIt2Ov80m/03sC96nKn5orGesMmg8jgIF77d7n8CkQHzu+xCVXrRvtJGeEa0dWuPt5I23ozeNbRsXEzoej3joU6cP+4L3sfnuZlVF/efrPs/U1lOrpV2kttxcAYEhXkO4FX8L/0T/Ip8ZKgxpXbu1pLQ7d6KxTeNiUTaFFUorIytOR54mIi0CW2NbVjy3Qns0U0oU/NoRspOh73zoOrP0B5WdChtegZBTUlG5kX9Bg/5av1JWQSgnP4fPznym8iy/2+pd3m7xdqXOJUUK/IkiBf8KqG1am0ktJjHUa2jZaq+Uw5D2162/WH59OVn5Wegr9JnQdAKTWkwqdUSQKIrciLvB1rtbORByQPU8GigM6FunLy/Xf5n2ju1V57PwfKeTMOpSkJmXyewTszkeIbVBm9NhTvHWgBXEP8Gf1/97ndScVDo7d2Zp76VFDX9lmJfylHlMPTKVM1FnsDOxY/2A9ap0Jl0Skx7DWwffIiQlhNqmtVnVf1Wxd1e5FYv71+HAXAg9I/1t5gDpsajvbfbw+G08pHs3xu/hz03pd/w99d9DARrnTwF6fwpmUtFXRPHRNlRqxeN/A2kP4ORC7ePUofxQRCYTRQj3eeg13wF5D+czA1No/jK0nQDObYpGFMQHkn/5T+7f8sGpcQf0oq5A6GmwcodJx8FMe9RUSk4KA7cPJDE7kTkd5jCmsXYjhFJUMuPYDI6HH8fRzJFNAzeVruBaOWSI2/G3eXXfq+Qp88pVELgwAYkBjNs/jrTcNAZ6DGRB1wVVri/ISvpTQE1X0qdtuMpe3yiNnvQXWzizVEt19wLvp72JPTuG7KiQ8parzOX7C9+z0X8jILXVmd95fqX3aNZU4TsrL4tDoYfYencrVx5cUa3vYOLAkPpDGFZ/mMZq17oMT67UAlqlFAKTs5MZuXskUelR9HbrzZJeS3T2QgxIDGDMvjFk5mUytslYZrefXXSFClQ91URpzqmbhRsJWQkEJQcRmBRIYFKg6v8FOaVlwdPaky86fUEr+1bVa3zSgdFj/e31fHfhO0REerv15rvu35X5Oc3Oz+ZgyEE2+m/EN/ZRbquXtRejG45moOdADoUeKpXBKyI1ggvRFzh//zwX7l8odn3MDcxpV7sd3k7edHDqwM24m8w/N1+lWEDRkN1OTp2Y0XZGlRfiepzSGPziMuPwue/DuahznLt/rljqjLWRNd5O3nRy6kRH545cuH+hSLRHwXFbGlqyfsB6jQU/AUno3DAa7h6QhMs3D4FeGcO1czNhy+twd7/kIRq6XBJYNa1eDkFIKSr5+crPrLq5CoDh9YfzScdPdO69hpILnb3d4m0mtpiotkiaVirg/YtIjeDbC9+qqk07mzkz13suPd16apzvkrOT2RO0h20B2whIfJQf62HlwfD6wxnkOUhjiGp1tCPKU+axwGcBW+5uAeD1Zq8zs81MnaR9haWEMW7/OOKz4mll34rl/ZYXj5wq47yUlpPGuAPjCEgMwMvai79e+AsLQ4sKj7WA6PRo3vjvDcJTw3Eyc2JV/1W4WRRPVyzz85QaA0e/hKvrkTo2mECXGdLx+e2AXVMp8z2akyGlvqiUdz+IvgFZSeU59Iph6QyefcCuvpQ/b1dfKm6pxZhWovzw/Hrcg89IueYPChXtq90c2k2A5iPBWLOsXOQa5aXBil6QGAwePWHMthLfuZv9N/PV+a+wNLRk79C9WtunrvBdwdKrSzFQGPDXC3/RzK6Z1m0DFZIhVt9czeLLizHRN2HboG24WZYupbYwsRmxjNk3hvvp92lbuy0r+q2olvabspL+FFDTlfSKeNIDEgMYuXskeWJe+VpOaGCz/2a+8fmGPDGPZrbNWNJrCbXNStenvayoa3GkFJV4O3rjl+CnKhKiEBR0c+nGyw1epqtL1wrnEZaVSvFWlFEI9IvzY+z+seQqc5nVbhbjm44v/wE9JDk7mdF7RhORFoG3oze/91MTZrX1DfD7V/OEUELVU01UJOIhKSuJwOSiivvVB1eLVOguTE3o76tCR0aPQ6GHmHNyDjnKHFrat2Rp76WlyjMLTw1ny90t/BvwL0nZSQDoK/Tp596PUY1G0cahTREjRlkNXqIoEpQchM99H3zu+3Ax5mKJxX4K87+u/2Owp5a+31VMWY5fFEWCU4I5F3WO81HnuRhzsdT9qAUE9gzdo125ur4J/p0Eeobw9klwKGfqS34u7JgCNzYDAgxcLIV8qqEigtCmO5tYcGEBSlFJF5cuLOqxSCc9q1NyUvCL88Mv3o+d93ZqTM0od6STjqKHjoYd5dsL33I//T4ADWs15G7iXdX8UTCftLRvye2E26r3l5GeEf3r9md4/eG0dmhdKqNidQisoijyx40/+Pnqz4Bk2P+6y9cV6hQTkx7DuP3jiEqPokGtBqzuv1p93+lyzEvR6dG8uvdVYjNj6ejUkd/6/qYTw1FkWiRv/vcmkWmRuJi7sKr/Ko0OhFJfp9wsOP8bnPoRctKkZc1HSHODVaGCwrrKHxdF2Pgq+O9HvZcdqfCkc4HT6OE9WfjeFITiyyMuQUpk2cai0JcUddv6YOf1SHm3rS958gWBHUc+4ovwvY9LT8xX1mJIVAA8jEBB3wSaDZfeby5tNefhF6LYNYq5BX/0kdpZdpkB/b7U+v08ZR4j94wkIDGAVxq9wsfeH6td70zkGSYfnqySe4bVH1a681MBGSJfmc9bB9/iUswlWti34M/n/yyTPJ2Rm8Eb/72BX7wfdS3r8veAvyvcF768PI1Kepk1m/z8fNauXcuRI0d48OABSmXRF+LRo0fLukmZQoxs58byE4FqPxNFkVEaCscpRSVfnvuSPDGPnm496euumyqQACMbjqSuZV3eP/E+N+Nv8sreV/ip1080t2+us32AZA2dd26eWmvo+ejzgJRDOKz+MIZ4DanWtj5DvIbQxqGN7rwV8YGSgq5OwNg1Fdw7Fptom9o15aP2H/G1z9csvryY5nbNy92HHaSX9eyTs4lIi8DF3IWFPRaqf1lbuasfJ0jLU6IgL6fMFWQLzml5Ih6sja1pa9yWtrXbqpaVFEJfGeGN5SI+UENFXgCxhFzDR/Sr0w+b52yYfnQ612OvM27/OD7t+Cnnos4V89LlK/M5E3WGjXc2cjrytMp762jmyIgGIxhWf5iqd/3juFu6l0nJEQRBqh1g7cmrjV8lX5nPncQ7XLh/QaW454l5ar+rJ+gRlKS+oGCpqISCR2U5fkEQ8LDywMPKgzGNx5CrzOVm3E3Jyx51juux1zW2y1IICrYHbNe8r9QY2P8wyqXH7PIr6CB5qYYuByMLuLQK9syErOSyhc6XglGNRuFg6sDsk7M5E3mGCQcm8LH3xxwPP17sHtVERm4GtxNu4xfnx834m/jF+RGWWrpnREQkKi2q5BUf5+o6VMpGMQTp81IY0nq796ajU0dW+K5g7c21qtSIx99R12KvAVIqzvD6w3nR48VqE3xVlOJZEgSBiS0m4mDqwLyz89gXvI/4zHgW91pcLi91YlYikw5NIio9CncLd5b3W675PBhZap6XEKQxP4ajmSO/9PmFCQcmcP7+eb4+/3XxQnRlJDw1nDf/e5P76fdxs3Bjdf/VFZNVRBFu74KDn0FSqLTMpS08/y24dSi+vq1nmSPZ1CIIUoG5u/9pVv5aji77vrQqlAqpCr1jC6m6elyAdN/lpksh+fH34O5j3zG2Ais3hsTcpI2+PtstzIjS18c5L49hqem454VL6zk0gbavQ4uRYGJdtjE/Tu0m8NKvsPV1OPMTOLWCZpoVan2FPh+1/4i3Dr7FZv/NjGo4Ck/ros9OZFokH536CBGR4fWHl15BB+mZ1Hjva5ch9BR6LOi6gOG7huMb68vKGyuZ3HJyqXabr8xnzqk5+MX7YW1kza99fq3+99RTRpmV9BkzZrB27VpefPFFmjVrVmNylJ8W6tmZ8d3wFny0zRdBEFAqlSgEARH4bngLjUXjtt7dyrXYa5jqm/KJ9yc6vy4dnDqw4cUNTD86nXtJ95hwYAJfdvmSZnbN1IbqlYa0nDTCU8MJSw0jPDWc/UH7NYYoAvSv25/vun1XvnzbShLWp7Waxr6ofQxoVUHLXTmFwJENR3L5wWX2B+/nwxMfsnnQ5nJXlv7p6k+cjTqLsZ4xP/X6SbMX1txB+4bCzsHy7vDSL+DarkxjKKsCqI2h9YeqcrUfR0Qs2yRYWdy/DsEaerEWoEa41ETb2m1Z98I63jn8DiEpIbx18C1VuKmAwJqba+hTpw9+cX5F8sa7OHdhZMORdHftXulRKXoKPZraNqWpbVNeb/Y6s47P4mDoQbXKarmVKlAfmXLmp4oVPKogBgoDWju0prVDa6a0msJ7x97jSNiRsh+7KMLe96VwVMcW0GVmxQenUMCLP0pC7+lFcPgLSVHv83mpvE2lpZd7L1b3X83Uo1O5nXCbsfvHFr1H/daoomey87O5m3BXpYz7xfsRlBykdp5wNXelqV1TErMSuRRzSWPtgHIZ55LC0OhNLIMhDcDUwJSZbWeSlJ3EtoBtatcREBjkMYivu1Z+m81SUcZn6SWvl7A3see94+/hE+3DhAMT+K3Pb2WKwEvPTWfK4SkEJQfhYOrAyudWajQccnsP+G7RsjVRmvPV0MS2CQu7L2T6selsD9iOm4UbbzV/q9TjLExoSihv/vcmMRkx1LWsyx/P/VGxqMP7vg/zzh+2krVwluSA5iOk57WyaT1Wus5q0XxOy79N4Lmvi8pmoigZ/lVK+72HvwOktpNZydIP4J6Xx8zEx+vWCJJiPnS5Tt9jNBsmFd08uxR2TgX7RpLyrgFvJ296u/XmaPhRFl5cyLK+y1TPdlZeFu8de4/k7GSa2TbT6GnXiMYq9A+x0h7C7mTuxKcdP+WjUx+x/PpyOjt3pqV9yxJ3++PlHzkWfgwDhQE/9/652tryPc2UWRrbuHEjmzdvZsCAAZUxHhlgRDs32te1YYNPKBdvBdK+iQeveNfRqKA/yHjA4suLAZjeZrpmq20FFVU3CzfWvbCOOafmcCLiBHNOzUFAKBKqV1jAAil8OiwljLBU6Sc85ZFSnpCVUOp9KwQFChTlU9BroLBejHIKgYIgMK/TPPwT/AlKDpJesn2Xl/k87Q/ez5qbkkL7VZevNPc0zkopNMEKkuW7cHBZm/Fwe7eU3/ZHX+g4WSouY1j1BdnqWNZhfuf5GtMSqnVCEUXJY3lgLuTnaFlPKeUalwEPaw++7fYt4w9I6Q+PKyuHQg8BUs7zUK+hjGg4oloKsRXgauGKQlDoNuKhHJEp1UEdyzrlO3a/7XBnjxQGOuQ3rfmaZUIQoO8XUn7m4XmSsp6VDAN+0KlS0Ny+Od91+46JhyYCxe/Rz858xtqbawlNDVUVDSyMg6kDTW2b0syuGc1sm9HEtokqz7MgP1Ud5TbOWTqX2UtbEhm5GSgEhXpjgiCQq8ytGQp6OZ+lzi6dWfP8Gt498i53E+/y2v7XWNZnGV61vErcZXZ+NtOPTudm/E1qGdViZb+V6p+F/Dw4Mk9SlEAqjJYYgmo+KhhzCbJOD7cezG4/m28vfMtPV37C1dyV5+s9X+I4CxOUHMRb/71FbGYsHlYerOq/SrNRoSTSHsDRr+DKOuk49I0f5p3PqNq51NZTkpM05bmX5x1a1m0KAli5SD8ePYt+lpsJCUGw5z0Iv4D6YnQCKPN0q6AX0GeeZEgJPgGbxsDEY1q99B+0+4CTkSc5E3WGU5Gn6O7aHVEU+er8V9xOuE0to1os7rW4bPncl9aA/z7Nn4tKyRGQEAw29TSuNsBjACciTrAveB9zT81l66CtWlORNtzZwLpb6wApJa2yWy8+q5RZSTc0NMTLq+SXrEzFqGtnxqzn6rMvL4ABz9XX6qX99sK3pOWm0cy2GaMbjla/ko4UVXNDc37q9RNfnf+KbQHbEBGLCZifnfmMv/z+IiYjhpQcDe0+HmJjbIObhRvuFu5EpEVw/cF1tdW4n2phPfKK9FNOIdDUwJRFPRfxyt5X8Lnvw7Lry5jaemqpd38n4Q6fn/kcgDeavaFdODn+LaRFQ616MHoD3NhU3OjT53NJ+fTdKOXQ3dkDA5eAV+W05dGGztMSdEFWMuyaLlWXBWjwAnj2hgMfUeT5LHiu9syUWuKU4T49GXESPUFPY7uwbi7dWNRzUaUXgSwNlRLxoKPw5MqmXMeeHgf7PpT+3+0DcNRt2hEAXd+TPOp73peMSdmpujUGAOfvn9eopAIEJktpX7WMatHUrqlKKW9q21Rr673HjXOP17co17Ofoi2ao3weRWdzZwQN92iNSsepwLPUxLYJfw/4m3cOSZE94w6M46deP9Hesb3G3eUqc5l1YhYXoi9gZmDGsn7L1LetS42WCh6GnZX+7jRVGkdS2CNnRFoMhJyGe4el4miGmpWOMY3HEJEawd+3/+aT059Q26x2qRWPe4n3eOvgW8RnxeNl7cUfz/1Ruoi2+EAUl/+kbbAPimOXodUrUnj5yR+goG5Hs5el47Iue0EvndB6jCQn6TISUVfbNDCR5sY6naVcd7XzXfmMaKVCTx9eXgMrekjGgu0T4ZVNGg2a7pbujG08ljV+a/j41Md0dO5IanYqZ++fRSEoWNhjYdlSI04vkSKeAOp2e1jpv7AMoZTe2VFXYFkX6Dcf2r2pcXyfdPyEKw+uEJ4azvcXv2de53lq1zsZcZJvL3wLwPTW03mh3gulH7NMmSizkv7BBx/w008/8csvv9QMK+8zztGwoxwKPYSeoMe8zvPUe1B1rKjqKfSwNrJGgUJje6uApEfVaB1MHHCzlBRxd0t3lVLuZuGGuaG5aj2VB0SNMfSpFNajb8Cxb8B/bwkrliwEFlQqn3NqDst9l9PKoRVdXUrueZyYlciMozPIys+ii3MXpreern28Pr9L/x/wA9RuDLXnFV/P1AaGLZdC8vbMlCbhv4dBy1eh//+kz6sQnaYlVJSoq7BlguTtUehLBWc6TpGs/F59igotTYdJz+f967BuqFS526J0oZNRaVFac53NDcxrhIIOlRTxUIEcvaqkXArlvg8hIx4cmkK3WZU3uHZvSHm+/74tFZTLSYPen6G4vvGRUtF2fLmFdW1pDAICLe1b8l3373AycyqzrFGR+hbF8N0CNwvC0h9GD4lKVBPVgB/KdQ6eiHQcqPCz5GLuwroX1jHt6DSuxV7j7UNvs6DbAp6vW9wYrBSVfH7mc46HH8dIz4ilvZeq7+gQfEoqFJf+AAwtYMiv0OQl6bPCOdm5WfBLe0gOg3O/Qo8PtY51VrtZRKZFciz8GNOPTmf9gPUl3jP+Cf5MOjSJhKwEGtk0YkW/FaUq2FngOFEg4CwqEc5dgLOFwsCd20h55+7eJW+rstFVnntlbbMywvJLi5ktjFoHq/pDwEE48S300hyu7mTuBEByTjL/hfynWt7XvS/eTqW81qIIR76UIp0Aur4vOUcSgoobPhR6Ujh+yCnYN0uqbzD4F6hVPHrO0tCSBV0X8OZ/b7ItYBvdXLrRp05R58qdhDtSa1tRyRCvIeVODZEpHWVW0k+fPs2xY8fYv38/TZs2LSbwbt++XWeDk9FOem46C3wWADC+6XjNIcqVoKhGpUU9MtgV26JAK4dWfNrxU1zNXUtdvbdSPCDawshFJcQ+XoWkCnhwG45/A7d2Sn8LCkmhdWgsvXgfD9Wr3UwK4yuBFz1e5ErMFTbf3czcU3PZPHCzakJQR54yjw9PfEhUehRuFm58111Lvr9SCXs/kCzVTV6C+qUoTFi/L0w5L4Xt+SyH6//AvUPwwvdSpd1nycgninBhBRz8VApvt3KHEWuK5uyrE1rGbIVVz0ntXtYPhwn7tLaKKeCJ8dI9RKcRDxkJEHUNzc+9KCmfNYQyKZS3d0uh7oKepJiUsThjmWn+Mhiaw5bxUkil/z4Ugt5DpeIinFta7rQhbfeoQlDQtnbbCt2nOqlv8eAO7J4h/b/HR9BilDRfJgRD4FHIToHU++XadKXMd7omLkBzGDFIc1RCkBTdYaY5tNva2JqVz61kzqk5HAk7wuwTs4nNiKW7a/ciNW1iMmLYE7QHfUGfH3v8WNzjrlTCmSXSnCIqJUPVyL+kit/qMDCW0je2vQmnF0ObcVoNnXoKPb7t9i2v//c6t+Jv8e6Rd1n3wjqNLbNux99m4qGJJGcn08S2CSv6rShd4axCjhNV/fPCjZb6fgmdp1VN3vnTQGWE5ZcF59YwaAnsmAwnvpMKyTUqnhIcmhKq8kA/zuHQw4SlhJX83CuVkrJ9SWpnSd95UuQTaDZ8jNsFF/+QvO7BJ2FZZyn3v+2EYnJYe8f2vN7sdVbfXM28c/NoYd9CFbkUnR7Nu0feJSMvA29Hbz7v+LnsrK1kyqykW1tbM3To0MoYi0wZWXp1KTEZMbiau/JOy3c0r6jDojcFlCRgtXFoQ4NaDcq8XZ16QABMbLR7Afz3wt8vS2FmDV+UJvXKIi5AChe/uQ3VJNJ0KPScI1VRBWg8+JEl1MAErm2EaF+49k+pBOHZHWZzM/4mt+JvMevELNY+v1Zj+5tFlxfhE+2Dib4JP/f6WbtwcW09hPuAgRn0/6b0x2xkDi98J7U82TUNYu9IFVFvbJGKVFnWLGWxUshMkoSH27ulvxsNlIrqmZTC22LuAGO3S4p69A3Y9BqM2QL62ns8PzFeukLoJOIh8Cj8O1lKydCIKOV6GltC9w+liubVTKkUyowEKfwcpNxUVeujSqbh8zD4Z9g+CQBBzH+oVDwMLS1n2lCNv0ez02DzOKmytEdPSUlX6D0Sgm/tgs1jpXzoNuPKFVKr8/lOV+RmwqlFkkKsrWYGSNFBP7WUws07vavRiGisb8yPPX7k2wvfstF/I99f/J7vL36PnqCHiIgoiqron6+7fk0Ptx5FN5CZKD3bd/dLf7d8VZpDtISwA9Lcc/43iLwMx/4n3ctaMDUw5ZfevzBm3xhCUkKYcWwGK59bWSxP2C/Oj4mHJpKak0pzu+b83u93LA1LafzT5jgR9CArUVbQy0plhOWXhVavSmmLF1dK0UcTj0pt4grxb8C/mo3ngqC9mweoaZW5SIp4KgmFArwnSdF6O9+VivvumSk5il76pWgLP2Bqq6mcjTrLnYQ7zDoxi9YOrQlLDeNyzGUSshLwsPJgUa9FFWqtKFM6yqykr1mjflKVqVpuxN7gn9v/APBZp88w0TfRvLK1e1ErrbrPy0hlClg6q/AdeflR3q827h2SfoytpBDjVq+Ca3vdeXoTguDE9+C76ZHBoPEg6DlXyqcqzOOWUFsvqYDTgTlQr1uJ18pIz4gfe/zIyD0j8Y3z5cfLPzKnw5xi6+0O3K0q+rGg6wLtxXwyEuCQlLNOr7lSAZey4tZB6uN8apHU59V/n5Qr2G8+tJnw9AokEZdh6wRJaFAYSNZr77fLdm/ZeEiK+dqBUoGaf9+B4au0nrMnwkunS/KypSiUc79If9s1kITzE99RzLNi3wge3JLCI69vhL7zJQ9pTb8HD8yRwnvtGkoKY1Xy4HahMO/HKV80Vo2+R0VRKkYV5y/1gh72h6SgF6bxICkPNOSU9H4csbZcu9JlRwudEHBY8tQlBkt/e/WVjvPIfIo9S53elTxz969LYb4XV0opGO3eUGvw1lPo8bH3xxjpGfHnrT+B4u3nBASa2z1WZyHqmmQwSQoFPSMYsFAyjJTmPSoI0H8BrO4v3afe72itwg1gb2rPr31+Zdz+cVx5cIXPznzG5JaT2XFvB1FpUegr9DkadpT0vHRa2rdkWd9lZWsxVwmOExkqJyy/LPRfIBnTw8/DxjEw8UgRI7C2NLQSO5nkZkmpcnf3S6lyQ5dLkU5lwdZTisbz+V16noOOwW+dpHG3fk31PBnoGfBdt+8Yvms4Vx5c4eqDq0XGPcRrSOkNUjIVoty9dmJjY/H3l3p8NmzYEHt7zYVcZHRLrjKXeefmISIy0GMgnZ07a/9C3e5SqJc6RKVUsVsUy6Q41GgBC+DGVslimJcFFo5StVR1YVDuHeH6Bri2AVIi4PIa6cfWC1q+IvUBfczKWGqSwiTl/No/j7xODQdInnOnkttbANB5Ovjvl7zYO6ZIYUslKBOuFq4s6LqAaUensf72elo7tKZ/3f6qz/3i/Jh3dh4Ab7d4m751SghdPzwPMhOkPqPeWiI2SkLfSFLym7wkedUjL0mC8I1tMOgnKWSxElrlVQuiKHlvDn0BylywriOFt7u0Lfm76nBuLeW9rR8hhTub14bnv9H6zNZYL52ueXAbtr0FMTelv9u9KRlDDE2lNJLH7ycbD6k4039zJQPajnek0MEXviv/9als/A9IRj5BIRVwq8yIH3VoVRrKr1TU2Hv08hrJWyXoSYWhzNXIN4IgPYPLu4Pfv9BhklTA6kklJUoq+Flg2LZwknKim7wkHWvjQerfzUol3N4JR7+W2mP9N1d69/WcAy1GS8W1CiEIAvoKfY01bRSC4pFHURTh8lrY/xHkZ0vv0ZF/gXOrsh2be0cpSu32LinlaGzJaZn1a9Xnx54/MuXwFPYF72N/8H5VocMChaWORR2W91uOmUEZK65baXOcVGKhM5nKRd9Quj+Xd5cMfDumSH8/nKfLnYaWnQobXpEMgvrG0jYb9Fe/bkkoFNBpCtR/TgrPj7ggRUPd2ilFmTyMbtRTPCo8+7hhYcmVJfRx71P97+lngDIr6enp6UybNo2//voLpVJ6werp6TFu3DiWLl2KqWnp8o9lys9ffn9xN/EuVkZWfNheeyEU4FHuCkhCR+HKjzxsBZWXJSlKZQhfqZECllIJxxfAyYXS3/Wfk7yO6bGalb/en0LPjyHkpKSs394lCRtHv5IED48e0GqMFKZcOLTu8cqsBUWUkiPh1A9SSK0yV1rXq5+koJZVCVDowZBl8HtX6QV9YQV0LFlR7unWkzeavcHqm6v5/MznmBuYczH6IsHJwZy/f54cZQ49XHswpdUU7RsKvwhX/pL+/+Ii3VR3rt0E3jwo5akf/UrqAbusszTp3NlDjW6VVxoyEiQDUUFblMaDYfBSra1ZSoVnb+le2D4RfJZJxqeuM7V+pcZ56XRJQZ7/oc+l95epnRS617BQpVlNnpWGz4NnL0mZOPkDRFyElX2k+6zPF1KaQU0hM0kKTQTJc1m4jkFVYe2O1romFVAqatw9GnVVUgpBunfqdNK8rmNzyaN7ea0U6TDxeM2PyHic/DzpOTr2P6k4oKAA78nSfFU4FUTTs6RQSGlbjQZJaVHHv4XkcOkdeOZn6POZNHcWMihqq2mj8ijmZEhGXN+N0gcNB0gGqtKkCamj33zJ4B14RKr27lVyXZXOzp2Z0nIKS68tVdvJJjw1nPjM+LIr6XoGaPWkV2ahM5nKxaK2ZFBfM0CSJU8vhm5SmlK5IlAzEuDv4VKFdkMLeHUj1C25KHCJ2HnBGwekgopHv5aiSX/tKBmrW47m34B/tbYHLTE0X0YnlFlJf//99zlx4gS7d++mS5cugFRMbvr06XzwwQcsW7ZM54OUeUR4SjjLrkvn+MN2H2JjXEKl7JAzkuIjKODVzVKLhsKKashpaSK8tl5qVzLiTymPuJTUKAErO03KBbqzR/q78zQplFWhJ+XJaQuDUiikvEOPnpD9g2RVvLZBUiCDjks/hhbQdIgUDh8fCLunF6rM+rCIUt3uUkuYgjw+j56SAaAiFVptPeG5r6TCbYe/kJQ1+5Lz/ae1nsb12OtcjrnMO4ffKdaSq6tLVxSCFoEyPw/2vg+IUv6fNmG1rCj0JGtuowGwe6YUdnV7l/p1a0qrvNIQfkGqOpwcDnqGUhhZ+7d0lzrRYqQUFXLwE+leMK8t1VN41kiNkZSAe1LPd7z6wku/lbr6PSBFdnR9T/L2HZ4nKQNX/5ZyjXt8JHlGK7swW2n47xOpOJmNJ/T6pHrGoK16sqh8epSKzETYPF56fzd8UZpDSqLXp3BzuxTyff0fKWz0SSH8Iux9TwrRBSnN68VF4NSi7NvS05cM1S1GwoWVUlpTnL9UR8OlrTT/1usOlMKjqGcCf/SR0lIEhWQ46zKjYu9RGw8p1ejcL3DwM/DoVTyFQQ0ZeRmqKMFiYy1NHvHjPLgDZwvy4gVEQYEoKhEEBUJVFTqTqVzcOkjK7t73JUeEU0vw6lP2CNSU+7BuiFTLx8QGXtsGLm10N06FHnSZLjlIdkyWUkR3vAO3dhJV2778ofkyOqPMJt9t27axatUqXnjhBSwtLbG0tGTAgAGsXLmSrVu3VsYYZR4iiiJfnv+S7PxsvB29Gew5WPsXlEoptAugzXio30+aKF9eLf229ZQm1dH/gL6JZF3+cyCkxVb2oeiepDBY/bykoOsZSh7H574u1SRcDCMLSdB6fS/MuC7ljlvXkfqWXl0Ha16QFEdRiSDmo0BEEPMlYTX4uCTg1ekCE/bCuJ26aaHS7k3w7CN5DP99W1KgS0Bfoc+M1jNUfz9uEf3mwjeEpWgJU720SipaZ2wltQqrDGrVhbH/Qn1toVsPc15rEvGBkmK39Q3pd2yApMSseUFS0GvVk1qmdZio+yr2nadKRZpAug8DDut2+zUd/wNS5MW9Q1J+6gsLpSr4ZVHQC2PpJLUMfPOQVJU3O0UygvzeRXonVicBh/k/e/cdHkXVBXD4N7vZ9EpJIYQOAiLSEbDTUZGmCCpFP6w0QUGkS6+ioqI0Gwgi2OkIKFKUJogISAslhfSebHbn+2OSQEghG3azm+S8z5Nnk9nZmbOZLXPm3nsuR78EFHj8A62YpD1kV09WdKiKHnNWcqFRIeyofeKyJlXVuqfGXdQ+73t8WLT3rmdleGCM9vuOt7WuqY4uJUarWr+8o5agu/rCo4vgua3FS9BvZHDTTvxH/KWNTze4ayf/nz2mTSV59Qg96/YsOAFQzfT6faWWoHv4w8AftR5D1vgcvf91rSU+8p8if6dcTbpaYAVri5OVzAytJ1RmmnZhcdghzG2GctW3NeY2Q2HowdLVa0wUrMVz2nmkatbOE2IvAFoP1B97/MigOwfRuXpnBt05iB97/EiPOj1yPz7mvFZH4dq/2tCTwZusm6DfqPId2nu//WTt/Pn0Jqr8uwWlgKLLiqo63AwxZZXFSXpKSgoBAXlPhvz9/UlJSbFKUCJ/P537if1h+3HRuzCpTRGmPjixIauLjGeh8zZyRxcY9BO4V9S6+i3vqCUhpUXoflj6MEQcB4/KMPAnrbXbGvxqaGPrhh/VCm40fUYrAFaYRr21BN0aXZKyKYrWldfVRzum2fNj3sLuy7sLbC3P7rKUr8RwrQsUZHX/tWHNCUXRem8U2KrvYIV0jnwJi1toXTlPfKvdftBC63ZtztSKD774q+XjJi3RcRrc9aS2v6+f1QrUlXUZKVp186/6QkqUNjXhi7u1qrXWOIEPaQVDdmrJqHsliDqtdTNc/ZR9Pg/TEuDH4drvrV+0bk+W4mj6NAw9eENSMUwbBgRacnv5oH3ju11739OGqOhdtDGflgxPafWi1lKbFKG1IDsqVdV6iC1uqXXRz+4lNfQgtBhs3a76br5aV/fhR7VeKTqDNvvCJw9SfctkplZsi05V0atqrtup16KolhqvXeh+6Tfrfo+6+V0vuvjLjCJdULHqdJa7ZmoXvt0qaBfdKtbG/NBEDtV8BfNDE6UFvSxRFOi2QJvvPi0O1jyjfYdxvQfq3AfmMrL5yLwt6JEntUanuIvaBf/nNoN/fdvGq3fSuuW/sBuCmtAzNhpVVfPWTlBV7UJaZTsMuyqHLP5EbtOmDZMnTyYtLS1nWWpqKlOnTqVNGzufRJRhsWmxzPtTG2f90t0v3XrctzENtk/Vfr935K3HWFZtoV1J862uVXVd3ql0nPgfWaVVvU6+BgF3aSfZ1mi5vplOBzXaaV+s9btR8PQpWW8pW8wd6V1F+9AHrWr11aO3fEhhV/kLbQXYOkFrUazSTJtL09YKG/OqqtrFF0dww/y2ZPeeuLGHwkNvaT1VijCX+W3R6bTXYu2HwZgCq5+AqP9su097CvsLPnngen2Ne16F/+0A/wbW3Y9OB82ehWGHtH3onLRquh/eo/WYSE/K24vCVgn8tomQcEW7UNh+km32Yambk4ru70O9rlrL4FdPOdbFNEtc3Hv9+7LrbMsvsDk5Q6cZ2u/7PtBawewp+iy6ndNofv5DdDunaa/Ra6e078rvXtIuclWur1147vmRbS/CegVo1diH/qnNooACJ76lx5+r+fFyGIPiE+icnMKg+AR+vBxGj6RkreffgB+0uhvW1uJ57YJKciTsWXTL1Qtt9bdkJpuLe6/v77F3bfPchGMxuGrj090raY1IP44ofKYl0M67V3bVphH1b6gl6H41SiRcQKsZ9L/tVK/SiqlRMegg94U0YGp0HNVOO1gPvvw+88oAi5P0d999l99//52qVavSvn172rdvT0hICHv37uXddwsYsyZu2/yD84lNj6WObx0G3jnw1g84sATiQ8GrinayWRSV6sD/tmvjZ1KitK7vp7feXuC2YjZpYzW/f0UrztbgMXh+C/iG2H7fFWoV0upr48qsd/XRqu2aM7Vu78a0QlcvVivAud3aPObZ83AWZ8iApZo+S6GFdI6u1i7I3OoLztYKnd9Wp80vbIsLNPnJriQb1ARSouHLntpYbVsrqSQVtCE7v7+rFXWLOg2egdrwiC4zbVvh3M1X28fLe7ULIaYMrQDQOw3h/ea5e1EsbqG9Nq3p3K6slk60ln1nCwtTlRSdHnov0y6QJl+D1X21HgClSVIkrBusXWxr3BeaDy7edu7oqtUgMWVoF1jsJaunj27fYqrEHUC3b7H2mv2wjVZjxclN6x314m/aheeSUqEm9PoEXtqjtQ4C1TIzGRkbz9xr0YyMjadaZqb2OepeIU9VeKtxcr4+fGvfYoi/XOjq2eOIdYoOvaLPdVvkmWzS4mHDi4AKTZ6BhrcYqijKDp+q8ORnWtHm419r5+YFOf8rfN5dq40R3ELrkWmPizl6A3gH0SM5teALaY50QTa/zzxbfC/bgcVJeqNGjThz5gyzZs2iSZMmNGnShNmzZ3PmzBnuvPPOW2/gJh988AE1atTA1dWV1q1b88cffxS6flxcHK+++ipBQUG4uLhQr149Nm7caPF+S5MD4Qf44ewPKChMbjMZw626WydHX+9y135S7orkt+Lpr30w1G6vtdB99ZRWpdyRpMVrcWXPifzAWHji85I7kb1VQmnLIkqKAo+8o43Vu/Yv7Jxe6OoWtwJkZmgF6kAreFalqTWivrUbxryi6HPfelfVWvW/f0Ub12jPK6RR/xUwV3SWkv7icvHSxmP71dT2vaq3bZOk/Lr6W+vL8OYr4Rd+hy8ezxpGYNQqRGcnzSWl8h3wzAZ46ivwDtY+e1Bz96JQzVptAGu9LtOTtN4aoLX61bzPOtu1FRdPreKwZ4A21nf980WqmeEQzCYt3qRwrWX50XeKf5FNUaDzLO0z6+SPcP4368ZaFDf09MlVLyX7NVvjfnj1gNat1V5FEQMbaWNrC/s/2/pztP6jUK2t1gNkx7Rbrl7kccQF2TRWazTxq6H11BDlS417tRpJoDUuXdiTd51Tm+DLPtoMCzXv1+oZud+iMLQtZfVuzPdCmmrWhpke/0Y7Z7Sngj7zrP29bCfFGoDk7u7OkCFDWLBgAQsWLOB///sfbm6WF7RZu3Yto0aNYvLkyRw+fJi7776bzp07ExkZme/6GRkZdOzYkQsXLvDNN99w6tQpli5dSnBwcHGeRqlgVI3M+EPrRtf3jr408W9y6wftnq0lNYF3ZXUvs5CLF/Rfq80Trpq0F/ruufZvxQTtDbesI5zZqs0X2WeF1sW4JKe9ybeIUlZCWRKVWT0qat1MAfYu1pKZAljcCrDvfYg+o3Uvf3iCDZ9EPrLGvNJuuDalT7vh2t8jjmotH05u2jR0H7bRptgryS+HxAhtCMDpzRR8gcZO89t6Vtbm/fWorBWBWvs0ZKZbfz8FdfW3xpdhnivh78On3bSWBYM7PPYe9P1Se+2XNEXRhrg06lVwYqGatWTv6GoIO3Z7//8dU7UkxaeaNm1UaeBTFfp9pb1Hz2zVCu+VBrtmZ73GPLReKbd7oTegoVYwCrQ5x815py+yqVv19KnaHPyql2hI+fKtRsGnnyXwOaoo0DlreMKxNVotnlu45Tjigpz4Fv76Svv/9/wk97R2ovy452W46wntO3PtM1pjSHZvtL3vw5qnwZSuzSrRf51FsyzZRKGNUWhDsdY/r/Uu2/G2fVrWk6O1z9kCcxMHLDpsoSL1J/rhhx/o2rUrBoOBH34oYJqkLN27F70bz8KFCxkyZAiDB2vdy5YsWcLPP//MihUrePPNN/Osv2LFCmJiYti7dy8Gg9aaXKNGjSLvrzS5mHCR9afW81PiT1wzX6OCawVGNBtx6wdGnYGDK7TfO80ofvKqN2gV0r2raK3yO2dob8puC2zXDe1Wzv8KXw/QugJ5BWlV6W1V7fJWmj4N1e7BfOgzwv45QFDD1uiz50kvCXd00T5Ej3yhTZ3x8u8FfvkXeT77uFDYnTW/fKcZtz+vd3EUNBdvuxHafOM/j9KKD/0yHY6vh+7vaQW/bCUuVOtuffgL7Qu0UHac37ZCLa1F/dNHtPfJty9C7xXWvXiV/bmSH9WsFVmr2kJ7Hbp4a7euPll/37jM+/rvTi65r4STlWLc+KXbdzXUech6z6O4Eq5S4OTOoJ3of/ey9rvOCSrdobUaBjTSLpgG3gUelfJ/bPRZ7b185TCc360t6/5u6TqhD24OPZfAuoFal86KdbTZDRzVme3w61zt98fe1XpNWMODb2nDhSKOw+HPtYJsJSUulEJPrB2li2phU/qV1OdocDOt+Obxr2HLBK14rrWHKiVc1aYYBbhvtG3q5YjSQVG0i80X92rn0n8uuz5sMrt3XuOntFoz9jrHvlF2Y9QPQ7n+vZd122m61mPv8Gfa9KC/LdCGg9XtrPXArP2w7RrOov7TCnye2giXDhTes9HRig4XQ5FeCT169CA8PBx/f3969OhR4HqKomAyFe3KcUZGBocOHWLcuHE5y3Q6HR06dGDfvn35PuaHH36gTZs2vPrqq3z//fdUrlyZ/v37M3bsWPT6/MfNpqenk55+/eQ6IUHrCmo0GjEajUWKtaR9f/Z7ph2YBgqYs16AsWmxbDm/he61Cr8Iot86CZ05E3OdTphC2sLtPsf7x6Fz90e35U2UQ59iTgjD1HOp1rpVgnSHVqDb+haKORNzlWaY+nymJer2PIbe1TDe+yaHUrfR8d6O2oWjkoyn/ds4nduFEncR86ZxmB55p8BVg9yCeLVx7toEN7/+9RvHoMtMxVytLaYGPe37v82PV1XouxblxDfot01EuXYSdXknzM0HY35wQqHF2rKfa5Hf89H/od/7HsrfX6OYta675uCWmNu9BilR6H8eyc1fXKZH3kX1rma//1vlO1H6fIZ+zVMoJ77F5F4Zc8cZxT/xTIlBubQf5fIBlND9KFcPa/P4FiT2vPZjAVXvop2oZCXoee5X9JjP7cRc3YoVnotJ510VXQEVHlRFhxrQGJzdUSJPoKTFQ+QJ7Ye119fzDED1b4QacCdqQCPUgEYol/9Av3EUoIBqynlVmWIvozrae5BbvJfqPYLuwfHod81A3TQWk3c11JIcnlBUCVdw2jAEBTA1G4y5QQ/rvW+dvdHdNwb9tvGov0wn847uti8kmUXn7o+uoPcSCmbvqpgd4TXlXQ3lkXfR/zwCu36OPvAWTid/QLm4h8wTP6De0c1621bN6L99CV1aHOagJpjajsr3OVn83SRKnNWOUWwoTolh19+fNySYKgqZbUeCWdWGdzmCRk9ClRbo/lqFEheK6lsN891Pa40CAG1GoJzZjO7QSnQXftWKrJ7ehOpbA3OzgZjv7q/NGnU7zCaUq4dQTm9Cd3ozSvSZXHer7pUhJSrfcxOH+sy7gSWvI0VV7dOH+erVqwQHB7N3795cVeHHjBnD7t27OXDgQJ7H1K9fnwsXLvD000/zyiuv8N9///HKK68wfPhwJk+enO9+pkyZwtSpebsMrl69Gnf3kk00iyLKFMW7ie/mO45YQWGk10gq6vN/0VdM/Jd7/5uJGR07G8wgydV6wwCC4g7S/MJH6FUjMe61OVB7FBlOtmnl8UgLp1rMr7inR5HqXAFXYywhsdqFm0t+bTla7TnMOjuNp3MwFRP/pd1/s1BQ2VdrNJE+dxdrOwHxR7jn3DuY0bOr/nQS3Rx7CIkhM5E7r6yheow27jPV4Mfxqs8Q5tPitlpDvFNDqRv+I8Fxf+R86F/zbMjpwO5EeTbI2bZHegTVonfjnh5FikslQis+QLJLMefptrLg2P20uPAhAGf8u6Iq+utxVrifZNd8CtGoKm4ZUVRMPk3FpNNUTD6FV1reyv/Zp9J5lytEeN9FlNedOJlScTKlYjCl4mTOujWlYjDfuLzwgofZzChc9W3NoZqvWPAfsA2PtHDanxyL9myv014lCjsaztVeA6qKmzEa79RL+KRexCf1Et6poXim51/UL7//aZ5tliaqStPQpVSL2YNR58Zv9SaS6FbV3lHlUMyZ3HtmBhVSzhLnVoPf6k3EfKs6L5buQ83koZPj8UoP4z//rpwI7mfV7efHkJlMuzMz8Um7lOc15aivJ0f4HG1wdR31In4kySWQXxrMRFWs04pZK3Izd11ZTabizO7600hyDbLKdkXp1eDq19SJ2IiOvK2/ZnT8F9CNk1WetENkt88z7So1on6hWsweDCZtqjmTYuCqbyvOV25PrHvt6+dPN5zj53deojenUznhbwLjjxCQcBTXzOt1dsyKnijPBoT7NCPcpwk6c2bRvpcdSEpKCv379yc+Ph5v78Iv4FqcpH/++ef07dsXFxeXXMszMjJYs2YNAwYMKNJ2ipOk16tXj7S0NM6fP5/Tcr5w4ULmzZtHWFhYvvvJryU9JCSEqKioW/5z7OH9o+/z+cnPMal5eyToFT0DGgxgWJNheR+omtGv7IQu7CimZoMwd51v9diUS/vRf/0MSlocaoXaZPb7GsyZ6P5afcNVtv5QofhdvpW/VudupbyhVcD04ATMbUeUXPXsIjAajWzbto2OHTvmDMEoabptE9D/sQTVw5/MF/ZYXmzEmILTx/eixIdiajMM88P5X/ByRMqFX9FvHI2S1YJrrtcVU+c52jCNG9zqOClXDqH7fSG6M1tylpnrdMJ87yjU4NI3H6jujyXot2k1BdScmQhuaKlq3BciT6K7dADl0j6USwdQEvNJyivVwxxyD2rIPaheVXBa3StPq7cKoOjIfGn/9Svst2I2QUYypCeg+20eumNfoeTTbU1V9JjbDNWm+3IAyl9fFdz6d/ctErH0RJRrJ1Ei/oaIE1qLe9jRnJ4aN3O0556tSJ95menoV/dGd2k/qk81MgdvcZhpFHVbx6P/82NUVx8yn/9Fm3bUBpT/tuO09ilUnYHMF/fc1vfiLaVE47S6D0rEcVSDB2SmAgqqakZRdBT5NVoepSfi9FErlORrmDrNwtzSCkM0Ik/itKIDiikdU5e5mJs/V+CqjnAOIQpnrWOk/3YIysnvC/iu06E2eFzrqVqaZSSj/PMt+kMrUMKP5SxWA+7C1HwwmE3ot4wlz3doh2ng7IHu9CaU87tRMq9fyFddvFHrdMRcrwtqrfZ5eibd+L1cGj7zEhISqFSpUpGSdIsvGQ4ePJguXbrg75973u3ExEQGDx5c5CS9UqVK6PV6IiJyty5EREQQGJj/lANBQUEYDIZcXdsbNGhAeHg4GRkZODvnbV11cXHJc0EBwGAwOOQHYnhKeKHVuMNTwvOP+9g6CDsKzl7oH56A3hbPrdZ98PxW+LI3SsxZDMse0ipR3vBm0+97XxvH0vRpy7cffRZ+HlnAGBMF/V290OdzjB2BXV9PHafAuZ0oUacwbH0Tnlhp2eN/fU+rPOtdFf2Db9rmtWMrddvDK/vg1/nw+yJ0pzehu/CbNqtBy//lmT4u13FSVa3K6m/ztSmvAFDgzh5w32h0gXcVr7KmI6jfLWsaKDXPCYHTT8Ng67is9+4NdE7adG7V20C1NhByD4pHRXL9B/MZo6agQvfFGAIsGdNrABdX8KoI94+GY1/lu5aCir75QMd5TbYYADXbaePH40K1AldNn8WpKLUoDBXAs532+GzrBsM/3+X7maegok+47DjP/SaFfuYZDFrNkGXtUWLPY1g/SJv32pbT5hXFie/gz48BUHp+jKFyHdvtq0FXqNMR5b9tGHZM0Srg20JSJKzqqVXW96iMMuB7cHLFdFO9lCK9RssjQwWt+OxPr6H/bR76pv3Aza/428tMhx9e1mqY1O2EvvUL6IvQsOCo56Tiuts+RhVqUFBhRwUFpUINdKX9NWDwhZaDocUgrcbKn8vg7/UoEcdx2jiqwIc5bbup2KhPNe085o5uKNXbougNBZ+PZX0vl5bPPEteQxYn6aqqouTzgXP58mV8fHyKvB1nZ2eaN2/Ojh07csa5m81mduzYwdChQ/N9TLt27Vi9ejVmsxldVlGC06dPExQUlG+CXhoVa15rY6pWERjg3pFatWdbqXwHPL8tayqsM/mv88NQqHaPVvE3JQZSYwq4jb1+mxoDcZcKLgKh6LQT4/yKipV3BjetYNOyDnBiA9R/RJtPvSiizlwv4NN1tv0rihaHwQ3aT4RGveHHEXD5D9g0Bo6t1Qq1GNzQHfqM5ucPoNt5CJoNgJhzWnJ+KavHjqLXZkK49zWoXM++z8cajnyRNda7gBohGUng7AlVW2oJefU22ryst5quMatg4s1J6m0VTLyhQI16w5Xw7OS/xIoxFlVBxQ2Lw686BVbjttdsAdbiURH6fw3LO2jvsx+GQq+l9usJFfUffJ91btFuhDavua11ngnndmpjNc/+Yv3pAxOuwmfdte9iz0AY+GPO55f5oYkcSt1It4e6OeyFHofRdAAc+Fib1vTX+dcrvxfHL9Mh4m9tLG73xQ7V80/YmSMUTCwpiqLNJlG1ufZ+OrpKm5UnLb7gx3gGQMsh2mdzwJ2WvXcq1i6Tn3lFTtKbNm2KoigoikL79u1xcrr+UJPJxPnz5+nSpYtFOx81ahQDBw6kRYsWtGrVikWLFpGcnJxT7X3AgAEEBwcza9YsAF5++WUWL17MiBEjGDZsGGfOnGHmzJkMHz7cov06sp51e7LyRP4tofnOaw1aJd34S9o8vveUwNhN7yCo27HgJF01wwetoIBunMVT+qs02lRwM3hgDOyapU3tUb2ddpwKo6raumYj1O2kzR1bmgU0hOe2wKEVsH0qXDkEH98HqopO0VFFNaPs+wP23vAlqXeBps9oJ+2OMDWRtRRa5VmB2g9p07wUp4qsNZPUbPaeLcFeyvpJW+V62tRmX/TSKp5XrAsPji35OIypWtX5jETts/HhSSWz38r1tJPOAx/B5rfgpT3Wq9wcd0m7WB57HryrwsAfyv77xVb0TlrF6lV94I9PtF5YFWpavp3zv2nTaYE2TaqXY42FFXZWWMV0R7wgbS3uFaDtMK1l/cS35Htuoui0+eQfeKPEw3NkRf62yG7tPnr0KJ07d8bT83qLm7OzMzVq1KB3794W7bxv375cu3aNSZMmER4eTpMmTdi8eTMBAdoHW2hoaE6LOUBISAhbtmzhtddeo3HjxgQHBzNixAjGjrXDl76NZM9rPXnvZBQUzKoZnaJDRc1/XuvkKPhtofZ7+0m3bgmzlqSInKrM+cpO0BW91nXMzU97o7pVyLrN5+9ja+HoVwW0/pXyVqWScN9oOLVJG/bww1BtSq7CrkSe2KBN9+TkCl3nlo0r/jqddoJ1xyPw/Sta6xWgZFXNzjW1V5NntBZ4r/yH15RqvtUodL7koLsdY5qXG5XRK+GFKg8nbbUehEcWwE8jYddM7TkVtaePtWx8XWvd9KgMfVaU7Gv/gTHaXNzXTsKhldaZli7mvNaCHh+qjakf+GPZushoD3U6QK2HtJ4P26fAk59Z9vjUOPj2JUDVemvVf8QGQYpSzxa90UoLv+qF9PCTc/z8FPmbKrt6eo0aNejbty+urtYZWzZ06NACu7fv2rUrz7I2bdqwf/9+q+zbUWXPa/3NqW84eOYgLeq2oM8dffIm6AC7ZkN6AgQ21ub8LCm3SgKaDdRa21y8iz5foncwHF1dwJ1loFXJ1vQG6PUJLLkP/tuunRC2KKBgTVqC1rIDWnJfnFYDR+YdpCWi53blfyFJ0WvDQspigg5lv4W2LCkPJ20tBkP0f7BvMXz3ipZYhrS07T6z554//6vWqwYFei8v+fe8ewV4aLx2oWDnTO0Cxe2MeY76T2tBT7yqFaMb+CP4OPZsHKWComit6Uvu1epEhB6wbF7zja9DwmXwqwmdZ9ksTFEG2KI3Wmkg5yUWs7gu0sCBA62WoIuCVfOuxrAmw+jr0ZdhTYbln6BHnYGDK7TfO88oejJsDU2fpeDutGhdW9x8LYspu1VJ0WlJ1I23ZaVVydYq3wEdsqqzb5mgjb3Oz65ZkBSuneS1LTvDRXIpdHhEGR8+Ie+l0iX7pK3PCu22LB6fjm9Dva5aQa01/SD2ou32deRLWNwCfn8vK0HPEn/ZdvssTPPBULmBVntl15zibyfyJKzsqiXolevD4I2SoFtTYCNt+BPA1vG5e14V5vg32nAORa/VXSiNtV2EsDU5L7GYxVmdyWRi/vz5tGrVisDAQCpUqJDrR5SgbZO1biP1ukLN+0t237Z6szV9GoYehHbD4c6e2u3Qg8WrFl9etX4Zqt8LxmT49mVtuqsbhR3T6hgAdJtn/4rLtlJYb4/y0LVK3kvCkej00HsZBNwFydfgq6e0Hj3WFn0Wfhim9aDJ1a1S1YYVRJ+1/j5vRe8EXWZqv/+5FK6dtnwb4cfh00cgOVL7Hw76uez2BLKnhyeAwQMu/6kNCbuV+MvwU1bV6vvfsH0PESFKMzkvsYjFSfrUqVNZuHAhffv2JT4+nlGjRtGrVy90Oh1TpkyxQYgiXxf2wKmftcS441T7xGCrN1t5aFWyJZ0OenwIzl5wab/WxTSb2awVi1PN2jGr095+cdpaob09yknXKnkvCUfi4qlNReYZqE0Z9s1zYLJCgdH4K9r0alvGa4lsQbVSULQu8PZQ+2Htgro5E7a8ZdljrxyGTx+FlGhtmsSBP4BHJZuEWe55BWqz5IA2Nt2YVvC6ZrM2Dj09Xpsd4/7XSyJCIUo3OS8pMourp6xatYqlS5fyyCOPMGXKFPr160ft2rVp3Lgx+/fvL1OV1h2W2aydjAA0H6R1cbaX8jq2xtH5VYcus7SWo1+mQ6U7tIT9/G7thM/grk0PVJaVtqm9hCgPfKpCv69gZTf4bxt8/6pWQ6Ko4/GNaRD2l9bSefkPuHwQEq4Uced2HubSeYZWL+S/bXBmmzZLyq1c+gO+7K3VnqnaCp75BlyLPt2tKIY2Q+HgSu218sfH2uwf+dn/AVz4Tfs+7fWJVhdGCCGsxOIkPTw8nLvuugsAT09P4uO1Oe8effRRJk6caN3oRP7+/kar4O3sBQ+Os3c0wlE1fQb+/QlOb4av+mq9LrK7fxpT4ezOst/FqLxO7SWEIwtuBj2XaNOiHVujDZMCQNEKC3VfrL13VVWbXvTyn3DpT+027C9t2sgbKTptXt2qrbTux/9td8wKwhVrQ+sXtd5NW97SKt8Xlthd2AOrntSGLlVvB/3XgotXiYVbbjm7azN/fPeyNm96k6fz9lwI/xt2vK393mWWfKcIIazO4iS9atWqhIWFUa1aNWrXrs3WrVtp1qwZf/75Jy4uLraIUdzImKrNAQ1w32tahWoh8qMocO8oLUmH/MdnVrun7J9clMepvYRwdIF3kTPd3M3d079/VSvEFXlSK3B5M4/KULXl9Z8qTa8X64o+C4u3FbBTBxjm8sAY+GsNRJ2GP5fBPS/nv97ZX+Cr/pCZqiXzT31VclOsCmj8FOz/CMKPabPoPDL/+n3GNNgwBEwZcEc3bTYbIYSwMovHpPfs2ZMdO3YAMGzYMCZOnEjdunUZMGAAzz1XwHRPwnr2f6RN8+FdFe55xd7RCEd3etMNrVQ3s+P4TCFE+Xbki0I+m1RtvuqkcNA5aUl4qxeg1zIYfhReP6N1mb9vFNS8L3c1bUevIOzqoxUnA22WjeTovOuc3gKrn9IS9LqdoN9aSdBLmk6nDU8AbRadG4v97Xhbq6ngURkee0+7IC6EEFZmcUv67Nmzc37v27cv1apVY9++fdStW5fHHnvMqsGJmyRHwW8Ltd/bTwKDm33jEY6vPE9DJoRwXHGhFDqNZ8Bd2uwTQXdbnqA6+tzzzQbAn8sh4jjsmgmPLLh+38kfYd1grUt//Ue14kpO0kvRLmrerxX7O71JK7hatblWA+HCb9r9j38gvRmFEDZjcZJ+szZt2tCmTRtrxCJuZdcsyEjUTlruesLe0YjSoLxPQyaEcEyFfTYpeqjbAarfxrmFIxc11em1ccyfPaol65npYEzRhrOd2gxkzb7Ra6kUI7O3jm9rQ8Yu/AoX99wwNEPRGk6EEMJGipWknzlzhp07dxIZGYnZnHss2aRJk6wSmLjJtdNatVGATjO0rlhC3ErTZ7VCTPlygPGZQojyqbx/NtW8T7vgHvZX1rCjrPH5oBXA67VMm19d2JdOf/33XLUTylFdFyGEXVj8DbB06VJefvllKlWqRGBgIMoNY3EURZEk3Va2T9YKf93RTftyF6IobpiG7PpJYNatI4zPFEKUT+X9syn6LIQfv2HBDV3/rxyEuItl/39QGmTXTihotoAjXzhujw0hRKlmcZI+ffp0ZsyYwdixY20Rj8iHcnEPnNqodQHsMNXe4YjSxtHHZwohyqfy/NmU03qeH0n+HEahtROkrosQwnYsTtJjY2N54gkZD11iVDP67Vm9E1oMhsr17BuPKJ0ceXymEKL8Kq+fTZL8lQ5S10UIYScWD2x+4okn2Lp1qy1iEfmoGrsPJfwYOHvBg+PsHY4QQgghbpckf6VD02cp9GJKWa+dIISwG4tb0uvUqcPEiRPZv38/d911FwZD7sqjw4cPt1pw5Vr0WXR/LOfu0Kxicc0Hgkcl+8YkhBBCiNtX3gvnlRblvXaCEMJuLE7SP/nkEzw9Pdm9eze7d+/OdZ+iKJKkW8ORL+GHYehUFQVV+0rY/yH4N9TG8AkhhBCi9JLkr/Qoz7UThBB2Y3GSfv78eVvEIbJFn4UfhoFqzukIp4A29YdM9yGEEEKUDZL8lR7ltXaCEMJuZBJORyMVX4UQQojyQZI/IYQQ+ShSkj5q1CimTZuGh4cHo0aNKnTdhQsXWiWwcksqvgohhBBCCCFEuVWkJP3IkSMYjcac3wuiKAW1AIsik4qvQgghhBBCCFFuFSlJ37lzZ76/CxuQiq9CCCGEEEIIUW5ZPE+6sLHsiq+KDlXRY0ZBVfSg6KTiqxBCCCGEEEKUcRYXjktLS+P9999n586dREZGYjabc91/+PBhqwVXbmVVfDUf+oywfw4Q1LA1+uYDJUEXQgghhBBCiDLO4iT9+eefZ+vWrfTp04dWrVrJOHRbqVgb80MTOZS6kW4PdUNvMNg7IiGEEEIIIYQQNmZxkv7TTz+xceNG2rVrZ4t4hBBCCCGEEEKIcsviMenBwcF4eXnZIhYhhBBCCCGEEKJcszhJX7BgAWPHjuXixYu2iEcIIYQQQgghhCi3LO7u3qJFC9LS0qhVqxbu7u4YbhorHRMTY7XghBBCCCGEEEKI8sTiJL1fv35cuXKFmTNnEhAQIIXjhBBCCCGEEEIIK7E4Sd+7dy/79u3j7rvvtkU8QgghhBBCCCFEuWVxkl6/fn1SU1NtEYsQQgghhBDCQWRcuEDc+g0Yr1zBEByMb+9eONeo4XDbFKKssThJnz17NqNHj2bGjBncddddecake3t7Wy04IYQQQgghRMmLW7+BsIkTQVFAVUFRiF6+nKDp0/Ht1dNhtilEWWRxkt6lSxcA2rdvn2u5qqooioLJZLJOZEIIIYQQQogSl3HhgpZMm8157gubMAH35s1wrl7d7tsUoqyyOEnfuXOnLeIQQgghhBBCOIC49Ru01u78mM2c7fYIOnd3FINB+3F2vuXv6WfPaq3n+VEU4r5Zj//oUbZ7UkKUIhYl6UajkbfffpslS5ZQt25dW8UkhBBCCCGEsBPjlSsFJ9QAJhPmxETr7VBVtX0KhyG1A+zLoiTdYDBw7NgxW8UihBDCwcmXthBClH36ihXy7ZYOgE6Hb98nqThwIOaMDFSjEYzGnN/V7FujETXDiGrU/k7ctp2UAwfyT/4VBUNwsG2flCgyqR1gfxZ3d3/mmWdYvnw5s2fPtkU8QgghHJR8aQshRNlnSkomed/+QtepOGiQxePHPdu142y3R/JP0lUV3z69LdqesA2pHeAYLE7SMzMzWbFiBdu3b6d58+Z4eHjkun/hwoVWC04IIYRjkC9tIYQo+8zp6VweOpSM//5DcXdHTU0FnS7nwiyqStD06cX6vHeuUYOg6dMJmzBB25bZnJOwV/zf/+Q75DZYo5ebOTWVzIgIrr2/uOCVpHZAibE4Sf/7779p1qwZAKdPn851n1JQgQkhhBClWqFFhORLWwghSj01M5Orr79Oyv796Nzdqfb55+i9PIn7Zv315K9P79tKpn179cS9ebOcbaaf/Y/0U6dJ3LaNSq++gs7FxYrPqHy4VS83VVUxJyWRGRGBMTyCzIhwjOHhZIZHYIwIJzMikszwcEzx8bfemdQOKDFS3V0IIcQtFVpESL60hRCiVFNVlbDJk0ncth3FYKDqhx/i1uhOAKtfgHWuXj1nm6b4eM4++igZ588T9dFH+I8cadV9lXWF9nJ76y2iPvoIU3Q05pSUIm1PcXNDcXbGXFDC7oC1AzIuXCB63TcEHjxI1OkzVHyiT5molWNxkp7tv//+4+zZs9x///24ubnlzJMuhBCi7DEEBxfaku5oX9pClFdl9YRV2Na1BQuIX78BdDqC31mIxz2tS2S/eh8fAidM5MqIEUQvW45316643nFHiezbHm6nW7o5JYWMCxdIP3uOjPPnSD97jpQ//ii4wB9gvHQp53edjw8Gf3+cAgMxBAbgFHD91inAH0NgIDovL4wXL2q1A/LbroPVDrixF4GX2Uzc338Tt3JlmaiVY3GSHh0dzZNPPsnOnTtRFIUzZ85Qq1Ytnn/+efz8/FiwYIHFQXzwwQfMmzeP8PBw7r77bt5//31atWqV77qffvopgwcPzrXMxcWFtLQ0i/crhBCiaHx79yJ6+fL87zSZHOpLW4jyqiyfsArbiV62jOhl2ud70LRpeHXoUKL79+7ciYSOHUjctp2w8ROosXYNil5fojGUhKIUX1VVFVNMDOlnz5Jx7jzp57TbjHPnMF69atkOFQX3Vq0InDIZQ0AAOnf3Ij2ssNoBvn37OkztgJt7ESgAJhNQNmrl6Cx9wGuvvYbBYCA0NBT3Gw5237592bx5s8UBrF27llGjRjF58mQOHz7M3XffTefOnYmMjCzwMd7e3oSFheX8XLx40eL9CiGEKLrsL+0ciqIVE8qSduKEHaISQmTLdcJqMqGoqnbCajYTNmECGXKuJPIRu24dkfO1Bjb/N97At3cvu8QRMGEiOi8v0v7+m5jPv7BLDLZ08/vzxtuw8eO5NGIEF/r15/Q9bTjT7l5CBwwkfMoUYj//guQ9e3ISdL2fH27Nm+P7xBP4jx2LV5cuub6Lc9HpcGvcGJeaNYucoGfz7dWT2ps2UvG557TeDXffDUDCjz+SERp6W/8LaylKrZzSzOKW9K1bt7JlyxaqVq2aa3ndunWLlSwvXLiQIUOG5LSOL1myhJ9//pkVK1bw5ptv5vsYRVEIDAy0eF9CCCGKz61pE+0XRcGzQ3tcatTElJRI3FdrCJs4Cdc77yzVV62FKM2kuKOwVMLWrYRPngJAxSH/o+Lzz9ktFkOAP/5j3iB84iSuvfsuXh3a4xwSYrd4rK3Q96eqkrRl6/W/s4aQOdeuhUvNWjjXqolL7do416qFk59frodmXLhA4tat5Os2u6bfWDtANRq5OHAQqYcPc2Xka1Rf8xU6Z+dib9saynqtHIuT9OTk5Fwt6NliYmJwsbAiY0ZGBocOHWLcuHE5y3Q6HR06dGDfvn0FPi4pKYnq1atjNptp1qwZM2fO5M4778x33fT0dNLT03P+TkhIAMBoNGI0Gi2Kt6Rlx+focZZncoxKBzlO1hH380YA3Nu2ITBruk01M5O0U6dJO3yYyyNHEvzFF8WuzivHyfHJMXJc6ZcvFTw2VVVJv3xJjpuDsef7KWX/fq6Ofh3MZrx798J32DC7vz48Hn8ctx9+JPXPP7k6cRJVPvnY7vWubvcYqSYTqX/8SfzGjTldsfNjqFOHCi++gHPNmhiqV0fn6pp3W/nEoQQH4z91KpGTJ+fqRo+q4j91KkqVKlY7rv5zZnPpiSdJ++cfwmfNpvJb4279IBtSKvgV/JmnKOiCguz+mr6ZJfEoqlrQJYj8devWjebNmzNt2jS8vLw4duwY1atX56mnnsJsNvPNN98UeVtXr14lODiYvXv30qZNm5zlY8aMYffu3Rw4cCDPY/bt28eZM2do3Lgx8fHxzJ8/n19//ZUTJ07kad0HmDJlClOnTs2zfPXq1flebBBCCJG/6u8swiU8nPA+fUho2SJnuVN8PNXefQ+n5GTi2txDZI8e9gtSiPJIVan6yVLcz53L/25FIfaBB4jq2qWEAxOOyOXSJUI+WYouI4PERncS9vTTBXeZLmGGqCiqv7MIXWZmnu+aUkNVcblyBe8jR/H66y+cEhMLX12nI/b++2/r/WmIisLnz4M4xcaS6edHfMsWGCtVKvb2CuLx778Er/wUgKtP9yepcWOr76MoFKORqkuW4Hb5CipZ49GzqACKwoXXR9vkf3A7UlJS6N+/P/Hx8Xh7exe6rsVJ+t9//0379u1p1qwZv/zyC927d+fEiRPExMTw+++/U7t27SJvqzhJ+s2MRiMNGjSgX79+TJs2Lc/9+bWkh4SEEBUVdct/jr0ZjUa2bdtGx44dMRgM9g5H5EOOUekgx+n2ZZw7R+jjPcDJiZq7dqH3yf35mbxnD2EvvwJA4IL5eHbqZPE+5Dg5PjlGjkc1mYiaO4/41asLXa/qmjW43tmwhKISRWGP91PGufNcHjgQc1wcbq1bU+XDD1Ds3G35ZrErVhD9ziJ0Xl5U++F7nOyYaFlyjIyXLpH480YSf/4Z44ULOct13t64tWlD8tat+XfP1umo9uMPOFerZuXobSPqnUXErViB4uFByNdrSzxu1WQi/I0xJG/bhuLigpqRATodqtmMotPl9CLw7vF4icZVFAkJCVSqVKlISbrF3d0bNWrE6dOnWbx4MV5eXiQlJdGrVy9effVVgoKCLNpWpUqV0Ov1RERE5FoeERFR5DHnBoOBpk2b8t9//+V7v4uLS77d8A0GQ6k5wShNsZZXcoxKB0c+TrczLUtJiNu2HQDPdu1wrVQxz/2+Dz1ExpAhRC9dSuTkKXjcdVexv7gd+TgJjRwjx2BOS+PqG2+QmPX+9OrWjcTNm0FRtBPW7MrMQNSsmVRbvgK9p4c9Qxb5KKn3k/HqVa6++CLmuDhc77qLkA8+QO/heK+Hys8/T/KWraT98w/Rs+dQ9d1F9g6pwGOUGR1NwsZNJPz0E6l//ZWzXHFxwfPhh/B57DE8770XxdmZuA3fXq+YfkO39KDp0/GwoJHT3gJfG0n60aOkHj5MxBtvUOOrr4o9zM1SqqoSMXeelqAbDIR8/DGGoECiv15H6MGDVGvRgopPPuGw9XEseZ9bnKSHhoYSEhLC+PHj872vmgUnZc7OzjRv3pwdO3bQI6t7pNlsZseOHQwdOrRI2zCZTBw/fpxu3boVeb9CCOFIijItiz2pqkrCpk0AeBXSHa/yiOGkHDqkFZZ5bRTVv1pt98IyQpRVmbGxXH75FVKPHkUxGKgydw7eXbuSMWJ4rhNWz5YtCRs7lrS/jnH5lVcI+eTjfMe7irItMyaG0Of/R2Z4OM61ahHyyccOe8FGcXIiaMZ0zvd5gsQtW0jcvr3Ep4UD7eJ59LpvCDx4kKjTZ6j4RB+ca9TAnJxM4o4dxP/4E8l7914fa67T4XHPPXg/9hheHTug9/TMtT3fXj1xb96MuG/WX78g36e3wyaUBVEMBoIXzOd8z16k/3OSyDlzCZw0sUT2HbNiJbFfaNX/g2bPwuOe1gBUGjmCPzZupFm3bmXmArLFSXrNmjUJCwvD398/1/Lo6Ghq1qyJqZCiCPkZNWoUAwcOpEWLFrRq1YpFixaRnJycU+19wIABBAcHM2vWLADefvtt7rnnHurUqUNcXBzz5s3j4sWL/O9//7P0qQghhN3dPM/njRxlns/0M2fIOHsWxWDAq337AtdTnJy0L+4ePUk7cYLIufMInJD3gq4Q4vZkXLrEpSEvkHHhAjpvb0I+WIx7y5aAVpH55hNWp2XLCB00iJQ//uDy8OGELF7scF2che2YkpK118v58zgFBVFt+bI8VcIdjWuDBlR87jmily4l/O1puLdujd7Lq8T2f+PFcy+zmbi//yZuxQpc77qL9DNnUFNTr8faqBE+jz2KV9euGG7Kj252Y8X00swQFESVObO59OJLxK5ejXurlnh3sW3Ni/iffiZy3jwA/MeMweeRR2y6P3uzuEqEqqr5VlpMSkrCtRhXZvv27cv8+fOZNGkSTZo04ejRo2zevJmAgABAa50PCwvLWT82NpYhQ4bQoEEDunXrRkJCAnv37qVhQxlnJYQofUrDPJ+JmzcD4HHffbc8STIEBRE0ZzYAsV9+SUJBU8MIIYol9fjfXHiqHxkXLuBUJYgaq1flJOgFcburESEfL0FxdSX519+48sYY1MzMEoq45GRcuEDkgoVcGTWayAULybhhXHB5ZU5P5/Krr5J24gR6Pz+qLV+OwcLhqfZS6dVXcK5enczISCLnzS+x/d48p7miqlpruaqSduwYamoqhmrVqPTKK9TauJGa36yjwsCBt0zQyxrPBx6g4hCtkTRs/AQyijEVd1El7z/A1azZwPwGPEuFwYNsti9HUeSW9FGjtKs+iqIwceLEXJXRTSYTBw4coEmTJsUKYujQoQV2b9+1a1euv9955x3eeeedYu1HCCEcjaPP86mqKgkbta7u3kWsPOv14INUeP45YpavIGz8BFwbNsQ5n9k3hBCWSdy1iyuvjUJNTcWlYQNCliwpcmLg3qIFVd9/n8uvvELili2EubkRNHOGVmipDHD0YUM3K6grtTWpmZlcGT2alAMH0Lm7E7J0KS61alp1H7akc3UlcNrbhA4YSNzXX+P96CN4tGpl8/3e6uK5T/fuBM2eZffp4RxB5eHDSTl0mNTDh7n82ms2GZ+eduo0l4cOBaMRr86dCXjzzXLxvy/yJ/ORI0c4cuQIqqpy/PjxnL+PHDnCv//+y913382nn35qw1CFEKLsMQQHF3ynohR+fwlIP3WKjAsXUJyd8Xzo4SI/zn/kSNyaNMGcmKglFRkZNoxSiLIvdu3XXH7lVdTUVDzuvZfqn39hccud5333EvzOQtDrif/uOyKmz8DCSX4c0s0tnzfehk2wbQtfccSt38DZbo8Q9+mneB07Rtynn2p/b/jWavtQVZWwyZNJ2r4DxWCg6ocf4tboTqttv6R4tGqF75NPAhA2cSLmtDSb7zMjNLTgOc0VBdVoLBdJYlFkj0/X+/rmjE+3JmNYGJdeeAFzUhJuLZpTZe6cMnNh8VaK/Cx37tzJzp07GThwIJs2bcr5e+fOnWzZsoWPP/6YunXr2jJWIYQoczwfejDf8egAqCq+fXqXZDh5ZLeiez5wv0VFhhSDgeCFC9D5+JB2/DiRCxbYKkQhyjRVVYl8913CJ08GsxmfXr0I+ejDYhf98urQgSqzZ4GiELt6NdcWLiz1iXppGDaUrcCu1Fa4oHBjd/8LfZ8ifv0G0OkIfmdhToGt0sj/jddx8vfHeDGUqA8+sOm+0v/7j5Q/CpkC2gEunjua7PHpALGrV5OQNUTudpkSErj0wgtkRkTgXLs2IYsXl1gVeUdg8aWIlStX4u3tzX///ceWLVtIzSqcUNo/4IUQoqSZU1OJmDX7+gK9Ptf9QdOn27VonKqqOV+23l27Wvx4Q5UqVMkq+hnz2eck7thh1fiEKOvUjAzC3hxH9EdLAKj06qsEzZiOcpvVi30ee4zAKVMAiF66jOiPP77dUO3KeOVKoRc77T1s6EaFXlAwm7k8YiSR775LzBdfkrBxI8kH/iD9v//IjI1FLeg5cr11PnrFChI2bSLt2DEAfHr2tEtldGvSe3kROHkSANErVpL2zz822U/chm85/8STmGLjCl7JAS6eOyJrj083Z2Rw+dWhpJ/5D6fKlam29BP0vr5WiLT0sLi6e0xMDE888QQ7d+5EURTOnDlDrVq1eP755/Hz82OBtJYIIcQtqSYTV954g7Tjx9H7+hK8cAHJ+/aTevw4Kfv3g4sLXu2L3r3cFtL++QdjaCiKqyueDzxQrG14PfwQFQYNIubTT7k67i1qbtiAc1VphRDiVkxJSVwZPkKb4kmvJ2jqFHz79LHa9v36Pok5JYXIOXO4tuhddO7uVBgwwGrbLympf58g9dixgmt7OFjLZ6F1SID0f/8l/d9/879Tr0dfwQ+nChVxqlgBfYWKOFWsiKrTEfvpp/luN/7bb6n0whC7zxJyu7zat8erSxcSN28mbMJEany9FsXJ4jQmX+bkZMLfnkb8998D4NG2DZ4PPkjE7Dla93azWetinTWneWn/X9qKtcanq2YzV8eOJeXPP9F5eBCy9BMMVarYIGLHZnFL+siRIzEYDISGhuYqHte3b182W6l7gxBClHWRc+ddHyv4wWI82rbFf/Qoqq1cgUu9epCeTty31hufWByJm7K7uj+AzqP48+n6j3oN18aNMSckcGW0jE8X4laMEZFcfOZZkvfuRXF3J+SjD62aoGerOHgQlbIK90bMnEXcN99YfR+2kn72LJdHjORCnz4YL18ueEUHa/k0BAcXekHBrXkz/Pr3x6tzZ9xbtMC5Vi10Pj7a/SYTpmtRpJ86RfLefST89BMxn31G7MqVhW7Tkbr7347ACeO1IVT//EOMlepgpZ06xfk+T2gJuk5H5ZEjCFm2jAoDBlB700Z8Bw0isXFjfAcN0v52wCKEjiJ7mNvtjk+PnDuPxE2bwcmJqu+/h2v9+laOtHSw+BLU1q1b2bJlC1VvqtRbt25dLjpYYQ4hhHBEMatWEfPZZwAEzZ6Fe/PmOfcpioLf008TPnkysau/osKAAXYpkqKqKgmbit/V/UaKszPBCxdyvlcv0v46RuQ7iwgYO8YaYYoSZItq1BkXLhC3fgPGK1cwBAfj27uX1StclzbpZ84Q+sKLZIaFoa9UiZAlS2xa8KvSq69gTk4mZuVKwiZOQnFzc+j5hzMuXyHqgw+0pMpszqq2/RjOdetxbeHC69Xds7qGuzVp4lAtn4aaNQpNqKvMnJlvvGpGBpmxcZhiosmMitZuo2MwxUSTsGlzwV36Hay7/+1wqlSJgDFjCBs/nmvvL8arQ4dif16oqkrc2q+JmDkTNSMDp4AAghfMx71Fi5x1nKtXp9LIEfyxcSPNunXDcJvDTMoDQ2AgVebO4dILLxZr/vSYzz7LuQBTZeYMPNq2tVGkjs/iJD05OTlXC3q2mJgYXMrRYH4hhCiOxJ07iZgxE4DKI0fmezLs89ijRM6fjzE0lOQ9e/C8//6SDpO048cxXrmC4u6O5wO3v3/nqsFUmTmDy0OHEbNyJe6tWuL10ENWiFSUhBunt/Iym4n7+2/iVq68remtStuUWSUh+Y8/uDx0GOaEBJxr1iRk6Sc2n75QURT8x7yBOSWFuLVruTr2TXRubng9bN/hNjfLvHaNqCUfE/v112A0AuDVsQOVhg3DtV49ALw7diDum/VaUurkRMIPP5B6+DBJv/2G53332TN8QBvCEPX+4usL9Poid6VWnJ0xBPhjCMinor+iI3rFivwrkjtYd//b5dOrJwk//0Ty3n2ETZpMtc8+tbjSuikpifBJk3IKo3rcfx9V5szByc/PFiGXO57330/FIUOIXrpUm4a1QYMiXShL2LxZG2IAVB49Cp/u3W0dqkOzuHnmvvvu4/PPP8/5W1EUzGYzc+fO5SE54RJCiAKlnjjBlVGjtQrNfXpT8cUX8l1P5+6ek6TErlpdkiHmyG5F93rwQXRublbZpleHDvgNeBaAsDfHYbx61SrbFXndWOU5csFCMi5cuK1tWbMatWo2k37mTKmaMstWbjxOl155ldDnnseckIBb06ZUX73K5gl6NkVRCJw8Ce/uj0FmJldGjNTGwjsAU1wckQsW8l/HTsSuWgVGIx5t21Lj67VUff/9nAQdtJZP/9GjCF64gOC5c6gwcCAAYZMmY0pKstdTyBE5Zy6ZYWEYqlWj5nffWa0rtW/vXgW3zjtYd//bpSgKgVOnori5kfLHH8StW2fR41NPnOB8r95agu7khP8brxOyZIkk6FZWecRw3Jo1w5yczOXXXsOcnl7o+il//snVN8aAquLXvz8V//e/EorUcVnckj537lzat2/PwYMHycjIYMyYMZw4cYKYmBh+//13W8QohBClnjEsjMsvvazNcdy2DUGTJxd69d+vXz9iPvucpF9/JSM0FOdq1Uos1hurunt1LXo3taIIeP11Ug8fIe3vv7kyajTVv/j8titVi9xup4VaVVXMycmYYmLIjI7GFBND7Jq1BScAZjMXnnkWp8qVwJiJajKhZmaiZhohM/t37Yfs20IqVAM5Y2j9R48q5n+gdMh1nMzmnP+x6513Um3lCnSuriUaj6LTUWXmTNTUVBK3befSq0OptnwZ7s2alWgc2czJycR88QXRy1dgTkwEwO3uu6n82mtFnk6s8sgRJP7yC8ZLl4icP5+grIr29pD0256chLLKjOm41r+DSrVrWaUrtXONGgRNn07YhAm53vdltdCZc0gIlYcPJ3LOHCLnzcfzgQfz72FwA1VVif1yFZFz56IajThVCSJ4wQLcmzYtoajLF8XJieCFCzjfo2fO+PTASRPzXTf9zBkuvToU1WjEq2MHAsa/JfPQU4wkvVGjRpw+fZrFixfj5eVFUlISvXr14tVXXyUoKMgWMQohRKlmSkri0osvkXntGi516xL87ru3TEyda9TA4777SP7tN2K/WlOiY7hTjx4lMywMnbu71buIKs7OBL+zkPM9e5F69CjX3n0X/9dft+o+yrNcrd43CRs/HjXTiKJ3whQbkzWeNYbMmBhM0dFkxsZiio62uLCf6do1TNeuWesplKkxtAUp7DilnTypzQtsh8RKcXKiyoIFXH7lVZL37OHSCy9S7bNPcbvTdmPib2ZOTydu7VqilnyMKSYGAJc77qDyyBF4PvigRSfvOjc3gqZNI3TQIOLWrMW7S1e7zBduSkzUjjfg9+yzuLdsafV9+PbqiXvzZjnd/Q3Bwfj26V3mEvRsFQY8S8LGjaQdP07E9GlUff/9Atc1xccTNmECidu2A+DZvj1VZkwvd1N6lbQ849NbtshT48YYEUHoCy/m9CCqMm8eyk3T0ZZXxZq7wMfHh/Hjx1s7FiGEKHNUo5ErI0aSfvo0+sqVCPl4CXovryI91u/p/iT/9htxGzZQefgwq3U7v5XErFZ0z/btbdKa5xwSQtCMGVwZMYLoZctxb9my2FO8idxiVhcyPEJVCZ80uUjbUdzccKpQAX3FiphiY7Xq2fm1put0eHXogO8TfbQTKycnFCcDipNemx4pv78NBqKWLCH2y1X5j6E1mzGnpxXxGZdOhc6VbeeeBDpnZ6q+/x6hQ4aQevAQl57/H1XmzCbl4CGrFvi7uWigT4/HST1yhGsffEhmWBgAhurVqDxsON7duha7gKbHPa3xfaovcWvWEjZxIrW+/w5dPrWVbClizhwyw8MxVKuG/2sjbbaf7O7+5YGi1xM0fRrne/chcdt2ErZsxbtzpzzrpf71F1dGjdYu/BkMBLzxOn7PPisttSUk1/j0CRNxbdgw58KRKTGRS1lFMp1r1qTqhx+UeA8iR1akJP3YsWNF3mDjxo2LHYwQQpQlqqoS/vY0kn//HcXNjZCPllg016fnffdhqFoV4+XLJPz8s02mYLqZajaTsHkLAN5W7up+I+/OnUh5+mliV63i6tg3CV78Pgk7d0nlcAtlRkWRcvAQKX/+ScrBg6SfOlXo+jpvb9zuvhunCn5ZcyxXQO9XAX3FCjhVrIjerwJOFfxyJTEZFy5wttsjBXZ59x89yuLWugr9+hH7xZcF3p+04xfCJk4i4K1xJXZxqiRlXL6U/wUKcIieBDo3N0KWLCF00GDS/v6bSy++BNlJshUK/OUZkgFEL12ac79TQACVXn0F3549rTIcxv/110na/SvGS5e49u67BIwbd9vbLKqk334j/pv1WZXbZ5T4BYKyzPWOO6g45H9Ef7SEsClTSDl0CFNUlHbRp1dPknbuInLhQsjMxBASQvDChbjd1cjeYZc7lUcMJ+XQIVIPHyb0lVfxeuABjFevknrsGJlXr2qzWCz9ROoC3KRISXqTJk1QFAVVVXNdeVKzPlhvXGYq6EtHCCHKmehly7QxiIpC8IL5Fk+jpOj1+PXrR+S8ecSsWo1P7942v/qfeuQImRER6Dw98bj3Xpvuy3/sGFKPHCHtn38IfeZZ0OvLZeVwSy4mGMPDtYT8z4Ok/PknGefPF31Hej1+ffta3NJ283jXolajtmSbN46h9bj/fpJ37yZu3TpSjx4heOFCXOrWtXgfjsqckUHaPycLXsFBqnHrPT0JmDSRi0/21Rbc1DU/bMIEXBs2wCkgADUjAzU9HTUjA3PWrZqegZpx4zIjano6xvBwoj/+uMCLPhVefIHKr7yCzoozBuk9PQl6eyqXhrxAzOdf4NW5C+7NbD8WWevmPgkAv2efyTW9l7COSi+9RNy6bzBFRRH7xRfa54ii5Lro49W5M0HTpxW5F5uwruzx6We7dsN49iwx587lev/79u1bYkUyS5MiJennbzgJOHLkCK+//jpvvPEGbdq0AWDfvn0sWLCAuXOLN2m9EEKUNQmbNnFtwUIAAsaNK/Z0Rr69e3HtvfdIP3mS1CNHbF7EKaeqe/v26JydbbovnbMzlV8fzaXnntcWmEwoWbegJQHuzZtZnAQWOi67mNu0lcIuJvj07IHx8mVS/tBayVP+/FPrdn4jRcGlXj3cW7bEvUULnAL8ufj0M/kXZ7uNKs/Z412jv15H6MGDVGvRgopPPnFb/8fCxtAm79vHlTFjSD/zH+efeJKA8W/h26dPqe+iak5N5fKw4RgLq17vQNW4k7ZtB70OTPm8nsxmzvew8gUvvR7FrFo1Qc/med99+PTsSfy33xI2fjw1v/vWJvu5UcTs2Vo39+rV8H/tNZvuq7zKDAvDFB2t/aGqeS7+VBo2jEqvvFzqPztKOzUtDTUtaxjTTcco+qOP8O3+mMN8LzuKIiXp1W/4pz3xxBO89957dOvWLWdZ48aNCQkJYeLEifTo0cPqQQohRGmScvgwV8e+CWhFgipkTTtWHHpfX7wfe5T4b9YTu2q1TZN01WQiYYttqroXJGXffq0bbX5JpdnMxUGDcQ4O1npuZVXAVlUzmLW/b/wd1YxqVsmMji64grgDVQ4v9GLCW28RuWDB9ZPPbDodrg0bakl5yxa4N2uWp/iRrao8O1evTqWRI6xSjfrGbeZ3LDzatKHWt99ydeybJP/+O+ETJ5Gybz+Bb09F7+l52/u1B3NyMpdefoWUP/5AcXPDr39/YlaudOhq3MYrV6CAwv65ODmhODujc3ZGcXZGcXHJunVG5+ySa1naP//kjDnPw8Zd/QPeHEvSnt/IOH+eqMUf2PRzIOnXX4nPqj1QZdasMjlswxHErd+gfYfk15NXp0NNS5ME3QEUepwc6HvZkVhcOO748ePUrFkzz/KaNWvyzz//WCUoIYTjKw/jfYsj4+JFLr/yKmpGBp4PP0zAm2Nve5sV+vcn/pv1JGzZgv/YMRj8C59qprhSDh3CdC0Knbc3nm3b2mQfN7vVCXlmWFjBJ/TFYTaTcSnUetu7DYUWDwMtQTcYcGvUKCcpd2va9JZJalmp8uyUNU4xZsUKIhe9S8LGjaQeP14qx5WaEhO5NOQFUo8eRefhQcgnH+PevDl+Tz7h0MfJEBxc8GtUp6PCwAH4jx6tFQUsosgFC4lesaLAk3VbdvXX+/gQNGUKl18dSvSKFXh16mST15IpIYGwCVo19woDBthtGrvyQLuQVPCVJHvXdxCaQo+TA9ThcEQWJ+kNGjRg1qxZLFu2DOesrpAZGRnMmjWLBg0aWD1AIYTjKU3jfUtSZmwsl154EVNcHK533knwfOtMJeLasCFuTZuSeuQIcevWUfnVV60QbV4JmzYB4NWhA4qNu7pnu1US4Pngg/h0fwwUHegUbSz0LX6PW7eOhJ83FtjlO3HnLiLmzMWv31MlOv/8jdL+/ZfE7dsLLh6mKLi3akXIko+K1QJXVqo8KzodFf/3P9xbtNAqNF+6xIX+/fEfPYoKAweWihayzNhYLv1vCGknTqDz8aHasqW43XUX4PjHybd3L6KXLy/wfr+nnrIoQb/lNkugq79X+/Z4d+tGwsaNWrf3b9ZZ/fMuYtZsMiMjca5encojR1h12yK3Qr9DHKS+g5DjVBwWz2exZMkStmzZQtWqVenQoQMdOnSgatWqbNmyhSVLltgiRiGEA8nVRddkynUbNmECGYWNtSzDzBkZXB42jIyLF3GqEqQlV1as4uv39NMAxK1Zi2o0Wm272dTMTBK3bgPIM4+pLfn27lVoK0jA2DF4d+mCd+dOeHfsiFf79ng9/BBeDz6I5/3343nfvXi2a4dHmzZ43NMaj1atbn0RIz2dmJUrOdu5C6EvvEDirl2oBXWPtyJjRCTRy1dw7vEenO/Rs/Cibzodbo0bSxfZLG5NmlDz2w14dewIRiORs+dw+aWXyYyNtXdohcqMjiZ04CDSTpxA7+dH9c8+zUnQS4PsAn/odKDX57q93aKB1tympQImjEfv50f66dNEfbL01g+wQOKuXcR/+y0oCkGzZsp72MYK/Q5xoPoO5Z0cJ8tZnKS3atWKc+fOMX36dBo3bkzjxo2ZMWMG586do1WrVraIUQjhQIoyv295o6oqYW+NJ/XgIXSenlT7+GOcKle26j68O3VEX6kSmdeukbhjh1W3DZBy8CCm6Gj0Pj543NPa6tsvyM0n7Kqi2DQJCJw+naoffYjHffeBqpL8629cfullznbqTPTy5VZP+swpKcT/+COh/xvCfw89ROS8eaSfOoViMODRrl3B7yU5aclD7+ND8HvvEjh5EoqzM0m7d3P+8R4k//GHvUPLlzEigovPDiD99Gn0lStR/YvPca1f395hWcy3V09qb9pIxeeew7tLFyo+9xy1N228rV5TttimJZwqVCBw4gQAopYsIe0WUxcWlSk+nvBJkwGoMHCgdHMvAY5w0Ufcmhwny1nc3R3Aw8ODF154wdqxCCFKARlXlNe1994j4aefwMmJqu+9a5PpohRnZ/yefIKoDz8i9stVeHexbmG3hI1ZXd07dbTKvMSWKOnK4QBeDz1ExsWLxH61hrgNGzBevkzkvPlce+99vLt1w69//2KPVVXNZlL++IP4774ncetWzCkpOfe5NWuGT/fueHftgt7Hh7gN39qkyFtZpSgKfv364da0KVdeG0XG+fOEDhpMpVdeodLLL1lleIk1GK9c4eKgwRgvXcIpKIjqK1eU6podtuiWb++u/l5du+K5cSNJ23cQ9tZ4aqxdY3HX/ZvldHOvUUO6uZegslKHo6yT42SZ2/s0EkKUO4WOG1JV9BX8Si4YO7mxaJ4pKYnkX38FIGjqFDxsWHDNt29foj7+hJSDB0k7dQrXO+6wyna1ru5bgZLt6n6jkqwcfuP9AW+OpfKI4ST8/DMxq1eT/s9J4r/9lvhvv8W1cWP8+vfDu2vXnKmaCiuYmP7ff8R//wPxP/5IZnh4zn4MISH4dO+OTz5TzMhJS/G41q9PzfXfED5tOvHffkvU4sWkHDhAlfnzMAQE2DW2jIsXuTh4MJlXwzCEhFBt5Uqcq8p4S0ejKAqBkyZx7o8/STtxguiVK6k0ZEixt5e4axfx332ndXOfOROdq6v1ghW3ZO+LPqJo5DgVnSTpQgiLeHd/jOilBYzhU1Xi1m/AqbI/FQYOKJMnKbmK5mVNCQbg8eCD+Pa2bfdkQ0AAXh07krh5M7GrVhP09lSrbDd5/wFMcXHoK1TAvRwOW9K5ueHbpw8+vXuTevQosau/InHzZtKOHSPs2DEi58zFt09vdD6+XFu4ME/BRO9u3cg4f560Eyeub9PbG++uXfF5vDtuTZsWWuBMTlqKR+fuTpVZM/Focw/hU6aS8uefnO/RkyqzZ+FcvbpdZp9IP3uW0EGDybx2DeeaNan26Uq7XzQQBTP4+xMwbhxh48YR9f5ivNq3x6VWLYu3Y4qPJ3ziJAAqDBqEe7Om1g5VCFHOSJIuhLBIdosroI0nyu6iazbjVKUKmVeucO2dd4hduwb/UaPxfqRbqajAXBSFzWud/OuvZFy8aPMWUL/+/UjcvJn4H3/E//XR6L29b3ubCZtv6Op+m909SzNFUXBv2hT3pk3JfHMsceu+IXbtWjLDwoheuqzAxyX89JP2i5MTnvffj8/jj+P54AM5re/Ctny6d8f1rru4Mno06f+c5NKLL2mfSTpdic4+kfbvv4Q+9zymmBhc6tWj2orlOFWqZLP9Cevw6fE4CRs3kvzbb4S9NZ7qq760eNhExMyZORdmKo8YbqNIhRDlSZEKx7333nukpaUBEBoailpIJV4hRNmVceEC0R9/AoD/uDdzF/3ZvIk627ZSZe4cnAIDybwaxtXXX+dC36dIOXzYzpFbhyMUzXNv2RKXunVRU1O1CsK3STUaSdy2HQDvLvbp6u6InCpWpNJLL1Jn21aqLn4fQ3CVQtd3b9uWur/uJuTDD/Du3EkS9BLmUrMmNdaswfvxx7UFqlqis0+kHv+biwMHYYqJwbVhQ6p99qkk6KWEoigETZ2CzsND60mzapVFj0/8ZSfx3/8AOh1VZkk3dyGEdRQpSR81ahQJCQkA1KxZk2vXrtk0KCGE41FVlbCpU1EzMvBo144KAwbgP3oUwQsX4D96FM7Vq6PodPh0707tTRupPHIEirs7aceOcbH/01we+RoZly7Z+2ncFkcomqcoSs50bDGrV9/21GHJ+/Zhjo9HX6kS7i1bWCPEMkVxcsKrQwfc7m5S6HzuTr6+OFWoUKKxidx0zs4Y/P21FvT8qCoxX3xp9f2mHD5M6ODBmOPjcbv7bqp9uhInv7Jfm6MsMVSpgv8bbwAQufAdMkJDi/Q4U1wcYZOzurkPHoRbkya2ClEIUc4UKUmvUqUK69ev5+LFi6iqyuXLlwkNDc33RwhRNiX89BMp+/ajuLhoUyAV0oVd5+ZGpZdeos6Wzfg+0QcUhcTNmznX7REi5s7DlHXRr7QxBAcXnKQrSuFF9azI57FH0Xl5YbwYSvLve29rWwmbNgPg3amTw1TGdkSG4OCCk78SPPaicIVeKFNVYr/8kgtP9SPq409IP3PmtnsGJu8/QOj/hmBOSsK9ZUtCli+3yhAUUfJ8n3wC99atUdPSCJs4qUgXQMNnzsR0LQrnWrWoPFy6uQshrKdISfqECRMYOXIktWrVQlEUWrZsSc2aNXP91KhRg5o1a9o6XiGEHZji44mYPQeASi+/jHO1akV6nFPlygRNm0bNbzfg0bYNqtFIzIoVnO3UmZhVq1CNRluGbXWuje4stCW9pOa11nl44NOzB4DFXTNvZM7IIHF7Vlf3rtad0q2s8e3dyyGOvSicITi44B4PWVKPHuXaO+9w7rHunO3YifCZM0net8/iz6Ok337j0osvoqak4NGuHSGffIze0+N2whd2pOh0BE17G8XNjZQDB4j7el2h6yf+8gsJP/x4vZu7DHERQlhRkSoEvfDCC/Tr14+LFy/SuHFjtm/fTsWKFW0dmxDCQUQuWIgpOhrnOrWp+Nxgix/vWr8+IcuXk/zrr0TMmUvGuXNETJtO7Jer8B/zBp4PPoiiKIVOb2Vvpvh4IufMvb7gxqJ5dpjX2q9fP2I//4Kk3bvJuHQJ55AQi7eR/PvvmBMTcapcGbfmzW0QZdnhXKMGQdOny5zmDs63dy+ily/P/06djmpffE7Gmf9I3PkLKfv2Y7x8mdjPvyD28y/QeXrief99eD70EJ733Yfe1zfXw2/8fFKNRhJ37oTMTDwfeojgRe9IklYGOFerhv9rI4mYOYvIefPwvP8+DFXy1qPIjI0lbPJkACo+Nxi3u+8u6VCFEGVckcv4enl50ahRI1auXEm7du1wkS8jIcqFlMNHiPv6awCCpkxBcXYu1nYURcHzgQfwaNuW2HXriHp/MRnnz3P55Vdwb3MP7i1aEPXBh3mmt7J1ReaiUFWVq+PHY7x6FUNICMHvLiJx02a7zmvtUrMmHvfeS/KePcR+tYaAMW9YvI3EzVpXd68uXVAK6sotcsic5o7vVhdTPJo3x6N5c/ye6os5JYXkvXtJ3LmTpF27MUVHk7BxEwkbN4Fej3uzZng+9BBeDz9EyqHD+U696NqoEVXfXVTsz0XhePyefpqETZtJPXKEsMlTCPnk4zzDuyJmZHVzr12bSsOG2SlSIURZZvFcOwMHDgTg0KFDnDx5EoCGDRvSrFkz60YmhLA71WgkPKu1wKd3L9xb3H5hMcVgoEL//vg89hjRH39MzGefk7JvPyn79ue7ftiECbg3b2bXRCj2iy9I2r4DxWAg+J13cGvYELeGDe0WTza/p/uTvGcPcevXU3nYUHRubkV+rDk9ncTtOwDw7ipV3YtK5jR3fEW9mKJzd8erQwe8OnRANZtJO36cxF92krRzJ+mnT5Py55+k/PknkXPnFrAnSPvnH4xhYXKhpgxR9HqCZkznfI+eJP/2G/HffY9v1vAigMTt27VpF6WbuxDChixuOomMjOThhx+mZcuWDB8+nOHDh9OiRQvat28vVd+FKGOiP/2U9DNn0Pv54f/661bdtt7LC//XX6fWpo041yqknkUJTW1WkNTjx4mYNx8A/7FjcWt0p91iuZnn/fdjCA7GHB9PwsaNFj02ec8ezMnJOAUG4tZEumqKsiX7YsqNs08URtHpcLv7bvxfG0mtH76n9vbtBIwfj0fbtgUXDAS7fz4J23CpVYtKw4YCEDFrFsbISCCrm/uUqQBUfP453Bo3tluMQoiyzeIkfdiwYSQmJnLixAliYmKIiYnh77//JiEhgeFS2VKIMiPj8mWt+zngP2aMzaYUcq5aFdf6DQou9lRCU5vlx5SQwJWRr4HRiFenTvg93d8ucRRE0evx698PgJgvV1lUqTph4yYAvDt3lq7uQtzEuWowFZ59hmorluPVoYNDfj4J26o4eDCud96JOSGBq2PfJGLBAs737oMpKgpDtRAqDR1q7xCFEGWYxWdmmzdv5sMPP6RBgwY5yxo2bMgHH3zApk2brBqcEMI+VFUlfNo01LQ03Fu1wqfH4zbdnyNOb6WqKmHjx2vdZatWJWjG9EKnnbMXn169UFxcSD95ktQjR4v0GHNamlb0CvDuJl3dhSiMc/XqDvf5JGxPcXIiaOZM0OtJ2bePmGXLybx6FQDjpcsk/GxZ7yUhhLCExUm62WzGYDDkWW4wGDAXYU5JIYTjS9yyleTdv4LBQOCUyTZPTgud3spstsv0VrFffEnitu2QNQ5d7+VV4jEUhZOfH96PPgJA7OrVRXpM0u5fUVNSMFSpgqt01xSiUDL9Xvmlc3HWCgVC7teAqhI2YQIZFy/aJzAhRJlncZL+8MMPM2LECK5mXU0EuHLlCq+99hrt27e3anBCiJJnSkoiYuZMACoN+R8utWrZfJ/ZFZnR6bSpzXS6691LVZX473+wqCv37dLGoc8DIGDMGNzualRi+y4Ov/5aN/yELVvILEJtkITNWq8nr65dHLJ3gBCOJN/Pp6xbmX6vbItbv6HQXhRSj0AIYSsWV3dfvHgx3bt3p0aNGoRkzct76dIlGjVqxJdffmn1AIUQJevaonfJjIzEUL0aFV98scT2m19FZlU1E7NsOVEffohqNlF5xAibJ5WmhASuvDZKG4fesSN+zzxt0/1Zg9udd+LWpAmpR48Su24dlV95pcB1zSkpJO3aDYB3F+nqLkRRyPR75ZPxypVCe1FIPQIhhK1YnKSHhIRw+PBhtm/fzr///gtAgwYN6NChg9WDE8KRZVy4QPS6bwg8eJCo02eo+EQfnGvUsHdYtyX1+N/ErloFQNDkySU+tUx+01s5VapE5Ow5RC/5GEwmKo8aZbNEXRuHPgHj5csOPQ49P35PP03q0aPErf2aSkOGoOQzLAkgafdu1NRUDCEhuDpQpXohHJ1Mv1f+GIKDCy4aKPUIhBA2ZHGSDqAoCh07dqRjx47WjkeIUiFu/QbCJk4ERcHLbCbu77+JW7mSoOnT8e3V097hFYuamanNia6qeD/6qDb1kAOoOGgQit6JiBkziF66DDXThP+YN2ySPMd+uYrEbduuj0P39rb6PmzFu3MnIubMITMigsQdv+DdpXO+6yVs2qyt30W6ugshRGF8e/cievny/O+UegRCCBuSeXeEsFDGhQtagm42g8mEoqpgMoHZXKoLycSuXk3aP/+g8/Ym4M2x9g4nlwrPPkPApIkAxKxcSeTs2VYfo556/G8i5s4FIOCNNxx+HPrNFGdnfJ/oA5DTG+JmpqRkknZndXXv2qXEYhNCiNJI6hEIIexFknQhLBS3fkOh3d9KYyEZY3g41xa9C4D/6NE4Vapk54jyqtC/P4FTpgAQ89nnREyfYbVEXRuHnjUfescO+D37jFW2W9L8+vbVpgv680/STp3Oc3/Srl2o6ek4V6+Oyw3TaAohhMifb6+e1N60kYrPPYd3ly5UfO45am/aWGp7zQkhSodidXcXojzLuHTp+pQsNyulhWQiZszEnJKCW9OmOa2xjsjvqb4oTnrCJk4idtUqVLOJwIkTUQqqvlsEqqoSNmGiNg49OJigGTNKbTdwQ2AgXh06kLhlC7FfrSYo66JGNqnqLoQQlpN6BEKIkiYt6UIUkSkpmejly7XuwgW14JbCQjKJv+zUxmE7ORE4ZcptJbwlwbdPH4JmzNB6LXy1hvDJU1ALumhSBLGrVpO4das2Dn1R6RqHnh+/p7Xp2OK//wFTQkLOclNSEsm//gaAd1ep6i6EEEII4agsPhvX6/VERkbmWR4dHY1ery9WEB988AE1atTA1dWV1q1b88cffxTpcWvWrEFRFHr06FGs/QpRFJmxsVx7fzH/tW9P5Lz5qKmpBa9cygrJmFNSCJ8+DYCKgwbiekc9O0dUNL69elJl9izQ6Yhbt46wiROLlain/n2CyDlzAAh443Xc7rrL2qGWOPeWLXGpWwc1NZX4777LWZ70yy+oGRk416qFS73ScZyFEEIIIcoji5P0gsaApqen4+zsbHEAa9euZdSoUUyePJnDhw9z991307lz53wvBNzowoULvP7669x3330W71OIojBGRhIxdx7/te9A1AcfYI6P14rIzJxJ4LRpOQVk1Bu6DbvdfXepKiRzbfEHZF4Nw1ClCpUKmVvbEfk8/jhV5swBnY749RsIe2s8qslU5MebEhO58tprqEYjnh3a4/fsszaMtuQoioLf09rc7rGrVudcvEjYqHV1l6ruQgghhBCOrchj0t977z1AOwFctmwZnp6eOfeZTCZ+/fVX6tevb3EACxcuZMiQIQwePBiAJUuW8PPPP7NixQrefPPNfB9jMpl4+umnmTp1Kr/99htxcXEW71eIgmRcvkz0smXEb/gWNSMDAJcGDaj04gt4deyIktVjxKNVS6K/XkfowYNUCQkh6eefST1yhITNWwqc/sqRpP37LzGffQZAwKSJ6Nzd7RyR5XweexRFr+PKG2OI/+47VJOJKrNmojgV/tGWMw790iUMwcFUKcXj0PPj89hjRM5fQMbFiyTv3Ydb47tI+v13QKq6CyGEEEI4uiIn6e+88w6gndwuWbIkV9d2Z2dnatSowZIlSyzaeUZGBocOHWLcuHE5y3Q6HR06dGDfvn0FPu7tt9/G39+f559/nt9++63QfaSnp5Oenp7zd0LWGE2j0YjRaLQo3pKWHZ+jx1lWZJw7R+yyZSRu3KRNqQa4NmmC35AhuN93L4qikGk25xSNU6pUwefVVwjfto27OnbEEBRE7NKlhE2ejKHxXThVrmzPp1Mo1Wzm6qTJYDLh0bEDru3aldrXmVvHjgTOm0v4mLEk/Pgj5kwjATNzJ+o3v5fivlpD4pYt4OREwLy5mN3dMZfS558vZ2e8ejxO/JeriP7yCzw7dACjEec6tdHVqOGwx1o+8xyfHKPSQY5T6SDHyfHJMSodSstxsiQ+RbVwDqOHHnqIDRs24OfnZ3FgN7t69SrBwcHs3buXNm3a5CwfM2YMu3fv5sCBA3kes2fPHp566imOHj1KpUqVGDRoEHFxcXx3w9jLG02ZMoWpU6fmWb569WrcS2HLobA+l8uXqbBzJ54n/tHmPAeS69Yl5uGHSK1Zs+Dp1m6WmUm1Dz/C9coVkuvV48pzg4v+2BLms38/Ad9+h8nFhYujR5Hp42PvkG6b599/E7RqNYrZTOJddxHW7yltPtubuFy5QsgHH6IzmYh89FHi7rvXDtHanuHaNWrOX4AKmN1c0aemEdO2LVGPd7d3aEIIIYQQ5U5KSgr9+/cnPj4e71sUKrZ4CradO3cWO7DblZiYyLPPPsvSpUupVMR5nMeNG8eoUdenzUhISCAkJIROnTrd8p9jb0ajkW3bttGxY0cMBoO9wynVMi5eJOHb78i8egWnKsF49+yB6do1YpctI+X3vTnrebRvj9+Q/+F6551F2u7Nxyjjzju51PcpPE6fpl1SEj59+9rqKRVbZlQUodNnYAYCXhvJHf362Tsk6+jWjeSWLQkbNRqv48cJDPAncO5cFIMh5zi1v+cewp95FqPJhMdDD9FmZtnq5n6jhG+/IxJQAH1qGgAV9u2jfteuePd43K6xFUQ+8xyfHKPSQY5T6SDHyfHJMSodSstxSrhh1p1bsThJN5lMfPrpp+zYsYPIyEjMN1VU/uWXX4q8rUqVKqHX64mIiMi1PCIigsDAwDzrnz17lgsXLvDYY4/lLMvev5OTE6dOnaJ27dq5HuPi4oKLi0uebRkMBoc+iDcqTbE6orj1GwibOFFr1c5qKY9bvvz6Cno93o90o9KQIbjUrVusfWQfI0P9+viPHk3EzJlEzV+AV9t2uNSqaY2nYTWR8xdgTkzE9c47qfTsszlj7MsC306d0C9+nyvDhpO8fQcRb4zBf+QI4r/7nsCDfxK+5GNtHHqVKgTPnoW+GMUuS4OMCxeIvGmOdABUlcjJk/Fq1dKhCxzKZ57jk2NUOshxKh3kODk+OUalg6MfJ0tiszhJHzFiBJ9++imPPPIIjRo1uq1WKGdnZ5o3b86OHTtyplEzm83s2LGDoUOH5lm/fv36HD9+PNeyCRMmkJiYyLvvvktISEixYxFlU8aFC1qCXsD0XF7duuH/2kicrfja8XvmaZJ27SR57z6ujh1LjdWrUOz8gZFx4QJx6zeQ+tdfpPzxBygKgVOnlqkEPZvXgw9S9cMPuPzqUJJ27CBpxw7Q6/EymcgeCeTd43H0ZaCLf0Hi1m8oeKiFohD3zXr8R4/K/34hhBBCCGFXFifpa9as4euvv6Zbt25WCWDUqFEMHDiQFi1a0KpVKxYtWkRycnJOtfcBAwYQHBzMrFmzcHV1pVGjRrke7+vrC5BnuRBwi2RFp8M5ONiqCTqAotMRNGsW57o/Ttrx40Qt+ZjKw/JedCopuXoSZE9Rpqqknz6NW6OidesvbTzvu4+AKZMJf2u8tsBk4sZXQfSSj/F9/HGHbk2+HcYrV3J6jeShqtr9QgghhBDCIVk8T7qzszN16tSxWgB9+/Zl/vz5TJo0iSZNmnD06FE2b95MQEAAAKGhoYSFhVltf6J8KTRZyb7fBgwBAQRNngRA1JIlpP71l032cyu5ehLcNId42IQJZFy8aJe4SoLx/AVtLvv8ZLUml1WG4OBCW9INwcElG5AQQgghhCgyi5P00aNH8+6772JhUfhCDR06lIsXL5Kens6BAwdo3bp1zn27du3i008/LfCxn376aYGV3YWwZ7Li3a0b3o8+CiYTV8eMxZySYrN9FaQo3Z7LqkIvwJTx1mTf3r0KbUn37dO7ZAMSQgghhBBFZnGSvmfPHlatWkXt2rV57LHH6NWrV64fIRyJb+9eBY5HL4lkJXDiBJwCA8m4eJGIefNsuq/8GK9cKfT5l+VEtTy3JjvXqEHQ9OlaTwK9Ptdt0PTpZbabvxBCCCFEWWDxmHRfX1969uxpi1iEsDrnGjVwvftu0o4e1RK27B9VLZFkRe/jQ5VZMwkd/BxxX63B66GH8Lz/fpvuM5uqqmTGxhbcolrGE1Xf3r2IvrGK/43KQWuyb6+euDdvRtw36zFeuYIhOBjfPr0lQRdCCCGEcHAWJ+krV660RRxC2ERmbCzp//wDgM/jj6NmZJR4suLRpg0VBg4g5rPPuTp+PLV++AEnPz+b7tOckUH422+Tsm9fwSuV8UQ1uzU5bMIEUBRUsxlFpyuxCzSOwLl6daniLoQQQghRylicpANkZmaya9cuzp49S//+/fHy8uLq1at4e3vj6elp7RiFKLb4b79DzcjAtWFDgmbNvK0pA29H5ddeI+n338n47yzhkyYR/N57NoslMyqKy8NHkHr4MOh0eHXpQuLmzdfniS/BngT2lt2aHP31OkIPHqRaixZUfPKJMv+8hRBCCCFE6WVxkn7x4kW6dOlCaGgo6enpdOzYES8vL+bMmUN6ejpLliyxRZxCWEw1m4lbuxYA36f62i1BB9C5uhI8dy7n+z5F4rbtxH/3Pb49e1h9P2n//MOlV4eSGRaGzsuL4IUL8LzvPjJGDC+33Z6dq1en0sgR/LFxI826dcNg5znrhRBCCCGEKIzFheNGjBhBixYtiI2Nxc3NLWd5z5492bFjh1WDE+J2pBw4QMbFi+g8PPB55BF7h4Nrw4ZUHjYMgIjp08m4bN2ibQmbN3Ph6WfIDAvDuUYNaqxdi+d99wHXuz0HL1yA/+hR5SZBF0IIIYQQorSxOEn/7bffmDBhAs7OzrmW16hRgytluFK0KH1i12it6D6PP47Ow8PO0WgqPv8cbs2aYU5O5uqbY1Fvmru8OFSzmWvvvc+Vka+hpqbice+91Ph6LS61alohYiGEEEIIIURJsjhJN5vNmPJJLC5fvoyXl5dVghLidhkjI0nM6tnh27evnaO5TtHrqTJnNjp3d1IPHiLm009va3vm5GSujBhB1IcfAlBh0CBClnyE3tvbCtEKIYQQQgghSprFSXqnTp1YtGhRzt+KopCUlMTkyZPp1q2bNWMTotjiN2yAzEzcmjXD9Y569g4nF+eQEALGvwVA5KJ3Sfv332JtJ+PyFS70f5rEbdtRDAaCZs4k4M2xKE7FqgcphBBCCCGEcAAWJ+kLFizg999/p2HDhqSlpdG/f/+cru5z5syxRYxCWEQ1mYj9+msA/J5ynFb0G/n06oVn+/ZgNHJ1zFjM6ekWPT7lzz+58MQTpJ86hb5SJap99hm+vXraKFohhBBCCCFESbG4ya1q1ar89ddfrFmzhmPHjpGUlMTzzz/P008/nauQnBD2kvTrr2ReDUPv44NX5872DidfiqIQ9PZUzh09Svrp01xb9C4BY8cU6bGxX39N+NvTIDMT14YNqfrBYgxBQTaOWAghhBBCCFESitUv1snJiWeeecbasQhhFXHZBeN69ULn4mLnaArmVLEiQdOmcfmVV4j59FM8H3gAj3taF7i+ajQSMXsOsatWAeDdrStBM2agk4tjQgghhBBClBlFStJ/+OEHunbtisFg4Icffih03e7du1slMCGKw3jlCkm//gqA75NP2DmaW/N6+CF8n3iCuHXruDpuHLV++B59PgUYM2NjufLaKFL27weg8sgRVHzxRbvO/S6EEEIIIYSwviIl6T169CA8PBx/f3969OhR4HqKouRb+V2IkhK7bh2oKu5t7sGlZumYgizgzbEkHziAMTSUiOnTqXJTbYf0M2e49OpQjKGh6NzdqTJvLl7t29spWiGEEEIIIYQtFSlJN5vN+f4uhCNRjUbivlkPgF/fp+wcTdHpPDyoMmc2F59+hvjvf8CUlIzOxQVDcDBOwcFcmzcPc3IyhuBgqn74ocNVqxdCCCGEEEJYj8zVJMqMxB2/YIqKQl+5El7tH7Z3OBZxb9oUjwfuJ3nnLpJ27ACdDlRV+wHcW7Yk+L13cfLzs3OkQgghhBBCCFuyeAq24cOH89577+VZvnjxYkaOHGmNmIQolti1awDw7d0bxWCwczSWybhwgeTdv15fYDbnJOgAgVMmS4IuhBBCCCFEOWBxkr5+/XratWuXZ3nbtm355ptvrBKUEJZKP3+elH37QVHwe8LxC8bdLG79BiioCJxeT/x335dsQEIIIYQQQgi7sDhJj46OxsfHJ89yb29voqKirBKUEJaK+3odAJ73348hONjO0VjOeOVKrpbzXFRVu18IIYQQQghR5lmcpNepU4fNmzfnWb5p0yZq1apllaCEsIQ5PZ34DRsA8H2qr52jKR5DcHDBLemKUiovPAghhBBCCCEsZ3HhuFGjRjF06FCuXbvGww9rxbl27NjBggULWLRokbXjE+KWErdswRQfj1NQEJ7332/vcIrFt3cvopcvz/9OVcW3T++SDUgIIYQQQghhFxYn6c899xzp6enMmDGDadOmAVCjRg0++ugjBgwYYPUAhbiV2DVrAfB78gkUvd7O0RSPc40aBE2fTtiECVqLuqrm3AZNn45z9er2DlEIIYQQQghRAoo1BdvLL7/Myy+/zLVr13Bzc8PT09PacQlRJGmnTpN6+DA4OeHTu3S3Nvv26ol782bEfbMe45UrGIKD8e3TWxJ0IYQQQgghypHbmie9cuXK1opDiGKJW6u1onu1b4/B39/O0dw+5+rV8R89yt5hCCGEEEIIIeykSEl6s2bN2LFjB35+fjRt2hSloAJXwOHDh60WnBCFMScnE/+9NjWZXyktGCeEEEIIIYQQNypSkv7444/j4uICQI8ePWwZjxBFFv/zz5iTk3GuXh331q3tHY4QQgghhBBC3LYiJel+fn7odNpsbYMHD6ZqsOY6BwAAU1JJREFU1ao5fwthD6qqErtmDQC+ffuiyOtRCCGEEEIIUQYUKbMZNWoUCQkJANSsWZOoqCibBiXEraT9/Tfp/5xEcXbGp2cPe4cjhBBCCCGEEFZRpJb0KlWqsH79erp164aqqly+fJm0tLR8161WrZpVAxQiP9mt6F5dOuPk52fnaIQQQgghhBDCOoqUpE+YMIFhw4YxdOhQFEWhZcuWedZRVRVFUTCZTFYPUogbmRISSPh5IwB+Tz1l52iEEEIIIYQQwnqKlKS/8MIL9OvXj4sXL9K4cWO2b99OxYoVbR2bEPmK//4H1LQ0XOrWxa1pU3uHI4QQQgghhBBWU6Qk/b333uOFF16gUaNGrFy5kjZt2uDm5mbr2ITIQ1VVYtdmFYx7qm+h0wEKIYQQQgghRGljceG45557jsTERJsGJURBUg8dIuO/syhubvh0727vcIQQQgghhBDCqqRwnChVYtesBcDn0UfQe3nZORohhBBCCCGEsC4pHCdKjcyYGBK3bAHAt68UjBNCCCGEEEKUPVI4TpQa8d9+i2o04tqoEW6N7rR3OEIIIYQQQghhdUVK0gG8vLxyCse1a9cOFxcXW8YlRC6q2Uzs2q8B8Huqr52jEUIIIYQQQgjbKFLhuBsNHDiQ1NRUli1bxrhx44iJiQHg8OHDXLlyxeoBCgGQvG8fxtBQdF5eeHfrZu9whBBCCCGEEMImitySnu3YsWN06NABHx8fLly4wJAhQ6hQoQIbNmwgNDSUzz//3BZxinIuLrtg3OOPo3N3t3M0QgghhBBCCGEbFrekv/baawwaNIgzZ87g6uqas7xbt278+uuvVg1OCABjRCSJv/wCgF/fJ+0cjRBCCCGEEELYjsUt6QcPHuSTTz7Jszw4OJjw8HCrBCXEjeK+WQcmE24tmuNSt669wxFCCCGEEEIIm7G4Jd3FxYWEhIQ8y0+fPk3lypWLFcQHH3xAjRo1cHV1pXXr1vzxxx8FrrthwwZatGiBr68vHh4eNGnShC+++KJY+xWOT83MJG7dNwD4ybRrQgghhBBCiDLO4iS9e/fuvP322xiNRgAURSE0NJSxY8fSu3dviwNYu3Yto0aNYvLkyRw+fJi7776bzp07ExkZme/6FSpUYPz48ezbt49jx44xePBgBg8ezJas+bNF2ZL0669khoej9/PDq3Mne4cjhBBCCCGEEDZlcZK+YMECkpKS8Pf3JzU1lQceeIA6derg5eXFjBkzLA5g4cKFDBkyhMGDB9OwYUOWLFmCu7s7K1asyHf9Bx98kJ49e9KgQQNq167NiBEjaNy4MXv27LF438Lxxa5ZA4BPr57onJ3tHI0QQgghhBBC2JbFY9J9fHzYtm0be/bs4dixYyQlJdGsWTM6dOhg8c4zMjI4dOgQ48aNy1mm0+no0KED+/btu+XjVVXll19+4dSpU8yZMyffddLT00lPT8/5O7urvtFozOkN4Kiy43P0OG3FePkyyb9pF188e/VyyP9DeT9GpYUcp9JBjpPjk2NUOshxKh3kODk+OUalQ2k5TpbEp6iqqtowlkJdvXqV4OBg9u7dS5s2bXKWjxkzht27d3PgwIF8HxcfH09wcDDp6eno9Xo+/PBDnnvuuXzXnTJlClOnTs2zfPXq1bjLVF4OreLmzVTcuYvkunW58r/n7R2OEEIIIYQQQhRLSkoK/fv3Jz4+Hm9v70LXtbglHWD37t3Mnz+fkydPAtCwYUPeeOMN7rvvvuJszmJeXl4cPXqUpKQkduzYwahRo6hVqxYPPvhgnnXHjRvHqFGjcv5OSEggJCSETp063fKfY29Go5Ft27bRsWNHDAaDvcMpMRkXL5Kwfj3xv+9FBao/0Ye7u3Wzd1j5Kq/HqLSR41Q6yHFyfHKMSgc5TqWDHCfHJ8eodCgtxym/4usFsThJ//LLLxk8eDC9evVi+PDhAOzZs4f27dvz6aef0r9//yJvq1KlSuj1eiIiInItj4iIIDAwsMDH6XQ66tSpA0CTJk04efIks2bNyjdJd3FxwcXFJc9yg8Hg0AfxRqUp1tsVt34DYRMnan+YzQBEzZ6DwdML31497RhZ4crTMSrN5DiVDnKcHJ8co9JBjlPpIMfJ8ckxKh0c/ThZEpvFheNmzJjB3LlzWbt2LcOHD2f48OF8/fXXzJ49m2nTplm0LWdnZ5o3b86OHTtylpnNZnbs2JGr+/utmM3mXOPORemUceGClqCbzTkJOgBmM2ETJpBx8aL9ghNCCCGEEEKIEmBxkn7u3Dkee+yxPMu7d+/O+fPnLQ5g1KhRLF26lM8++4yTJ0/y8ssvk5yczODBgwEYMGBArsJys2bNYtu2bZw7d46TJ0+yYMECvvjiC5555hmL9y0cgzEikvjvv+fysOG5k/MbKQpx36wv2cCEEEIIIYQQooRZ3N09JCSEHTt25HQ3z7Z9+3ZCQkIsDqBv375cu3aNSZMmER4eTpMmTdi8eTMBAQEAhIaGotNdv5aQnJzMK6+8wuXLl3Fzc6N+/fp8+eWX9O3b1+J9C/swJSaS8uefJO/dR/L+fWT8d/bWD1JVjFeu2D44IYQQQgghhLAji5P00aNHM3z4cI4ePUrbtm0B+P333/n000959913ixXE0KFDGTp0aL737dq1K9ff06dPZ/r06cXaj7C+jAsXiFu/AeOVKxiCg/Ht3QvnGjVyrWPOyCD1yFGS9+0lZd9+Uv/+G0ym6ysoCq533gl6PWnHj+ffmq4oGIKDbftkhBBCCCGEEMLOLE7SX375ZQIDA1mwYAFff/01AA0aNGDt2rU8/vjjVg9QWE9REmpL5BR5UxRQVVAUopcvJ3Da27jWr0/K/v0k791HyqFDqGlpuR7rXL067m3b4NGmDR6tWqH39SXjwgXOdnsk/52pKr59ehc7ViGEEEIIIYQoDYo1BVvPnj3p2dNxK22LvApKqIOmTy9W1fRcRd5uEj5+Qp5l+kqVtIT8nnvwaHMPhipV8qzjXKMGQdOnEzZhQq44UVWCpk/HuXp1i+MUQgghhBBCiNLE4iT9zz//xGw207p161zLDxw4gF6vp0WLFlYLTlhHYQl12FtvkbT3d/RubqjGTFSjETUzU/sxZkBmZj7LjWReu1ZwkTcAgwHPdu3waNsG93vuwaVuXRRFuWWsvr164t68GXHfrL/e4t+ntyToQgghhBBCiHLB4iT91VdfZcyYMXmS9CtXrjBnzhwOHDhgteCEdcSt36C1SBcg8aefrbtDRcGrQ3uqvvNOsR7uXL06/qNHWTcmIYQQQgghhCgFLE7S//nnH5o1a5ZnedOmTfnnn3+sEpSwLuOVK1rX8fwoCs61a+PzSDcUgwGcnFCcDCgGA4qTE4rB6YblTigGZxQnJ+LWryfh55/zb03X6XCuanmlfyGEEEIIIYQo7yxO0l1cXIiIiKBWrVq5loeFheHkVKwh7sLGDMHBBbek63R4PfQQlV5+2bJtBgZoSXp+pMibEEIIIYQQQhSL7tar5NapUyfGjRtHfHx8zrK4uDjeeustOnbsaNXghHX49u5VcEt6MRPq7CJv6HSg1+e6lSJvQgghhBBCCFE8Fjd9z58/n/vvv5/q1avTtGlTAI4ePUpAQABffPGF1QMUt8+5Rg38x4whcvZsbYFOZ5Wq6VLkTQghhBBCCCGsy+IkPTg4mGPHjrFq1Sr++usv3NzcGDx4MP369cNgMNgiRmEFLrVqAqDz9cWzbVurJdRS5E0IIYQQQgghrKdYg8g9PDx44YUXrB2LsKG0f08B4Nm2LcELF9g5GiGEEEIIIYQQ+bF4TLoondL//RcAl/r17RyJEEIIIYQQQoiCSJJeTqRlJemu9e+wcyRCCCGEEEIIIQoiSXo5YE5LI+PCBUBa0oUQQgghhBDCkUmSXg6knzkDZjP6ChVwqlzZ3uEIIYQQQgghhCiAJOnlwI1d3RVFsXM0QgghhBBCCCEKUqTq7n5+fkVO7mJiYm4rIGF96VmV3V3ukK7uQgghhBBCCOHIipSkL1q0KOf36Ohopk+fTufOnWnTpg0A+/btY8uWLUycONEmQYrbI0XjhBBCCCGEEKJ0KFKSPnDgwJzfe/fuzdtvv83QoUNzlg0fPpzFixezfft2XnvtNetHKYpNVVXST2W1pNdvYOdohBBCCCGEEEIUxuIx6Vu2bKFLly55lnfp0oXt27dbJShhPcYrVzAnJaEYDLjUqmnvcIQQQgghhBBCFMLiJL1ixYp8//33eZZ///33VKxY0SpBCetJz+rq7lynDorBYOdohBBCCCGEEEIUpkjd3W80depU/ve//7Fr1y5at24NwIEDB9i8eTNLly61eoDi9qRlFY1zvUPGowshhBBCCCGEo7M4SR80aBANGjTgvffeY8OGDQA0aNCAPXv25CTtwnGk/XsSABcpGieEEEIIIYQQDs/iJB2gdevWrFq1ytqxCBvInn7NVYrGCSGEEEIIIYTDs3hMOsDZs2eZMGEC/fv3JzIyEoBNmzZx4sQJqwYnbo8pKQnj5cuATL8mhBBCCCGEEKWBxUn67t27ueuuuzhw4ADr168nKSkJgL/++ovJkydbPUBRfNlTrzkFBqL39bVvMEIIIYQQQgghbsniJP3NN99k+vTpbNu2DWdn55zlDz/8MPv377dqcOL2pGVVdpeicUIIIYQQQghROlicpB8/fpyePXvmWe7v709UVJRVghLWkT0e3aV+fTtHIoQQQgghhBCiKCxO0n19fQkLC8uz/MiRIwQHB1slKGEdOS3pMh5dCCGEEEIIIUoFi5P0p556irFjxxIeHo6iKJjNZn7//Xdef/11BgwYYIsYRTGoJhPpZ84A0pIuhBBCCCGEEKWFxUn6zJkzqV+/PiEhISQlJdGwYUPuv/9+2rZty4QJE2wRoyiGjIsXUdPSUNzccK5Wzd7hCCGEEEIIIYQoAovnSXd2dmbp0qVMmjSJ48ePk5SURNOmTalbt64t4hPFlJ7V1d2lXl0Uvd7O0QghhBBCCCGEKAqLW9LffvttUlJSCAkJoVu3bjz55JPUrVuX1NRU3n77bVvEKIohLatonOsd0tVdCCGEEEIIIUoLi5P0qVOn5syNfqOUlBSmTp1qlaDE7Uv79yQALlI0TgghhBBCCCFKDYuTdFVVURQlz/K//vqLChUqWCUocfuyp19zrd/AzpEIIYQQQgghhCiqIo9J9/PzQ1EUFEWhXr16uRJ1k8lEUlISL730kk2CFJbJjI0lMzISAJd69ewcjRBCCCGEEEKIoipykr5o0SJUVeW5555j6tSp+Pj45Nzn7OxMjRo1aNOmjU2CFJbJLhpnqFYNvaeHnaMRQgghhBBCCFFURU7SBw4cCEDNmjVp164dTk4WF4YXJeR60TgZjy6EEEIIIYQQpYnFY9K9vLw4efJkzt/ff/89PXr04K233iIjI8OqwYniSZeicUIIIYQQQghRKlmcpL/44oucPn0agHPnztG3b1/c3d1Zt24dY8aMsXqAwnI5LekNpGicEEIIIYQQQpQmFvdZP336NE2aNAFg3bp1PPDAA6xevZrff/+dp556ikWLFlk5RGEJNSOD9HPnAOnuLoQQQgghHIfJZMJoNNo7jCIzGo04OTmRlpaGyWSydziiAI50nJydndHpLG4Hz8PiJF1VVcxmMwDbt2/n0UcfBSAkJISoqKjbDkjcnvRz58BoROftjVOVKvYORwghhBBClHOqqhIeHk5cXJy9Q7GIqqoEBgZy6dKlfKegFo7BkY6TTqejZs2aODs739Z2LE7SW7RowfTp0+nQoQO7d+/mo48+AuD8+fMEBAQUK4gPPviAefPmER4ezt133837779Pq1at8l136dKlfP755/z9998ANG/enJkzZxa4fnmTllXZ3fWmafKEEEIIIYSwh+wE3d/fH3d391Jzjmo2m0lKSsLT09MqraPCNhzlOJnNZq5evUpYWBjVqlW7rde5xUn6okWLePrpp/nuu+8YP348derUAeCbb76hbdu2Fgewdu1aRo0axZIlS2jdujWLFi2ic+fOnDp1Cn9//zzr79q1i379+tG2bVtcXV2ZM2cOnTp14sSJEwQHB1u8/7Im/aSWpLvUr2/nSIQQQgghRHlnMplyEvSKFSvaOxyLmM1mMjIycHV1lSTdgTnScapcuTJXr14lMzMTg8FQ7O1YnKQ3btyY48eP51k+b9489Hq9xQEsXLiQIUOGMHjwYACWLFnCzz//zIoVK3jzzTfzrL9q1apcfy9btoz169ezY8cOBgwYYPH+y5q0U9lF4yRJF0IIIYQQ9pU9Bt3d3d3OkQhhe9nd3E0mU8km6QVxdXW1+DEZGRkcOnSIcePG5SzT6XR06NCBffv2FWkbKSkpGI1GKlSokO/96enppKen5/ydkJAAaB8Yjl64Iju+osapqmpOd3d97ToO//zKAkuPkbAPOU6lgxwnxyfHqHSQ41Q6lJfjZDQaUVU1V12r0kJV1Zzb0hZ7eeJIxyn7tW40GvM0YFvyXrc4SdfpdIX2r7ekol5UVBQmkynPWPaAgAD+zUo2b2Xs2LFUqVKFDh065Hv/rFmzmDp1ap7lW7duLTVX9LZt21ak9Zzi46kVF4eq07HzvzOoFy/YNjCRo6jHSNiXHKfSQY6T45NjVDrIcSodyvpxcnJyIjAwkKSkJDIyMuwdTrEkJibaOwRRBI5wnDIyMkhNTeXXX38lMzMz130pKSlF3o7FSfq3336b62+j0ciRI0f47LPP8k2GbWn27NmsWbOGXbt2FdiSP27cOEaNGpXzd0JCAiEhIXTq1Alvb++SCrVYjEYj27Zto2PHjkXqLpH862+EAS41a9L18cdtH6Cw+BgJ+5DjVDrIcXJ8coxKBzlOpUN5OU5paWlcunQJT0/PYvW8tSdVVUlMTMTLy6vUFLsrjxzpOKWlpeHm5sb999+f5/We3aO7KCxO0h/PJ/nr06cPd955J2vXruX5558v8rYqVaqEXq8nIiIi1/KIiAgCAwMLfez8+fOZPXs227dvp3HjxgWu5+LigouLS57lBoOh1HwgFjXWzDNnAHCtX7/UPLeyojS9nsozOU6lgxwnxyfHqHSQ41Q6lPXjZDKZUBQFnU5n96JelnrwwQdp0KABH3zwQamLvbimTJnCd999x9GjR+0dSpFld3HPfp3ZU3av8/ze15a8z632LO655x527Nhh0WOcnZ1p3rx5rseZzWZ27NhBmzZtCnzc3LlzmTZtGps3b6ZFixbFjrmsSTuVNf2aFI0TQgghhBBlzPmoZOZs/pdhXx1hzuZ/OR+VbLdYpkyZgqIovPTSS7mWHz16FEVRuHDhAgAXLlxAURT8/f3zdMdu0qQJU6ZMyfn7wQcfRFEUFEXB1dWVevXqMWvWrJwx144g+/nc/LN//357h1amWCVJT01N5b333ivWFGijRo1i6dKlfPbZZ5w8eZKXX36Z5OTknGrvAwYMyFVYbs6cOUycOJEVK1ZQo0YNwsPDCQ8PJykpyRpPpVRL/1er7O5yhyTpQgghhBCi7Pj64CXaL9jFJ7+e4+djV/nk13O0X7CLdQcv2S0mV1dXli9fzpms3qyFSUxMZP78+bdcb8iQIYSFhXHq1CnGjRvHpEmTWLJkiTXCtart27cTFhaW89O8eXN7h1SmWJyk+/n5UaFChZwfPz8/vLy8WLFiBfPmzbM4gL59+zJ//nwmTZpEkyZNOHr0KJs3b84pJhcaGkpYWFjO+h999BEZGRn06dOHoKCgnJ+ivOjLMnNqKhkXLwLgWv8OO0cjhBBCCCFE/lRVJSUjs8g//4TF8+b6Y5hVMJnVXLdj1x/jZFh8kbZzuy3SP//8Mz4+PjlTQt9xxx089NBDjB8//paPHTZsGAsXLiQyMrLQ9dzd3QkMDKR69eoMHjyYxo0b5youmJ6ezuuvv05wcDAeHh60bt2aXbt25drG0qVLCQkJwd3dnZ49e7Jw4UJ8fX0tfr4AZ8+epVatWgwdOjTX/69ixYoEBgbm/JTlIRv2YPGY9HfeeSfXgHydTkflypVp3bo1fn5+xQpi6NChDB06NN/7bn7RZXcdEbmlnzkDZjP6ihVxqlzZ3uEIIYQQQgiRr1SjiYaTtlhlW2YVur67p0jr/vN2Z9ydizcD9erVq3nppZdYvXo1jz76aE439dmzZ9OyZUsOHjxY6DDcfv36sW3bNt5++20WL158y/2pqsqePXv4999/qVu3bs7yoUOH8s8//7BmzRqqVKnCt99+S5cuXTh+/Dh169bl999/56WXXmLOnDl0796d7du3M3HixGI952PHjtG5c2eef/55pk+fnuu+7t27k5aWRr169RgzZgzdu3cv1j5E/ix+lQ4aNMgGYYjblXYyazz6HdKKLoQQQgghhLV8+OGHTJgwgR9//JEHHngg133NmjXjySefZOzYsYXW51IUhdmzZ/PYY4/x2muvUbt27QL3tWzZMjIyMjAajbi6ujJ8+HBA62G8cuVKQkNDqVKlCgCvv/46mzdvZuXKlcycOZP333+frl278vrrrwNQr1499u7dy08//WTRc967dy+PPvoo48ePZ/To0TnLPT09WbBgAe3atUOn07F+/Xp69OjBd999J4m6FRUpST927BiNGjVCp9Nx7NixQtf19PQkJCREujyUsPSsonEu9WU8uhBCCCGEcFxuBj3/vN25yOsv3HaalXsuYMqnu7peURh8bw1GdaxXpP1a6ocffmD58uX8/vvvtGzZMt91pk+fToMGDdi6dSv+/v4Fbqtz587ce++9TJw4kdWrV+e7ztNPP8348eOJjY1l8uTJtG3blrZt2wJw/PhxTCYT9erlfq7p6elUrFgRgFOnTtGzZ89c97dq1cqiJD00NJSOHTsyY8YMRo4cmeu+SpUq5ZreumXLlly9epV58+ZJkm5FRUrSmzRpQnh4OP7+/jRp0gRFUQod0+Hj48OSJUvo27ev1QIVhUvLKhonld2FEEIIIYQjUxTFom7nT7euzoo95/O9T0XlmdbVi92N/Vbuuusujh8/zooVK2jRokW+83DXrl2bIUOG8Oabb7J8+fJCtzd79mzatGnDG2+8ke/9Pj4+1KlTB4Cvv/6aOnXqcM8999ChQweSkpLQ6/UcOnQIvT73BQdPT89iPsO8KleuTJUqVfjqq6947rnn8Pb2LnT91q1b5xo3L25fkV7N58+fp3LWOOfz5/N/g2RLT09n3bp1jB07VpL0EqKazaSfyq7sLt3dhRBCCCFE2VGzkgdzejdm7PpjOY2F2bdzejemRiUP2+27Zk0WLVrEww8/jF6vL3A8+aRJk6hduzZr1qwpdHutWrWiV69evPnmm7fct6enJyNGjOD111/nyJEjNG3aFJPJRGRkJPfdd1++j7njjjv4888/cy27+e9bcXNz46effqJbt27/b+++w6K42jaA38PigrAUAaUoTakiiIoQS5TYkFhi91MTiRjU2FHsUbEFX6PGqDFYsMTYY0mikISILWpUNGuJr6hEJLFrjCBI253vD2RfV7qCuwv377r2gp2ZPfPMPp7kejhnziAoKAg///wzTExMij1eLpfD1ta2XOegkpWpSHd0dCzy9+KMHDkSZ8+effWoqFxyb92CMiMDQo0aMHB21nQ4REREREQVqq+fPZo7WWBH4l/4+/Ez1KtVE/397Cu1QC/g5uaGQ4cOITAwEPr6+li2bFmhY6ytrTFhwoQyPe1qwYIF8PLygr5+6aXY8OHDMW/ePOzevRt9+vTBoEGDMHjwYCxZsgRNmjTBgwcPcPDgQfj4+KBLly4YM2YM2rRpg6VLl6Jbt25ISEhAXFxckTMASmJsbIwDBw4gODgYwcHB+PHHHyGTybBp0yZIpVI0adIEALBnzx6sX78e69atK1f7VLIy35NeVj4+PqhVqxb27NnzykFR+WT9978AAKmrCwSuBUBEREREVZCTlTGmdNbMrZ3u7u5ISEhAYGAgJBJJkSPLERER+Oqrr5CVlVViW25ubggNDcWaNWtKPa+FhQUGDx6MyMhI9OrVCxs2bMD8+fMxceJE3Lp1C1ZWVnjrrbfQtWtXAECrVq0QHR2NOXPm4JNPPkFQUBDCw8PLtKL8y2QyGeLi4hAUFIQuXbogNjYWADBv3jzcvHkT+vr68PDwwI4dO9CnT59yt0/FK/M96S9OLSmJQqGokMCo7LIL7kd35/3oREREREQVISEhAWlpaar3np6euHfvnup9wWPYCpiamuLBgwdq25ycnIpcy2v16tVYvXq12raXHz1dIDo6WvW7np4e5syZgzlz5hQbd1hYGMLCwtTeF9znXprIyEi165LJZDh+/LjqfUhICEJCQsrUFr26Mt+TXuD3339HREQEJk2ahBYtWgAATp48iSVLlmDRokWVEyWVKCuJi8YRERERERGwePFidOzYEcbGxoiLi8OmTZuwatUqTYdF5VDue9L79u2L5cuX491331Vt8/Hxgb29PWbOnIkePXpUeJBUsuwrzx+/xpF0IiIiIqJq7fTp01i0aBHS09NRv359LF++HB999BEAwMvLCzdv3izyc6tXr8agQYPeZKhUjHI/q+DixYtwLmJxMmdnZ1y+fLlCgqKyU6SlIffWLQCAoQdXdiciIiIiqs527txZ7L7Y2Fjk5uYWuc/a2rqyQqJyKneR7unpiaioKKxbtw5SqRQAkJOTg6ioKHh6elZ4gFSygkev6dvaQmJmpuFoiIiIiIhIW5XlSV2keeUu0qOjo9GtWzfUq1cPPj4+APJXfxcEAT/88EOFB0gly1ItGsdRdCIiIiIiIl1X7iLd398ff/75J7Zs2YIrz++F7t+/PwYOHAhj48p/TiGpy0p6fj86F40jIiIiIiLSeeUu0oH8h9sPGzasomOhV8DHrxEREREREVUdr1SkA8Dly5eRmpqKnJwcte3du3d/7aCobMS8PGRfuwaAi8YRERERERFVBeUu0v/880/07NkTFy9ehCAIEEURACAIAgBAoVBUbIRUrJyUFIjZ2RCMjFDDwUHT4RAREREREdFr0ivvB8aNGwdnZ2fcv38fRkZG+OOPP3D06FH4+fnh8OHDlRAiFUe1aJyrKwS9cqeSiIiIiIiK0a5dO0ybNk3TYbxRkZGR8PX11XQY1V65K7uTJ09i7ty5sLKygp6eHvT09NC6dWtERUVh7NixlREjFSObi8YRERERUXXxKBn4JRL4NjT/56NkjYUSGRkJQRAwYsQIte1yuRyCICAlJQUAkJKSAkEQUKdOHaSnp6sd6+vri8jISNX7wMBACIIAQRBgaGgINzc3REVFqWYua4PDhw/jvffeg62tLYyNjeHr64stW7ZoOqwqp9xFukKhgImJCQDAysoKt2/fBpD/zL2k58/spjdDNZLuwSKdiIiIiKqw378BVvoBx5cDf+zN/7nSD/hdcwWioaEhYmJicO35GlElSU9Px+LFi0s9LiwsDHfu3EFSUhKmTZuGWbNmITo6uiLCrRAnTpyAj48Pdu/ejQsXLmDIkCEYPHgw9u/fr+nQqpRyF+mNGjXC+fPnAQABAQFYtGgRjh8/jrlz56J+/foVHiAVL/v5I/AM+Ix0IiIiItIVogjkZJT9dfci8P0YQFQCokL95/ejgbuXytbOa45IHzhwAGZmZqqRY3d3d7zzzjuYMWNGqZ8dM2YMli5divv375d4nJGREWxsbODo6IghQ4bAx8cH8fHxqv3Z2dmIiIhA3bp1YWxsjICAgEK3HK9duxb29vYwMjJCz549sXTpUpibm5f7egEgOTkZ9evXx+jRoyGKIqZPn4558+ahZcuWaNCgAcaNG4fOnTtjz549r9Q+Fa3cC8d98sknyMjIAADMnTsXXbt2xdtvvw1LS0vs2LGjwgOkouU9eoS8Bw8AQYChm5umwyEiIiIiKpvcTOBTu4ppS1QC0a3Kduz024DU+JVOs3XrVowYMQJbt25F165dVdPUFy5ciObNmyMxMRF+fn7Ffn7AgAGIj4/H3LlzsXLlylLPJ4oifv31V1y5cgWurq6q7aNHj8bly5exfft22NnZYe/evejcuTMuXrwIV1dXHD9+HCNGjMB//vMfdO/eHb/88gtmzpz5Std84cIFBAUFYejQoZg/f36xxz158gSenp6vdA4qWrlH0oOCgtCrVy8AgIuLC65cuYKHDx/i/v37aNeuXYUHSEXLej6KXsPBHnrGr/YfGyIiIiIiKtmqVaswcuRI/PDDD+jatavavqZNm6Jfv36YMmVKiW0IgoCFCxdizZo1SE4u/l76VatWQSaTwcDAAG3atIFSqVSt+5WamooNGzZg165dePvtt9GgQQNERESgdevW2LBhAwBgxYoVCA4ORkREBNzc3DBy5EgEBweX+5pPnDiBwMBARERElFig79y5E2fOnMGQIUPKfQ4q3is/J/1FFhYWFdEMlUO26n50/tWKiIiIiHRIDaP8Ue2yOvQp8NtX+VPcXyZIgLc+Bt6ZXrbzltP333+PmJgYHD9+HM2bNy/ymPnz58PT0xM///wz6tSpU2xbQUFBaN26NWbOnImtW7cWecygQYMwY8YMPH78GLNnz0bLli3RsmVLAMDFixehUCjg9tIs2uzsbFhaWgIAkpKS0LNnT7X9/v7+5bpnPDU1FR07dsSCBQswfvz4Yo87dOgQhgwZgrVr18LLy6vM7VPpKqRIpzcv6/nK7oYevB+diIiIiHSIIJRv2rlfKPDbqmJ2ivn7X3Eae2m8vb1x8eJFrF+/Hn5+fhAEodAxDRo0QFhYGKZOnYqYmJgS21u4cCFatGiBSZMmFbnfzMwMLi4uAPJHqV1cXPDWW2+hQ4cOePr0KSQSCc6ePQuJRKL2OZlM9opXWFjt2rVhZ2eHbdu2ITQ0FKampoWOOXLkCLp164bPP/8cgwcPrrBzUz4+XFtHFYykG7hzZXciIiIiqsIsGwDdVwKCXv7I+Ys/u6/M319JnJ2dcfDgQXz33XcYM2ZMscfNmjULV69exfbt20tsz9/fH7169cLUqVNLPbdMJsO4ceMQEREBURTRpEkTKBQK3L9/Hy4uLmovGxsbAPmL2Z05c0atnZffl6ZmzZrYv38/DA0NERQUVOjRcYcPH0aXLl3wn//8B8OGDStX21Q2LNJ1kDInB9l//gmAI+lEREREVA00GQSMTgRajQW8eub/HJ2Yv72Subm54dChQ9i9e3ex07+tra0xYcIELF++vNT2FixYgISEhDI9vnr48OG4evUqdu/eDTc3NwwaNAiDBw/Gnj17cOPGDZw+fRpRUVE4cOAAgPxV5GNjY7F06VJcu3YNq1evRlxcXJEzAEpibGyMAwcOQF9fH8HBwXj69CmA/CnuXbp0wdixY9G7d2/cvXsXd+/exT///FOu9qlkLNJ1UM7160BeHvRMTaFva6vpcIiIiIiIKp9lA6BDJNBnff7PShxBf5m7uzsSEhKwbds2TJw4schjIiIiyjTt3M3NDaGhocjKyir1WAsLCwwePBiRkZFQKpXYsGEDBg8ejIkTJ8Ld3R09evTAmTNn4ODgAABo1aoVoqOjsXTpUjRu3Bg//vgjwsPDYWhoWL4LRv5IflxcHERRRJcuXZCRkYFNmzYhMzMTUVFRsLW1Vb0KFhanisF70nVQlmrROI9y/1WMiIiIiIhKl5CQgLS0NNV7T09P3Lt3T/W+4DFsBUxNTfHgwQO1bU5OThCLeD776tWrsXr1arVtLz/vvEB0dLTqdz09PcyZMwdz5swpNu6wsDCEhYWpvS+4z700kZGRatclk8lw/Phx1fuNGzdi48aNZWqLXh2LdB2U/XzROANOdSciIiIiohcsXrwYHTt2hLGxMeLi4rBp0yasWlXcwnukjTjdXQepRtK5aBwREREREb3g9OnT6NixI7y9vREdHY3ly5fjo48+AgB4eXlBJpMV+dqyZYuGI6cCHEnXMaIoIvsKR9KJiIiIiKiwnTt3FrsvNjYWubm5Re6ztraurJConFik65i8u3ehePIEkEhgUMZ7S4iIiIiIiBwdHTUdApUBp7vrmKyCUfT6ztAzMNBwNERERERERFSRWKTrmOznz1M08PDUcCRERERERERU0Vik65j/PX6N96MTERERERFVNSzSdYxq0Tiu7E5ERERERFTlsEjXIcrMTOTcvAmAI+lERERERERVEYt0HZJ99SogipBYWUHfykrT4RARERERVVnt2rXDtGnTNB3GGxUZGQlfX19Nh1EhPvzwQ/To0UP1PjAwEOPHj9dYPOXBIl2H/O9+dE51JyIiIqLq5WbaTSw7uwyTj0zGsrPLcDPtpsZiiYyMhCAIGDFihNp2uVwOQRCQkpICAEhJSYEgCKhTpw7S09PVjvX19UVkZKTqfWBgIARBgCAIMDQ0hJubG6KioiCKYmVfTpkVXM/Lr99++03ToZVqz549mDdvnqbDKBMW6TokKyn/fnROdSciIiKi6mTvtb3ovq87Nv6xET/d/Akb/9iI7vu6Y9/1fRqLydDQEDExMbh27Vqpx6anp2Px4sWlHhcWFoY7d+4gKSkJ06ZNw6xZsxAdHV0R4VaoX375BXfu3FG9mjVrpumQSmVhYQETExNNh1EmLNJ1SPbzkXQuGkdEREREukoURWTmZpb5deXRFUSeiIRSVEIhKtR+zj4+G0n/JJWpndcdkT5w4ADMzMywZcsWAIC7uzveeecdzJgxo9TPjhkzBkuXLsX9+/dLPM7IyAg2NjZwdHTEkCFD4OPjg/j4eNX+7OxsREREoG7dujA2NkZAQAAOHz6s1sbatWthb28PIyMj9OzZE0uXLoW5uXm5rxcAkpOTUb9+fYwePVrt+7O0tISNjY3qVaNGjTK1VzAF/dNPP4W1tTXMzc0xd+5c5OXlYdKkSbCwsEC9evWwYcMGtc/99ddf6NevH8zNzWFhYYH33ntPNVsBABQKBSZOnAhzc3NYWlpi8uTJhfL98nT3zZs3w8/PDyYmJrCxscHAgQPV8nP48GEIgoCDBw/Cz88PRkZGaNmyJZKePxK7Mmm8SP/yyy/h5OQEQ0NDBAQE4PTp08Ue+8cff6B3795wcnKCIAhYtmzZmwtUw0SlEllJfPwaEREREem2Z3nPELA1oMyvvvv7QgllkW0poUSfH/qUqZ1nec9eOeatW7diwIAB2LJlCwYNGqTavnDhQuzevRuJiYklfn7AgAFwcXHB3Llzy3Q+URRx7NgxXLlyBVKpVLV99OjROHnyJLZv344LFy6gb9++6Ny5s2o0//jx4xgxYgTGjRsHuVyOjh07YsGCBa9wxcCFCxfQunVrDBw4ECtXroQgCKp93bt3R506ddC6dWt8//335Wo3ISEBt2/fxtGjR7F06VLMnj0bXbt2Ra1atXDq1CmMGDECw4cPx99//w0AyM3NRVBQEExMTHDs2DEcP34cMpkMnTt3Rk5ODgBg5cqV2LRpE9avX49ff/0V//zzD/bu3VtiHLm5uZg3bx7Onz+Pffv2ISUlBR9++GGh42bMmIElS5YgMTER+vr6CA0NLdf1vgqNFuk7duzAhAkTMHv2bJw7dw6NGzdGUFBQsX9hyszMRP369bFw4ULY2Ni84Wg1K/evvyBmZkKQSiF1dtZ0OERERERE1cKqVaswcuRI/PDDD+jatavavqZNm6Jfv36YMmVKiW0IgoCFCxdizZo1SE5OLvFcMpkMBgYGaNOmDZRKJcaOHQsASE1NxYYNG7Br1y68/fbbaNCgASIiItC6dWvVyPOKFSsQHByMiIgIuLm5YeTIkQgODi73NZ84cQKBgYGIiIjA/PnzVdtlMhmWLFmCXbt24cCBA2jdujV69OhRrkLdwsICy5cvh7u7O0JDQ+Hu7o7MzExMnz4drq6umDZtGqRSKX799VcA+TWjUqnEunXr4O3tDU9PT2zYsAGpqamqWQTR0dGYOnUqevXqBU9PT0RHR8PMzKzEOEJDQxEcHIz69evjrbfewvLlyxEXF4enT5+qHbdgwQK0bdsWDRs2xNSpU3HixAlkZWWV+XpfhX6ltl6KpUuXIiwsDEOGDAGQ/+UeOHAA69evx9SpUwsd37x5czRv3hwAitxflRUsGmfg6gpBX6NpIyIiIiJ6ZTX1a+LUwFNlPv5L+ZfY8t8tUIiKQvskggSDPAdhlO+oMp23vL7//nvExMTg+PHjqjrkZfPnz4enpyd+/vln1KlTp9i2goKC0Lp1a8ycORNbt24t8phBgwZhxowZePz4MWbPno2WLVuiZcuWAICLFy9CoVDAzc1N7TPZ2dmwtLQEACQlJaFnz55q+/39/bF///4yX3NqaqpqBP7l1dCtrKwwYcIE1fvmzZvj9u3b+Oyzz9C9e/cyte/l5QU9vf+NFVtbW6NRo0aq9xKJBJaWlqqB2/Pnz+P69euF7ifPyspCcnIymjdvjrt378Lf31+1T19fH35+fiXe4nD27FlERkbi/PnzePz4MZRKper6GzZsqDrOx8dH9butrS0A4P79+3BwcCjT9b4KjVV7OTk5OHv2rNpjDfT09NChQwecPHmyws6TnZ2N7Oxs1fu0tDQA+dMbcnNzK+w8laEgvtzcXGRevgwAqOHmpvVxVycv5oi0F/OkG5gn7ccc6QbmSTdUlzzl5uZCFEUolUpVEQQAhhLDMrfRx7UPvrn8TZH7RFFEX9e+ZWpPFMVy3ZcuiiK8vb1x8eJFxMTEoGnTpqop3wXtKJVKODs746OPPsLUqVOxdu1a1fYXr7ng908//RStWrXCxIkTVe28+L2Ympqifv36AIDt27fDzc0N/v7+6NChA9LS0iCRSHDmzBlIJBK1WGUymaqdl9t8MdayXHPt2rVhZ2eHbdu24cMPP4SpqWmJn/H390d8fHyZ29fX11c7VhCEIrcpFAoolUqkp6ejWbNm2Lx5c6H2ateurbq+oq67qG1KpRIZGRkICgpCp06dsHnzZtSuXRupqakIDg5GVlaWWu4kEonadwsAeXl5RV6vUqmEKIrIzc0tlKPy9HWNFekPHz6EQqGAtbW12nZra2tcuXKlws4TFRWFOXPmFNr+888/w8jIqMLOU5ni4+Nhd+woZACSFXk4Gxur6ZDoJS8u6EHai3nSDcyT9mOOdAPzpBuqep709fVhY2ODp0+fqu4fLq9aqIUpTabgP7//B4Ig5BdKAgARmNJkCsxhrhqIq0gKhQLOzs6YP38+unXrBoVCgc8++wxA/kCgQqFQnXf8+PFo2rQpvv76awDA06dPkZaWppo6nZGRgbS0NHh4eKBbt26YPHkyFAoFsrOzVW3k5eUhJydH7VqGDRuGiRMn4ujRo3B1dYVCocCNGzdUo+svSktLg7OzM06ePKnWxokTJyCKYpm+o+zsbEilUnzzzTfo168fOnXqhN27d5e4KvqZM2dQp06dMrWfm5uLvLw8tWOLum6lUomsrCykpaXB09MTO3bsgKGhYZF/MBAEATY2Njh27BiaNGmiajMxMRGNGzcu8vuVy+V49OgRpk+fjnr16gEAjh07BuB/ucrMzASQvzJ/wch/RkYGgP/l92U5OTl49uwZjh49iry8PLV9Be2VRZWfNz1t2jS1KRlpaWmwt7dHp06dSv2rkKbl5uYiPj4eHTt2xK1lXyAPQJMePVGzuZ+mQ6PnXsxRWVe1pDePedINzJP2Y450A/OkG6pLnrKysvDXX39BJpPB0LDso+cv+79G/4eWDi2x9/pe3M64DTtjO/R06QkH08qbclwwEtqkSRMkJCSgXbt2MDIywueffw4DAwNIJBJVPWFqaorw8HDVY9ZkMhlMTU0hk8kAAMbGxqpjFy5cCG9vb+jr68PAwEC1XV9fH1KpVK1GGTt2LBYvXoz4+Hj06dMHAwcOxKhRo/DZZ5+hSZMmePDgARISEuDt7Y0uXbogPDwcgYGBiImJQdeuXZGQkICDBw9CT0+vTLVPwXXZ2toiLi4OXbp0wYABAxAbGwuZTIZNmzZBKpWqiuG9e/fim2++wZo1a8rUfo0aNaCvr692bFHXraenpyrKhw4dii+//BIhISGIjIxEvXr1cPPmTezduxeTJk1C3bp1MXz4cHzxxRdo1KgRPDw88PnnnyMtLU3tXC+ex9PTE1KpFJs2bcLw4cNx6dIlLF26VC1XBQO6JiYmqjaMjY3V8vuyrKws1KxZE23atCn07708f0jSWJFuZWUFiUSCe/fuqW2/d+9ehS4KZ2BgAAMDg0Lba9SooTP/QdTLfIa8O3cAAMZeDSHRkbirE13691SdMU+6gXnSfsyRbmCedENVz5NCoYAgCNDT01O7D/lVOJk7IdwvvIIiK13B1HZBEODp6YmEhAQEBgZCX19fNbL84jVNmjQJ0dHRyMrKUl1vwf4Xf/fw8EBoaCjWrFmj+m5ePOeL762srDB48GDMnTsXffr0wcaNGzF//nxMmjQJt27dgpWVFd566y1069YNenp6ePvttxEdHY05c+Zg5syZCAoKQnh4OFauXFmm77/gmguK+ri4OAQFBaFbt26IjY2Fnp4eFixYgJs3b0JfXx8eHh7YsWMH+vTpU+bv9OVrLOq6X9wmk8lw9OhRTJkyBX369EF6ejrq1q2L9u3bw9zcHIIgYPTo0Xj8+DGGDBkCPT09hIaGomfPnnjy5EmR36+1tTU2btyI6dOnY8WKFWjatCkWL16M7t27l5i7ora9SE9PD4IgFNmvy9PPBfF1Hxj4GgICAuDv748VK1YAyJ/W4ODggNGjR5e6MJyTkxPGjx9faDGD0qSlpcHMzAxPnjzRiZH02NhYvFO7Dm6FhqKGnR1cEg5qOix6QUGO3n333Sr9P1hdxzzpBuZJ+zFHuoF50g3VJU9ZWVm4ceMGnJ2dX2skXROUSiXS0tJgamr62n9g0KSwsDBcuXJFNZ27qtGmPJX07708dahGp7tPmDABISEh8PPzg7+/P5YtW4aMjAzVau+DBw9G3bp1ERUVBSB/jv/l5wuo5eTk4NatW5DL5ZDJZHBxcdHYdVS27KvPV3b38NBwJEREREREpM0WL16Mjh07wtjYGHFxcdi0aRNWrVql6bCoHDRapPfv3x8PHjzArFmzcPfuXfj6+uLHH39ULSaXmpqq9teQ27dvq+5/APL/AS5evBht27ZVPSOvKspOugoAMPRw13AkRERERESkzU6fPo1FixYhPT0d9evXx/Lly/HRRx8ByH/82c2bN4v83OrVqzFo0KDXOnfBPfhFiYuLw9tvv/1a7VcXGl84bvTo0Rg9enSR+14uvJ2cnMr12ISqIuf5avcG7hxJJyIiIiKi4u3cubPYfbGxscU+Cuzlp269CrlcXuy+unXrvnb71YXGi3QqhUKBnORkABxJJyIiIiKiV+fo6Fip7VflW5DfJN1dAaGakD54CDEnB3pGRqhhb6/pcIiIiIiIiKgSsUjXcgbPH71m4O4OQYdXlSQiIiIiIqLSserTcqoinVPdiYiIiIiIqjwW6VrO4PZtAIAhF40jIiIiIiKq8lika7mCkXQuGkdERERERFT1sUjXYnkPH0H/6VNAEGDg5qbpcIiIiIiIiKiSsUjXYjlXkwAANRwdoGdkpOFoiIiIiIg0JyclBfeXLMWtCRNxf8lS5KSkvJHznjx5EhKJBF26dFHbnpKSAkEQVC8LCwu0bdsWx44dUzsuMjJS7biC1y+//FJov0Qigb29PYYNG4Z//vnnjVxfWTg5ORWKf+HChZoOq8ric9K1WHZSfpFu4Map7kRERERUff27ew/uzJwJCAIgioAg4FFMDGznz4d5r56Veu7169djzJgxiImJwe3bt2FnZ6e2/5dffoGXlxcePnyIBQsWoGvXrrh69Sqsra1Vx3h5eamK8gIWFhaF9isUCvz3v/9FaGgonjx5gh07dlTqtZXH3LlzERYWpnpvYmKiwWiqNo6ka7HsK/lFutSdU92JiIiIqGoQRRHKzMwyv7KuXMkv0JVKQKFQ+3nnk0+QdSWpTO2IoljuWJ8+fYqdO3fi448/RpcuXbBx48ZCx1haWsLGxgaNGjXC9OnTkZaWhlOnTqkdo6+vDxsbG7WXVCottL9u3bro0KED+vbti/j4eLU21q1bB09PTxgaGsLDwwOrVq1S23/ixAn4+vrC0NAQfn5+2LdvHwRBgFwuL/U6Hz9+jEGDBqF27dqoWbMmXF1dsWHDBrVjTExM1OI3NjYutV16NRxJ11I5KSnIPHECQH6xnpOSAqmTk2aDIiIiIiJ6TeKzZ0hq2qxiGlMqcaNHjzId6n7uLIRy3kK6b98+eHh4wN3dHe+//z7Gjx+PadOmQRCEQsc+e/YMX3/9NQCoFeDllZKSgp9++kmtjS1btmDWrFlYuXIlmjRpgt9//x1hYWEwNjZGSEgI0tLS0K1bN7z77rvYunUrbt68ifHjx5f5nDNnzsTly5cRFxcHKysrXL9+Hc+ePVM7ZuHChZg3bx4cHBwwcOBAhIeHQ1+f5WRl4LeqhVTTeZRKAEDGwYNIPnjwjUznISIiIiKifJs3b8agQYMAAJ07d8aTJ09w5MgRBAYGqo5p2bIl9PT0kPl8tL5Zs2Zo3769WjsXL16ETCZTvW/YsCFOnz5daL9CoUBWVhYAYOnSpar9s2fPxpIlS9CrVy8AgLOzMy5fvozVq1cjJCQEW7duhSAIWLt2LQwNDdGwYUPcunVLbXp6SVJTU9GkSRP4+fkByL8H/UVjx45F06ZNYWFhgRMnTmDatGm4c+eOWoxUcVika5mclBS1Ah2A6vc7n3wCo2ZNIXV01FB0RERERESvR6hZE+7nzpb5+AcrVuCfrzfnT3F/mUQCi8EfoPaYMWU6b3kkJSXh3Llz+O677wDkT0nv378/YmJi1Ir0HTt2wMPDA5cuXcLkyZOxceNG1KhRQ60td3d3fP/996r3BgYGRe7PysrCN998A7lcjjHPrykjIwPJyckYOnSoWtGdl5cHMzMzVaw+Pj4wNDRU7ff39y/ztX788cfo3bs3zp07h06dOqFHjx5o2bKlav+ECRNUv/v4+EAqlWL48OGIiooqdC30+lika5l/d+/JXxCjKIKAf7/djToTJxS9n4iIiIhIywmCUK5p57X698c/m74ueqcootb//V+lPAlp/fr1yMvLQ7169V44nQgDAwOsXLlStc3e3h6urq5wdXVFXl4eevbsiUuXLqkVr1KpFC4uLsWe68X9CxcuRJcuXTBnzhzMmzcPT58+BQCsXbsWAQEBap+TSCQVcq3BwcG4efMmYmNjER8fj/bt22PUqFFYvHhxkccHBAQgLy8PKSkpcHfnItcVjQvHaZncW7fyV6wsiijm7yciIiIiqiakTk6wnT8f0NMDJBK1n7bz51fKLNO8vDxs3rwZ8+fPx7lz5yCXyyGXy3H+/HnY2dlh27ZtRX6uT58+0NfXL7SoW3l98sknWLx4MW7fvg1ra2vY2dnhzz//hIuLi9rL2dkZQP5I/MWLF5Gdna1q48yZM+U6Z+3atRESEoJvvvkGy5Ytw5o1a4o9Vi6XQ09PD3Xq1Hm1C6QScSRdy9SoW7fEkfQadeu+2YCIiIiIiDTMvFdPGDVrin+/3Y3cW7dQo25dmPfpXWm3ge7fvx+PHz/G+++/D3t7e+jp/W9ss3fv3oiJiUHnzp0LfU4QBIwdOxaRkZEYPnw4jF5xhL9Fixbw8fHBp59+ipUrV2LOnDkYO3YszMzM0LlzZ2RnZyMxMRGPHz/GhAkTMHDgQMyYMQPDhg3D1KlTkZqaqhoFL2qRu5fNmjULzZo1g5eXF7Kzs7F//354enoCyH9O/KlTp/DOO+/AxMQEJ0+eRHh4ON5//33UqlXrla6PSsaRdC1j3rtXiSPp5n16v9mAiIiIiIi0gNTREXUmTkDdpUtQZ+KESl2nKSYmBu3bt1fd8/2i3r17IzExEWlpaUV+NiQkBLm5uWpT4l9FeHg41q1bh7/++gsfffQR1q1bhw0bNsDb2xtt27bFxo0bVSPppqam+OGHHyCXy+Hr64sZM2Zg1qxZAKB2n3pxpFIppk2bBh8fH7Rp0wYSiQTbt28HkH///Pbt29G2bVt4eXlhwYIFCA8PL3GknV6PIL7KAwN1WFpaGszMzPDkyROYmppqOpwi/btnL+588gkgCBCVSgh6eoAocnV3LZSbm4vY2Fi8++67hRYIIe3BPOkG5kn7MUe6gXnSDdUlT1lZWbhx4wacnZ3LVCxqE6VSibS0NJiamqqNpOuKLVu2YMiQIXjy5AlqlnPRPF2iTXkq6d97eepQTnfXQgXTeR7t3IXUxEQ4+PnBsl9frupORERERERF+vrrr1G/fn3UrVsX58+fx5QpU9CvX78qXaBXVbr3J6FqQuroCKvx43B34ABYjR/HAp2IiIiIiIp19+5dvP/++/D09ER4eDj69u2rmpI+YsQIyGSyIl8jRozQcOT0Mo6kExERERER6bjJkydj8uTJRe6bO3cuIiIiitynrbcAV2cs0omIiIiIiKqwOnXq8HFpOoTT3YmIiIiIqFJVs7WqqZqqqH/nLNKJiIiIiKhSFKxcn5mZqeFIiCpfTk4OAEAikbxWO5zuTkRERERElUIikcDc3Bz3798HABgZGUEQBA1HVTZKpRI5OTnIysrS+KO9qHjakielUokHDx7AyMgI+vqvV2azSCciIiIiokpjY2MDAKpCXVeIoohnz56hZs2aOvOHhepIm/Kkp6cHBweH146DRToREREREVUaQRBga2uLOnXqIDc3V9PhlFlubi6OHj2KNm3aqKbtk/bRpjxJpdIKGc1nkU5ERERERJVOIpG89r26b5JEIkFeXh4MDQ01XvxR8apinnhzBREREREREZGWYJFOREREREREpCVYpBMRERERERFpiWp3T3rBA+bT0tI0HEnpcnNzkZmZibS0tCpzf0VVwxzpBuZJNzBP2o850g3Mk25gnrQfc6QbdCVPBfVnQT1akmpXpKenpwMA7O3tNRwJERERERERVSfp6ekwMzMr8RhBLEspX4UolUrcvn0bJiYmGn+OXmnS0tJgb2+Pv/76C6amppoOh4rAHOkG5kk3ME/ajznSDcyTbmCetB9zpBt0JU+iKCI9PR12dnalPqat2o2k6+npoV69epoOo1xMTU21+h8cMUe6gnnSDcyT9mOOdAPzpBuYJ+3HHOkGXchTaSPoBbhwHBEREREREZGWYJFOREREREREpCVYpGsxAwMDzJ49GwYGBpoOhYrBHOkG5kk3ME/ajznSDcyTbmCetB9zpBuqYp6q3cJxRERERERERNqKI+lEREREREREWoJFOhEREREREZGWYJFOREREREREpCVYpBMRERERERFpCRbpWurLL7+Ek5MTDA0NERAQgNOnT2s6JHpBZGQkBEFQe3l4eGg6rGrv6NGj6NatG+zs7CAIAvbt26e2XxRFzJo1C7a2tqhZsyY6dOiAa9euaSbYaqq0HH344YeF+lbnzp01E2w1FhUVhebNm8PExAR16tRBjx49kJSUpHZMVlYWRo0aBUtLS8hkMvTu3Rv37t3TUMTVT1lyFBgYWKg/jRgxQkMRV09fffUVfHx8YGpqClNTU7Ro0QJxcXGq/exH2qG0PLEvaZ+FCxdCEASMHz9eta0q9ScW6Vpox44dmDBhAmbPno1z586hcePGCAoKwv379zUdGr3Ay8sLd+7cUb1+/fVXTYdU7WVkZKBx48b48ssvi9y/aNEiLF++HNHR0Th16hSMjY0RFBSErKysNxxp9VVajgCgc+fOan1r27ZtbzBCAoAjR45g1KhR+O233xAfH4/c3Fx06tQJGRkZqmPCw8Pxww8/YNeuXThy5Ahu376NXr16aTDq6qUsOQKAsLAwtf60aNEiDUVcPdWrVw8LFy7E2bNnkZiYiHbt2uG9997DH3/8AYD9SFuUlieAfUmbnDlzBqtXr4aPj4/a9irVn0TSOv7+/uKoUaNU7xUKhWhnZydGRUVpMCp60ezZs8XGjRtrOgwqAQBx7969qvdKpVK0sbERP/vsM9W2f//9VzQwMBC3bdumgQjp5RyJoiiGhISI7733nkbioeLdv39fBCAeOXJEFMX8vlOjRg1x165dqmP++9//igDEkydPairMau3lHImiKLZt21YcN26c5oKiItWqVUtct24d+5GWK8iTKLIvaZP09HTR1dVVjI+PV8tLVetPHEnXMjk5OTh79iw6dOig2qanp4cOHTrg5MmTGoyMXnbt2jXY2dmhfv36GDRoEFJTUzUdEpXgxo0buHv3rlrfMjMzQ0BAAPuWljl8+DDq1KkDd3d3fPzxx3j06JGmQ6r2njx5AgCwsLAAAJw9exa5ublq/cnDwwMODg7sTxryco4KbNmyBVZWVmjUqBGmTZuGzMxMTYRHABQKBbZv346MjAy0aNGC/UhLvZynAuxL2mHUqFHo0qWLWr8Bqt7/l/Q1HQCpe/jwIRQKBaytrdW2W1tb48qVKxqKil4WEBCAjRs3wt3dHXfu3MGcOXPw9ttv49KlSzAxMdF0eFSEu3fvAkCRfatgH2le586d0atXLzg7OyM5ORnTp09HcHAwTp48CYlEounwqiWlUonx48ejVatWaNSoEYD8/iSVSmFubq52LPuTZhSVIwAYOHAgHB0dYWdnhwsXLmDKlClISkrCnj17NBht9XPx4kW0aNECWVlZkMlk2Lt3Lxo2bAi5XM5+pEWKyxPAvqQttm/fjnPnzuHMmTOF9lW1/y+xSCd6BcHBwarffXx8EBAQAEdHR+zcuRNDhw7VYGREuu3//u//VL97e3vDx8cHDRo0wOHDh9G+fXsNRlZ9jRo1CpcuXeK6G1qsuBwNGzZM9bu3tzdsbW3Rvn17JCcno0GDBm86zGrL3d0dcrkcT548wbfffouQkBAcOXJE02HRS4rLU8OGDdmXtMBff/2FcePGIT4+HoaGhpoOp9JxuruWsbKygkQiKbQS4b1792BjY6OhqKg05ubmcHNzw/Xr1zUdChWjoP+wb+mW+vXrw8rKin1LQ0aPHo39+/fj0KFDqFevnmq7jY0NcnJy8O+//6odz/705hWXo6IEBAQAAPvTGyaVSuHi4oJmzZohKioKjRs3xhdffMF+pGWKy1NR2JfevLNnz+L+/fto2rQp9PX1oa+vjyNHjmD58uXQ19eHtbV1lepPLNK1jFQqRbNmzXDw4EHVNqVSiYMHD6rdF0Pa5enTp0hOToatra2mQ6FiODs7w8bGRq1vpaWl4dSpU+xbWuzvv//Go0eP2LfeMFEUMXr0aOzduxcJCQlwdnZW29+sWTPUqFFDrT8lJSUhNTWV/ekNKS1HRZHL5QDA/qRhSqUS2dnZ7EdariBPRWFfevPat2+PixcvQi6Xq15+fn4YNGiQ6veq1J843V0LTZgwASEhIfDz84O/vz+WLVuGjIwMDBkyRNOh0XMRERHo1q0bHB0dcfv2bcyePRsSiQQDBgzQdGjV2tOnT9X+qn3jxg3I5XJYWFjAwcEB48ePx/z58+Hq6gpnZ2fMnDkTdnZ26NGjh+aCrmZKypGFhQXmzJmD3r17w8bGBsnJyZg8eTJcXFwQFBSkwairn1GjRmHr1q347rvvYGJiorqfz8zMDDVr1oSZmRmGDh2KCRMmwMLCAqamphgzZgxatGiBt956S8PRVw+l5Sg5ORlbt27Fu+++C0tLS1y4cAHh4eFo06ZNoccWUeWZNm0agoOD4eDggPT0dGzduhWHDx/GTz/9xH6kRUrKE/uSdjAxMVFbcwMAjI2NYWlpqdpepfqTppeXp6KtWLFCdHBwEKVSqejv7y/+9ttvmg6JXtC/f3/R1tZWlEqlYt26dcX+/fuL169f13RY1d6hQ4dEAIVeISEhoijmP4Zt5syZorW1tWhgYCC2b99eTEpK0mzQ1UxJOcrMzBQ7deok1q5dW6xRo4bo6OgohoWFiXfv3tV02NVOUTkCIG7YsEF1zLNnz8SRI0eKtWrVEo2MjMSePXuKd+7c0VzQ1UxpOUpNTRXbtGkjWlhYiAYGBqKLi4s4adIk8cmTJ5oNvJoJDQ0VHR0dRalUKtauXVts3769+PPPP6v2sx9ph5LyxL6kvV5+NF5V6k+CKIrim/yjABEREREREREVjfekExEREREREWkJFulEREREREREWoJFOhEREREREZGWYJFOREREREREpCVYpBMRERERERFpCRbpRERERERERFqCRToRERERERGRlmCRTkRERERERKQlWKQTERERERERaQkW6URERFrqwYMHkEqlyMjIQG5uLoyNjZGamlriZyIjI+Hr61thMQQGBmL8+PEV1h4RERGVjEU6ERGRljp58iQaN24MY2NjnDt3DhYWFnBwcNB0WERERFSJWKQTERFpqRMnTqBVq1YAgF9//VX1e3l8+OGH6NGjBxYvXgxbW1tYWlpi1KhRyM3NVR2zatUquLq6wtDQENbW1ujTp4/qs0eOHMEXX3wBQRAgCAJSUlKgUCgwdOhQODs7o2bNmnB3d8cXX3xR7vNmZ2djypQpsLe3h4GBAVxcXBATE6Paf+nSJQQHB0Mmk8Ha2hoffPABHj58qNr/7bffwtvbGzVr1oSlpSU6dOiAjIyMcn9HRERE2kRf0wEQERHR/6SmpsLHxwcAkJmZCYlEgo0bN+LZs2cQBAHm5uYYOHAgVq1aVeY2Dx06BFtbWxw6dAjXr19H//794evri7CwMCQmJmLs2LHYvHkzWrZsiX/++QfHjh0DAHzxxRe4evUqGjVqhLlz5wIAateuDaVSiXr16mHXrl2wtLTEiRMnMGzYMNja2qJfv35lOi8ADB48GCdPnsTy5cvRuHFj3LhxQ1WE//vvv2jXrh0++ugjfP7553j27BmmTJmCfv36ISEhAXfu3MGAAQOwaNEi9OzZE+np6Th27BhEUayQPBAREWmKIPL/ZkRERFojLy8Pf//9N9LS0uDn54fExEQYGxvD19cXBw4cgIODA2QyGaysrIr8fGRkJPbt2we5XA4gf0T78OHDSE5OhkQiAQD069cPenp62L59O/bs2YMhQ4bg77//homJSaH2AgMD4evri2XLlpUY9+jRo3H37l18++23ZTrv1atX4e7ujvj4eHTo0KFQe/Pnz8exY8fw008/qbb9/fffsLe3R1JSEp4+fYpmzZohJSUFjo6OpX6vREREuoLT3YmIiLSIvr4+nJyccOXKFTRv3hw+Pj64e/curK2t0aZNGzg5ORVboBfHy8tLVSgDgK2tLe7fvw8A6NixIxwdHVG/fn188MEH2LJlCzIzM0tt88svv0SzZs1Qu3ZtyGQyrFmzptCidiWdVy6XQyKRoG3btkW2f/78eRw6dAgymUz18vDwAAAkJyejcePGaN++Pby9vdG3b1+sXbsWjx8/Ltf3QkREpI1YpBMREWkRLy8vyGQyfPDBBzh9+jRkMhnat2+PlJQUyGQyeHl5lbvNGjVqqL0XBAFKpRIAYGJignPnzmHbtm2wtbXFrFmz0Lh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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "stream = stream_from_file(path_to_csv_or_arff=csv_fried_path, enforce_regression=True)\n", - "\n", - "# Define the learners + an alias (dictionary key)\n", - "learners = {\n", - " 'kNNReg_k5': KNNRegressor(schema=stream.get_schema(), k=5),\n", - " 'kNNReg_k2': KNNRegressor(schema=stream.get_schema(), k=2),\n", - " 'kNNReg_k5_median': KNNRegressor(schema=stream.get_schema(), CLI='-k 5 -m'),\n", - " 'ARFReg_s5': AdaptiveRandomForestRegressor(schema=stream.get_schema(), ensemble_size=5)\n", - "}\n", - "\n", - "results = prequential_evaluation_multiple_learners(stream, learners)\n", - "\n", - "for learner_id in results.keys():\n", - " print(f\"{learner_id}: {results[learner_id]['cumulative']} \\n\")\n", - " results[learner_id]['learner']=learner_id # sets the identifiers correctly for plotting\n", - "\n", - "plot_windowed_results(results['kNNReg_k5'], results['kNNReg_k2'], results['kNNReg_k5_median'], \n", - " results['ARFReg_s5'], metric=\"adjusted coefficient of determination\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b84569df-4d2d-4e01-a049-a64750e7f3ea", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/index.rst b/notebooks/index.rst index 93f5e251..226f2e8e 100644 --- a/notebooks/index.rst +++ b/notebooks/index.rst @@ -10,7 +10,7 @@ These tutorials will show you how to get started with the CapyMOA library. :caption: Tutorials: 00_getting_started.ipynb - 01_evaluation_and_data_reading.ipynb + 01_evaluation.ipynb 02_learners_api_examples.ipynb 03_using_sklearn_pytorch.ipynb 04_drift_streams.ipynb diff --git a/pyproject.toml b/pyproject.toml index 805811b0..30f2ba08 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,14 +33,15 @@ dev=[ "jupyter", "nbmake", "pytest-xdist", - "invoke" + "invoke", + "wget" ] doc=[ # Documentation generator "sphinx", # Theme for the documentation - "sphinx-book-theme", + "pydata-sphinx-theme", # Allows to include Jupyter notebooks in the documentation "sphinx-autobuild", # Allows to include Jupyter notebooks in the documentation diff --git a/src/capymoa/__init__.py b/src/capymoa/__init__.py index 4f3a3a77..757193c7 100644 --- a/src/capymoa/__init__.py +++ b/src/capymoa/__init__.py @@ -1,6 +1,4 @@ from .prepare_jpype import _start_jpype - +# It is important that this is called before importing any other module _start_jpype() -"""Whenever capymoa is imported, start jpype. -""" diff --git a/src/capymoa/_utils.py b/src/capymoa/_utils.py index 0a5f9609..e663904b 100644 --- a/src/capymoa/_utils.py +++ b/src/capymoa/_utils.py @@ -37,7 +37,7 @@ def _get_moa_creation_CLI(moa_object): >>> from moa.streams import ConceptDriftStream ... - >>> stream = ConceptDriftStream() + >>> stream = ConceptDriftStream() >>> _get_moa_creation_CLI(stream) 'streams.ConceptDriftStream' """ diff --git a/src/capymoa/learner/learners.py b/src/capymoa/base.py similarity index 97% rename from src/capymoa/learner/learners.py rename to src/capymoa/base.py index 8de20f19..358cbd96 100644 --- a/src/capymoa/learner/learners.py +++ b/src/capymoa/base.py @@ -5,9 +5,8 @@ from moa.classifiers import Classifier as MOA_Classifier_Interface from moa.core import Utils -from capymoa.stream.instance import (Instance, LabeledInstance, - RegressionInstance) -from capymoa.stream.stream import Schema +from capymoa.instance import Instance, LabeledInstance, RegressionInstance +from capymoa.stream._stream import Schema from capymoa.type_alias import LabelIndex, LabelProbabilities, TargetValue ############################################################## @@ -154,7 +153,9 @@ def train(self, instance): self.moa_learner.trainOnInstance(instance.java_instance) def predict(self, instance): - return Utils.maxIndex(self.moa_learner.getVotesForInstance(instance.java_instance)) + return Utils.maxIndex( + self.moa_learner.getVotesForInstance(instance.java_instance) + ) def predict_proba(self, instance): return self.moa_learner.getVotesForInstance(instance.java_instance) diff --git a/src/capymoa/classifier/__init__.py b/src/capymoa/classifier/__init__.py new file mode 100644 index 00000000..3fd5388d --- /dev/null +++ b/src/capymoa/classifier/__init__.py @@ -0,0 +1,16 @@ +from ._adaptive_random_forest import AdaptiveRandomForest +from ._efdt import EFDT +from ._hoeffding_tree import HoeffdingTree +from ._naive_bayes import NaiveBayes +from ._online_bagging import OnlineBagging +from ._passive_aggressive_classifier import PassiveAggressiveClassifier + +__all__ = [ + "AdaptiveRandomForest", + "AdaptiveRandomForest", + "EFDT", + "HoeffdingTree", + "NaiveBayes", + "OnlineBagging", + "PassiveAggressiveClassifier", +] diff --git a/src/capymoa/learner/classifier/classifiers.py b/src/capymoa/classifier/_adaptive_random_forest.py similarity index 71% rename from src/capymoa/learner/classifier/classifiers.py rename to src/capymoa/classifier/_adaptive_random_forest.py index 8fe9cf95..50ed0549 100644 --- a/src/capymoa/learner/classifier/classifiers.py +++ b/src/capymoa/classifier/_adaptive_random_forest.py @@ -1,18 +1,9 @@ -# Library imports -from capymoa.learner.learners import ( +from capymoa.base import ( MOAClassifier, - MOARegressor, - _get_moa_creation_CLI, _extract_moa_learner_CLI, ) -# MOA/Java imports -from moa.classifiers import Classifier -from moa.classifiers.meta import AdaptiveRandomForest as MOA_AdaptiveRandomForest -from moa.classifiers.meta import OzaBag as MOA_OzaBag -from moa.classifiers.meta import ( - AdaptiveRandomForestRegressor as MOA_AdaptiveRandomForestRegressor, -) +from moa.classifiers.meta import AdaptiveRandomForest as _MOA_AdaptiveRandomForest # TODO: replace the m_features_mode logic such that we can infer from m_features_per_tree_size, e.g. if value is double between 0.0 and 1.0 = percentage @@ -85,29 +76,5 @@ def __init__( schema=schema, CLI=CLI, random_seed=random_seed, - moa_learner=MOA_AdaptiveRandomForest(), - ) - - -class OnlineBagging(MOAClassifier): - def __init__( - self, schema=None, CLI=None, random_seed=1, base_learner=None, ensemble_size=100 - ): - # This method basically configures the CLI, object creation is delegated to MOAClassifier (the super class, through super().__init___())) - # Initialize instance attributes with default values, if the CLI was not set. - if CLI is None: - self.base_learner = ( - "trees.HoeffdingTree" - if base_learner is None - else _extract_moa_learner_CLI(base_learner) - ) - self.ensemble_size = ensemble_size - CLI = f"-l {self.base_learner} -s {self.ensemble_size}" - - super().__init__( - schema=schema, CLI=CLI, random_seed=random_seed, moa_learner=MOA_OzaBag() + moa_learner=_MOA_AdaptiveRandomForest(), ) - - def __str__(self): - # Overrides the default class name from MOA (OzaBag) - return "OnlineBagging" diff --git a/src/capymoa/learner/classifier/efdt.py b/src/capymoa/classifier/_efdt.py similarity index 97% rename from src/capymoa/learner/classifier/efdt.py rename to src/capymoa/classifier/_efdt.py index 28eb13b4..ba399790 100644 --- a/src/capymoa/learner/classifier/efdt.py +++ b/src/capymoa/classifier/_efdt.py @@ -1,8 +1,8 @@ from __future__ import annotations from typing import Union -from capymoa.learner import MOAClassifier -from capymoa.learner.splitcriteria import SplitCriterion, _split_criterion_to_cli_str +from capymoa.base import MOAClassifier +from capymoa.splitcriteria import SplitCriterion, _split_criterion_to_cli_str from capymoa.stream import Schema from capymoa._utils import build_cli_str_from_mapping_and_locals diff --git a/src/capymoa/learner/classifier/hoeffding_tree.py b/src/capymoa/classifier/_hoeffding_tree.py similarity index 96% rename from src/capymoa/learner/classifier/hoeffding_tree.py rename to src/capymoa/classifier/_hoeffding_tree.py index 3e7656f8..0fc0fe5b 100644 --- a/src/capymoa/learner/classifier/hoeffding_tree.py +++ b/src/capymoa/classifier/_hoeffding_tree.py @@ -1,8 +1,8 @@ from __future__ import annotations from typing import Union -from capymoa.learner import MOAClassifier -from capymoa.learner.splitcriteria import SplitCriterion, _split_criterion_to_cli_str +from capymoa.base import MOAClassifier +from capymoa.splitcriteria import SplitCriterion, _split_criterion_to_cli_str from capymoa.stream import Schema from capymoa._utils import build_cli_str_from_mapping_and_locals diff --git a/src/capymoa/learner/classifier/naive_bayes.py b/src/capymoa/classifier/_naive_bayes.py similarity index 74% rename from src/capymoa/learner/classifier/naive_bayes.py rename to src/capymoa/classifier/_naive_bayes.py index 4320a4a0..f18bf68f 100644 --- a/src/capymoa/learner/classifier/naive_bayes.py +++ b/src/capymoa/classifier/_naive_bayes.py @@ -1,7 +1,7 @@ from __future__ import annotations import typing -from capymoa.learner import MOAClassifier +from capymoa.base import MOAClassifier from capymoa.stream import Schema import moa.classifiers.bayes as moa_bayes @@ -12,18 +12,14 @@ class NaiveBayes(MOAClassifier): Performs classic Bayesian prediction while making the naive assumption that all inputs are independent. Naive Bayes is a classifier algorithm known for its simplicity and low computational cost. Given n different classes, the trained Naive Bayes classifier predicts, for every unlabeled instance I, the class C to which it belongs with high accuracy. :param schema: The schema of the stream, defaults to None. - :type schema: object, optional :param random_seed: The random seed passed to the MOA learner, defaults to 0. - :type random_seed: int, optional """ - def __init__(self, schema: typing.Union[Schema, None] = None, random_seed: int = 0): - super(NaiveBayes, self).__init__(moa_learner=moa_bayes.NaiveBayes(), - schema=schema, - random_seed=random_seed) + super(NaiveBayes, self).__init__( + moa_learner=moa_bayes.NaiveBayes(), schema=schema, random_seed=random_seed + ) def __str__(self): # Overrides the default class name from MOA (OzaBag) return "Naive Bayes CapyMOA Classifier" - diff --git a/src/capymoa/classifier/_online_bagging.py b/src/capymoa/classifier/_online_bagging.py new file mode 100644 index 00000000..f27c1b34 --- /dev/null +++ b/src/capymoa/classifier/_online_bagging.py @@ -0,0 +1,29 @@ +from capymoa.base import ( + MOAClassifier, + _extract_moa_learner_CLI, +) + +from moa.classifiers.meta import OzaBag as _MOA_OzaBag + +class OnlineBagging(MOAClassifier): + def __init__( + self, schema=None, CLI=None, random_seed=1, base_learner=None, ensemble_size=100 + ): + # This method basically configures the CLI, object creation is delegated to MOAClassifier (the super class, through super().__init___())) + # Initialize instance attributes with default values, if the CLI was not set. + if CLI is None: + self.base_learner = ( + "trees.HoeffdingTree" + if base_learner is None + else _extract_moa_learner_CLI(base_learner) + ) + self.ensemble_size = ensemble_size + CLI = f"-l {self.base_learner} -s {self.ensemble_size}" + + super().__init__( + schema=schema, CLI=CLI, random_seed=random_seed, moa_learner=_MOA_OzaBag() + ) + + def __str__(self): + # Overrides the default class name from MOA (OzaBag) + return "OnlineBagging" diff --git a/src/capymoa/learner/classifier/sklearn.py b/src/capymoa/classifier/_passive_aggressive_classifier.py similarity index 95% rename from src/capymoa/learner/classifier/sklearn.py rename to src/capymoa/classifier/_passive_aggressive_classifier.py index 07acb964..30826e63 100644 --- a/src/capymoa/learner/classifier/sklearn.py +++ b/src/capymoa/classifier/_passive_aggressive_classifier.py @@ -1,10 +1,10 @@ from typing import Optional, Dict, Union, Literal -from capymoa.learner.learners import Classifier +from capymoa.base import Classifier from sklearn.linear_model import ( PassiveAggressiveClassifier as skPassiveAggressiveClassifier, ) -from capymoa.stream.instance import Instance, LabeledInstance -from capymoa.stream.stream import Schema +from capymoa.instance import Instance, LabeledInstance +from capymoa.stream._stream import Schema from capymoa.type_alias import LabelIndex, LabelProbabilities import numpy as np @@ -21,7 +21,7 @@ class PassiveAggressiveClassifier(Classifier): `_ >>> from capymoa.datasets import ElectricityTiny - >>> from capymoa.learner.classifier import PassiveAggressiveClassifier + >>> from capymoa.classifier import PassiveAggressiveClassifier >>> from capymoa.evaluation import prequential_evaluation >>> stream = ElectricityTiny() >>> schema = stream.get_schema() diff --git a/src/capymoa/datasets/__init__.py b/src/capymoa/datasets/__init__.py index c188cb33..cc1ee4cc 100644 --- a/src/capymoa/datasets/__init__.py +++ b/src/capymoa/datasets/__init__.py @@ -1,4 +1,4 @@ -from .datasets import ( +from ._datasets import ( CovtFD, Covtype, RBFm_100k, @@ -6,11 +6,9 @@ Hyper100k, Sensor, ElectricityTiny, - Fried -) -from .downloader import ( - get_download_dir + Fried, ) +from .downloader import get_download_dir __all__ = [ "Hyper100k", @@ -21,5 +19,5 @@ "Sensor", "ElectricityTiny", "Fried", - "get_download_dir" + "get_download_dir", ] diff --git a/src/capymoa/datasets/datasets.py b/src/capymoa/datasets/_datasets.py similarity index 99% rename from src/capymoa/datasets/datasets.py rename to src/capymoa/datasets/_datasets.py index 1d7d90e8..e006ca4e 100644 --- a/src/capymoa/datasets/datasets.py +++ b/src/capymoa/datasets/_datasets.py @@ -51,6 +51,7 @@ class ElectricityTiny(DownloadARFFGzip): filename = "electricity_tiny.arff" remote_url = ROOT_URL + class CovtypeTiny(DownloadARFFGzip): """A truncated version of the Covtype dataset with 1000 instances.""" diff --git a/src/capymoa/datasets/downloader.py b/src/capymoa/datasets/downloader.py index 83e5d94b..07c2d8e9 100644 --- a/src/capymoa/datasets/downloader.py +++ b/src/capymoa/datasets/downloader.py @@ -5,12 +5,12 @@ from pathlib import Path from tempfile import TemporaryDirectory from typing import Any, Optional -import shutil import wget from moa.streams import ArffFileStream -from capymoa.stream.stream import Stream +from capymoa.stream._stream import Stream + def get_download_dir(): """A default directory to store datasets in. Defaults to `./data` when the @@ -18,6 +18,7 @@ def get_download_dir(): """ return environ.get("CAPYMOA_DATASETS_DIR", "data") + class DownloadableDataset(ABC, Stream): filename: str = None """Name of the dataset in the capymoa dataset directory""" @@ -54,7 +55,7 @@ def _resolve_dataset(self, auto_download: bool, directory: Path): ) return stream - + def get_path(self): return self._path diff --git a/src/capymoa/evaluation/__init__.py b/src/capymoa/evaluation/__init__.py index 2cbfe737..d7a8a120 100644 --- a/src/capymoa/evaluation/__init__.py +++ b/src/capymoa/evaluation/__init__.py @@ -2,24 +2,20 @@ test_then_train_evaluation, prequential_evaluation, windowed_evaluation, - test_then_train_evaluation_fast, prequential_evaluation_multiple_learners, - prequential_evaluation_fast, - prequential_SSL_evaluation, + prequential_ssl_evaluation, ClassificationEvaluator, ClassificationWindowedEvaluator, RegressionWindowedEvaluator, RegressionEvaluator, ) -__ALL__ = [ +__all__ = [ "prequential_evaluation", - "prequential_SSL_evaluation", + "prequential_ssl_evaluation", "test_then_train_evaluation", "windowed_evaluation", - "test_then_train_evaluation_fast", "prequential_evaluation_multiple_learners", - "prequential_evaluation_fast", "ClassificationEvaluator", "ClassificationWindowedEvaluator", "RegressionWindowedEvaluator", diff --git a/src/capymoa/evaluation/evaluation.py b/src/capymoa/evaluation/evaluation.py index 67157218..58475e61 100644 --- a/src/capymoa/evaluation/evaluation.py +++ b/src/capymoa/evaluation/evaluation.py @@ -2,10 +2,11 @@ import pandas as pd import numpy as np import time +import warnings import random -from capymoa.stream.stream import Schema, Stream -from capymoa.learner.learners import ClassifierSSL +from capymoa.stream import Schema, Stream +from capymoa.base import ClassifierSSL from com.yahoo.labs.samoa.instances import Instances, Attribute, DenseInstance from moa.core import InstanceExample @@ -28,10 +29,14 @@ def _is_fast_mode_compilable(stream: Stream, learner, optimise=True) -> bool: is_moa_learner = hasattr(learner, "moa_learner") and learner.moa_learner is not None return is_moa_stream and is_moa_learner and optimise +# class Evaluator: +# + class ClassificationEvaluator: """ - Wrapper for the Classification Performance Evaluator from MOA. By default uses the BasicClassificationPerformanceEvaluator + Wrapper for the Classification Performance Evaluator from MOA. By default, it uses the + BasicClassificationPerformanceEvaluator """ def __init__( @@ -39,10 +44,6 @@ def __init__( schema: Schema = None, window_size=None, allow_abstaining=True, - recall_per_class=False, - precision_per_class=False, - f1_precision_recall=False, - f1_per_class=False, moa_evaluator=None, ): self.instances_seen = 0 @@ -89,15 +90,7 @@ def __init__( self._instance.setDataset(self._header) def __str__(self): - return str( - { - header: value - for header, value in zip(self.metrics_header(), self.metrics()) - } - ) - - # # def metrics_with_header(self): - # return {header: value for header, value in zip(self.metrics_header(), self.metrics())} + return str(self.metrics_dict()) def get_instances_seen(self): return self.instances_seen @@ -113,19 +106,26 @@ def update(self, y_target_index: int, y_pred_index: Optional[int]): :raises ValueError: If the values are not valid indexes in the schema. """ if not isinstance(y_target_index, (np.integer, int)): - raise ValueError(f"y_target_index must be an integer, not {type(y_target_index)}") + raise ValueError( + f"y_target_index must be an integer, not {type(y_target_index)}" + ) if not (y_pred_index is None or isinstance(y_pred_index, (np.integer, int))): - raise ValueError(f"y_pred_index must be an integer, not {type(y_pred_index)}") + raise ValueError( + f"y_pred_index must be an integer, not {type(y_pred_index)}" + ) - # If the prediction is invalid, it could mean the classifier is abstaining from making a prediction; + # If the prediction is invalid, it could mean the classifier is abstaining from making a prediction; # thus, it is allowed to continue (unless parameterized differently). - if y_pred_index is not None and not self.schema.is_y_index_in_range(y_pred_index): + if y_pred_index is not None and not self.schema.is_y_index_in_range( + y_pred_index + ): if self.allow_abstaining: y_pred_index = None else: raise ValueError(f"Invalid prediction y_pred_index = {y_pred_index}") - # Notice, in MOA the class value is an index, not the actual value (e.g. not "one" but 0 assuming labels=["one", "two"]) + # Notice, in MOA the class value is an index, not the actual value + # (e.g. not "one" but 0 assuming labels=["one", "two"]) self._instance.setClassValue(y_target_index) example = InstanceExample(self._instance) @@ -134,14 +134,16 @@ def update(self, y_target_index: int, y_pred_index: Optional[int]): # (may or may not be normalised, but for our purposes it doesn't matter) prediction_array = self.pred_template[:] - # if y_pred is None, it indicates the learner did not produce a prediction for this instace, count as an error + # if y_pred is None, it indicates the learner did not produce a prediction for this instance, + # count as an error if y_pred_index is None: - # TODO: I'm not sure what the actual logic should be here, but for + # TODO: I'm not sure what the actual logic should be here, but for # now I'm just setting the prediction to the first class since this # does not break the tests. y_pred_index = 0 # Set y_pred_index to any valid prediction that is not y (force an incorrect prediction) - # This does not affect recall or any other metrics, because the selected value is always incorrect. + # This does not affect recall or any other metrics, because the selected value is always + # incorrect. # Create an intermediary array with indices excluding the y # indexesWithoutY = [ @@ -157,9 +159,7 @@ def update(self, y_target_index: int, y_pred_index: Optional[int]): # If the window_size is set, then check if it should record the intermediary results. if self.window_size is not None and self.instances_seen % self.window_size == 0: - performance_values = ( - self.metrics() - ) + performance_values = self.metrics() self.result_windows.append(performance_values) def metrics_header(self): @@ -174,9 +174,12 @@ def metrics(self): measurement.getValue() for measurement in self.moa_basic_evaluator.getPerformanceMeasurements() ] - + def metrics_dict(self): - return {header: value for header, value in zip(self.metrics_header(), self.metrics())} + return { + header: value + for header, value in zip(self.metrics_header(), self.metrics()) + } def metrics_per_window(self): return pd.DataFrame(self.result_windows, columns=self.metrics_header()) @@ -198,38 +201,10 @@ def kappa_M(self): return self.metrics()[index] -class ClassificationWindowedEvaluator(ClassificationEvaluator): - """ - The results for the last window are always through ```metrics()```, if the window_size does not perfectly divides the stream, i.e. - there are remaining instances are the last window, then we can obtain the results for this last window by invoking ```metrics()``` - """ - - def __init__( - self, - schema=None, - window_size=1000, - recall_per_class=False, - precision_per_class=False, - f1_precision_recall=False, - f1_per_class=False, - ): - self.moa_evaluator = WindowClassificationPerformanceEvaluator() - self.moa_evaluator.widthOption.setValue(window_size) - - super().__init__( - schema=schema, - window_size=window_size, - recall_per_class=recall_per_class, - precision_per_class=precision_per_class, - f1_precision_recall=f1_precision_recall, - f1_per_class=f1_per_class, - moa_evaluator=self.moa_evaluator, - ) - - class RegressionEvaluator: """ - Wrapper for the Regression Performance Evaluator from MOA. By default uses the BasicRegressionPerformanceEvaluator + Wrapper for the Regression Performance Evaluator from MOA. + By default, it uses the MOA BasicRegressionPerformanceEvaluator as moa_evaluator. """ def __init__(self, schema=None, window_size=None, moa_evaluator=None): @@ -241,8 +216,6 @@ def __init__(self, schema=None, window_size=None, moa_evaluator=None): if self.moa_basic_evaluator is None: self.moa_basic_evaluator = BasicRegressionPerformanceEvaluator() - # self.moa_basic_evaluator.prepareForUse() - _attributeValues = ArrayList() self.schema = schema @@ -257,7 +230,6 @@ def __init__(self, schema=None, window_size=None, moa_evaluator=None): attSub.append(_targetAttribute) self._header = Instances("", attSub, 1) self._header.setClassIndex(self.schema.get_num_attributes()) - # print(self._header) else: raise ValueError("Schema was not set for a regression task") else: @@ -271,12 +243,7 @@ def __init__(self, schema=None, window_size=None, moa_evaluator=None): self._instance.setDataset(self._header) def __str__(self): - return str( - { - header: value - for header, value in zip(self.metrics_header(), self.metrics()) - } - ) + return str(self.metrics_dict()) def get_instances_seen(self): return self.instances_seen @@ -288,18 +255,11 @@ def update(self, y, y_pred): self._instance.setClassValue(y) example = InstanceExample(self._instance) - # if y_pred is None, it indicates the learner did not produce a prediction for this instace + # The learner did not produce a prediction for this instance, thus y_pred is None if y_pred is None: - # In classification it is rather easy to deal with this, but + warnings.warn("The learner did not produce a prediction for this instance") - # Create an intermediary array with indices excluding the y - indexesWithoutY = [ - i for i in range(len(self.schema.get_label_indexes())) if i != y_index - ] - random_y_pred = random.choice(indexesWithoutY) - y_pred_index = self.schema.get_label_indexes()[random_y_pred] - - # Different from classification, there is no need to make a shallow copy of the prediction array, just override the value. + # Different from classification, there is no need to copy the prediction array, just override the value. self.pred_template[0] = y_pred self.moa_basic_evaluator.addResult(example, self.pred_template) @@ -326,9 +286,18 @@ def metrics(self): for measurement in self.moa_basic_evaluator.getPerformanceMeasurements() ] + def metrics_dict(self): + return {header: value for header, value in zip(self.metrics_header(), self.metrics())} + def metrics_per_window(self): return pd.DataFrame(self.result_windows, columns=self.metrics_header()) + def predictions(self): + return self.predictions + + def ground_truth_y(self): + return self.gt_y + def MAE(self): index = self.metrics_header().index("mean absolute error") return self.metrics()[index] @@ -350,10 +319,37 @@ def adjusted_R2(self): return self.metrics()[index] +class ClassificationWindowedEvaluator(ClassificationEvaluator): + """ + Uses the ClassificationEvaluator to perform a windowed evaluation. + + IMPORTANT: The results for the last window are always through ```metrics()```, if the window_size does not + perfectly divide the stream, the metrics corresponding to the last remaining instances in the last window can + be obtained by invoking ```metrics()``` + """ + + def __init__( + self, + schema=None, + window_size=1000 + ): + self.moa_evaluator = WindowClassificationPerformanceEvaluator() + self.moa_evaluator.widthOption.setValue(window_size) + + super().__init__( + schema=schema, + window_size=window_size, + moa_evaluator=self.moa_evaluator, + ) + + class RegressionWindowedEvaluator(RegressionEvaluator): """ - The results for the last window are always through ```metrics()```, if the window_size does not perfectly divides the stream, i.e. - there are remaining instances are the last window, then we can obtain the results for this last window by invoking ```metrics()``` + Uses the RegressionEvaluator to perform a windowed evaluation. + + IMPORTANT: The results for the last window are always through ```metrics()```, if the window_size does not + perfectly divide the stream, the metrics corresponding to the last remaining instances in the last window can + be obtained by invoking ```metrics()``` """ def __init__(self, schema=None, window_size=1000): @@ -398,7 +394,8 @@ def test_then_train_evaluation( """ if _is_fast_mode_compilable(stream, learner, optimise): - return test_then_train_evaluation_fast( + return _test_then_train_evaluation_fast( + stream, learner, max_instances, sample_frequency, evaluator ) @@ -407,7 +404,7 @@ def test_then_train_evaluation( instancesProcessed = 1 - if stream.has_more_instances() == False: + if not stream.has_more_instances(): stream.restart() if evaluator is None: @@ -425,7 +422,6 @@ def test_then_train_evaluation( instance = stream.next_instance() prediction = learner.predict(instance) - # TODO: The multiple if statements based on the type of stream is ugly. if stream.get_schema().is_classification(): y = instance.y_index else: @@ -446,8 +442,8 @@ def test_then_train_evaluation( "cumulative": evaluator, "wallclock": elapsed_wallclock_time, "cpu_time": elapsed_cpu_time, - "max_instances":max_instances, - "stream":stream, + "max_instances": max_instances, + "stream": stream, } return results @@ -455,9 +451,10 @@ def test_then_train_evaluation( def windowed_evaluation(stream, learner, max_instances=None, window_size=1000): """ - Prequential evaluation (window). Returns a dictionary with the results. + Windowed evaluation. Returns a dictionary with the results. """ - # Run test-then-train evaluation, but change the underlying evaluator + # Run test-then-train evaluation, but change the underlying MOA evaluator. + # This is a workaround to avoid redundant code. evaluator = None if stream.get_schema().is_classification(): evaluator = ClassificationWindowedEvaluator( @@ -476,8 +473,9 @@ def windowed_evaluation(stream, learner, max_instances=None, window_size=1000): ) results["windowed"] = results["cumulative"] - # Add the results corresponding to the remainder of the stream in case the number of processed instances is not perfectly divisible by - # the window_size (if it was, then it is already be in the result_windows variable). + # Add the results corresponding to the remainder of the stream in case the number of + # processed instances is not perfectly divisible by the window_size (if it was, then + # it is already in the result_windows variable). if evaluator.get_instances_seen() % window_size != 0: results["windowed"].result_windows.append(results["windowed"].metrics()) @@ -490,14 +488,23 @@ def windowed_evaluation(stream, learner, max_instances=None, window_size=1000): def prequential_evaluation( - stream, learner, max_instances=None, window_size=1000, optimise=True + stream, learner, max_instances=None, window_size=1000, optimise=True, store_predictions=False, store_y=False ): """ Calculates the metrics cumulatively (i.e. test-then-train) and in a window-fashion (i.e. windowed prequential evaluation). Returns both evaluators so that the caller has access to metric from both evaluators. """ + if _is_fast_mode_compilable(stream, learner, optimise): - return prequential_evaluation_fast(stream, learner, max_instances, window_size) + return _prequential_evaluation_fast(stream, learner, max_instances, window_size) + + predictions = None + if store_predictions: + predictions = [] + + ground_truth_y = None + if store_y: + ground_truth_y = [] # Start measuring time start_wallclock_time, start_cpu_time = start_time_measuring() @@ -532,8 +539,6 @@ def prequential_evaluation( prediction = learner.predict(instance) - # TODO: The multiple if statements based on the type of stream is not - # ideal. if stream.get_schema().is_classification(): y = instance.y_index else: @@ -544,6 +549,14 @@ def prequential_evaluation( evaluator_windowed.update(y, prediction) learner.train(instance) + # Storing predictions if store_predictions was set to True during initialisation + if predictions is not None: + predictions.append(prediction) + + # Storing ground-truth if store_y was set to True during initialisation + if ground_truth_y is not None: + ground_truth_y.append(y) + instancesProcessed += 1 # Stop measuring time @@ -551,9 +564,9 @@ def prequential_evaluation( start_wallclock_time, start_cpu_time ) - # Add the results corresponding to the remainder of the stream in case the number of processed instances is not perfectly divisible by - # the window_size (if it was, then it is already be in the result_windows variable). - # The evaluator_windowed will be None if the window_size is None (it will not be created) + # Add the results corresponding to the remainder of the stream in case the number of processed + # instances is not perfectly divisible by the window_size (if it was, then it is already be in + # the result_windows variable). The evaluator_windowed will be None if the window_size is None. if ( evaluator_windowed is not None and evaluator_windowed.get_instances_seen() % window_size != 0 @@ -566,8 +579,10 @@ def prequential_evaluation( "windowed": evaluator_windowed, "wallclock": elapsed_wallclock_time, "cpu_time": elapsed_cpu_time, - "max_instances":max_instances, - "stream":stream, + "max_instances": max_instances, + "stream": stream, + "predictions": predictions, + "ground_truth_y": ground_truth_y } return results @@ -589,7 +604,7 @@ def test_then_train_SSL_evaluation( Test-then-train SSL evaluation. Returns a dictionary with the results. """ if _is_fast_mode_compilable(stream, learner, optimise): - return test_then_train_SSL_evaluation_fast( + return _test_then_train_ssl_evaluation_fast( stream, learner, max_instances, @@ -604,7 +619,7 @@ def test_then_train_SSL_evaluation( raise ValueError("test_then_train_SSL_evaluation(...) not fully implemented yet!") -def prequential_SSL_evaluation( +def prequential_ssl_evaluation( stream, learner, max_instances=None, @@ -619,10 +634,10 @@ def prequential_SSL_evaluation( If the learner is not a SSL learner, then it will just train on labeled instances. """ if _is_fast_mode_compilable(stream, learner, optimise): - return prequential_evaluation_fast(stream, learner, max_instances, window_size) + return _prequential_ssl_evaluation_fast(stream, learner, max_instances, window_size) # IMPORTANT: delay_length and initial_window_size have not been implemented in python yet - # In MOA it is implemented so prequential_SSL_evaluation_fast works just fine. + # In MOA it is implemented so _prequential_ssl_evaluation_fast works just fine. if initial_window_size != 0: raise ValueError( "Initial window size must be 0 for this function as the feature is not implemented yet." @@ -705,8 +720,8 @@ def prequential_SSL_evaluation( "windowed": evaluator_windowed, "wallclock": elapsed_wallclock_time, "cpu_time": elapsed_cpu_time, - "max_instances":max_instances, - "stream":stream, + "max_instances": max_instances, + "stream": stream, "unlabeled": unlabeled_counter, "unlabeled_ratio": unlabeled_counter / instancesProcessed, } @@ -719,7 +734,7 @@ def prequential_SSL_evaluation( ############################################################## -def test_then_train_evaluation_fast( +def _test_then_train_evaluation_fast( stream, learner, max_instances=None, sample_frequency=None, evaluator=None ): """ @@ -751,9 +766,9 @@ def test_then_train_evaluation_fast( sample_frequency, ) # Reset the windowed_evaluator result_windows - if moa_results != None: + if moa_results is not None: evaluator.result_windows = [] - if moa_results.windowedResults != None: + if moa_results.windowedResults is not None: for entry_idx in range(len(moa_results.windowedResults)): evaluator.result_windows.append( moa_results.windowedResults[entry_idx] @@ -780,14 +795,14 @@ def test_then_train_evaluation_fast( "cumulative": evaluator, "wallclock": elapsed_wallclock_time, "cpu_time": elapsed_cpu_time, - "max_instances":max_instances, - "stream":stream, + "max_instances": max_instances, + "stream": stream, } return results -def prequential_evaluation_fast(stream, learner, max_instances=None, window_size=1000): +def _prequential_evaluation_fast(stream, learner, max_instances=None, window_size=1000): """ Prequential evaluation fast. """ @@ -847,14 +862,14 @@ def prequential_evaluation_fast(stream, learner, max_instances=None, window_size "windowed": windowed_evaluator, "wallclock": elapsed_wallclock_time, "cpu_time": elapsed_cpu_time, - "max_instances":max_instances, - "stream":stream, + "max_instances": max_instances, + "stream": stream, } return results -def test_then_train_SSL_evaluation_fast( +def _test_then_train_ssl_evaluation_fast( stream, learner, max_instances=None, @@ -871,7 +886,7 @@ def test_then_train_SSL_evaluation_fast( if not _is_fast_mode_compilable(stream, learner): raise ValueError( - "`test_then_train_SSL_evaluation_fast` requires the stream object to have a`Stream.moa_stream`" + "`_test_then_train_ssl_evaluation_fast` requires the stream object to have a`Stream.moa_stream`" ) if max_instances is None: @@ -934,8 +949,8 @@ def test_then_train_SSL_evaluation_fast( "cumulative": evaluator, "wallclock": elapsed_wallclock_time, "cpu_time": elapsed_cpu_time, - "max_instances":max_instances, - "stream":stream, + "max_instances": max_instances, + "stream": stream, } for measure in moa_results.otherMeasurements.keySet(): @@ -947,7 +962,7 @@ def test_then_train_SSL_evaluation_fast( return results -def prequential_SSL_evaluation_fast( +def _prequential_ssl_evaluation_fast( stream, learner, max_instances=None, @@ -972,7 +987,8 @@ def prequential_SSL_evaluation_fast( start_wallclock_time, start_cpu_time = start_time_measuring() basic_evaluator = ClassificationEvaluator(schema=stream.get_schema()) - # Always create the windowed_evaluator, even if window_size is None. TODO: may want to avoid creating it if window_size is None. + # Always create the windowed_evaluator, even if window_size is None. + # TODO: may want to avoid creating it if window_size is None. windowed_evaluator = ClassificationWindowedEvaluator( schema=stream.get_schema(), window_size=window_size ) @@ -992,9 +1008,9 @@ def prequential_SSL_evaluation_fast( ) # Reset the windowed_evaluator result_windows - if moa_results != None: + if moa_results is not None: windowed_evaluator.result_windows = [] - if moa_results.windowedResults != None: + if moa_results.windowedResults is not None: for entry_idx in range(len(moa_results.windowedResults)): windowed_evaluator.result_windows.append( moa_results.windowedResults[entry_idx] @@ -1011,8 +1027,8 @@ def prequential_SSL_evaluation_fast( "windowed": windowed_evaluator, "wallclock": elapsed_wallclock_time, "cpu_time": elapsed_cpu_time, - "max_instances":max_instances, - "stream":stream, + "max_instances": max_instances, + "stream": stream, "other_measurements": dict(moa_results.otherMeasurements), } @@ -1024,17 +1040,21 @@ def prequential_SSL_evaluation_fast( ######################################################################################## -# TODO: review if we want to keep this method. def prequential_evaluation_multiple_learners( stream, learners, max_instances=None, window_size=1000 ): """ - Calculates the metrics cumulatively (i.e., test-then-train) and in a window-fashion (i.e., windowed prequential evaluation) for multiple streams and learners. - Returns the results in a dictionary format. Infers whether it is a Classification or Regression problem based on the stream schema. + Calculates the metrics cumulatively (i.e., test-then-train) and in a windowed-fashion for multiple streams and + learners. It behaves as if we invoked prequential_evaluation() multiple times, but we only iterate through the + stream once. + This function is useful in situations where iterating through the stream is costly, but we still want to assess + several learners on it. + Returns the results in a dictionary format. Infers whether it is a Classification or Regression problem based on the + stream schema. """ results = {} - if stream.has_more_instances() == False: + if not stream.has_more_instances(): stream.restart() for learner_name, learner in learners.items(): @@ -1057,6 +1077,7 @@ def prequential_evaluation_multiple_learners( results[learner_name]["windowed"] = RegressionWindowedEvaluator( schema=stream.get_schema(), window_size=window_size ) + results[learner_name]['learner'] = learner_name instancesProcessed = 1 while stream.has_more_instances() and ( @@ -1074,7 +1095,6 @@ def prequential_evaluation_multiple_learners( else: y = instance.y_value - results[learner_name]["cumulative"].update(y, prediction) if window_size is not None: results[learner_name]["windowed"].update(y, prediction) diff --git a/src/capymoa/evaluation/visualization.py b/src/capymoa/evaluation/visualization.py index a045ed1c..a49b849c 100644 --- a/src/capymoa/evaluation/visualization.py +++ b/src/capymoa/evaluation/visualization.py @@ -1,50 +1,60 @@ import matplotlib.pyplot as plt -import os +from datetime import datetime from capymoa.stream.drift import DriftStream +from com.yahoo.labs.samoa.instances import InstancesHeader -def plot_windowed_results(*results, metric="classifications correct (percent)", - plot_title=None, xlabel=None, ylabel=None, - figure_path="./", figure_name=None, save_only=True - # , - # drift_locations=None, gradual_drift_window_lengths=None - ): + +def plot_windowed_results( + *results, + metric="classifications correct (percent)", + plot_title=None, + xlabel=None, + ylabel=None, + figure_path="./", + figure_name=None, + save_only=True, + # , + # drift_locations=None, gradual_drift_window_lengths=None +): """ Plot a comparison of values from multiple evaluators based on a selected column using line plots. It assumes the results contain windowed results ('windowed') which often originate from metrics_per_window() and the learner identification ('learner'). - + If figure_path is provided, the figure will be saved at the specified path instead of displaying it. """ dfs = [] labels = [] - - num_instances = results[0].get('max_instances', None) - stream = results[0].get('stream', None) + + num_instances = results[0].get("max_instances", None) + stream = results[0].get("stream", None) if num_instances is not None: - window_size = results[0]['windowed'].window_size - num_windows = results[0]['windowed'].metrics_per_window().shape[0] + window_size = results[0]["windowed"].window_size + num_windows = results[0]["windowed"].metrics_per_window().shape[0] x_values = [] - for i in range(1, num_windows+1): + for i in range(1, num_windows + 1): x_values.append(i * window_size) # print(f'x_values: {x_values}') # Check if the given metric exists in all DataFrames for result in results: - df = result['windowed'].metrics_per_window() + df = result["windowed"].metrics_per_window() if metric not in df.columns: - print(f"Column '{metric}' not found in metrics DataFrame for {result['learner']}. Skipping.") + print( + f"Column '{metric}' not found in metrics DataFrame for {result['learner']}. Skipping." + ) else: dfs.append(df) - if 'experiment_id' in result: - labels.append(result['experiment_id']) + if "experiment_id" in result: + labels.append(result["experiment_id"]) else: - labels.append(result['learner']) - + labels.append(result["learner"]) + if not dfs: print("No valid DataFrames to plot.") return - + # Create a figure plt.figure(figsize=(12, 5)) @@ -52,10 +62,23 @@ def plot_windowed_results(*results, metric="classifications correct (percent)", for i, df in enumerate(dfs): # print(f'df.index: {df.index}') if num_instances is not None: - plt.plot(x_values, df[metric], label=labels[i], marker='o', linestyle='-', markersize=5) + plt.plot( + x_values, + df[metric], + label=labels[i], + marker="o", + linestyle="-", + markersize=5, + ) else: - plt.plot(df.index, df[metric], label=labels[i], marker='o', linestyle='-', markersize=5) - + plt.plot( + df.index, + df[metric], + label=labels[i], + marker="o", + linestyle="-", + markersize=5, + ) if stream is not None and isinstance(stream, DriftStream): drifts = stream.get_drifts() @@ -66,41 +89,119 @@ def plot_windowed_results(*results, metric="classifications correct (percent)", # Add vertical lines at drift locations if drift_locations: for location in drift_locations: - plt.axvline(location, color='red', linestyle='-') - + plt.axvline(location, color="red", linestyle="-") + # Add gradual drift windows as 70% transparent rectangles if gradual_drift_window_lengths: if not drift_locations: - print("Error: gradual_drift_window_lengths is provided, but drift_locations is not.") + print( + "Error: gradual_drift_window_lengths is provided, but drift_locations is not." + ) return - + if len(drift_locations) != len(gradual_drift_window_lengths): - print("Error: drift_locations and gradual_drift_window_lengths must have the same length.") + print( + "Error: drift_locations and gradual_drift_window_lengths must have the same length." + ) return - + for i in range(len(drift_locations)): location = drift_locations[i] window_length = gradual_drift_window_lengths[i] - + # Plot the 70% transparent rectangle - plt.axvspan(location - window_length / 2, location + window_length / 2, alpha=0.2, color='red') - + plt.axvspan( + location - window_length / 2, + location + window_length / 2, + alpha=0.2, + color="red", + ) + # Add labels and title - xlabel = xlabel if xlabel is not None else '# Instances' + xlabel = xlabel if xlabel is not None else "# Instances" plt.xlabel(xlabel) ylabel = ylabel if ylabel is not None else metric plt.ylabel(ylabel) plot_title = plot_title if plot_title is not None else metric plt.title(plot_title) - + # Add legend plt.legend() plt.grid(True) - + # Show the plot or save it to the specified path if save_only == False: plt.show() elif figure_path is not None: if figure_name is None: - figure_name = result['learner'] + "_" + ylabel.replace(' ', '') + figure_name = result["learner"] + "_" + ylabel.replace(" ", "") plt.savefig(figure_path + figure_name) + + +# TODO: Update this function so that it works properly with DriftStreams +# TODO: Once Schema is updated to provide an easier access to the target name should remove direct access to MOA +def plot_predictions_vs_ground_truth(*results, ground_truth=None, plot_interval=None, plot_title=None, + xlabel=None, ylabel=None, figure_path="./", figure_name=None, save_only=False + ): + """ + Plot predictions vs. ground truth for multiple results. + + If ground_truth is None, then the code should check if "ground_truth_y" is not None in the first result, + i.e. results[0]["ground_truth_y"], and use it instead. If ground_truth is None and there is no data in + results[0]["ground_truth_y"] (also None) then it raises an error stating that the ground truth y is None. + + The plot_interval parameter is a tuple (start, end) that determines when to start and stop plotting predictions. + + If save_only is True, then a figure will be saved at the specified path + """ + # Determine ground truth y + if ground_truth is None: + if results and "ground_truth_y" in results[0]: + ground_truth = results[0]["ground_truth_y"] + + # Check if ground truth y is available + if ground_truth is None: + raise ValueError("Ground truth y is None.") + + # Create a figure + plt.figure(figsize=(20, 6)) + + # Determine indices to plot based on plot_interval + start, end = plot_interval or (0, len(ground_truth)) + + # Check if predictions have the same length as ground truth + for i, result in enumerate(results): + if "predictions" in result: + predictions = result["predictions"][start:end] + if len(predictions) != len(ground_truth[start:end]): + raise ValueError(f"Length of predictions for result {i + 1} does not match ground truth.") + + # Plot ground truth y vs. predictions for each result within the specified interval + instance_numbers = list(range(start, end)) + for i, result in enumerate(results): + if "predictions" in result: + predictions = result["predictions"][start:end] + plt.plot(instance_numbers, predictions, label=f"{result['learner']} predictions", alpha=0.7) + + # Plot ground truth y + plt.scatter(instance_numbers, ground_truth[start:end], label="ground truth", marker='*', s=20, color='red') + + # TODO: Once Schema is updated to provide an easier access to the target name should remove direct access to MOA + output_name = str(InstancesHeader.getClassNameString(results[0]['stream'].get_schema().get_moa_header())) + output_name = output_name[output_name.find(":") + 1:-1] + + # Add labels and title + plt.xlabel(xlabel if xlabel else "# Instance") + plt.ylabel(ylabel if ylabel else output_name) + plt.title(plot_title if plot_title else "Predictions vs. Ground Truth") + plt.grid(True) + plt.legend() + + # Show the plot or save it to the specified path + if not save_only: + plt.show() + elif figure_path: + current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + figure_name = figure_name if figure_name else f"predictions_vs_ground_truth_{current_time}.pdf" + plt.savefig(figure_path + figure_name) + diff --git a/src/capymoa/stream/instance.py b/src/capymoa/instance.py similarity index 97% rename from src/capymoa/stream/instance.py rename to src/capymoa/instance.py index f33b8091..d582919d 100644 --- a/src/capymoa/stream/instance.py +++ b/src/capymoa/instance.py @@ -62,7 +62,7 @@ def from_array(cls, schema: "Schema", instance: FeatureVector) -> "Instance": >>> from capymoa.stream import Schema ... - >>> from capymoa.stream.instance import Instance + >>> from capymoa.instance import Instance >>> import numpy as np >>> schema = Schema.from_custom( ... ["f1", "f2"], @@ -146,7 +146,7 @@ class LabeledInstance(Instance): >>> from capymoa.datasets import ElectricityTiny ... - >>> from capymoa.stream.instance import LabeledInstance + >>> from capymoa.instance import LabeledInstance >>> stream = ElectricityTiny() >>> instance: LabeledInstance = stream.next_instance() >>> instance.y_label @@ -182,7 +182,7 @@ def from_array( >>> from capymoa.stream import Schema ... - >>> from capymoa.stream.instance import LabeledInstance + >>> from capymoa.instance import LabeledInstance >>> import numpy as np >>> schema = Schema.from_custom( ... ["f1", "f2"], @@ -253,7 +253,7 @@ class RegressionInstance(Instance): >>> from capymoa.datasets import Fried ... - >>> from capymoa.stream.instance import RegressionInstance + >>> from capymoa.instance import RegressionInstance >>> stream = Fried() >>> instance: RegressionInstance = stream.next_instance() >>> instance.y_value @@ -286,7 +286,7 @@ def from_array( >>> from capymoa.stream import Schema ... - >>> from capymoa.stream.instance import LabeledInstance + >>> from capymoa.instance import LabeledInstance >>> import numpy as np >>> schema = Schema.from_custom( ... ["f1", "f2"], diff --git a/src/capymoa/learner/__init__.py b/src/capymoa/learner/__init__.py deleted file mode 100644 index e8d2355e..00000000 --- a/src/capymoa/learner/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -from .learners import ( - Classifier, - MOAClassifier, - ClassifierSSL, - MOAClassifierSSL, - Regressor, - MOARegressor, - SKClassifier, -) - - -__ALL__ = [ - "Classifier", - "MOAClassifier", - "ClassifierSSL", - "MOAClassifierSSL", - "Regressor", - "MOARegressor", - "SKClassifier", -] diff --git a/src/capymoa/learner/classifier/__init__.py b/src/capymoa/learner/classifier/__init__.py deleted file mode 100644 index 51c8b531..00000000 --- a/src/capymoa/learner/classifier/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -from .classifiers import AdaptiveRandomForest, OnlineBagging, AdaptiveRandomForest -from .efdt import EFDT -from .sklearn import PassiveAggressiveClassifier -from .hoeffding_tree import HoeffdingTree -from .naive_bayes import NaiveBayes - -__all__ = [ - "AdaptiveRandomForest", - "OnlineBagging", - "AdaptiveRandomForest", - "EFDT", - "HoeffdingTree", - "NaiveBayes", - "PassiveAggressiveClassifier", -] diff --git a/src/capymoa/learner/regressor/__init__.py b/src/capymoa/learner/regressor/__init__.py deleted file mode 100644 index cead1fa7..00000000 --- a/src/capymoa/learner/regressor/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -from .regressors import ( - KNNRegressor, - AdaptiveRandomForestRegressor, - FIMTDD, - ARFFIMTDD, - ORTO, - SOKNLBT, - SOKNL, -) - -__all__ = [ - "KNNRegressor", - "AdaptiveRandomForestRegressor", - "FIMTDD", - "ARFFIMTDD", - "ORTO", - "SOKNLBT", - "SOKNL", -] diff --git a/src/capymoa/learner/regressor/regressors.py b/src/capymoa/learner/regressor/regressors.py deleted file mode 100644 index d717ab48..00000000 --- a/src/capymoa/learner/regressor/regressors.py +++ /dev/null @@ -1,493 +0,0 @@ -# Library imports -from typing import Optional, Union - -from capymoa.learner.learners import ( - MOARegressor, -) - -from capymoa.learner.splitcriteria import SplitCriterion, _split_criterion_to_cli_str -from capymoa.stream.stream import Schema -from moa.classifiers.lazy import kNN as MOA_kNN -from moa.classifiers.meta import ( - AdaptiveRandomForestRegressor as MOA_AdaptiveRandomForestRegressor, - SelfOptimisingKNearestLeaves as MOA_SOKNL, -) -from moa.classifiers.trees import ( - FIMTDD as _MOA_FIMTDD, - ARFFIMTDD as _MOA_ARFFIMTDD, - ORTO as _MOA_ORTO, - SelfOptimisingBaseTree as _MOA_SelfOptimisingBaseTree, -) - - -######################## -######### TREES ######## -######################## -class FIMTDD(MOARegressor): - """Implementation of the FIMT-DD tree as described by Ikonomovska et al.""" - - def __init__( - self, - schema: Schema, - split_criterion: Union[SplitCriterion, str] = "VarianceReductionSplitCriterion", - grace_period: int = 200, - split_confidence: float = 1.0e-7, - tie_threshold: float = 0.05, - page_hinckley_alpha: float = 0.005, - page_hinckley_threshold: int = 50, - alternate_tree_fading_factor: float = 0.995, - alternate_tree_t_min: int = 150, - alternate_tree_time: int = 1500, - regression_tree: bool = False, - learning_ratio: float = 0.02, - learning_ratio_decay_factor: float = 0.001, - learning_ratio_const: bool = False, - random_seed: Optional[int] = None, - ) -> None: - """ - Construct FIMTDD. - - :param split_criterion: Split criterion to use. - :param grace_period: Number of instances a leaf should observe between split attempts. - :param split_confidence: Allowed error in split decision, values close to 0 will take long to decide. - :param tie_threshold: Threshold below which a split will be forced to break ties. - :param page_hinckley_alpha: Alpha value to use in the Page Hinckley change detection tests. - :param page_hinckley_threshold: Threshold value used in the Page Hinckley change detection tests. - :param alternate_tree_fading_factor: Fading factor used to decide if an alternate tree should replace an original. - :param alternate_tree_t_min: Tmin value used to decide if an alternate tree should replace an original. - :param alternate_tree_time: The number of instances used to decide if an alternate tree should be discarded. - :param regression_tree: Build a regression tree instead of a model tree. - :param learning_ratio: Learning ratio to used for training the Perceptrons in the leaves. - :param learning_ratio_decay_factor: Learning rate decay factor (not used when learning rate is constant). - :param learning_ratio_const: Keep learning rate constant instead of decaying. - """ - cli = [] - - cli.append(f"-s ({_split_criterion_to_cli_str(split_criterion)})") - cli.append(f"-g {grace_period}") - cli.append(f"-c {split_confidence}") - cli.append(f"-t {tie_threshold}") - cli.append(f"-a {page_hinckley_alpha}") - cli.append(f"-h {page_hinckley_threshold}") - cli.append(f"-f {alternate_tree_fading_factor}") - cli.append(f"-y {alternate_tree_t_min}") - cli.append(f"-u {alternate_tree_time}") - cli.append("-e") if regression_tree else None - cli.append(f"-l {learning_ratio}") - cli.append(f"-d {learning_ratio_decay_factor}") - cli.append("-p") if learning_ratio_const else None - - self.moa_learner = _MOA_FIMTDD() - - super().__init__( - schema=schema, - CLI=" ".join(cli), - random_seed=random_seed, - moa_learner=self.moa_learner, - ) - - -class ARFFIMTDD(MOARegressor): - """Modified Fast Incremental Model Tree with Drift Detection for basic - learner for ARF-Regas described by Ikonomovska et al.""" - - def __init__( - self, - schema: Schema, - subspace_size_size: int = 2, - split_criterion: Union[SplitCriterion, str] = "VarianceReductionSplitCriterion", - grace_period: int = 200, - split_confidence: float = 1.0e-7, - tie_threshold: float = 0.05, - page_hinckley_alpha: float = 0.005, - page_hinckley_threshold: int = 50, - alternate_tree_fading_factor: float = 0.995, - alternate_tree_t_min: int = 150, - alternate_tree_time: int = 1500, - learning_ratio: float = 0.02, - learning_ratio_decay_factor: float = 0.001, - learning_ratio_const: bool = False, - random_seed: Optional[int] = None, - ) -> None: - """ - Construct ARFFIMTDD. - - :param subspace_size_size: Number of features per subset for each node split. Negative values = #features - k - :param split_criterion: Split criterion to use. - :param grace_period: Number of instances a leaf should observe between split attempts. - :param split_confidence: Allowed error in split decision, values close to 0 will take long to decide. - :param tie_threshold: Threshold below which a split will be forced to break ties. - :param page_hinckley_alpha: Alpha value to use in the Page Hinckley change detection tests. - :param page_hinckley_threshold: Threshold value used in the Page Hinckley change detection tests. - :param alternate_tree_fading_factor: Fading factor used to decide if an alternate tree should replace an original. - :param alternate_tree_t_min: Tmin value used to decide if an alternate tree should replace an original. - :param alternate_tree_time: The number of instances used to decide if an alternate tree should be discarded. - :param learning_ratio: Learning ratio to used for training the Perceptrons in the leaves. - :param learning_ratio_decay_factor: Learning rate decay factor (not used when learning rate is constant). - :param learning_ratio_const: Keep learning rate constant instead of decaying. - """ - cli = [] - - cli.append(f"-k {subspace_size_size}") - cli.append(f"-s ({_split_criterion_to_cli_str(split_criterion)})") - cli.append(f"-g {grace_period}") - cli.append(f"-c {split_confidence}") - cli.append(f"-t {tie_threshold}") - cli.append(f"-a {page_hinckley_alpha}") - cli.append(f"-h {page_hinckley_threshold}") - cli.append(f"-f {alternate_tree_fading_factor}") - cli.append(f"-y {alternate_tree_t_min}") - cli.append(f"-u {alternate_tree_time}") - cli.append(f"-l {learning_ratio}") - cli.append(f"-d {learning_ratio_decay_factor}") - cli.append("-p") if learning_ratio_const else None - - self.moa_learner = _MOA_ARFFIMTDD() - - super().__init__( - schema=schema, - CLI=" ".join(cli), - random_seed=random_seed, - moa_learner=self.moa_learner, - ) - - -class ORTO(MOARegressor): - """Implementation of the ORTO tree as described by Ikonomovska et al.""" - - def __init__( - self, - schema: Schema, - max_trees: int = 10, - max_option_level: int = 10, - option_decay_factor: float = 0.9, - option_fading_factor: float = 0.9995, - split_criterion: Union[SplitCriterion, str] = "VarianceReductionSplitCriterion", - grace_period: int = 200, - split_confidence: float = 1.0e-7, - tie_threshold: float = 0.05, - page_hinckley_alpha: float = 0.005, - page_hinckley_threshold: int = 50, - alternate_tree_fading_factor: float = 0.995, - alternate_tree_t_min: int = 150, - alternate_tree_time: int = 1500, - regression_tree: bool = False, - learning_ratio: float = 0.02, - learning_ratio_decay_factor: float = 0.001, - learning_ratio_const: bool = False, - random_seed: Optional[int] = None, - ) -> None: - """ - Construct ORTO. - - :param max_trees: The maximum number of trees contained in the option tree. - :param max_option_level: The maximal depth at which option nodes can be created. - :param option_decay_factor: The option decay factor that determines how many options can be selected at a given level. - :param option_fading_factor: The fading factor used for comparing subtrees of an option node. - :param split_criterion: Split criterion to use. - :param grace_period: Number of instances a leaf should observe between split attempts. - :param split_confidence: Allowed error in split decision, values close to 0 will take long to decide. - :param tie_threshold: Threshold below which a split will be forced to break ties. - :param page_hinckley_alpha: Alpha value to use in the Page Hinckley change detection tests. - :param page_hinckley_threshold: Threshold value used in the Page Hinckley change detection tests. - :param alternate_tree_fading_factor: Fading factor used to decide if an alternate tree should replace an original. - :param alternate_tree_t_min: Tmin value used to decide if an alternate tree should replace an original. - :param alternate_tree_time: The number of instances used to decide if an alternate tree should be discarded. - :param regression_tree: Build a regression tree instead of a model tree. - :param learning_ratio: Learning ratio to used for training the Perceptrons in the leaves. - :param learning_ratio_decay_factor: Learning rate decay factor (not used when learning rate is constant). - :param learning_ratio_const: Keep learning rate constant instead of decaying. - """ - cli = [] - - cli.append(f"-m {max_trees}") - cli.append(f"-x {max_option_level}") - cli.append(f"-z {option_decay_factor}") - cli.append(f"-q {option_fading_factor}") - cli.append(f"-s ({_split_criterion_to_cli_str(split_criterion)})") - cli.append(f"-g {grace_period}") - cli.append(f"-c {split_confidence}") - cli.append(f"-t {tie_threshold}") - cli.append(f"-a {page_hinckley_alpha}") - cli.append(f"-h {page_hinckley_threshold}") - cli.append(f"-f {alternate_tree_fading_factor}") - cli.append(f"-y {alternate_tree_t_min}") - cli.append(f"-u {alternate_tree_time}") - cli.append("-e") if regression_tree else None - cli.append(f"-l {learning_ratio}") - cli.append(f"-d {learning_ratio_decay_factor}") - cli.append("-p") if learning_ratio_const else None - - self.moa_learner = _MOA_ORTO() - - super().__init__( - schema=schema, - CLI=" ".join(cli), - random_seed=random_seed, - moa_learner=self.moa_learner, - ) - - -class SOKNLBT(MOARegressor): - """Implementation of the FIMT-DD tree as described by Ikonomovska et al.""" - - def __init__( - self, - schema: Schema, - subspace_size_size: int = 2, - split_criterion: Union[SplitCriterion, str] = "VarianceReductionSplitCriterion", - grace_period: int = 200, - split_confidence: float = 1.0e-7, - tie_threshold: float = 0.05, - page_hinckley_alpha: float = 0.005, - page_hinckley_threshold: int = 50, - alternate_tree_fading_factor: float = 0.995, - alternate_tree_t_min: int = 150, - alternate_tree_time: int = 1500, - learning_ratio: float = 0.02, - learning_ratio_decay_factor: float = 0.001, - learning_ratio_const: bool = False, - random_seed: Optional[int] = None, - ) -> None: - """ - Construct SelfOptimisingBaseTree. - - :param subspace_size_size: Number of features per subset for each node split. Negative values = #features - k - :param split_criterion: Split criterion to use. - :param grace_period: Number of instances a leaf should observe between split attempts. - :param split_confidence: Allowed error in split decision, values close to 0 will take long to decide. - :param tie_threshold: Threshold below which a split will be forced to break ties. - :param page_hinckley_alpha: Alpha value to use in the Page Hinckley change detection tests. - :param page_hinckley_threshold: Threshold value used in the Page Hinckley change detection tests. - :param alternate_tree_fading_factor: Fading factor used to decide if an alternate tree should replace an original. - :param alternate_tree_t_min: Tmin value used to decide if an alternate tree should replace an original. - :param alternate_tree_time: The number of instances used to decide if an alternate tree should be discarded. - :param learning_ratio: Learning ratio to used for training the Perceptrons in the leaves. - :param learning_ratio_decay_factor: Learning rate decay factor (not used when learning rate is constant). - :param learning_ratio_const: Keep learning rate constant instead of decaying. - """ - cli = [] - - cli.append(f"-k {subspace_size_size}") - cli.append(f"-s ({_split_criterion_to_cli_str(split_criterion)})") - cli.append(f"-g {grace_period}") - cli.append(f"-c {split_confidence}") - cli.append(f"-t {tie_threshold}") - cli.append(f"-a {page_hinckley_alpha}") - cli.append(f"-h {page_hinckley_threshold}") - cli.append(f"-f {alternate_tree_fading_factor}") - cli.append(f"-y {alternate_tree_t_min}") - cli.append(f"-u {alternate_tree_time}") - cli.append(f"-l {learning_ratio}") - cli.append(f"-d {learning_ratio_decay_factor}") - cli.append("-p") if learning_ratio_const else None - - self.moa_learner = _MOA_SelfOptimisingBaseTree() - - super().__init__( - schema=schema, - CLI=" ".join(cli), - random_seed=random_seed, - moa_learner=self.moa_learner, - ) - - -######################## -######### LAZY ######### -######################## - - -class KNNRegressor(MOARegressor): - """ - The default number of neighbors (k) is set to 3 instead of 10 (as in MOA) - """ - - def __init__( - self, schema=None, CLI=None, random_seed=1, k=3, median=False, window_size=1000 - ): - # Important, should create the MOA object before invoking the super class __init__ - self.moa_learner = MOA_kNN() - super().__init__( - schema=schema, - CLI=CLI, - random_seed=random_seed, - moa_learner=self.moa_learner, - ) - - # Initialize instance attributes with default values, CLI was not set. - if self.CLI is None: - self.k = k - self.median = median - self.window_size = window_size - self.moa_learner.getOptions().setViaCLIString( - f"-k {self.k} {'-m' if self.median else ''} -w \ - {self.window_size}" - ) - self.moa_learner.prepareForUse() - self.moa_learner.resetLearning() - - def __str__(self): - # Overrides the default class name from MOA - return "kNNRegressor" - - -######################## -####### ENSEMBLES ###### -######################## - - -# TODO: replace the m_features_mode logic such that we can infer from m_features_per_tree_size, e.g. if value is double between 0.0 and 1.0 = percentage -class AdaptiveRandomForestRegressor(MOARegressor): - def __init__( - self, - schema=None, - CLI=None, - random_seed=1, - tree_learner=None, - ensemble_size=100, - max_features=0.6, - lambda_param=6.0, # m_features_mode=None, m_features_per_tree_size=60, - drift_detection_method=None, - warning_detection_method=None, - disable_drift_detection=False, - disable_background_learner=False, - ): - # Important: must create the MOA object before invoking the super class __init__ - self.moa_learner = MOA_AdaptiveRandomForestRegressor() - super().__init__( - schema=schema, - CLI=CLI, - random_seed=random_seed, - moa_learner=self.moa_learner, - ) - - # Initialize instance attributes with default values, CLI was not set. - if self.CLI is None: - self.tree_learner = ( - ARFFIMTDD(schema, grace_period=50, split_confidence=0.01) - if tree_learner is None - else tree_learner - ) - self.ensemble_size = ensemble_size - - self.max_features = max_features - if isinstance(self.max_features, float) and 0.0 <= self.max_features <= 1.0: - self.m_features_mode = "(Percentage (M * (m / 100)))" - self.m_features_per_tree_size = int(self.max_features * 100) - elif isinstance(self.max_features, int): - self.m_features_mode = "(Specified m (integer value))" - self.m_features_per_tree_size = max_features - elif self.max_features in ["sqrt"]: - self.m_features_mode = "(sqrt(M)+1)" - self.m_features_per_tree_size = -1 # or leave it unchanged - elif self.max_features is None: - self.m_features_mode = "(Percentage (M * (m / 100)))" - self.m_features_per_tree_size = 60 - else: - # Handle other cases or raise an exception if needed - raise ValueError("Invalid value for max_features") - - # self.m_features_mode = "(Percentage (M * (m / 100)))" if m_features_mode is None else m_features_mode - # self.m_features_per_tree_size = m_features_per_tree_size - self.lambda_param = lambda_param - self.drift_detection_method = ( - "(ADWINChangeDetector -a 1.0E-3)" - if drift_detection_method is None - else drift_detection_method - ) - self.warning_detection_method = ( - "(ADWINChangeDetector -a 1.0E-2)" - if warning_detection_method is None - else warning_detection_method - ) - self.disable_drift_detection = disable_drift_detection - self.disable_background_learner = disable_background_learner - - self.moa_learner.getOptions().setViaCLIString( - f"-l {self.tree_learner} -s {self.ensemble_size} -o {self.m_features_mode} -m \ - {self.m_features_per_tree_size} -a {self.lambda_param} -x {self.drift_detection_method} -p \ - {self.warning_detection_method} {'-u' if self.disable_drift_detection else ''} {'-q' if self.disable_background_learner else ''}" - ) - self.moa_learner.prepareForUse() - self.moa_learner.resetLearning() - - -class SOKNL(MOARegressor): - def __init__( - self, - schema=None, - CLI=None, - random_seed=1, - tree_learner=None, - ensemble_size=100, - max_features=0.6, - lambda_param=6.0, # m_features_mode=None, m_features_per_tree_size=60, - drift_detection_method=None, - warning_detection_method=None, - disable_drift_detection=False, - disable_background_learner=False, - self_optimising=True, - k_value=10, - ): - # Important: must create the MOA object before invoking the super class __init__ - self.moa_learner = MOA_SOKNL() - super().__init__( - schema=schema, - CLI=CLI, - random_seed=random_seed, - moa_learner=self.moa_learner, - ) - - # Initialize instance attributes with default values, CLI was not set. - if self.CLI is None: - self.tree_learner = ( - # "(SelfOptimisingBaseTree -s VarianceReductionSplitCriterion -g 50 -c 0.01)" - SOKNLBT(schema, grace_period=50, split_confidence=0.01) - if tree_learner is None - else tree_learner - ) - self.ensemble_size = ensemble_size - - self.max_features = max_features - if isinstance(self.max_features, float) and 0.0 <= self.max_features <= 1.0: - self.m_features_mode = "(Percentage (M * (m / 100)))" - self.m_features_per_tree_size = int(self.max_features * 100) - elif isinstance(self.max_features, int): - self.m_features_mode = "(Specified m (integer value))" - self.m_features_per_tree_size = max_features - elif self.max_features in ["sqrt"]: - self.m_features_mode = "(sqrt(M)+1)" - self.m_features_per_tree_size = -1 # or leave it unchanged - elif self.max_features is None: - self.m_features_mode = "(Percentage (M * (m / 100)))" - self.m_features_per_tree_size = 60 - else: - # Handle other cases or raise an exception if needed - raise ValueError("Invalid value for max_features") - - # self.m_features_mode = "(Percentage (M * (m / 100)))" if m_features_mode is None else m_features_mode - # self.m_features_per_tree_size = m_features_per_tree_size - self.lambda_param = lambda_param - self.drift_detection_method = ( - "(ADWINChangeDetector -a 1.0E-3)" - if drift_detection_method is None - else drift_detection_method - ) - self.warning_detection_method = ( - "(ADWINChangeDetector -a 1.0E-2)" - if warning_detection_method is None - else warning_detection_method - ) - self.disable_drift_detection = disable_drift_detection - self.disable_background_learner = disable_background_learner - - self.self_optimising = self_optimising - self.k_value = k_value - - self.moa_learner.getOptions().setViaCLIString( - f"-l {self.tree_learner} -s {self.ensemble_size} {'-f' if self.self_optimising else ''} -k {self.k_value} -o {self.m_features_mode} -m \ - {self.m_features_per_tree_size} -a {self.lambda_param} -x {self.drift_detection_method} -p \ - {self.warning_detection_method} {'-u' if self.disable_drift_detection else ''} {'-q' if self.disable_background_learner else ''}" - ) - self.moa_learner.prepareForUse() - self.moa_learner.resetLearning() diff --git a/src/capymoa/learner/ssl/classifier/CPSSDS.py b/src/capymoa/learner/ssl/classifier/CPSSDS.py deleted file mode 100644 index 8f55fb30..00000000 --- a/src/capymoa/learner/ssl/classifier/CPSSDS.py +++ /dev/null @@ -1,296 +0,0 @@ -import typing as t -from typing import Dict, Literal - -import numpy as np -from river.base import Classifier -from river.naive_bayes import GaussianNB -from river.tree import HoeffdingTreeClassifier - -from capymoa.learner.ssl.classifier.batch import BatchClassifierSSL -from capymoa.stream import Schema -from capymoa.stream.instance import Instance - - -def shuffle_split( - split_proportion: float, x: np.ndarray, y: np.ndarray -) -> t.Tuple[t.Tuple[np.ndarray, np.ndarray], t.Tuple[np.ndarray, np.ndarray]]: - """Shuffle and split the data into two parts. - - :param split_proportion: The proportion of the dataset to be included in - the first part. - :param x: The instances to split. - :param y: The labels to split. - :raises LengthMismatchError: The length of x and y must be the same. - :return: Two tuples containing the instances and labels of the two parts. - """ - assert len(x) == len(y), "x and y must have the same length" - indices = np.arange(len(x)) - np.random.shuffle(indices) - split_index = int(len(x) * split_proportion) - idx_a = indices[:split_index] - idx_b = indices[split_index:] - return (x[idx_a], y[idx_a]), (x[idx_b], y[idx_b]) - - -def split_by_label_presence( - x: np.ndarray, y: np.ndarray -) -> t.Tuple[t.Tuple[np.ndarray, np.ndarray], np.ndarray]: - """Split the data into labeled and unlabeled instances. - - :param x: A batch of instances. - :param y: A batch of labels where -1 means that the instance is unlabeled. - :raises LengthMismatchError: The length of x and y must be the same. - :return: - - A tuple containing the labeled instances and labels. - - A numpy array containing the unlabeled instances. - """ - assert len(x) == len(y), "x and y must have the same length" - labeled_mask = y != -1 - return (x[labeled_mask], y[labeled_mask]), x[~labeled_mask] - - -def Unlabeling_data(X_train, Y_train, Percentage, chunk_size, class_count): - labeled_count = round(Percentage * chunk_size) - TLabeled = X_train[0 : labeled_count - 1] - Y_TLabeled = Y_train[0 : labeled_count - 1] - X_Unlabeled = X_train[labeled_count : Y_train.shape[0] - 1] - - cal_count = round(0.3 * TLabeled.shape[0]) - X_cal = TLabeled[0 : cal_count - 1] - Y_cal = Y_TLabeled[0 : cal_count - 1] - X_L = TLabeled[cal_count : TLabeled.shape[0] - 1] - Y_L = Y_TLabeled[cal_count : TLabeled.shape[0] - 1] - - return X_Unlabeled, X_L, Y_L, X_cal, Y_cal - - -def Prediction_by_CP(num, classifier, X, Y, X_Unlabeled, class_count, sl): - row = X_Unlabeled.shape[0] - col = class_count - p_values = np.zeros([row, col]) - labels = np.ones((row, col), dtype=bool) - alphas = NCM(num, classifier, X, Y, 1, class_count) - for elem in range(row): - c = [] - for o in range(class_count): - a_test = NCM( - num, classifier, np.array([X_Unlabeled[elem, :]]), o, 2, class_count - ) - idx = np.argwhere(Y == o).flatten() - temp = alphas[idx] - p = len(temp[temp >= a_test]) - if idx.shape[0] == 0: - s = 0 - else: - s = p / idx.shape[0] - c.append(s) - if s < sl: - labels[elem, int(o)] = False - p_values[elem, :] = np.array(c) - return p_values, labels - - -def NCM(num, classifier, X, Y, t, class_count): - if num == 1: - if t == 1: - p = np.zeros([X.shape[0], 1]) - alpha = np.zeros([X.shape[0], 1]) - for g in range(X.shape[0]): - dic_vote = classifier.predict_proba_one(np_to_dict(X[g, :])) - vote = np.fromiter(dic_vote.values(), dtype=float) - vote_keys = np.fromiter(dic_vote.keys(), dtype=int) - Sum = np.sum(vote) - keys = np.argwhere(vote_keys == int(Y[g])).flatten() - if keys.size == 0: - p[g] = (1) / (Sum + class_count) - else: - for key, val in dic_vote.items(): - if key == float(Y[g]): - p[g] = (val + 1) / (Sum + class_count) - alpha[g] = 1 - p[g] - - else: - dic_vote = classifier.predict_proba_one(np_to_dict(X[0, :])) - vote = np.fromiter(dic_vote.values(), dtype=float) - vote_keys = np.fromiter(dic_vote.keys(), dtype=int) - Sum = np.sum(vote) - keys = np.argwhere(vote_keys == int(Y)).flatten() - if keys.size == 0: - p = (1) / (Sum + class_count) - else: - for key, val in dic_vote.items(): - if key == float(Y): - p = (val + 1) / (Sum + class_count) - alpha = 1 - p - - else: - if t == 1: - prediction = predict_many(classifier, X) - P = np.max(prediction, axis=1) - alpha = 1 - P - elif t == 2: - prediction = predict_many(classifier, X) - # TODO: This is a hacky patch because river tries to be smart and - # infer the number of classes from the data. This is silly because - # CPSSDS assumes that the number of classes is known. Future work - # will replace river with MOA. - if prediction.shape[1] <= Y: - P = 0 - else: - P = prediction[0, int(Y)] - alpha = 1 - P - return alpha - - -def Informatives_selection(X_Unlabeled, p_values, labels, class_count): - row = X_Unlabeled.shape[0] - X = np.empty([1, X_Unlabeled.shape[1]]) - Y = np.empty([1]) - for elem in range(row): - l = np.argwhere(labels[elem, :] == True).flatten() - if len(l) == 1: - pp = p_values[elem, l] - X = np.append(X, [X_Unlabeled[elem, :]], axis=0) - Y = np.append(Y, [l[0]], axis=0) - Informatives = X[1 : X.shape[0], :] - Y_Informatives = Y[1 : Y.shape[0]] - return Informatives, Y_Informatives - - -def Appending_informative_to_nextchunk( - X_Currentchunk_Labeled, Y_Currentchunk_Labeled, Informatives, Y_Informatives -): - X = np.append(X_Currentchunk_Labeled, Informatives, axis=0) - Y = np.append(Y_Currentchunk_Labeled, Y_Informatives, axis=0) - return X, Y - - -def np_to_dict(x): - return dict(enumerate(x)) - - -def predict_many(classifier: Classifier, x: np.ndarray) -> np.ndarray: - """Predict the labels of a batch of instances. - - :param classifier: The classifier to use. - :param x: A batch of instances. - :return: A numpy array containing the predicted labels. - """ - if len(x) == 0: - return np.array([]) - results = [] - for x_i in x: - y_hat = classifier.predict_proba_one(np_to_dict(x_i)) - y_hat_skmf = np.array(list(y_hat.values())) - results.append(y_hat_skmf) - return np.stack(results) - - -class CPSSDS(BatchClassifierSSL): - """Conformal prediction for semi-supervised classification on data streams. - - Tanha, J., Samadi, N., Abdi, Y., & Razzaghi-Asl, N. (2021). CPSSDS: - Conformal prediction for semi-supervised classification on data streams. - Information Sciences, 584, 212–234. https://doi.org/10.1016/j.ins.2021.10.068 - """ - - def __init__( - self, - base_model: Literal["NaiveBayes", "HoeffdingTree"], - batch_size: int, - schema: Schema, - significance_level: float = 0.98, - calibration_split: float = 0.3, - random_seed=1, - ) -> None: - """Constructor for CPSSDS. - - :param base_model: An underlying model which is augmented with - self-labeled data from conformal prediction. - :param batch_size: The number of instances to train on at a time. - :param schema: The schema of the data stream. - :param significance_level: Controls the required confidence level for - unlabeled instances to be labeled. Must be between 0 and 1. defaults to 0.98 - :param calibration_split: The proportion of the labeled data to be used - for calibration. defaults to 0.3 - :param random_seed: The random seed to use for reproducibility. - :raises ValueError: `base_model` must be either NaiveBayes or HoeffdingTree - """ - super().__init__(batch_size, schema, random_seed) - self.significance_level: float = significance_level - self.chunk_id = 0 - self.class_count = schema.get_num_classes() - self.calibration_split = calibration_split - - # TODO: These classifiers should be replaced with MOA classifiers - if base_model == "NaiveBayes": - self.classifier = GaussianNB() - self._num = 2 - elif base_model == "HoeffdingTree": - self.classifier = HoeffdingTreeClassifier() - self._num = 1 - else: - raise ValueError("`base_model` must be either NaiveBayes or HoeffdingTree") - - # Self-labeled data, initialized as empty - self.self_labeled_x: np.array = None - self.self_labeled_y: np.array = None - - # Set seed for reproducibility - np.random.seed(random_seed) - - def train_on_batch(self, x_batch, y_indices): - (x_label, y_label), x_unlabeled = split_by_label_presence(x_batch, y_indices) - (x_cal, y_cal), (x_train, y_train) = shuffle_split( - self.calibration_split, x_label, y_label - ) - - # Add self-labeled data to training data - if self.self_labeled_x is not None and self.self_labeled_y is not None: - x_train = np.concatenate((x_train, self.self_labeled_x)) - y_train = np.concatenate((y_train, self.self_labeled_y)) - - for x_one, y_one in zip(x_train, y_train): - self.classifier.learn_one(dict(enumerate(x_one)), y_one) - - assert x_cal.shape[0] > 0, "Calibration data must not be empty" - assert x_unlabeled.shape[0] > 0, "Unlabeled data must not be empty" - """Issues arise when not enough labeled data is available for calibration. - This can be solved by increasing the calibration split or increasing the - batch size. - """ - - # Use conformal prediction to label some unlabeled data - p_values, labels = Prediction_by_CP( - self._num, - self.classifier, - x_cal, - y_cal, - x_unlabeled, - self.class_count, - self.significance_level, - ) - - # Add newly labeled data to self-labeled data - self.self_labeled_x, self.self_labeled_y = Informatives_selection( - x_unlabeled, p_values, labels, self.class_count - ) - - def instance_to_dict(self, instance: Instance) -> Dict[str, float]: - """Convert an instance to a dictionary with the feature names as keys.""" - return dict(enumerate(instance.x)) - - def skmf_to_river(self, x): - return dict(enumerate(x)) - - def predict(self, instance: Instance): - class_index = self.classifier.predict_one(self.instance_to_dict(instance)) - if class_index is None: - return None - return class_index - - def predict_proba(self, instance): - raise NotImplementedError() - - def __str__(self): - return f"CPSSDS(significance_level={self.significance_level})" diff --git a/src/capymoa/learner/ssl/classifier/__init__.py b/src/capymoa/learner/ssl/classifier/__init__.py deleted file mode 100644 index 42d00b44..00000000 --- a/src/capymoa/learner/ssl/classifier/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .CPSSDS import CPSSDS -from .OSNN import OSNN -from .batch import BatchClassifierSSL - -__all__ = ["BatchClassifierSSL", "CPSSDS", "OSNN"] diff --git a/src/capymoa/prepare_jpype.py b/src/capymoa/prepare_jpype.py index dccfddf4..af257429 100644 --- a/src/capymoa/prepare_jpype.py +++ b/src/capymoa/prepare_jpype.py @@ -1,9 +1,7 @@ # Python imports -import subprocess import configparser import jpype import jpype.imports -from jpype.types import * import os from pathlib import Path diff --git a/src/capymoa/regressor/__init__.py b/src/capymoa/regressor/__init__.py new file mode 100644 index 00000000..8ea9dd43 --- /dev/null +++ b/src/capymoa/regressor/__init__.py @@ -0,0 +1,16 @@ +from ._soknl import SOKNL, SOKNLBT +from ._orto import ORTO +from ._knn import KNNRegressor +from ._fimtdd import FIMTDD +from ._arffimtdd import ARFFIMTDD +from ._adaptive_random_forrest import AdaptiveRandomForestRegressor + +__all__ = [ + "SOKNLBT", + "SOKNL", + "ORTO", + "KNNRegressor", + "FIMTDD", + "ARFFIMTDD", + "AdaptiveRandomForestRegressor", +] diff --git a/src/capymoa/regressor/_adaptive_random_forrest.py b/src/capymoa/regressor/_adaptive_random_forrest.py new file mode 100644 index 00000000..294c6020 --- /dev/null +++ b/src/capymoa/regressor/_adaptive_random_forrest.py @@ -0,0 +1,84 @@ +# Library imports + +from capymoa.base import MOARegressor +from ._arffimtdd import ARFFIMTDD + +from moa.classifiers.meta import ( + AdaptiveRandomForestRegressor as MOA_AdaptiveRandomForestRegressor, +) + + +# TODO: replace the m_features_mode logic such that we can infer from m_features_per_tree_size, e.g. if value is double between 0.0 and 1.0 = percentage +class AdaptiveRandomForestRegressor(MOARegressor): + def __init__( + self, + schema=None, + CLI=None, + random_seed=1, + tree_learner=None, + ensemble_size=100, + max_features=0.6, + lambda_param=6.0, # m_features_mode=None, m_features_per_tree_size=60, + drift_detection_method=None, + warning_detection_method=None, + disable_drift_detection=False, + disable_background_learner=False, + ): + # Important: must create the MOA object before invoking the super class __init__ + self.moa_learner = MOA_AdaptiveRandomForestRegressor() + super().__init__( + schema=schema, + CLI=CLI, + random_seed=random_seed, + moa_learner=self.moa_learner, + ) + + # Initialize instance attributes with default values, CLI was not set. + if self.CLI is None: + self.tree_learner = ( + ARFFIMTDD(schema, grace_period=50, split_confidence=0.01) + if tree_learner is None + else tree_learner + ) + self.ensemble_size = ensemble_size + + self.max_features = max_features + if isinstance(self.max_features, float) and 0.0 <= self.max_features <= 1.0: + self.m_features_mode = "(Percentage (M * (m / 100)))" + self.m_features_per_tree_size = int(self.max_features * 100) + elif isinstance(self.max_features, int): + self.m_features_mode = "(Specified m (integer value))" + self.m_features_per_tree_size = max_features + elif self.max_features in ["sqrt"]: + self.m_features_mode = "(sqrt(M)+1)" + self.m_features_per_tree_size = -1 # or leave it unchanged + elif self.max_features is None: + self.m_features_mode = "(Percentage (M * (m / 100)))" + self.m_features_per_tree_size = 60 + else: + # Handle other cases or raise an exception if needed + raise ValueError("Invalid value for max_features") + + # self.m_features_mode = "(Percentage (M * (m / 100)))" if m_features_mode is None else m_features_mode + # self.m_features_per_tree_size = m_features_per_tree_size + self.lambda_param = lambda_param + self.drift_detection_method = ( + "(ADWINChangeDetector -a 1.0E-3)" + if drift_detection_method is None + else drift_detection_method + ) + self.warning_detection_method = ( + "(ADWINChangeDetector -a 1.0E-2)" + if warning_detection_method is None + else warning_detection_method + ) + self.disable_drift_detection = disable_drift_detection + self.disable_background_learner = disable_background_learner + + self.moa_learner.getOptions().setViaCLIString( + f"-l {self.tree_learner} -s {self.ensemble_size} -o {self.m_features_mode} -m \ + {self.m_features_per_tree_size} -a {self.lambda_param} -x {self.drift_detection_method} -p \ + {self.warning_detection_method} {'-u' if self.disable_drift_detection else ''} {'-q' if self.disable_background_learner else ''}" + ) + self.moa_learner.prepareForUse() + self.moa_learner.resetLearning() diff --git a/src/capymoa/regressor/_arffimtdd.py b/src/capymoa/regressor/_arffimtdd.py new file mode 100644 index 00000000..c228f69e --- /dev/null +++ b/src/capymoa/regressor/_arffimtdd.py @@ -0,0 +1,73 @@ +# Library imports +from typing import Optional, Union + +from capymoa.base import MOARegressor + +from capymoa.splitcriteria import SplitCriterion, _split_criterion_to_cli_str +from capymoa.stream._stream import Schema +from moa.classifiers.trees import ARFFIMTDD as _MOA_ARFFIMTDD + + +class ARFFIMTDD(MOARegressor): + """Modified Fast Incremental Model Tree with Drift Detection for basic + learner for ARF-Regas described by Ikonomovska et al.""" + + def __init__( + self, + schema: Schema, + subspace_size_size: int = 2, + split_criterion: Union[SplitCriterion, str] = "VarianceReductionSplitCriterion", + grace_period: int = 200, + split_confidence: float = 1.0e-7, + tie_threshold: float = 0.05, + page_hinckley_alpha: float = 0.005, + page_hinckley_threshold: int = 50, + alternate_tree_fading_factor: float = 0.995, + alternate_tree_t_min: int = 150, + alternate_tree_time: int = 1500, + learning_ratio: float = 0.02, + learning_ratio_decay_factor: float = 0.001, + learning_ratio_const: bool = False, + random_seed: Optional[int] = None, + ) -> None: + """ + Construct ARFFIMTDD. + + :param subspace_size_size: Number of features per subset for each node split. Negative values = #features - k + :param split_criterion: Split criterion to use. + :param grace_period: Number of instances a leaf should observe between split attempts. + :param split_confidence: Allowed error in split decision, values close to 0 will take long to decide. + :param tie_threshold: Threshold below which a split will be forced to break ties. + :param page_hinckley_alpha: Alpha value to use in the Page Hinckley change detection tests. + :param page_hinckley_threshold: Threshold value used in the Page Hinckley change detection tests. + :param alternate_tree_fading_factor: Fading factor used to decide if an alternate tree should replace an original. + :param alternate_tree_t_min: Tmin value used to decide if an alternate tree should replace an original. + :param alternate_tree_time: The number of instances used to decide if an alternate tree should be discarded. + :param learning_ratio: Learning ratio to used for training the Perceptrons in the leaves. + :param learning_ratio_decay_factor: Learning rate decay factor (not used when learning rate is constant). + :param learning_ratio_const: Keep learning rate constant instead of decaying. + """ + cli = [] + + cli.append(f"-k {subspace_size_size}") + cli.append(f"-s ({_split_criterion_to_cli_str(split_criterion)})") + cli.append(f"-g {grace_period}") + cli.append(f"-c {split_confidence}") + cli.append(f"-t {tie_threshold}") + cli.append(f"-a {page_hinckley_alpha}") + cli.append(f"-h {page_hinckley_threshold}") + cli.append(f"-f {alternate_tree_fading_factor}") + cli.append(f"-y {alternate_tree_t_min}") + cli.append(f"-u {alternate_tree_time}") + cli.append(f"-l {learning_ratio}") + cli.append(f"-d {learning_ratio_decay_factor}") + cli.append("-p") if learning_ratio_const else None + + self.moa_learner = _MOA_ARFFIMTDD() + + super().__init__( + schema=schema, + CLI=" ".join(cli), + random_seed=random_seed, + moa_learner=self.moa_learner, + ) diff --git a/src/capymoa/regressor/_fimtdd.py b/src/capymoa/regressor/_fimtdd.py new file mode 100644 index 00000000..172b48c6 --- /dev/null +++ b/src/capymoa/regressor/_fimtdd.py @@ -0,0 +1,71 @@ +from typing import Optional, Union + +from capymoa.base import MOARegressor + +from capymoa.splitcriteria import SplitCriterion, _split_criterion_to_cli_str +from capymoa.stream._stream import Schema +from moa.classifiers.trees import FIMTDD as _MOA_FIMTDD + + +class FIMTDD(MOARegressor): + """Implementation of the FIMT-DD tree as described by Ikonomovska et al.""" + + def __init__( + self, + schema: Schema, + split_criterion: Union[SplitCriterion, str] = "VarianceReductionSplitCriterion", + grace_period: int = 200, + split_confidence: float = 1.0e-7, + tie_threshold: float = 0.05, + page_hinckley_alpha: float = 0.005, + page_hinckley_threshold: int = 50, + alternate_tree_fading_factor: float = 0.995, + alternate_tree_t_min: int = 150, + alternate_tree_time: int = 1500, + regression_tree: bool = False, + learning_ratio: float = 0.02, + learning_ratio_decay_factor: float = 0.001, + learning_ratio_const: bool = False, + random_seed: Optional[int] = None, + ) -> None: + """ + Construct FIMTDD. + + :param split_criterion: Split criterion to use. + :param grace_period: Number of instances a leaf should observe between split attempts. + :param split_confidence: Allowed error in split decision, values close to 0 will take long to decide. + :param tie_threshold: Threshold below which a split will be forced to break ties. + :param page_hinckley_alpha: Alpha value to use in the Page Hinckley change detection tests. + :param page_hinckley_threshold: Threshold value used in the Page Hinckley change detection tests. + :param alternate_tree_fading_factor: Fading factor used to decide if an alternate tree should replace an original. + :param alternate_tree_t_min: Tmin value used to decide if an alternate tree should replace an original. + :param alternate_tree_time: The number of instances used to decide if an alternate tree should be discarded. + :param regression_tree: Build a regression tree instead of a model tree. + :param learning_ratio: Learning ratio to used for training the Perceptrons in the leaves. + :param learning_ratio_decay_factor: Learning rate decay factor (not used when learning rate is constant). + :param learning_ratio_const: Keep learning rate constant instead of decaying. + """ + cli = [] + + cli.append(f"-s ({_split_criterion_to_cli_str(split_criterion)})") + cli.append(f"-g {grace_period}") + cli.append(f"-c {split_confidence}") + cli.append(f"-t {tie_threshold}") + cli.append(f"-a {page_hinckley_alpha}") + cli.append(f"-h {page_hinckley_threshold}") + cli.append(f"-f {alternate_tree_fading_factor}") + cli.append(f"-y {alternate_tree_t_min}") + cli.append(f"-u {alternate_tree_time}") + cli.append("-e") if regression_tree else None + cli.append(f"-l {learning_ratio}") + cli.append(f"-d {learning_ratio_decay_factor}") + cli.append("-p") if learning_ratio_const else None + + self.moa_learner = _MOA_FIMTDD() + + super().__init__( + schema=schema, + CLI=" ".join(cli), + random_seed=random_seed, + moa_learner=self.moa_learner, + ) diff --git a/src/capymoa/regressor/_knn.py b/src/capymoa/regressor/_knn.py new file mode 100644 index 00000000..b641cf76 --- /dev/null +++ b/src/capymoa/regressor/_knn.py @@ -0,0 +1,36 @@ +from capymoa.base import MOARegressor +from moa.classifiers.lazy import kNN as _moa_kNN + + +class KNNRegressor(MOARegressor): + """ + The default number of neighbors (k) is set to 3 instead of 10 (as in MOA) + """ + + def __init__( + self, schema=None, CLI=None, random_seed=1, k=3, median=False, window_size=1000 + ): + # Important, should create the MOA object before invoking the super class __init__ + self.moa_learner = _moa_kNN() + super().__init__( + schema=schema, + CLI=CLI, + random_seed=random_seed, + moa_learner=self.moa_learner, + ) + + # Initialize instance attributes with default values, CLI was not set. + if self.CLI is None: + self.k = k + self.median = median + self.window_size = window_size + self.moa_learner.getOptions().setViaCLIString( + f"-k {self.k} {'-m' if self.median else ''} -w \ + {self.window_size}" + ) + self.moa_learner.prepareForUse() + self.moa_learner.resetLearning() + + def __str__(self): + # Overrides the default class name from MOA + return "kNNRegressor" diff --git a/src/capymoa/regressor/_orto.py b/src/capymoa/regressor/_orto.py new file mode 100644 index 00000000..172bf38b --- /dev/null +++ b/src/capymoa/regressor/_orto.py @@ -0,0 +1,83 @@ +from typing import Optional, Union + +from capymoa.stream import Schema +from capymoa.base import MOARegressor +from capymoa.splitcriteria import SplitCriterion, _split_criterion_to_cli_str + +from moa.classifiers.trees import ORTO as _MOA_ORTO + + +class ORTO(MOARegressor): + """Implementation of the ORTO tree as described by Ikonomovska et al.""" + + def __init__( + self, + schema: Schema, + max_trees: int = 10, + max_option_level: int = 10, + option_decay_factor: float = 0.9, + option_fading_factor: float = 0.9995, + split_criterion: Union[SplitCriterion, str] = "VarianceReductionSplitCriterion", + grace_period: int = 200, + split_confidence: float = 1.0e-7, + tie_threshold: float = 0.05, + page_hinckley_alpha: float = 0.005, + page_hinckley_threshold: int = 50, + alternate_tree_fading_factor: float = 0.995, + alternate_tree_t_min: int = 150, + alternate_tree_time: int = 1500, + regression_tree: bool = False, + learning_ratio: float = 0.02, + learning_ratio_decay_factor: float = 0.001, + learning_ratio_const: bool = False, + random_seed: Optional[int] = None, + ) -> None: + """ + Construct ORTO. + + :param max_trees: The maximum number of trees contained in the option tree. + :param max_option_level: The maximal depth at which option nodes can be created. + :param option_decay_factor: The option decay factor that determines how many options can be selected at a given level. + :param option_fading_factor: The fading factor used for comparing subtrees of an option node. + :param split_criterion: Split criterion to use. + :param grace_period: Number of instances a leaf should observe between split attempts. + :param split_confidence: Allowed error in split decision, values close to 0 will take long to decide. + :param tie_threshold: Threshold below which a split will be forced to break ties. + :param page_hinckley_alpha: Alpha value to use in the Page Hinckley change detection tests. + :param page_hinckley_threshold: Threshold value used in the Page Hinckley change detection tests. + :param alternate_tree_fading_factor: Fading factor used to decide if an alternate tree should replace an original. + :param alternate_tree_t_min: Tmin value used to decide if an alternate tree should replace an original. + :param alternate_tree_time: The number of instances used to decide if an alternate tree should be discarded. + :param regression_tree: Build a regression tree instead of a model tree. + :param learning_ratio: Learning ratio to used for training the Perceptrons in the leaves. + :param learning_ratio_decay_factor: Learning rate decay factor (not used when learning rate is constant). + :param learning_ratio_const: Keep learning rate constant instead of decaying. + """ + cli = [] + + cli.append(f"-m {max_trees}") + cli.append(f"-x {max_option_level}") + cli.append(f"-z {option_decay_factor}") + cli.append(f"-q {option_fading_factor}") + cli.append(f"-s ({_split_criterion_to_cli_str(split_criterion)})") + cli.append(f"-g {grace_period}") + cli.append(f"-c {split_confidence}") + cli.append(f"-t {tie_threshold}") + cli.append(f"-a {page_hinckley_alpha}") + cli.append(f"-h {page_hinckley_threshold}") + cli.append(f"-f {alternate_tree_fading_factor}") + cli.append(f"-y {alternate_tree_t_min}") + cli.append(f"-u {alternate_tree_time}") + cli.append("-e") if regression_tree else None + cli.append(f"-l {learning_ratio}") + cli.append(f"-d {learning_ratio_decay_factor}") + cli.append("-p") if learning_ratio_const else None + + self.moa_learner = _MOA_ORTO() + + super().__init__( + schema=schema, + CLI=" ".join(cli), + random_seed=random_seed, + moa_learner=self.moa_learner, + ) diff --git a/src/capymoa/regressor/_soknl.py b/src/capymoa/regressor/_soknl.py new file mode 100644 index 00000000..dcac87ee --- /dev/null +++ b/src/capymoa/regressor/_soknl.py @@ -0,0 +1,156 @@ +# Library imports +from typing import Optional, Union + +from capymoa.base import ( + MOARegressor, +) + +from capymoa.splitcriteria import SplitCriterion, _split_criterion_to_cli_str +from capymoa.stream._stream import Schema +from moa.classifiers.meta import SelfOptimisingKNearestLeaves as _MOA_SOKNL +from moa.classifiers.trees import SelfOptimisingBaseTree as _MOA_SelfOptimisingBaseTree + + +class SOKNLBT(MOARegressor): + """Implementation of the FIMT-DD tree as described by Ikonomovska et al.""" + + def __init__( + self, + schema: Schema, + subspace_size_size: int = 2, + split_criterion: Union[SplitCriterion, str] = "VarianceReductionSplitCriterion", + grace_period: int = 200, + split_confidence: float = 1.0e-7, + tie_threshold: float = 0.05, + page_hinckley_alpha: float = 0.005, + page_hinckley_threshold: int = 50, + alternate_tree_fading_factor: float = 0.995, + alternate_tree_t_min: int = 150, + alternate_tree_time: int = 1500, + learning_ratio: float = 0.02, + learning_ratio_decay_factor: float = 0.001, + learning_ratio_const: bool = False, + random_seed: Optional[int] = None, + ) -> None: + """ + Construct SelfOptimisingBaseTree. + + :param subspace_size_size: Number of features per subset for each node split. Negative values = #features - k + :param split_criterion: Split criterion to use. + :param grace_period: Number of instances a leaf should observe between split attempts. + :param split_confidence: Allowed error in split decision, values close to 0 will take long to decide. + :param tie_threshold: Threshold below which a split will be forced to break ties. + :param page_hinckley_alpha: Alpha value to use in the Page Hinckley change detection tests. + :param page_hinckley_threshold: Threshold value used in the Page Hinckley change detection tests. + :param alternate_tree_fading_factor: Fading factor used to decide if an alternate tree should replace an original. + :param alternate_tree_t_min: Tmin value used to decide if an alternate tree should replace an original. + :param alternate_tree_time: The number of instances used to decide if an alternate tree should be discarded. + :param learning_ratio: Learning ratio to used for training the Perceptrons in the leaves. + :param learning_ratio_decay_factor: Learning rate decay factor (not used when learning rate is constant). + :param learning_ratio_const: Keep learning rate constant instead of decaying. + """ + cli = [] + + cli.append(f"-k {subspace_size_size}") + cli.append(f"-s ({_split_criterion_to_cli_str(split_criterion)})") + cli.append(f"-g {grace_period}") + cli.append(f"-c {split_confidence}") + cli.append(f"-t {tie_threshold}") + cli.append(f"-a {page_hinckley_alpha}") + cli.append(f"-h {page_hinckley_threshold}") + cli.append(f"-f {alternate_tree_fading_factor}") + cli.append(f"-y {alternate_tree_t_min}") + cli.append(f"-u {alternate_tree_time}") + cli.append(f"-l {learning_ratio}") + cli.append(f"-d {learning_ratio_decay_factor}") + cli.append("-p") if learning_ratio_const else None + + self.moa_learner = _MOA_SelfOptimisingBaseTree() + + super().__init__( + schema=schema, + CLI=" ".join(cli), + random_seed=random_seed, + moa_learner=self.moa_learner, + ) + + +class SOKNL(MOARegressor): + def __init__( + self, + schema=None, + CLI=None, + random_seed=1, + tree_learner=None, + ensemble_size=100, + max_features=0.6, + lambda_param=6.0, # m_features_mode=None, m_features_per_tree_size=60, + drift_detection_method=None, + warning_detection_method=None, + disable_drift_detection=False, + disable_background_learner=False, + self_optimising=True, + k_value=10, + ): + # Important: must create the MOA object before invoking the super class __init__ + self.moa_learner = _MOA_SOKNL() + super().__init__( + schema=schema, + CLI=CLI, + random_seed=random_seed, + moa_learner=self.moa_learner, + ) + + # Initialize instance attributes with default values, CLI was not set. + if self.CLI is None: + self.tree_learner = ( + # "(SelfOptimisingBaseTree -s VarianceReductionSplitCriterion -g 50 -c 0.01)" + SOKNLBT(schema, grace_period=50, split_confidence=0.01) + if tree_learner is None + else tree_learner + ) + self.ensemble_size = ensemble_size + + self.max_features = max_features + if isinstance(self.max_features, float) and 0.0 <= self.max_features <= 1.0: + self.m_features_mode = "(Percentage (M * (m / 100)))" + self.m_features_per_tree_size = int(self.max_features * 100) + elif isinstance(self.max_features, int): + self.m_features_mode = "(Specified m (integer value))" + self.m_features_per_tree_size = max_features + elif self.max_features in ["sqrt"]: + self.m_features_mode = "(sqrt(M)+1)" + self.m_features_per_tree_size = -1 # or leave it unchanged + elif self.max_features is None: + self.m_features_mode = "(Percentage (M * (m / 100)))" + self.m_features_per_tree_size = 60 + else: + # Handle other cases or raise an exception if needed + raise ValueError("Invalid value for max_features") + + # self.m_features_mode = "(Percentage (M * (m / 100)))" if m_features_mode is None else m_features_mode + # self.m_features_per_tree_size = m_features_per_tree_size + self.lambda_param = lambda_param + self.drift_detection_method = ( + "(ADWINChangeDetector -a 1.0E-3)" + if drift_detection_method is None + else drift_detection_method + ) + self.warning_detection_method = ( + "(ADWINChangeDetector -a 1.0E-2)" + if warning_detection_method is None + else warning_detection_method + ) + self.disable_drift_detection = disable_drift_detection + self.disable_background_learner = disable_background_learner + + self.self_optimising = self_optimising + self.k_value = k_value + + self.moa_learner.getOptions().setViaCLIString( + f"-l {self.tree_learner} -s {self.ensemble_size} {'-f' if self.self_optimising else ''} -k {self.k_value} -o {self.m_features_mode} -m \ + {self.m_features_per_tree_size} -a {self.lambda_param} -x {self.drift_detection_method} -p \ + {self.warning_detection_method} {'-u' if self.disable_drift_detection else ''} {'-q' if self.disable_background_learner else ''}" + ) + self.moa_learner.prepareForUse() + self.moa_learner.resetLearning() diff --git a/src/capymoa/learner/splitcriteria.py b/src/capymoa/splitcriteria.py similarity index 94% rename from src/capymoa/learner/splitcriteria.py rename to src/capymoa/splitcriteria.py index b4894179..e50c3311 100644 --- a/src/capymoa/learner/splitcriteria.py +++ b/src/capymoa/splitcriteria.py @@ -4,8 +4,9 @@ import moa.classifiers.core.splitcriteria as moa_split -class SplitCriterion(): +class SplitCriterion: """Split criterions are used to evaluate the quality of a split in a decision tree.""" + _java_object: Optional[moa_split.SplitCriterion] = None def java_object(self) -> moa_split.SplitCriterion: @@ -65,4 +66,6 @@ def _split_criterion_to_cli_str(split_criterion: Union[str, SplitCriterion]) -> elif isinstance(split_criterion, str): return split_criterion.strip().strip("() ") else: - raise TypeError(f"Expected a string or SplitCriterion, got {type(split_criterion)}") + raise TypeError( + f"Expected a string or SplitCriterion, got {type(split_criterion)}" + ) diff --git a/src/capymoa/ssl/classifier/__init__.py b/src/capymoa/ssl/classifier/__init__.py new file mode 100644 index 00000000..5c33de26 --- /dev/null +++ b/src/capymoa/ssl/classifier/__init__.py @@ -0,0 +1,4 @@ +from ._osnn import OSNN +from ._batch import BatchClassifierSSL + +__all__ = ["BatchClassifierSSL", "OSNN"] diff --git a/src/capymoa/learner/ssl/classifier/batch.py b/src/capymoa/ssl/classifier/_batch.py similarity index 92% rename from src/capymoa/learner/ssl/classifier/batch.py rename to src/capymoa/ssl/classifier/_batch.py index 37a0c785..09ec351e 100644 --- a/src/capymoa/learner/ssl/classifier/batch.py +++ b/src/capymoa/ssl/classifier/_batch.py @@ -1,12 +1,11 @@ from abc import ABC, abstractmethod -from typing import Any import numpy as np from numpy.typing import NDArray -from capymoa.learner import ClassifierSSL -from capymoa.stream.instance import Instance, LabeledInstance -from capymoa.stream.stream import Schema +from capymoa.base import ClassifierSSL +from capymoa.instance import Instance, LabeledInstance +from capymoa.stream._stream import Schema from capymoa.type_alias import FeatureVector diff --git a/src/capymoa/learner/ssl/classifier/OSNN.py b/src/capymoa/ssl/classifier/_osnn.py similarity index 65% rename from src/capymoa/learner/ssl/classifier/OSNN.py rename to src/capymoa/ssl/classifier/_osnn.py index 8b673138..9ced0e30 100644 --- a/src/capymoa/learner/ssl/classifier/OSNN.py +++ b/src/capymoa/ssl/classifier/_osnn.py @@ -6,28 +6,30 @@ CapyMOA implementation by Botao, Anton """ + import numpy as np import random import torch.nn as nn import torch from scipy.spatial.distance import cdist -from capymoa.learner import ClassifierSSL +from capymoa.base import ClassifierSSL def kernel_fun(a, b, sigma): A = torch.sum((a - b) ** 2, dim=1) - B = A / (2 * sigma ** 2) + B = A / (2 * sigma**2) C = torch.exp(-B) return C -def Euclidean_Distances(a,b): - dis = torch.sqrt(torch.sum((a-b)**2, dim=1)) + +def Euclidean_Distances(a, b): + dis = torch.sqrt(torch.sum((a - b) ** 2, dim=1)) return dis -class OSNeuralNetwork(nn.Module): - def __init__(self, num_center, n_out, window_size, beta=1, gamma = 1): +class OSNeuralNetwork(nn.Module): + def __init__(self, num_center, n_out, window_size, beta=1, gamma=1): super(OSNeuralNetwork, self).__init__() self.n_out = n_out self.num_centers = num_center @@ -66,37 +68,52 @@ def initialize_weights(self): m.bias.data.zero_() def update_sigma(self): - #The width of basis function is set to a proportion β of the mean of the Euclidean distances to the other centers. + # The width of basis function is set to a proportion β of the mean of the Euclidean distances to the other centers. self.sigma = torch.ones(1, self.num_centers) for i in range(self.num_centers): dis = Euclidean_Distances(self.centers[i], self.centers) - dis = torch.sum(dis)/(self.num_centers) - self.sigma[0][i] = dis*self.beta + dis = torch.sum(dis) / (self.num_centers) + self.sigma[0][i] = dis * self.beta def window_update(self, data, label): - #The window is updated according to random sampling, and the first-in-first-out principle is adopted. + # The window is updated according to random sampling, and the first-in-first-out principle is adopted. if self.i == 0: - self.data_window = torch.zeros([self.window_size, data.size(1)], dtype=torch.float32) - self.label_window = torch.zeros([self.window_size, self.n_out], dtype=torch.float32) + self.data_window = torch.zeros( + [self.window_size, data.size(1)], dtype=torch.float32 + ) + self.label_window = torch.zeros( + [self.window_size, self.n_out], dtype=torch.float32 + ) self.label_index = torch.zeros((self.window_size, 1), dtype=torch.float32) - self.linear = nn.Sequential(nn.Linear(self.num_centers + data.size(1), self.n_out, bias=True) - , nn.Sigmoid()) + self.linear = nn.Sequential( + nn.Linear(self.num_centers + data.size(1), self.n_out, bias=True), + nn.Sigmoid(), + ) for i in range(data.size(0)): - - self.data_window = torch.cat([self.data_window[1:, :], data[i:i+1, :]], dim=0) - self.label_window = torch.cat([self.label_window[1:, :], label[i:i+1, :]], dim=0) + self.data_window = torch.cat( + [self.data_window[1:, :], data[i : i + 1, :]], dim=0 + ) + self.label_window = torch.cat( + [self.label_window[1:, :], label[i : i + 1, :]], dim=0 + ) if label[i] != -1: - self.label_index = torch.cat([self.label_index[1:, :], torch.ones(1, 1)], dim=0) + self.label_index = torch.cat( + [self.label_index[1:, :], torch.ones(1, 1)], dim=0 + ) else: - self.label_index = torch.cat([self.label_index[1:, :], torch.zeros(1, 1)], dim=0) + self.label_index = torch.cat( + [self.label_index[1:, :], torch.zeros(1, 1)], dim=0 + ) self.i = self.i + 1 if self.i == self.window_size: - index = torch.LongTensor(random.sample(range(self.data_window.size(0)), self.num_centers)) + index = torch.LongTensor( + random.sample(range(self.data_window.size(0)), self.num_centers) + ) self.centers = torch.index_select(self.data_window, 0, index) self.initialize_weights() @@ -110,17 +127,25 @@ def window_update(self, data, label): return update def center_adjustment(self): - #The samples are assigned to the nearest RBF centers, and then each center is updated according to the assigned samples. - distances = np.linalg.norm(self.data_window[:, np.newaxis] - self.centers, axis=2) + # The samples are assigned to the nearest RBF centers, and then each center is updated according to the assigned samples. + distances = np.linalg.norm( + self.data_window[:, np.newaxis] - self.centers, axis=2 + ) nearest_centers = np.argmin(distances, axis=1) - assigned_samples = [self.data_window[nearest_centers == i] for i in range(len(self.centers))] - assigned_labels = [self.label_window[nearest_centers == i] for i in range(len(self.centers))] - assigned_label_index = [self.label_index[nearest_centers == i] for i in range(len(self.centers))] + assigned_samples = [ + self.data_window[nearest_centers == i] for i in range(len(self.centers)) + ] + assigned_labels = [ + self.label_window[nearest_centers == i] for i in range(len(self.centers)) + ] + assigned_label_index = [ + self.label_index[nearest_centers == i] for i in range(len(self.centers)) + ] for i in range(self.num_centers): if len(assigned_samples) > 0: - unlabel_index = torch.squeeze(assigned_label_index[i] == 0., 1) - label_index = torch.squeeze(assigned_label_index[i] == 1., 1) + unlabel_index = torch.squeeze(assigned_label_index[i] == 0.0, 1) + label_index = torch.squeeze(assigned_label_index[i] == 1.0, 1) unlabel_sample = assigned_samples[i][unlabel_index] label_sample = assigned_samples[i][label_index] @@ -133,30 +158,42 @@ def center_adjustment(self): majorit_class = unique[np.argmax(counts)] minorit_class = unique[np.argmin(counts)] if majorit_class == minorit_class: - self.centers[i] = (torch.mean(unlabel_sample, axis=0) + torch.mean(label_sample, axis=0))/2 + self.centers[i] = ( + torch.mean(unlabel_sample, axis=0) + + torch.mean(label_sample, axis=0) + ) / 2 else: majorit_sample = label_sample[labels.flatten() == majorit_class] minorit_sample = label_sample[labels.flatten() == minorit_class] - a = (majorit_sample.sum(dim=0) + minorit_sample.sum(dim=0))/len(label_sample) + a = ( + majorit_sample.sum(dim=0) + minorit_sample.sum(dim=0) + ) / len(label_sample) b = torch.mean(unlabel_sample, axis=0) - c = ((len(majorit_sample) - len(minorit_sample))/len(label_sample)) + 1 - self.centers[i] = (a + b)/c + c = ( + (len(majorit_sample) - len(minorit_sample)) + / len(label_sample) + ) + 1 + self.centers[i] = (a + b) / c elif len(label_sample) > 0 and len(unlabel_sample == 0): unique, counts = np.unique(labels, return_counts=True) majorit_class = unique[np.argmax(counts)] minorit_class = unique[np.argmin(counts)] majorit_sample = label_sample[labels.flatten() == majorit_class] minorit_sample = label_sample[labels.flatten() == minorit_class] - a = (majorit_sample.sum(dim=0) + minorit_sample.sum(dim=0)) / len(label_sample) - c = ((len(majorit_sample) - len(minorit_sample)) / len(label_sample)) + a = (majorit_sample.sum(dim=0) + minorit_sample.sum(dim=0)) / len( + label_sample + ) + c = (len(majorit_sample) - len(minorit_sample)) / len(label_sample) self.centers[i] = a / c else: - self.centers[i] = self.data_window[torch.randint(self.data_window.shape[0], size=(1,))][0] + self.centers[i] = self.data_window[ + torch.randint(self.data_window.shape[0], size=(1,)) + ][0] self.update_sigma() def pseudo_label(self): - #Pseudo-labels for unlabeled samples are calculated based on the true labels of labeled samples and the output of the network on unlabeled samples. + # Pseudo-labels for unlabeled samples are calculated based on the true labels of labeled samples and the output of the network on unlabeled samples. V = torch.cat([self.data_window, self.centers], dim=0) label = np.vstack((self.label_window, np.zeros((self.num_centers, 1)))) label_index = np.vstack((self.label_index, np.zeros((self.num_centers, 1)))) @@ -167,18 +204,19 @@ def pseudo_label(self): nearest_distances = np.sort(distances, axis=1)[:, 1] nearest_distances = self.gamma * nearest_distances.reshape(-1, 1) - S = np.exp(-1 * np.square(distances)/(nearest_distances+1e-8)) + S = np.exp(-1 * np.square(distances) / (nearest_distances + 1e-8)) y = np.where(label_index, label, pre.detach().numpy()) - U = np.dot(S, y)/np.sum(S, axis=1).reshape(-1, 1) + U = np.dot(S, y) / np.sum(S, axis=1).reshape(-1, 1) U = np.where(label_index, label, U) - self.plabel_window = torch.from_numpy(U[:len(U)-self.num_centers]) + self.plabel_window = torch.from_numpy(U[: len(U) - self.num_centers]) def return_window(self): - #Returns the samples, pseudo-labels and true labels within the windows. + # Returns the samples, pseudo-labels and true labels within the windows. return self.data_window, self.plabel_window, self.label_index + class def_loss(nn.Module): def __init__(self, model, lam=0.3, alpha=0.2): super().__init__() @@ -190,14 +228,14 @@ def L2loss(self): # l2 regularization on the network weights. l2_loss = torch.tensor(0.0, requires_grad=True) for name, parma in self.model.named_parameters(): - if 'bias' not in name: + if "bias" not in name: l2_loss = l2_loss + (0.5 * torch.sum(torch.pow(parma, 2))) return l2_loss def forward(self, y_pred, y_true, label_index): - #Computes supervised loss for labeled samples and unsupervised loss for unlabeled samples. - labeled = torch.squeeze(label_index == 1., 1) - unlabeled = torch.squeeze(label_index == 0., 1) + # Computes supervised loss for labeled samples and unsupervised loss for unlabeled samples. + labeled = torch.squeeze(label_index == 1.0, 1) + unlabeled = torch.squeeze(label_index == 0.0, 1) y_pred_labeled = y_pred[labeled] y_true_label = y_true[labeled] @@ -205,17 +243,24 @@ def forward(self, y_pred, y_true, label_index): y_pred_unlabeled = y_pred[unlabeled] y_sudo_unlabeled = y_true[unlabeled] - first_item = -torch.mean(y_true_label * torch.log(y_pred_labeled + 1e-8) + (1 - y_true_label) * torch.log(1 - y_pred_labeled + 1e-8)) - second_item = -torch.mean(y_sudo_unlabeled * torch.log(y_pred_unlabeled + 1e-8) + (1 - y_sudo_unlabeled) * torch.log(1 - y_pred_unlabeled + 1e-8)) + first_item = -torch.mean( + y_true_label * torch.log(y_pred_labeled + 1e-8) + + (1 - y_true_label) * torch.log(1 - y_pred_labeled + 1e-8) + ) + second_item = -torch.mean( + y_sudo_unlabeled * torch.log(y_pred_unlabeled + 1e-8) + + (1 - y_sudo_unlabeled) * torch.log(1 - y_pred_unlabeled + 1e-8) + ) l2_loss = self.L2loss() / len(y_pred) - loss = first_item + self.lam*second_item + self.alpha*l2_loss + loss = first_item + self.lam * second_item + self.alpha * l2_loss return loss class OSNN(ClassifierSSL): def __init__( self, + schema=None, num_center=10, n_out=1, window_size=200, diff --git a/src/capymoa/stream/PytorchStream.py b/src/capymoa/stream/PytorchStream.py index 3d664c4f..cf94a494 100644 --- a/src/capymoa/stream/PytorchStream.py +++ b/src/capymoa/stream/PytorchStream.py @@ -1,11 +1,8 @@ -from jpype import JObject - -import numpy as np import torch from capymoa.stream import Stream, Schema -from capymoa.stream.stream import _init_moa_stream_and_create_moa_header -from capymoa.stream.instance import ( +from capymoa.stream._stream import _init_moa_stream_and_create_moa_header +from capymoa.instance import ( LabeledInstance, RegressionInstance, ) diff --git a/src/capymoa/stream/__init__.py b/src/capymoa/stream/__init__.py index 2e5c4cb0..dc19e74e 100644 --- a/src/capymoa/stream/__init__.py +++ b/src/capymoa/stream/__init__.py @@ -1,11 +1,4 @@ -from .stream import ( - Stream, - Schema, - ARFFStream, - stream_from_file, - CSVStream -) -from .generator import RandomTreeGenerator +from ._stream import Stream, Schema, ARFFStream, stream_from_file, CSVStream from .PytorchStream import PytorchStream __all__ = [ @@ -13,7 +6,6 @@ "Schema", "stream_from_file", "ARFFStream", - "RandomTreeGenerator", "PytorchStream", - "CSVStream" + "CSVStream", ] diff --git a/src/capymoa/stream/stream.py b/src/capymoa/stream/_stream.py similarity index 86% rename from src/capymoa/stream/stream.py rename to src/capymoa/stream/_stream.py index c8900b5d..15027262 100644 --- a/src/capymoa/stream/stream.py +++ b/src/capymoa/stream/_stream.py @@ -15,7 +15,7 @@ # MOA/Java imports -from capymoa.stream.instance import ( +from capymoa.instance import ( Instance, LabeledInstance, RegressionInstance, @@ -35,9 +35,9 @@ class Schema: """ def __init__(self, moa_header: InstancesHeader): - """Construct a schema by wrapping a :class:`InstancesHeader`. + """Construct a schema by wrapping a ``InstancesHeader``. - To create a schema without an :class:`InstancesHeader` use + To create a schema without an ``InstancesHeader`` use :meth:`from_custom` method. :param moa_header: A Java MOA header object. @@ -218,7 +218,7 @@ def __init__( ): """Construct a Stream from a MOA stream object. - Usually, you will want to construct a Stream using the :func:`stream_from_file` + Usually, you will want to construct a Stream using the :func:`capymoa.stream.stream_from_file` function. :param moa_stream: The MOA stream object to read instances from. Is None @@ -429,7 +429,10 @@ def stream_from_file( targets = targets.astype(int) x_features = x_features[:, :-1] return NumpyStream( - x_features, targets, dataset_name=dataset_name, enforce_regression=enforce_regression + x_features, + targets, + dataset_name=dataset_name, + enforce_regression=enforce_regression, ) @@ -571,18 +574,20 @@ def _add_instances_to_moa_stream(moa_stream, moa_header, X, y): moa_stream.add(instance) -class CSVStream(Stream): - def __init__(self, - csv_file_path, - dtypes: list = None, # [('column1', np.float64), ('column2', np.int32), ('column3', np.float64), ('column3', str)] reads nomonal attributes as str - values_for_nominal_features={}, # {i: [1,2,3], k: [Aa, BB]}. Key is integer. Values are turned into strings - class_index: int = -1, - values_for_class_label: list = None, - target_attribute_name=None, - enforce_regression=False, - skip_header: bool = False, - delimiter=','): +class CSVStream(Stream): + def __init__( + self, + csv_file_path, + dtypes: list = None, # [('column1', np.float64), ('column2', np.int32), ('column3', np.float64), ('column3', str)] reads nomonal attributes as str + values_for_nominal_features={}, # {i: [1,2,3], k: [Aa, BB]}. Key is integer. Values are turned into strings + class_index: int = -1, + values_for_class_label: list = None, + target_attribute_name=None, + enforce_regression=False, + skip_header: bool = False, + delimiter=",", + ): self.csv_file_path = csv_file_path self.values_for_nominal_features = values_for_nominal_features self.class_index = class_index @@ -592,56 +597,96 @@ def __init__(self, self.skip_header = skip_header self.delimiter = delimiter - self.dtypes = [] # [('column1', np.float64), ('column2', np.int32), ('column3', np.float64), ('column3', str)] reads nomonal attributes as str - if dtypes is None or len(dtypes) == 0: # data definition for each column not provided - if len(self.values_for_nominal_features) == 0: # data definition for nominal features are given + self.dtypes = ( + [] + ) # [('column1', np.float64), ('column2', np.int32), ('column3', np.float64), ('column3', str)] reads nomonal attributes as str + if ( + dtypes is None or len(dtypes) == 0 + ): # data definition for each column not provided + if ( + len(self.values_for_nominal_features) == 0 + ): # data definition for nominal features are given # need to infer number of columns, then generate full data definition using nominal information # LOADS FIRST TWO ROWS INTO THE MEMORY - data = np.genfromtxt(self.csv_file_path, delimiter=self.delimiter, dtype=None, names=True, - skip_header=0, max_rows=2) + data = np.genfromtxt( + self.csv_file_path, + delimiter=self.delimiter, + dtype=None, + names=True, + skip_header=0, + max_rows=2, + ) if not self.enforce_regression and self.values_for_class_label is None: # LOADS THE FULL FILE INTO THE MEMORY - data = np.genfromtxt(self.csv_file_path, delimiter=self.delimiter, dtype=None, names=True, - skip_header=1 if skip_header else 0) + data = np.genfromtxt( + self.csv_file_path, + delimiter=self.delimiter, + dtype=None, + names=True, + skip_header=1 if skip_header else 0, + ) y = data[data.dtype.names[self.class_index]] self.values_for_class_label = [str(value) for value in np.unique(y)] for i, data_info in enumerate(data.dtype.descr): column_name, data_type = data_info - if self.values_for_nominal_features.get(i) is not None: # i is in nominal feature keys - self.dtypes.append((column_name, 'str')) + if ( + self.values_for_nominal_features.get(i) is not None + ): # i is in nominal feature keys + self.dtypes.append((column_name, "str")) else: self.dtypes.append((column_name, data_type)) - else: # need to infer data definitions + else: # need to infer data definitions # LOADS THE FULL FILE INTO THE MEMORY - data = np.genfromtxt(self.csv_file_path, delimiter=self.delimiter, dtype=None, names=True, - skip_header=1 if skip_header else 0) + data = np.genfromtxt( + self.csv_file_path, + delimiter=self.delimiter, + dtype=None, + names=True, + skip_header=1 if skip_header else 0, + ) self.dtypes = data.dtype if not self.enforce_regression and self.values_for_class_label is None: y = data[data.dtype.names[self.class_index]] self.values_for_class_label = [str(value) for value in np.unique(y)] - else: # data definition for each column are provided + else: # data definition for each column are provided self.dtypes = dtypes self.total_number_of_lines = 0 if self.skip_header: self.n_lines_to_skip = 1 else: - row1_data = np.genfromtxt(self.csv_file_path, delimiter=self.delimiter, dtype=None, names=True, skip_header=0,max_rows=1) - row2_data = np.genfromtxt(self.csv_file_path, delimiter=self.delimiter, dtype=None, names=True, skip_header=1, max_rows=1) + row1_data = np.genfromtxt( + self.csv_file_path, + delimiter=self.delimiter, + dtype=None, + names=True, + skip_header=0, + max_rows=1, + ) + row2_data = np.genfromtxt( + self.csv_file_path, + delimiter=self.delimiter, + dtype=None, + names=True, + skip_header=1, + max_rows=1, + ) if row1_data.dtype.names != row2_data.dtype.names: self.n_lines_to_skip = 1 else: self.n_lines_to_skip = 0 - self.__moa_stream_with_only_header, self.moa_header = _init_moa_stream_and_create_moa_header( - number_of_instances=1, # we only need this to initialize the MOA header - feature_names = [data_info[0] for data_info in self.dtypes], - values_for_nominal_features = self.values_for_nominal_features, - values_for_class_label = self.values_for_class_label, + self.__moa_stream_with_only_header, self.moa_header = ( + _init_moa_stream_and_create_moa_header( + number_of_instances=1, # we only need this to initialize the MOA header + feature_names=[data_info[0] for data_info in self.dtypes], + values_for_nominal_features=self.values_for_nominal_features, + values_for_class_label=self.values_for_class_label, dataset_name="CSVDataset", - target_attribute_name = self.target_attribute_name, - enforce_regression = self.enforce_regression, + target_attribute_name=self.target_attribute_name, + enforce_regression=self.enforce_regression, ) + ) self.schema = Schema(moa_header=self.moa_header) super().__init__(schema=self.schema, CLI=None, moa_stream=None) @@ -660,15 +705,32 @@ def next_instance(self): if not self.has_more_instances(): return None # skip header - data = np.genfromtxt(self.csv_file_path, delimiter=self.delimiter, dtype=self.dtypes, names=None, skip_header=self.n_lines_to_skip, max_rows=1) + data = np.genfromtxt( + self.csv_file_path, + delimiter=self.delimiter, + dtype=self.dtypes, + names=None, + skip_header=self.n_lines_to_skip, + max_rows=1, + ) # np.genfromtxt() returns a structured https://numpy.org/doc/stable/user/basics.rec.html#structured-arrays self.n_lines_to_skip += 1 # data = np.expand_dims(data, axis=0) # y = data[[data.dtype.names[self.class_index]]].view('i4') - y = rfn.structured_to_unstructured(data[[data.dtype.names[self.class_index]]])[0] + y = rfn.structured_to_unstructured(data[[data.dtype.names[self.class_index]]])[ + 0 + ] # X = data[[item for item in data.dtype.names if item != data.dtype.names[self.class_index]]].view('f4') - X = rfn.structured_to_unstructured(data[[item for item in data.dtype.names if item != data.dtype.names[self.class_index]]]) + X = rfn.structured_to_unstructured( + data[ + [ + item + for item in data.dtype.names + if item != data.dtype.names[self.class_index] + ] + ] + ) if self.schema.is_classification(): return LabeledInstance.from_array(self.schema, X, y) @@ -688,4 +750,4 @@ def get_moa_stream(self): def restart(self): self.total_number_of_lines = 0 - self.n_lines_to_skip = 1 if self.skip_header else 0 \ No newline at end of file + self.n_lines_to_skip = 1 if self.skip_header else 0 diff --git a/src/capymoa/stream/drift.py b/src/capymoa/stream/drift.py index f60a77c7..81451b0c 100644 --- a/src/capymoa/stream/drift.py +++ b/src/capymoa/stream/drift.py @@ -2,7 +2,7 @@ import re -from capymoa.stream.stream import Stream +from capymoa.stream._stream import Stream from capymoa._utils import _get_moa_creation_CLI from moa.streams import ConceptDriftStream as MOA_ConceptDriftStream diff --git a/tasks.py b/tasks.py index fc4e71fe..7c3a9d65 100644 --- a/tasks.py +++ b/tasks.py @@ -34,13 +34,14 @@ def all_exist(files: List[str] = None, directories: List[str] = None) -> bool: def docs_build(ctx: Context, ignore_warnings: bool = False): """Build the documentation using Sphinx.""" warn = "-W" if not ignore_warnings else "" + nitpicky = "-n" if not ignore_warnings else "" doc_dir = Path("docs/_build") doc_dir.mkdir(exist_ok=True, parents=True) cpu = cpu_count() // 2 print("Building documentation...") - ctx.run(f"python -m sphinx build {warn} --color -E -b html docs {doc_dir}") + ctx.run(f"python -m sphinx build {warn} {nitpicky} --color -E -b html docs {doc_dir}") print("-" * 80) print("Documentation is built and available at:") diff --git a/test_utility/ssl_helpers.py b/test_utility/ssl_helpers.py deleted file mode 100644 index 560ae67a..00000000 --- a/test_utility/ssl_helpers.py +++ /dev/null @@ -1,23 +0,0 @@ -import pytest -from capymoa.evaluation.evaluation import prequential_SSL_evaluation -from capymoa.learner import ClassifierSSL -from capymoa.stream import Stream - -def assert_ssl_evaluation( - learner: ClassifierSSL, - stream: Stream, - expectation: float, - label_probability: float = 0.01, - max_instances: int = 1000, -): - results = prequential_SSL_evaluation( - stream=stream, - learner=learner, - label_probability=label_probability, - window_size=10, - max_instances=max_instances, - ) - - assert results["cumulative"].accuracy() == pytest.approx(expectation), \ - f"Expected accuracy of {expectation} but got {results['cumulative'].accuracy()}" + \ - f" for learner {learner} on stream {stream}" diff --git a/tests/test_CPSSDS.py b/tests/test_CPSSDS.py deleted file mode 100644 index 81cd87cc..00000000 --- a/tests/test_CPSSDS.py +++ /dev/null @@ -1,28 +0,0 @@ -from capymoa.datasets.datasets import ElectricityTiny, CovtypeTiny -from capymoa.learner.ssl.classifier.CPSSDS import CPSSDS -from test_utility.ssl_helpers import assert_ssl_evaluation -import pytest - - -@pytest.mark.parametrize( - "learner, stream, expectation", - [ - ("NaiveBayes", ElectricityTiny(), 76.6), - ("HoeffdingTree", ElectricityTiny(), 66.2), - ("NaiveBayes", CovtypeTiny(), 55.7), - ("HoeffdingTree", CovtypeTiny(), 53.3), - ], - ids=[ - "ElectricityTiny-NaiveBayes", - "ElectricityTiny-HoeffdingTree", - "CovtypeTiny-NaiveBayes", - "CovtypeTiny-HoeffdingTree", - ], -) -def test_CPSSDS(learner, stream, expectation): - assert_ssl_evaluation( - CPSSDS(learner, 100, schema=stream.schema), - stream, - expectation, - label_probability=0.5, - ) diff --git a/tests/test_OSNN.py b/tests/test_OSNN.py deleted file mode 100644 index a75c0556..00000000 --- a/tests/test_OSNN.py +++ /dev/null @@ -1,23 +0,0 @@ -from capymoa.datasets.datasets import ElectricityTiny, CovtypeTiny -from test_utility.ssl_helpers import assert_ssl_evaluation -import pytest -import importlib - -@pytest.mark.parametrize( - "stream, expectation", - [ - (ElectricityTiny(), 46.1), - (CovtypeTiny(), 26.3), - ], - ids=["ElectricityTiny", "CovtypeTiny"], -) -def test_OSNN(stream, expectation): - pytest.importorskip("torch.nn", reason="PyTorch not installed. Skipping test.") - OSNN = importlib.import_module("capymoa.learner.ssl.classifier.OSNN").OSNN - # The optimizer steps are set to 10 to speed up the test - learner = OSNN(optim_steps=10) - assert_ssl_evaluation( - learner, - stream, - expectation, - ) diff --git a/tests/test_batch.py b/tests/test_batch.py index ad72451c..40a8bf61 100644 --- a/tests/test_batch.py +++ b/tests/test_batch.py @@ -1,7 +1,7 @@ -from capymoa.datasets.datasets import ElectricityTiny -from capymoa.learner.ssl.classifier.batch import BatchClassifierSSL -from capymoa.stream.stream import Schema, NumpyStream -from capymoa.evaluation.evaluation import prequential_SSL_evaluation +from capymoa.datasets._datasets import ElectricityTiny +from capymoa.ssl.classifier._batch import BatchClassifierSSL +from capymoa.stream._stream import Schema, NumpyStream +from capymoa.evaluation.evaluation import prequential_ssl_evaluation import numpy as np @@ -69,7 +69,7 @@ def test_batch_basic(): stream = NumpyStream(x, y) learner = _DummyBatchClassifierSSL(batch_size, stream.schema, class_value_type=str) - prequential_SSL_evaluation( + prequential_ssl_evaluation( stream=stream, learner=learner, label_probability=0.01, window_size=100 ) @@ -83,7 +83,7 @@ def test_batch_real(): assert stream.schema.get_num_attributes() == 6 learner = _DummyBatchClassifierSSL(128, stream.schema, class_value_type=str) - prequential_SSL_evaluation( + prequential_ssl_evaluation( stream=stream, learner=learner, label_probability=0.01, diff --git a/tests/test_classifiers.py b/tests/test_classifiers.py index ac8599a3..b619d227 100644 --- a/tests/test_classifiers.py +++ b/tests/test_classifiers.py @@ -1,22 +1,23 @@ from capymoa.evaluation import ClassificationEvaluator, ClassificationWindowedEvaluator -from capymoa.learner.classifier import ( +from capymoa.classifier import ( EFDT, HoeffdingTree, AdaptiveRandomForest, OnlineBagging, NaiveBayes, ) -from capymoa.learner import Classifier, MOAClassifier +from capymoa.base import Classifier +from capymoa.base import MOAClassifier from capymoa.datasets import ElectricityTiny import pytest from functools import partial from typing import Callable, Optional -from capymoa.learner.learners import _extract_moa_learner_CLI -from capymoa.learner.splitcriteria import InfoGainSplitCriterion +from capymoa.base import _extract_moa_learner_CLI +from capymoa.splitcriteria import InfoGainSplitCriterion -from capymoa.stream.stream import Schema +from capymoa.stream._stream import Schema -from capymoa.learner.classifier.sklearn import PassiveAggressiveClassifier +from capymoa.classifier import PassiveAggressiveClassifier @pytest.mark.parametrize( @@ -34,8 +35,14 @@ ), (partial(NaiveBayes), 84.0, 91.0, None), ], - ids=["OnlineBagging", "AdaptiveRandomForest", "HoeffdingTree", "EFDT", "EFDT_gini", "NaiveBayes"], - + ids=[ + "OnlineBagging", + "AdaptiveRandomForest", + "HoeffdingTree", + "EFDT", + "EFDT_gini", + "NaiveBayes", + ], ) def test_classifiers( learner_constructor: Callable[[Schema], Classifier], @@ -62,7 +69,6 @@ def test_classifiers( ) learner: Classifier = learner_constructor(schema=stream.get_schema()) - # learner = learner_constructor(schema=stream.get_schema()) while stream.has_more_instances(): instance = stream.next_instance() @@ -71,6 +77,7 @@ def test_classifiers( win_evaluator.update(instance.y_index, prediction) learner.train(instance) + # Check if the accuracy matches the expected value for both evaluator types actual_acc = evaluator.accuracy() actual_win_acc = win_evaluator.accuracy() assert actual_acc == pytest.approx( @@ -80,11 +87,7 @@ def test_classifiers( win_accuracy, abs=0.1 ), f"Windowed Eval: Expected accuracy of {win_accuracy:0.1f} got {actual_win_acc:0.1f}" + # Optionally check the CLI string if it was provided if isinstance(learner, MOAClassifier) and cli_string is not None: cli_str = _extract_moa_learner_CLI(learner).strip("()") - assert ( - cli_str == cli_string - ), "CLI does not match expected value" - - # assert evaluator.accuracy() == pytest.approx(accuracy, abs=0.1) - # assert win_evaluator.accuracy() == pytest.approx(win_accuracy, abs=0.1) + assert cli_str == cli_string, "CLI does not match expected value" diff --git a/tests/test_regressors.py b/tests/test_regressors.py index de601c10..14ba21ca 100644 --- a/tests/test_regressors.py +++ b/tests/test_regressors.py @@ -1,6 +1,6 @@ from capymoa.evaluation import RegressionEvaluator, RegressionWindowedEvaluator from capymoa.datasets import Fried -from capymoa.learner.regressor import ( +from capymoa.regressor import ( KNNRegressor, AdaptiveRandomForestRegressor, FIMTDD, @@ -12,7 +12,7 @@ import pytest from functools import partial -from capymoa.learner import Regressor +from capymoa.base import Regressor @pytest.mark.parametrize( diff --git a/tests/test_ssl_classifiers.py b/tests/test_ssl_classifiers.py new file mode 100644 index 00000000..47973eb0 --- /dev/null +++ b/tests/test_ssl_classifiers.py @@ -0,0 +1,55 @@ +from capymoa.datasets._datasets import ElectricityTiny, CovtypeTiny +from capymoa.ssl.classifier import OSNN + +import pytest +from capymoa.evaluation.evaluation import prequential_ssl_evaluation +from capymoa.base import ClassifierSSL +from capymoa.stream import Stream +from functools import partial + + +def assert_ssl_evaluation( + learner: ClassifierSSL, + stream: Stream, + expectation: float, + label_probability: float = 0.01, + max_instances: int = 1000, +): + results = prequential_ssl_evaluation( + stream=stream, + learner=learner, + label_probability=label_probability, + window_size=10, + max_instances=max_instances, + ) + + assert results["cumulative"].accuracy() == pytest.approx(expectation), ( + f"Expected accuracy of {expectation} but got {results['cumulative'].accuracy()}" + + f" for learner {learner} on stream {stream}" + ) + +@pytest.mark.parametrize( + "learner_constructor, stream_constructor, expectation, label_probability", + [ + (partial(OSNN, optim_steps=10), ElectricityTiny, 46.1, None), + (partial(OSNN, optim_steps=10), CovtypeTiny, 26.3, None), + ], + ids=[ + "OSNN_ElectricityTiny", + "OSNN_CovtypeTiny", + ], +) +def test_ssl_classifiers(learner_constructor, stream_constructor, expectation, label_probability): + # The optimizer steps are set to 10 to speed up the test + stream = stream_constructor() + learner = learner_constructor(schema=stream.get_schema()) + + if label_probability is None: + label_probability = 0.01 + + assert_ssl_evaluation( + learner, + stream, + expectation, + label_probability=label_probability, + ) diff --git a/tests/test_stream.py b/tests/test_stream.py index 4801bd90..eb5356d6 100644 --- a/tests/test_stream.py +++ b/tests/test_stream.py @@ -1,5 +1,6 @@ """This module is for testing the speeds of different stream implementations. """ + import time from capymoa.stream import stream_from_file from cProfile import Profile @@ -7,33 +8,41 @@ import numpy as np from capymoa.stream import Stream -from capymoa.stream.instance import Instance -from capymoa.stream.stream import CSVStream +from capymoa.instance import Instance +from capymoa.stream._stream import CSVStream import csv + def _get_streams() -> List[Stream]: return [ stream_from_file("data/electricity_tiny.csv"), stream_from_file("data/electricity_tiny.arff"), - CSVStream("data/electricity_tiny.csv") + CSVStream("data/electricity_tiny.csv"), ] + def test_stream_consistency(): streams = _get_streams() def _has_more_instance(): return [stream.has_more_instances() for stream in streams] - + def _next_instance(): return [stream.next_instance() for stream in streams] - + i = 0 while any(_has_more_instance()): - assert all(_has_more_instance()), "Not all streams have the same number of instances" + assert all( + _has_more_instance() + ), "Not all streams have the same number of instances" i += 1 instances = _next_instance() prototype = instances.pop() for instance in instances: - assert np.allclose(prototype.x, instance.x), f"Streams are not consistent at instance {i}" - assert prototype.y_index == instance.y_index, f"Streams are not consistent at instance {i}" + assert np.allclose( + prototype.x, instance.x + ), f"Streams are not consistent at instance {i}" + assert ( + prototype.y_index == instance.y_index + ), f"Streams are not consistent at instance {i}"