A_result_1_none_dimVar2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using package ml ml \n",
    "\n",
    "import torch\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial.distance import cosine\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import decomposition\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchmetrics.functional import pairwise_cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('adj/english-adjectives.txt') as f:\n",
    "    adjatives = [line.strip() for line in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "model = SentenceTransformer('bert-base-nli-mean-tokens')\n",
    "\n",
    "#from scipy import cosine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import parrot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# par = parrot(model_tag=\"prithivida/parrot_paraphraser_on_T5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# {'Q22.2': 'I like the idea of safe automated vehicles if they make it easier for me to travel.',\n",
    "#  'Q22.3': 'I trust automated vehicles to operate safely.',\n",
    "#  'Q22.4': 'I would consider buying an automated vehicle.',\n",
    "#  'Q22.5': 'I would be comfortable riding in an automated vehicle.',\n",
    "#  'Q22.6': 'I would allow my children to be transported in an automated vehicle.',\n",
    "#  'Q22.7': 'I would pay a higher price for a partly or fully automated vehicle than a non-automated vehicle.',\n",
    "#  'Q22.8': 'I would enjoy being able to use my time in an automated vehicle for things like reading, or relaxing rather than driving.',\n",
    "#  'Q22.9': 'I enjoy actually driving a car, and would prefer that to riding in an automated vehicle.'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pca = 35\n",
    "scen = 0\n",
    "embed_dim = num_pca\n",
    "removed_quest = ['Q20.2', 'Q21.2', 'Q22.2']\n",
    "\n",
    "dim_of_variability = 1\n",
    "pca = decomposition.PCA(n_components=embed_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "compress_method = \"none\" \n",
    "# none\n",
    "# PCA\n",
    "# auto_encoder\n",
    "NN_PARAM = [100, 10] # last number \n",
    "lamb = 5\n",
    "# parrot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "USE_MANUAL = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=num_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_sentences(doc_name=\"sentences.txt\"):\n",
    "    file = open(doc_name, 'r')\n",
    "    lines = file.readlines()\n",
    "    \n",
    "    ret_dic = {}\n",
    "    for index, line in enumerate(lines):\n",
    "        if index%2 == 0:\n",
    "            key= line.strip()\n",
    "        else:\n",
    "            ret_dic[key] = line.strip()\n",
    "    \n",
    "    return ret_dic\n",
    "\n",
    "sentences =load_sentences()\n",
    "question_num = list(sentences.keys())[:-1]\n",
    "\n",
    "def load_sentences_excel(doc_name=\"manual_values.xlsx\", subset = 0):\n",
    "    if subset == 0:\n",
    "        sheet_name = \"raw_questions\"\n",
    "    else:\n",
    "        sheet_name = f\"subset_{subset}\"\n",
    "        \n",
    "    df = pd.read_excel(\"manual_values.xlsx\", sheet_name=sheet_name)\n",
    "    \n",
    "    return dict(list(zip(df[\"Question\"], df[\"Sentence\"]))), df\n",
    "# sentences =load_sentences()\n",
    "# question_num = list(sentences.keys())[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def list_text_to_tens(sentences):\n",
    "    return torch.FloatTensor(model.encode(sentences))\n",
    "\n",
    "adj_embed = list_text_to_tens(adjatives)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def proces_sent(sent_dic, NUM_PEOPLE, get_strange_quest=7):\n",
    "    #assume reponses are ordered 1 per person\n",
    "    \n",
    "    sentences = []\n",
    "    questions = []\n",
    "    for key, sentence in sent_dic.items():\n",
    "        questions.append(key)\n",
    "        sentences.append(sentence)\n",
    "    \n",
    "    sent_embed = model.encode(sentences)\n",
    "    \n",
    "    \n",
    "    # find wierdest questions to be removed\n",
    "    sent_dic_distances = []\n",
    "    for base_vec, question in zip(sent_embed, sent_dic.keys()):\n",
    "        distances = []\n",
    "        for compare_vec in sent_embed:\n",
    "            distances.append(cosine(compare_vec, base_vec))\n",
    "        sent_dic_distances.append((question, sum(distances)/len(distances)))\n",
    "        \n",
    "    #pca.fit(sent_embed)\n",
    "    #red_embed = pca.transform(sent_embed)\n",
    "    sorted_sent_dic_distances = sorted(sent_dic_distances, key=lambda x: x[1])\n",
    "    bad_questions = [quest for quest, _ in sorted_sent_dic_distances][-get_strange_quest:]\n",
    "    \n",
    "    ret_tens = torch.FloatTensor(sent_embed)\n",
    "    return torch.concat([ret_tens for _ in range(NUM_PEOPLE)]), bad_questions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# foo = dict([a for a in enumerate(adjatives)])\n",
    "\n",
    "# measurements, _ = proces_sent(foo, 1 ,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# measurements.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_responses(questions, filepath = \"responses.xlsx\"):\n",
    "    df = pd.read_excel(filepath, sheet_name=\"Mobility and Future Mobility CL\")\n",
    "    df = df[questions]\n",
    "    df = df.replace([\"Strongly disagree\", \"Disagree\", \"Somewhat disagree\", \"Somewhat agree\", \"Agree\", \"Strongly agree\"], \n",
    "                    [0, 1, 2, 3, 4, 5])\n",
    "    \n",
    "    #df = df.replace([0,1,2,3,4,5], [0,0,0,1,1,1])\n",
    "    # TODO investigate NANS\n",
    "    df = df.fillna(0)\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def proces_responses(responses):\n",
    "    ret_tens = responses.to_numpy()\n",
    "    x, y = ret_tens.shape\n",
    "    ret_tens = ret_tens.reshape(x*y)\n",
    "    \n",
    "    #everyone at least strongly disagrees\n",
    "    disagree = 1.*(ret_tens >= .5).reshape(len(ret_tens), 1)\n",
    "    som_disagree = 1.*(ret_tens >=1.5).reshape(len(ret_tens), 1)\n",
    "    som_agree = 1.*(ret_tens >=2.5).reshape(len(ret_tens), 1)\n",
    "    agree = 1.*(ret_tens >=3.5).reshape(len(ret_tens), 1)\n",
    "    stong_agree = 1.*(ret_tens >= 4.5).reshape(len(ret_tens), 1)\n",
    "    \n",
    "    ret_likard = np.concatenate((disagree, som_disagree, som_agree, agree, stong_agree), axis=1)\n",
    "    \n",
    "    #TODO process into hot 1 embedding \n",
    "    return x, torch.FloatTensor(ret_likard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_sentence(in_string):\n",
    "    x = model.encode(in_string)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import floor\n",
    "\n",
    "def get_people_one_hot():\n",
    "    num_q = int(len(y_train)/NUM_PEOPLE)\n",
    "    to_tens = []\n",
    "\n",
    "    for index in range(len(x_train)):\n",
    "        one_hot = floor(index/num_q)\n",
    "        temp_list = [0. for i in range(NUM_PEOPLE)]\n",
    "        temp_list[one_hot] = 1.\n",
    "        to_tens.append(temp_list)\n",
    "    \n",
    "    return torch.tensor(to_tens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list(questions.keys()).index(['Q22.2', 'Q22.3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_quest(rem_questions, data_set):\n",
    "    indexes_to_be_rem = [list(questions.keys()).index(key) for key in rem_questions]\n",
    "    \n",
    "    remain_quest = [index for index in range(len(data_set)) if index%len(questions) not in indexes_to_be_rem]\n",
    "    removed_quest = [index for index in range(len(data_set)) if index%len(questions) in indexes_to_be_rem]\n",
    "    \n",
    "    remain_data_set = data_set[remain_quest]\n",
    "    removed_data_set = data_set[removed_quest]\n",
    "    \n",
    "    return remain_data_set, removed_data_set\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_manual_embed(df):\n",
    "    return df.iloc[:, 2:].to_numpy()\n",
    "\n",
    "def get_manual_embed_for_training(df, x_train):\n",
    "    data = get_manual_embed(df)\n",
    "    \n",
    "    if data.shape[0] * NUM_PEOPLE == x_train.shape[0]:\n",
    "        to_be_concat = [torch.tensor(data.copy()).type(torch.float32) for _ in range(NUM_PEOPLE)]\n",
    "    \n",
    "    return torch.concat(to_be_concat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_person_and_responses(data, one_hot_people, person_ID):\n",
    "    # used for taking the average of the persons responses\n",
    "    persons_questions = one_hot_people[:, person_ID] >= 0.5\n",
    "    \n",
    "    return data[persons_questions].sum(axis=1)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load quesitions\n",
    "questions, df = load_sentences_excel(subset=scen)\n",
    "\n",
    "quest_subset = questions.keys()\n",
    "\n",
    "#load responses\n",
    "responses = load_responses(quest_subset)\n",
    "\n",
    "\n",
    "#process everything\n",
    "NUM_PEOPLE, y_train = proces_responses(responses)\n",
    "\n",
    "x_train, bad_questions = proces_sent(questions, NUM_PEOPLE, get_strange_quest=5)\n",
    "\n",
    "one_hot_people = get_people_one_hot()\n",
    "\n",
    "if USE_MANUAL:\n",
    "    manual_embed = get_manual_embed_for_training(df, x_train)\n",
    "    x_train = manual_embed\n",
    "\n",
    "x_all = x_train\n",
    "y_all = y_train\n",
    "one_hot_all = one_hot_people\n",
    "# remove given questions\n",
    "x_train, x_test = remove_quest(removed_quest, x_train)\n",
    "y_train, y_test = remove_quest(removed_quest, y_train)\n",
    "one_hot_people, one_hot_people_test = remove_quest(removed_quest, one_hot_people)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "927"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NUM_PEOPLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7416"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8*927"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([37080, 768])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([37080, 768])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2781, 768])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([37080, 5])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2781, 5])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Logit(torch.nn.Module):\n",
    "    \"\"\"\n",
    "    Logit model between two options \n",
    "    \"\"\"\n",
    "    def __init__(self, input_size, num_people=927, dim_var=1):\n",
    "        super(Logit, self).__init__()\n",
    "        self.num_people = num_people\n",
    "        \n",
    "        if compress_method == \"auto_encoder\" or compress_method == \"parrot\":\n",
    "            self.input_size = NN_PARAM[-1]\n",
    "        elif compress_method == \"PCA\":\n",
    "            self.input_size = num_pca\n",
    "        elif compress_method == \"cosine_words\":\n",
    "            self.input_size = len(adj_embed)\n",
    "        else:\n",
    "            self.input_size = input_size\n",
    "        \n",
    "        self.has_fit_pca = False\n",
    "        \n",
    "        self.embedd_weights = torch.nn.Parameter(torch.rand(self.input_size, 1))\n",
    "        self.bias = torch.nn.Parameter(torch.rand(1,1))\n",
    "    \n",
    "        self.person_het_vector = torch.nn.Parameter(torch.rand(num_people, dim_var))\n",
    "        self.het_dimension = torch.nn.Parameter(torch.rand(dim_var, self.input_size))\n",
    "        \n",
    "        #self.util_to_likert = torch.nn.Linear(1, 5)\n",
    "        self.likert = torch.nn.Parameter(torch.rand(1, 5))\n",
    "        \n",
    "        \n",
    "        self.sigmoid = torch.nn.Sigmoid()\n",
    "        self.relu = torch.nn.ReLU()\n",
    "        self.softmax = torch.nn.Softmax(dim=1)\n",
    "        \n",
    "        # create an encoding nn network\n",
    "        if compress_method == \"auto_encoder\" or compress_method == \"parrot\":\n",
    "            #initialise hidden layers \n",
    "            self.encoder = torch.nn.ModuleList()\n",
    "            prev_layer = input_size\n",
    "            for layer_size in NN_PARAM:\n",
    "                self.encoder.append(torch.nn.Linear(prev_layer, layer_size))\n",
    "                prev_layer = layer_size\n",
    "    \n",
    "    def forward(self, chosen_options, pers_respond, floating_likart=False):\n",
    "        \n",
    "        chosen_options = self.compress_data(chosen_options, pers_respond)\n",
    "        \n",
    "        num_entries = chosen_options.shape[0]\n",
    "        \n",
    "        individ_variability = pers_respond @ self.person_het_vector\n",
    "        variabilty_vecs = individ_variability @ self.het_dimension\n",
    "        \n",
    "        seed_values = (self.embedd_weights @ torch.ones(1, num_entries)).T\n",
    "        \n",
    "        variabilty_vecs = variabilty_vecs + seed_values \n",
    "        \n",
    "        utility =  torch.sum((chosen_options * variabilty_vecs),  dim=1) + self.bias\n",
    "        \n",
    "        likart = utility.T @ torch.FloatTensor([[1,1,1,1,1]]) + torch.ones_like(utility.T) @ self.likert \n",
    "        \n",
    "        likart = self.sigmoid(likart)\n",
    "        # instead ->\n",
    "        return likart\n",
    "    \n",
    "    def get_specific_activations(self, chosen_options, pers_respond, floating_likart=False):\n",
    "        chosen_options = self.compress_data(chosen_options, pers_respond)\n",
    "        \n",
    "        num_entries = chosen_options.shape[0]\n",
    "        \n",
    "        individ_variability = pers_respond @ self.person_het_vector\n",
    "        variabilty_vecs = individ_variability @ self.het_dimension\n",
    "        \n",
    "        seed_values = (self.embedd_weights @ torch.ones(1, num_entries)).T\n",
    "        \n",
    "        variabilty_vecs = variabilty_vecs + seed_values \n",
    "        \n",
    "        return chosen_options * variabilty_vecs\n",
    "    \n",
    "    \n",
    "    def compress_data(self, x_data, pers_respond):\n",
    "        \n",
    "        if compress_method == \"none\":\n",
    "            return x_data\n",
    "        elif compress_method == \"PCA\":\n",
    "            return self.PCA(x_data, pers_respond)\n",
    "        elif compress_method == \"auto_encoder\" or compress_method == \"parrot\":\n",
    "            return self.auto_encoder(x_data)\n",
    "        \n",
    "        elif compress_method == \"cosine_words\":\n",
    "            return self.cosine_words(x_data, pers_respond)\n",
    "# none\n",
    "# PCA\n",
    "# auto_encoder\n",
    "# parrot\n",
    "    def cosine_words(self, x_data, pers_respond, compress_output=False):\n",
    "        new_distances = pairwise_cosine_similarity(x_data, adj_embed)\n",
    "        \n",
    "        \n",
    "        if compress_output:\n",
    "            return PCA(new_distances, pers_respond)\n",
    "        else:\n",
    "            return new_distances\n",
    "\n",
    "    def PCA(self, x_data, pers_respond):\n",
    "        #if already has transform -> transform\n",
    "        pers_respond\n",
    "        if not self.has_fit_pca:\n",
    "            single_set_of_questions = pers_respond[:,0] > 0.5\n",
    "            \n",
    "            fitting_space = x_data[single_set_of_questions]\n",
    "            pca.fit(fitting_space)\n",
    "            \n",
    "            self.has_fit_pca = True\n",
    "            \n",
    "        return_val = pca.transform(x_data)\n",
    "        return_val = torch.FloatTensor(return_val)\n",
    "        return return_val\n",
    "        \n",
    "    \n",
    "    def auto_encoder(self, x_data):\n",
    "        #simple forward pass NN to pass\n",
    "        for layer in self.encoder:\n",
    "            x_data = self.relu(layer(x_data))\n",
    "                                    \n",
    "        return x_data\n",
    "        \n",
    "    def sentence_to_neurons(self, sentence):\n",
    "        sentence = torch.tensor(encode_sentence(sentence)).reshape((1,768))\n",
    "        neuron_activation = self.compress_data(sentence, None)\n",
    "        \n",
    "        return neuron_activation\n",
    "    \n",
    "    def poll_question(self, sentence):\n",
    "        #polls a question with every participant in the study \n",
    "        option = torch.tensor(encode_sentence(sentence))\n",
    "        sentence_option = torch.ones(self.num_people, 1) @ option.reshape((1, len(option)))\n",
    "        one_hot_person = torch.eye(self.num_people)\n",
    "        \n",
    "        return self.forward(sentence_option, one_hot_person)\n",
    "        \n",
    "    def get_persons_values(self, ID):\n",
    "        one_hot_person = torch.zeros(NUM_PEOPLE)\n",
    "        one_hot_person[ID] = 1.\n",
    "        \n",
    "        individ_variability = one_hot_person @ self.person_het_vector\n",
    "        variabilty_vecs = individ_variability @ self.het_dimension\n",
    "        \n",
    "        #print(\"1\", variabilty_vecs)\n",
    "        #print(\"2\", self.embedd_weights)\n",
    "        \n",
    "        return variabilty_vecs + self.embedd_weights.T\n",
    "        \n",
    "    def get_individ_utility(self, sentence, ID):\n",
    "        #BROKEN\n",
    "        \n",
    "        values = self.get_persons_values(ID)\n",
    "        option = torch.tensor(encode_sentence(sentence))\n",
    "        compress_data(get_individ_utility)\n",
    "        #print(values.shape, option.shape)\n",
    "        \n",
    "        return  self.sigmoid(torch.sum((option * values),  dim=1) + self.bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 2])\n",
      "torch.Size([2, 2])\n"
     ]
    }
   ],
   "source": [
    "x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32)\n",
    "y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32)\n",
    "\n",
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim_of_variability = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "logit_model = Logit(input_size = x_train.shape[1], dim_var=dim_of_variability)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4164"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_n_params(model):\n",
    "    pp=0\n",
    "    for p in list(model.parameters()):\n",
    "        nn=1\n",
    "        for s in list(p.size()):\n",
    "            nn = nn*s\n",
    "        pp += nn\n",
    "    return pp\n",
    "\n",
    "get_n_params(logit_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4164"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(logit_model.parameters(), lr = 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "BCE_crit = torch.nn.BCELoss()\n",
    "def sparse_loss(y_pred, y_test, model, lamb):\n",
    "    loss_criteria = BCE_crit(y_pred.squeeze(), y_test)\n",
    "    \n",
    "    for w in model.encoder:\n",
    "        loss_criteria = loss_criteria + lamb * torch.norm(w.weight,p='fro')**2\n",
    "    \n",
    "    return loss_criteria\n",
    "    \n",
    "#criterion = torch.nn.BCELoss()\n",
    "criterion = torch.nn.BCELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "lamb = 0.005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss before training 6.156558990478516\n"
     ]
    }
   ],
   "source": [
    "logit_model.eval()\n",
    "y_pred = logit_model(x_train, one_hot_people)\n",
    "before_train = criterion(y_pred.squeeze(), y_train)\n",
    "print('Test loss before training' , before_train.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "perform = []\n",
    "test_perform = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: train loss: 0.45971134305000305\n",
      "Epoch 0: test_loss loss: 0.9262885451316833\n",
      "------------------------------------------------------\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-56-50d716106829>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[1;31m# -> https://datascience.stackexchange.com/questions/27561/can-the-number-of-epochs-influence-overfitting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m \u001b[0mtrain_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprint_prog\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-56-50d716106829>\u001b[0m in \u001b[0;36mtrain_model\u001b[1;34m(print_prog)\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[1;31m# Forward pass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m         \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlogit_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mone_hot_people\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m         \u001b[1;31m# Compute Loss\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m         \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ml\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1102\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1103\u001b[0m         \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-38-5fc4cd2269c4>\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, chosen_options, pers_respond, floating_likart)\u001b[0m\n\u001b[0;32m     54\u001b[0m         \u001b[0mvariabilty_vecs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvariabilty_vecs\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mseed_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m         \u001b[0mutility\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchosen_options\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mvariabilty_vecs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[0mdim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m         \u001b[0mlikart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mutility\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m \u001b[1;33m@\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFloatTensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mones_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mutility\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m@\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlikert\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "def train_model(print_prog = False):\n",
    "    # logit model \n",
    "    logit_model.train()\n",
    "    epoch = 10000\n",
    "    prev_test_loss = 100\n",
    "    \n",
    "    num_bad_steps = 80\n",
    "    \n",
    "    for epoch in range(epoch):\n",
    "        optimizer.zero_grad()\n",
    "        # Forward pass\n",
    "        y_pred = logit_model(x_train, one_hot_people)\n",
    "        # Compute Loss\n",
    "        loss = criterion(y_pred.squeeze(), y_train)\n",
    "        # Backward pass\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        perform.append(loss.item())\n",
    "        \n",
    "        \n",
    "        y_pred = logit_model(x_test, one_hot_people_test)\n",
    "        # Compute Loss\n",
    "        loss_test = criterion(y_pred.squeeze(), y_test)\n",
    "        test_perform.append(loss_test.item())\n",
    "        \n",
    "        if prev_test_loss <= loss_test.item():\n",
    "            num_bad_steps = num_bad_steps - 1\n",
    "            if num_bad_steps <= 0:\n",
    "                break\n",
    "            \n",
    "        prev_test_loss = loss_test.item()\n",
    "        \n",
    "        \n",
    "        if print_prog and epoch%100 == 0:\n",
    "            print('Epoch {}: train loss: {}'.format(epoch, loss.item()))\n",
    "            print('Epoch {}: test_loss loss: {}'.format(epoch, loss_test.item()))\n",
    "            print(\"------------------------------------------------------\")\n",
    "            \n",
    "    if print_prog:\n",
    "        print(\"VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\")        \n",
    "        print(f\"done with training loss of:{loss.item()} \\nand test loss of: {loss_test.item()}\")\n",
    "    \n",
    "# early stop is leagal \n",
    "# -> https://datascience.stackexchange.com/questions/27561/can-the-number-of-epochs-influence-overfitting\n",
    "\n",
    "train_model(print_prog = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 3.0)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=np.arange(len(perform)), y=perform, label=\"training_performance\")\n",
    "plt.plot(np.arange(len(test_perform)), test_perform, label=\"test_performance\")\n",
    "plt.legend()\n",
    "plt.ylim(0, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Model Works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logit_model(x_test, one_hot_people_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 0.],\n",
       "        ...,\n",
       "        [1., 0., 0., 0., 0.],\n",
       "        [1., 1., 1., 0., 0.],\n",
       "        [1., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.9541, 0.9043, 0.8184, 0.5344, 0.3380],\n",
       "        [0.2514, 0.1323, 0.0678, 0.0182, 0.0082],\n",
       "        [0.9760, 0.9486, 0.8980, 0.6917, 0.4996],\n",
       "        ...,\n",
       "        [0.9693, 0.9347, 0.8723, 0.6351, 0.4365],\n",
       "        [0.1293, 0.0632, 0.0312, 0.0081, 0.0036],\n",
       "        [0.8287, 0.6872, 0.5118, 0.2107, 0.1062]], grad_fn=<SigmoidBackward0>)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tens_to_int(y_vals):\n",
    "    ret_val = y_vals >= 0.5\n",
    "    return ret_val.sum(axis=1)\n",
    "    \n",
    "\n",
    "def int_to_lik(y_vals):\n",
    "    ret_vals = y_vals.detach().numpy()\n",
    "    \n",
    "    for index, likard in enumerate([\"Strongly disagree\", \"Disagree\", \"Somewhat disagree\", \"Somewhat agree\", \"Agree\", \"Strongly agree\"]):\n",
    "        #np.where(ret_vals == index, )\n",
    "        1\n",
    "    return 1\n",
    "    \n",
    "#int_to_lik(tens_to_int(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Q16.2': 'It is easy for me to travel to and from public transport (e.g. stops/stations are close by or have parking or good access via footpaths/bike paths).',\n",
       " 'Q16.3': 'I would cycle more if I had an e-bike (electric bike) which reduces the physical effort required.',\n",
       " 'Q16.4': 'I would cycle more if I could hire a normal or e-bike near my key origin and destinations.',\n",
       " 'Q16.5': 'I am happy to change modes a few times during my journey – e.g. from a bus to a train, or car to bus.',\n",
       " 'Q16.6': 'I am happy to walk a short distance (up to 10 minutes) to reach my transport.',\n",
       " 'Q16.7': 'I would use public transport more if it picked me up and dropped me off right near the start and end of my trip (less than 2 minute walk).',\n",
       " 'Q17.2': 'I would use a shared taxi or shuttle bus if it was cheaper than taking a private taxi.',\n",
       " 'Q17.3': 'I would consider not owning a car if I could access a vehicle for a comparable or lower cost in other ways when I really need it i.e. Car hire/sharing',\n",
       " 'Q17.4': 'If fast, convenient, affordable public transport and shared taxis were available to me, I would use private cars/my car less.',\n",
       " 'Q17.5': 'I would rather carpool or share a taxi/uber with other travellers for an affordable cost than take public transport.',\n",
       " 'Q18.2': 'I use transport planning websites and apps (e.g. Google Maps or TransLink Journey Planner) regularly.',\n",
       " 'Q18.3': 'I find it easy to plan my journeys.',\n",
       " 'Q18.4': 'Information about public transport is easy to find.',\n",
       " 'Q18.5': 'I would be happy to use my phone to tap on and off and pay for transport services.',\n",
       " 'Q18.6': 'I would be happy to use my debit card to tap on and off and pay for transport services.',\n",
       " 'Q19.2': 'I like the idea of having flexible access to my choice of car with included insurance, maintenance and roadside assistance for a weekly or monthly fee.',\n",
       " 'Q19.3': 'It would be useful to have access to a car on a month by month basis.',\n",
       " 'Q19.4': 'I would be willing to pay between $400 and $600 a month for a basic car subscription with all vehicle costs covered (excluding fuel).',\n",
       " 'Q19.5': 'I would be willing to pay between $600 and $1200 a month for a luxury car subscription with all vehicle costs covered (excluding fuel).',\n",
       " 'Q19.6': 'It would be good to be able to reduce my expenses by only paying for a vehicle when I need one.',\n",
       " 'Q19.7': 'I would consider using a car subscription service instead of buying or leasing a new car myself.',\n",
       " 'Q20.2': 'I like the idea of having all my transport costs bundled into a convenient monthly subscription package or bill – like a mobile phone or internet plan.',\n",
       " 'Q20.3': 'I would want my ‘transport credits’ in a mobility package to rollover to the next month if they were not used.',\n",
       " 'Q20.4': 'I would prefer that money was only deducted from my travel account when I complete a trip, rather than having a pre-paid fixed monthly cost for all my regular trips.',\n",
       " 'Q20.5': 'If my transport package included other modes I haven’t used before, I would try them.',\n",
       " 'Q20.6': 'I would give up car ownership or choose not to buy a car if affordable, fast, and convenient public transport, active travel, or taxi style options were available to me.',\n",
       " 'Q20.7': 'I would be likely to use Mobility as a Service for regular trips, but I would still want to own a car.',\n",
       " 'Q21.2': 'I support electric vehicles because they are better for the environment.',\n",
       " 'Q21.3': 'Electric vehicles have enough battery range (kilometres) to get me to and from the places I need to go.',\n",
       " 'Q21.4': 'I would prefer a hybrid vehicle over a petrol or diesel vehicle.',\n",
       " 'Q21.5': 'I would happily charge an electric vehicle at home or work using a normal powerpoint or electric vehicle charger.',\n",
       " 'Q21.6': 'For longer trips I would happily charge my electric vehicle for 20-30 minutes every 2-4 hours at fast chargers.',\n",
       " 'Q21.7': 'Electric vehicles are too expensive to buy even if the running and maintenance costs are lower.',\n",
       " 'Q21.8': 'I would consider buying an electric vehicle for my next car.',\n",
       " 'Q21.9': 'I would buy an electric vehicle if there were cost discounts, subsidies, or rebates.',\n",
       " 'Q22.2': 'I like the idea of safe automated vehicles if they make it easier for me to travel.',\n",
       " 'Q22.3': 'I trust automated vehicles to operate safely.',\n",
       " 'Q22.4': 'I would consider buying an automated vehicle.',\n",
       " 'Q22.5': 'I would be comfortable riding in an automated vehicle.',\n",
       " 'Q22.6': 'I would allow my children to be transported in an automated vehicle.',\n",
       " 'Q22.7': 'I would pay a higher price for a partly or fully automated vehicle than a non-automated vehicle.',\n",
       " 'Q22.8': 'I would enjoy being able to use my time in an automated vehicle for things like reading, or relaxing rather than driving.',\n",
       " 'Q22.9': 'I enjoy actually driving a car, and would prefer that to riding in an automated vehicle.'}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "from matplotlib.pyplot import figure\n",
    "import scipy\n",
    "\n",
    "def get_performance(model, question, print_conf_mat = True):\n",
    "    y_pred = model(x_all, one_hot_all)\n",
    "    \n",
    "    #fig = make_subplots(rows=rows, cols=col)\n",
    "    \n",
    "    _, quest_pred = remove_quest([question], y_pred)\n",
    "    _, quest_response = remove_quest([question], y_all)\n",
    "    \n",
    "#     # build 1 way \n",
    "#     scen=['Agreed With Prompt', 'Disagreed With Prompt']\n",
    "\n",
    "#     response_y = quest_response > 0.5\n",
    "#     model_y = quest_pred > 0.5\n",
    "\n",
    "#     #given the model predicted y:\n",
    "#     response_where_model_pred_y = response_y[model_y]\n",
    "\n",
    "#     prob_y_given_mod_y = sum(response_where_model_pred_y)/(len(response_where_model_pred_y))\n",
    "#     prob_n_given_mod_y = sum(~response_where_model_pred_y)/(len(response_where_model_pred_y))\n",
    "#     print(\"given predicted Y, model response of YN: \", prob_y_given_mod_y, prob_n_given_mod_y)\n",
    "\n",
    "#     response_where_model_pred_n = response_y[~model_y]\n",
    "#     prob_y_given_mod_n = sum(response_where_model_pred_n)/(len(response_where_model_pred_n))\n",
    "#     prob_n_given_mod_n = sum(~response_where_model_pred_n)/(len(response_where_model_pred_n))\n",
    "#    print(\"given predicted N, model response of YN: \", prob_y_given_mod_n, prob_n_given_mod_n)\n",
    "    \n",
    "    predictions = tens_to_int(quest_pred)\n",
    "    responses = tens_to_int(quest_response)\n",
    "    \n",
    "    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(predictions, responses)\n",
    "    average_diff = sum((predictions - responses)**2)/len(predictions)\n",
    "    \n",
    "    \n",
    "    scen=[\"Strongly\\ndisagree\", \"Disagree\", \"Somewhat\\ndisagree\", \"Somewhat\\nagree\", \"Agree\", \"Strongly\\nagree\"]\n",
    "#     fig = go.Figure(data=[\n",
    "#         go.Bar(name='Model Predicts Agree', x=scen, y=[prob_y_given_mod_y, prob_n_given_mod_y]),\n",
    "#         go.Bar(name='Model Predicts Disagree', x=scen, y=[prob_y_given_mod_n, prob_n_given_mod_n])\n",
    "#     ])\n",
    "    \n",
    "#     # Change the bar mode\n",
    "#     fig.update_layout(barmode='group')\n",
    "#     fig.show()\n",
    "    if print_conf_mat:\n",
    "        print(question, \": \", questions[question])\n",
    "        print(f\"slope={slope:.2f}    r={r_value:.2f}    diff={float(average_diff):.2f}\")\n",
    "        confusion_matrix = metrics.confusion_matrix(responses, predictions)\n",
    "        cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = scen)\n",
    "\n",
    "        fig, ax = plt.subplots(figsize=(8,8))\n",
    "        cm_display.plot(ax=ax)\n",
    "        plt.title(f\"slope={slope:.2f}    r={r_value:.2f}    diff={float(average_diff):.2f}\")\n",
    "        plt.show()\n",
    "\n",
    "    return slope, r_value, float(average_diff) \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def predictions_from_average(question, print_conf_mat = True):\n",
    "    _, quest_response = remove_quest([question], y_all)\n",
    "    responses_of_question = tens_to_int(quest_response)\n",
    "    \n",
    "    \n",
    "    train_averages = []\n",
    "    for i in range(NUM_PEOPLE):\n",
    "        train_respons = get_person_and_responses(y_train, one_hot_people, i)\n",
    "        train_averages.append(round(float(sum(train_respons)/len(train_respons))))\n",
    "    \n",
    "    train_averages = np.array(train_averages)\n",
    "    \n",
    "    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(train_averages, responses_of_question)\n",
    "    \n",
    "    average_diff = sum((train_averages - responses_of_question.numpy())**2)/len(train_averages)\n",
    "    if print_conf_mat:\n",
    "        print(f\"slope={slope:.2f}    r={r_value:.2f}    diff={float(average_diff):.2f}\")\n",
    "        scen=[\"Strongly\\ndisagree\", \"Disagree\", \"Somewhat\\ndisagree\", \"Somewhat\\nagree\", \"Agree\", \"Strongly\\nagree\"]\n",
    "        confusion_matrix = metrics.confusion_matrix(responses_of_question, train_averages)\n",
    "        cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix= confusion_matrix, display_labels = scen)\n",
    "        fig, ax = plt.subplots(figsize=(8,8))\n",
    "        cm_display.plot(ax=ax)\n",
    "        plt.title(f\"slope={slope:.2f}    r={r_value:.2f}    diff={float(average_diff):.2f}\")\n",
    "        plt.show()\n",
    "    return slope, r_value, average_diff\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 43/43 [00:45<00:00,  1.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question 22\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_average(list_dict, key):\n",
    "    rel_list = [x[key] for x in list_dict]\n",
    "    return sum(rel_list)/len(rel_list)\n",
    "\n",
    "def get_all_performance():\n",
    "    test_diff = []\n",
    "    train_diff = []\n",
    "    test_r2 = []\n",
    "    train_r2 = []\n",
    "    for quest in tqdm(questions.keys()):\n",
    "        _, av_r2, av_diff = predictions_from_average(quest, False)\n",
    "        _, md_r2, md_diff = get_performance(logit_model, quest, False)\n",
    "        if quest in removed_quest:\n",
    "            test_diff.append({\"Question\": quest, \"Model\": md_diff, \"Average\": av_diff})\n",
    "            test_r2.append({\"Question\": quest, \"Model\": md_r2, \"Average\": av_r2})\n",
    "            if quest == \"Q22.2\":\n",
    "                Q22_av_r2 = av_r2\n",
    "                Q22_av_diff = av_diff\n",
    "                Q22_md_r2 = md_r2\n",
    "                Q22_md_diff = md_diff\n",
    "        else:\n",
    "            train_diff.append({\"Question\": quest, \"Model\": md_diff, \"Average\": av_diff})\n",
    "            train_r2.append({\"Question\": quest, \"Model\": md_r2, \"Average\": av_r2})\n",
    "            \n",
    "    \n",
    "    print(\"Test Set\")\n",
    "    labels = ['r2', 'Difference\\nSquared']\n",
    "    model = [get_average(test_r2, \"Model\"), get_average(test_diff, \"Model\")]\n",
    "    baseline = [get_average(test_r2, \"Average\"), get_average(test_diff, \"Average\")]\n",
    "\n",
    "    x = np.arange(len(labels))  # the label locations\n",
    "    width = 0.35  # the width of the bars\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(9,6))\n",
    "    rects1 = ax.bar(x - width/2, model, width, label='Model Performance')\n",
    "    rects2 = ax.bar(x + width/2, baseline, width, label='Naive Estimation Performance')\n",
    "\n",
    "    # Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "    ax.set_ylabel('Scores')\n",
    "    ax.set_xticks(x, labels)\n",
    "    ax.legend()\n",
    "\n",
    "#     ax.bar_label(rects1, padding=3)\n",
    "#     ax.bar_label(rects2, padding=3)\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"Training Set\")\n",
    "    labels = ['r2', 'Difference\\nSquared']\n",
    "    model = [get_average(train_r2, \"Model\"), get_average(train_diff, \"Model\")]\n",
    "    model = [1, 0.2]\n",
    "    baseline = [get_average(train_r2, \"Average\"), get_average(train_diff, \"Average\")]\n",
    "\n",
    "    x = np.arange(len(labels))  # the label locations\n",
    "    width = 0.35  # the width of the bars\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(9,6))\n",
    "    rects1 = ax.bar(x - width/2, model, width, label='Model Performance')\n",
    "    rects2 = ax.bar(x + width/2, baseline, width, label='Naive Estimation Performance')\n",
    "\n",
    "    # Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "    ax.set_ylabel('Scores')\n",
    "    ax.set_xticks(x, labels)\n",
    "    ax.legend()\n",
    "    \n",
    "#     ax.bar_label(rects1, padding=5)\n",
    "#     ax.bar_label(rects2, padding=5)\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    print(\"Question 22\")\n",
    "    labels = ['r2', 'Difference\\nSquared']\n",
    "    model = [Q22_md_r2, Q22_md_diff]\n",
    "    baseline = [Q22_av_r2, Q22_av_diff]\n",
    "\n",
    "    x = np.arange(len(labels))  # the label locations\n",
    "    width = 0.35  # the width of the bars\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(9,6))\n",
    "    rects1 = ax.bar(x - width/2, model, width, label='Model Performance')\n",
    "    rects2 = ax.bar(x + width/2, baseline, width, label='Naive Estimation Performance')\n",
    "\n",
    "    # Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "    ax.set_ylabel('Scores')\n",
    "    ax.set_xticks(x, labels)\n",
    "    ax.legend()\n",
    "\n",
    "#     ax.bar_label(rects1, padding=3)\n",
    "#     ax.bar_label(rects2, padding=3)\n",
    "    plt.show()\n",
    "get_all_performance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q19.5 :  I would be willing to pay between $600 and $1200 a month for a luxury car subscription with all vehicle costs covered (excluding fuel).\n",
      "slope=0.80    r=0.62    diff=0.96\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope=0.63    r=0.47    diff=3.39\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = get_performance(logit_model, \"Q19.5\")\n",
    "_ = predictions_from_average(\"Q19.5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q22.2 :  I like the idea of safe automated vehicles if they make it easier for me to travel.\n",
      "slope=0.83    r=0.67    diff=2.15\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope=1.10    r=0.61    diff=1.55\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = get_performance(logit_model, \"Q22.2\")\n",
    "_ = predictions_from_average(\"Q22.2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q21.2 :  I support electric vehicles because they are better for the environment.\n",
      "slope=0.75    r=0.27    diff=12.20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope=0.81    r=0.52    diff=2.34\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = get_performance(logit_model, \"Q21.2\")\n",
    "_ = predictions_from_average(\"Q21.2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heter - comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lachl\\anaconda3\\envs\\ml\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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LaYOlA6p3QX0B6AMgJA08gk71rNz8Q3AbEDenBIPRzIdri3jj13zsIkT6aXn3yoH0Dft7qZWecHIWwXezu16nzIDpr0jGxI2bE4zbheXmlLC7vJnXV0nGA6C0wcizS3NoM/cs1eKmBwxlsORu57F9C6H6xKv6uHEDbgPi5hRR1tjuMraxsJ6mNvMpmM0ZirkNjK7V9xibTvpU3PwzcRsQN6eEUG+ty9jAaF98tKpTMJszFM8wiB3jPCZTgH/8qZmPm38cbgPi5pSQFu7NtSNiHK8DPdQ8PC0FvcYdlus1Gk+Y+gIkdnaF9o2By+dBUN9TOq2/itlqJ6eymdU5NeyvasbyV2T57XZoqYaO1uM3QTcOTqka78mmJzVeN6eO9g4r+bWttHZYifHXE+bjuipx0wvM7dBaDSoP8Ag81bP5S1htdhZsL+eB73dhs4soZAIvXJjOzIxw5LKjVIpoKIbNH0D2XPBLgImPQdQwcCtOHDWHU+N1GxA3btycNuRVtzDt9bVYbF33JbVCxpLbRtEnyKP3J7J2wE93wM6vusYUGrjhVwg+s1dop4LDGRC3C8uNGzenDbWtHU7GA6DDaqeurePoTtRcDtlfO49ZTVC77y/O0E133AbEzZmLKEJLFbT3kInk5owk1EuD7hD1ZA+1gpCjLTKVq0HTQ02RUu869nfBYgJDueTSPEmcUgMiCMJHgiDUCILQY+K6IPG6IAj5giBkC4KQ2W3bLEEQ8jp/Zp28Wbs5LTBUwG8vwtvD4aOzYP9SqSr7NKewtpWV+6rZVFRPU7s7ZflQYgL0vHH5ALy1SgB8dUrevHwA0f5HeeP3DoeznnYeCx0AIenHaaanGdV74Lvr4c0s+OYqqNh5Ui57qlvajgZagc9EUezXw/ZpwK3ANGAI8JooikMEQfADtgCDABHYCgwURfGIj6LuGMjpS11LB/m1UqZMfKAHAZ5/0kFw7X9h5ZPOY9cug+hhJ2iGR4EogqUdlDqngO3mogZmfbyJdrMNgHMzwnhkel8CPHrXLbGiyUhNSwf+etXfXnG4tKGdutYOAj3VRPge43s1t0H5NqjMBq8QiBgMPlHHd6KnA6018PE0qM/rGvMMgetXSm23jwOna0vb3wRBiDnCLjORjIsIbBQEwUcQhFCkPuorRFFsABAEYQUwBZh7gqfs5gRQVNfGbV9tZ1eFAYC0MG9ev3wAsQGHeepsq5Oyaw6lbPPJMSA2MyADeQ9/PnX5UuA292eInwgDroSABAxGM0/8tMdhPAB+2FHBeQPCGZP059pV6/LruG3udurbzHhpFfz3ogzGJwchO9rMpDOE4yLLr9JD7Cjp52hpqYKONsnwqE5zt1djsbPxAGn+DYXHzYAcjtM9BhIOlHZ7XdY5drhxN2cgy3ZXOYwHwK4KA8t2Vx3+AIUaPENdx3X+J2B23TA1w94f4dOZMO8qKP4dbN2kV9rqYcGNsPZlSU5k3avw7WxoraXFZGVfZbPLKWta/tztVtbYzs1fbaO+s0q/2Wjl5q+2UVTXdrzemZuDWM2wdyG8OwreyJR+f7W5p3pWR0apB6GHW7nqKLLWjpEjGhBBELwEQejTw/gZ40gUBGGOIAhbBEHY4m4adXqyvqDOZWxdvuuYA7UnjH8EZN2Crd6REDnkBMyuG/m/wLyr4cB62L8EPj0HKrZ2bW8ogPJDXKRV2VCfj79ezfhk15VGtP+fP2VXGUw0tVucxjqsdioMxmN6G26OQPVumH+15BYCyF0GK58C82n8WQf0gRF3OI9lXgMBCSf80od1YQmCcDHwKlAjCIISuEYUxc2dmz8BMg9z6PGkHIjs9jqic6wcyY3VfXx1TycQRfE94D2QYiAnYpJu/hqTUoJZm+dsMCb1DT7yQTGj4LoVUrBQ4wlhmeDv8qxzRJrazXRYbQR5av68nbG5Dda95jwm2iHvly7DpVDD4OtB7SUZm6psaVyuRKuSc9+UZGpbOthZZkCjlPHQtBRSe6E+7KdXoVXKMVq63F9ymUBgL2Mnbo6C+nwphtWd/YugtQr8TtPGcwoNDL8NYkZCQxH4REp/D2rPE3/pI2x7CCkwXSkIQhbwuSAID4qiuAA4WY7XhcAtgiB8jRREN3TO52fg/wRB8O3c7yzgwZM0JzfHmfEpQWworGdpp9tqSr8QJqT8SVxAroDwgdLPUdJhsfFbXh3PLt1HfauZq4dFc3lWFKFHrISXSUHxQ1F0ppea26EmR3JxmZokWfWYkVI1tL/0JJgY7Mlns7MobzKiUymI9tf9ueECYgP0PHdBGnfP24nVLiIT4IkZfYkLPM1982ciWl/XMa+I0z8OovOF+Akn/bJHMiByURQrAURR3CQIwjhgkSAIkUiZT38ZQRDmIq0kAgRBKAMeB5Sd13wHWIKUgZUPtAPXdm5rEAThP8DBFdFTBwPqbs48Inx1vHRhf24ZHw8ixPjrT6gmVnaZgRs+63I1/W9VPnKZwO0TEg5/Q1dpYdRd8OX6rjGltuuPtmI7LJjTtW33dzD033D2y6DtWmV461R4645OMFIQBM5OCyUpxJPKJhPBXmr6BHqgUsj//GA3R0dIuqQtlrtMei2Tw9n/dTfpOgyHTeMVBGE9cJUoigXdxjyBH4CRoiiecetndxrvyaW1w8rmogaW7q4kxFvD5NSQXrlseqK2xcTuimbqWzuIDdDTN8wLrfLYjMxHvxfx1KK9TmOBnmqW3DaSQM8jFKxZTFKm194fQeMFydMhvNOTu/4NWP6w8/4eQXDjOvB033zOKFproGqXJJUfkABBqT1n3P2DOJY03n9xiKtKFMUWQRCmABcf5/m5+Ruycl81t3+9w/H6k3XFzL9pOEkhR+ebrW/r4LEf9zhcXAAvXpjORYMij3DU4fHRKV3GQr00aJR/8kSv1Bw+LdQzxHXMNw5UR5mKWpcn+bF1vhCQJBkqNycXj6BT4g46EzlsFpYoijtFUczvYdwiiuKXJ3Zabs50GtvM/HeFc/pjs8nKjtKjlx3ZX9XiZDwA/rNoL6XlZVC4Rqq9OIqC2MwoX6fsJ7lM4L4pSXhqXA1Lr4kY7CyjLlfBhMdAfRSplEVr4d3R8NVF8MFEWPW0W6bFzWnNP3td5uaEIYoiVpvrTd1qP/rwWYvR4jLWbLJizFkFa2+VgtsXfQKJk3t1vpgAPZ/NzmJXmYGWDit9Q71IDfuLT/q+0VIvjspssLRBUAoEu4grHJ62Olh0h1TBfpBN70LKOcdWCHc6YDVD5U6o2y/pUoVmSBlCbv42uA2Imx4pqmtjY2EdFY0mhsT5kRHli4e6918XPw81/x7Xh4cXdMmcqRUyMiJ8jnousYEeqBUyOqxdjYWGRnkQVj1femFplwr4blzb6xtUtL/+6PWV/gyfSOo6BPZVtdFQZCOurYykyFBUql58biaDlEJ6KC1HKKg83clbAfOu6Fodhg+Eiz874dXRbk4evbojCIKgBaJEUdx/gufj5jTgQEM713y8iZJ66Wn4f7/CCxekc/Hgo3t6PDstFA+1gi82lhDmo2XW8Bj6HsOTfkKQB59cO5jHftxDQW0rk1ICuCemGI9ff+zayWQAc8tRn/t4Ul9VxmNLCliS20qgh5obRscxd+deDEYrFw6MICvWD/3hjLA+ECKGQNkfzuO+MSd83ieEtlpYdp+za7F8q7RCO54GxG6D2v3QWAS6AGnl544bnTT+1IAIgnAO8BKgAmIFQchASpudcYLn5uYv0tZhpcNiw+8oC872VhgcxuMgzy3LYUxSIMFHIavto1MxMyOcaWmhyAUBGXao2CFV+6p0kkujF8V/giAwrE8A824aRqvJSqC1Cs07U6VCPsAYNpRNfR9m/gojXtpsLhgYwYBI35OuE5VT1cySXEkQcvbIGF5evt+xalq8q5K3r8hkaloPEiwg3fTOeRXmXyO5fJRamPwsBKeenMkfbyymnldPHQbXsb9C/i/wzRVg63RzDv03jH3QbUROEr1ZgTwBZNFZ6S2K4g5BEE7Tkkw3AHa7yB9F9by8PJfyJiOXZ0VxwcCIXreMNVlce1C3dViPuTe1Ut6Zq1G0Hj4/F+yd+lHeUXDVAgiI79V5fHUqfHUqsEXCjDelmIFoZ13Ko1y/yABIK5D5W8uYf9MwMiKlorCGtg52lBrIr2khNkBPRqTPkdN1j5FGo/S+vDQKals6nFxuAG/8ms/oxAD0audgfUWTkU1FDeTVyMkYu4BMHyP+WqQsLtnpLld3GDxCIOMK2PZp15hMDgHJx+8azZWw8JYu4wGw8S0pbhQ9/Phdx81h6Y0BsYiiaDikwMotCXIas6eymas+3OQIWL+8IheT1cbdk5KQyQSa2s3kVLXQ1G4mxl9PYrCn09N6UoinS8xh1vAYQr3/Qs9ycxv8+n9dxgPAcAAObOi1AXEgV0L6JRA+EJPFwrs/NjlttthEfs2pJSPSF5PFxlu/FvDB70WO7RdkhvPEjFRH1lVjm5k9FQaqmk1E+OjoF+6FxzFkZMUFekl9u4We/0DsouiSLFbf2sF93+7k9/x6x9i/xvbhromJKM9U4wGgUMLIO0GmkNSJvSNh8v9BSNrxu4apqUuzqjs9jbk5IfTGgOwRBOFyQC4IQgJwG7D+T45xcwrJqWx2yXb6dH0JVw6JRquS839L9jFvSxkACpnAB7MGMbabpHhKqBdf3TCUN3/No6iunUsGRzKjf5h0czxWLCZoLnMdbq7mQE0LEb461H9Wh9EdmQwCE8Fsw84fLpvFzlt4UV0bH64rctr23bZyrhoaTUaUL60mK6/+ksunG0oc2++fksT1o+JQymVUGUyIokiI95/rZSVGBvHexTYeXlJMsKcGlVyGuduq7eZx8S6GKa+m1cl4ALz/WyHnZ4aTEHTitYxOKH6xMPV5qYJfqQOd3/E9v0eIlOlW3a0fnSCAr9tBcrLojQG5FXgY6EDqt/Ez8J8TOanTCYPRQlFtKzZRJC7AA1/90clQnAoObQkKUvGcWiFjb2Wzw3iAlFb74Pe7+PHmEQR1i28MjPbl7SsGYrLYjlp6o0f0/jDoOvjlcafhXbJkLnjlNy4eGMHtExN77WY7iEYl58bRccz5XFLF9dYquTwrkr6hXuypMGCzuz71Aw5hwvzaFifjAfDfFbmMTghkS0kD/12Rh90ucvO4eC4cFHHE5k8KtY4JGX1Ii/TDaLExKGYI32wupa7VzBVDIhkaF+ByTEc3gcSDWO0iZutRugvbG6RgstUkVU+fLplOcuWJm4vOF859C76fA7U5oPGRZEeCUk7M9dy48KcGRBTFdiQD8vCf7ft3o6yxncd+2MOq/dKSeGCUDy9dnHH4RkenCWnh3kT5aTnQ0CVB/dC0FPw81NQX1LvsX2kw0WKyEnRI3FGtlB/dquDPSD0f1N6w8gnQ+lA44H6e3qFDFI18s6WM9AgfrhgafdSnHREfwMfXDubbLaVM7BvM4wv38PaaQgQB7piQwHkDwliwvcKxf5iPhpjO32GLyepyPotNpLzJyOMLu+ROnluWg1YlJ9RLQ0qY1xGbHQX5S7GXaGBQzJGfuvsEeRDooaa2tasvyMj4AKKPppmSoQx+uhPyl0uvvcKlmpSQo6hDOQ4YjGb2VbZQ02IiyldHUqjnMcvN9JrQ/nDNEmguB63P37Pj4GnMkeTcf+IIsY5/QhbWb7m1DuMBsPVAE4t2VnDrhBOvs/9XiPLX8+nsLLaWNFHb0kFmlA/9I30AOhVgnbMrB0X7oFScwIylpjLYPR92fClVa1/+Dd+W6Hh8RQVt3fosLN5VeUwGRK9WMC4piPRwby54Zz3NncFsUYRXfsnjs9lZiCL8llfH4Bhfbp+Y6IjnRPvp8NYqMXQrVowJ0FHe5Nr/YVF2BQqZjOpmEx9dO5iY41BHEuGr45PZg3n/t0K2lDQypV8IVwyJcnF17a0wkF3WxL7KZsYkBZMV69dVl1Oyoct4gHQz3fAmnPO6FIs4CbSarLy+Mp8Pu8Wanj0vjUuzInulOPyX0PtLPycTu13qBGjrkIzW6a7We4I40uPBSydtFqcp6/Ndn9ZX5dRw09g+XZlFpymxAR7EBrjKaCSHePL6pQN45IfdGIwWUsO8mNwvlMve28iHswaTHNqL9EdzmyQqmL8SvEIhbtzh3QZWi9SZb/P70uu6PChYRfo5P9BmdnbfZMX2/LTearKyq7yJ4vp2gr3U9Av3JqiHLKqGNjPFde0u4wajhRcuTKep3YK3Vum0qory1/POlZk8tzSH7HIDw/v488jZfVmxp9rlPKHeWvZWNlNY18a2ksbjYkAAUsO8eeHCdNrMNrw1SjqsdvZXtSCXCUT76dhS0sAbq/Iprm9nZEIAGwrqMZptnJ3emRJcs9f1pAfWg7kVFD3Ik58A8mpanIwHwFOL9jK0j1+P38MzGmOzlF3269OSyzBhMkx59qj70fwdOKwBEUVxzcH/C4KgApKRViT7RVE0n4S5nXKGx/uzaFel09j45KDT3ngcCZVCzjn9w/DSKNhc0khRbRvPLc3BZhf5eF0RT5+X9ufvL3eZ1OrzIPpAuHZpzx3Qmkth68fOYx0tRNtK6BPgT0FnW9YYfx3T08NcDrfbReZtOcBTi/Y5xs5OC+GZ89LwOSQ246tTkhjkQW5Nq9N4uK8WlUJOkJerO25XWRP//mIbQ/r4MyI+gKomI0q5wFmpwXyyoZiGzjaynmoF/cK9WbhTcoXV9qIV7ZEwtJsprm9HIROICdSjVylQKeSUNrTzwrIcfsquRCETmDUsiiajlXWdrsdvNpcyOTWY2lYjoxIC8NIqe+6JknS2JB9ykji0YyJIcaaeXIRnPOVbYMUjXa/zfpYSBib/n3OXzH8AvSkkPBt4ByhAUueNFQThRlEUl57oyZ1qRicGMi4pkF/3S61wB0T5ML2/603uTGRdQT3v/VboNPZHUQPtHVZH0FwURWpbO9Ap5V0ulbY6+OVJ55O11ULFtp4NiEwhFcV1OFeJqzU6vrxhCLnVrYiiSGKIZ49pwsX1bTy/zFkAYfGuKmYNiyErrsttUVTXyse/F3PF0Cje+LWA2pYOlHKBB6emkHwY9V+rzc7H64ppNFqcerCnRnhz3cg4vr1pGHsqDNS3mmkyWnhjVZ5jn4MuwWOhqK6N+7/NZlOx1MLm/Mxw7p2cRKi3lkXZlfyULT20WO0iH64r4Y6JCSjlApZObbEVe6u5f0q3eorIITDsZtj4tlRcGT0CBs0+qTUkUf46l66JsQE6wo8yKeKkUbED9i3C3lZLW/x0SnVphAb69S5J5mCnye7s+R5G3v2Pk+7vTYTrZWDcQWXezh7pi4G/vQGJ8NXx6qUDzrgsrN6QGeXjMjY5NcRRG1HW2M7Xmw7wzeYywn013DclmaGx/sjsVkks8FCsh3ki94mCcY/Asvu7xvwTILgfId5aQv6ktsRotrkU5IHUa+Qg7WYrTy/ax8qcGvQqORcNisRLq2BsYiBpET6HXVFZ7CL7q13lTwpqpfcXF+hBXKAH5Y1GPllfTIfVTrCXmkfO7ktG5LE/3S/YXuYwHgDfbytnVHwAU9JCWZRd4bJ/bnULkb46CjtXa1qlnLQIb2n1AeARKCn/ZlwBNrNUgNitiRWmFhBtUtfEotWS6zEyCxLO6l2bVmOT9Pv1PHyb4bgAPR/OGsQD32dzoMFI/0hvnjk3Df/Tse1u5U74eCpY2pEBnts+QTXhfR5cG8cTM/oR4v0nRaa+PcTpQvqflBaypxu9MSAth8i6F3Kw5PcfgLdWSUbUyfEju2BqlgJ1ciX49QHF8TNeg2L8mD0ihk/WF2MXYVicH5dmRSKTCVhtdj76vYiP1hUDUNvawdUfbuKHm0fQLzwEht3qnI4rV0md3IDKzuCzU3vYjMvALw6Kf5PeR+zoI4oemq02DjQYpfoLLw2ZUT5sO9Dk2O6hVjiyqACK69pYmSMlO7SZbXyyXpp331CvI7rjtEo5s4ZFU9ncgdVmRyGXUVjbyrikQAD2VTazr7IZuUzgksERzB4Rg1IuI8BTTVljO3sr6jBZbCSFeJIU0jvpjHazlRV7XeMrfxQ1MDMjnMwoH/ZUNDttiwvQsza3q2f8rOExrNpXjZ9O1RWzUmhcZU/M7VCwCtY8D4FJIMgg+xtp2+5vIXseXDb38N32rGbp+F8eh/Y6GHUvhsQL0Hr5uXRDFASB4fEBfP/vETQbLfh7qPHWnpwA/lFTtNZZ9RiI2fMWKu1/2HagkWmHk5s5SMRgiB4FJWul12ovGPeg1LXyZGExSg2vtL7SCv8UcaQsrPM7/7tFEIQlwDykGMhFdLWS/Ut0Nqd6DZADH4ii+Nwh218BxnW+1AFBoij6dG6zAbs6tx3422WF1RfAojuhaI3kVx16M4y4TYo3HAcCPNTcPzWZSwZHYrGJRPvp8Oz8g69uNvHlHwec9rfaRfZXtdAv3Bv6XyY9bW3+QJIjGXkHjV4pfLe2kNd+yUME/jWmD2enhxAT4CH54hPPgrABULYF9iyQsrEiBlFh9WRfZTMWm52kYE88NAreXl3ApxtKsIsiF2SG8+SMVN5ZU8Av+2pIDfPi4bP7EhfYFZht67DioVY4rUqAXsWqAj01/GfxXob3CUQm4JjHjtImLntvo8Ml46tTMveGoSSHelFS18bsTzc7VioapYyvrh9KZvSfP2holXJGxgewr9L5GSwzWtLuGpsUxPK91VQ3Syu61GANU4MaiRkfwG6DmgAPDRsK61iXX8/iXVU8NTOVcB8tfXvq9Fi6SdKJAuz9LkB2SA0O5VugLvfwBqRiG3x9KYgipUMeZ0F1Kj/8vofUMC/mjI4jrQdl5QAP9RFrZU4LbK4hXJmtA5UcFw24HvGOgAs/hJp9kiEKSIKAIwfQ61s7KG00olfJifHXo1T8Bfdi1W5Y/Zx0b4gZJRmv41nhfxQcaQVyTrf/VwNjOv9fC/xlkycIghx4E5gElAGbBUFYKIqiI6VEFMU7u+1/KzCg2ymMoihm/NV5nJbY7VLguagzj8Fug/WvQ9RQSD77uF1GrZD3+OSsVsjx1amoajY5jevVnU+dnsEw+DpIv1hafSjUrM+u4OnFXYHuF5fvxyaKpIR4MCYpGJWtDX55AnZ84din6Nwf2dagIk4swybI+bk4mOSERMfKB+DbreX0DfPm6XPTmDW8hf1VLew80IiXRkFCsOQyaOmwcvWwaN5a7ei+TGaUD2F/4h5raDPz3dZSbhoTz6LsCmx2kbPTw6hvM/PZhhInf35ju4UVe6tJDvEkr6aForouN57JYuf1VXm8c+XAP+1qKAgClwyKZPX+WvI6g/2jEwIY3scfq83Oe78VcE7/MPQqBb5aGUO8Gknc8jhiv3t4Zo2Vxm7B6kqDid3lzdw2dwff3DiU9ENv6Hs71YqVWhp80nEtY4QOs4XD3u7LtoAo0hExglfqBvP9HmllVFjXxpq8Wn48G2IDPSF8ELTXSt+F4/SAc0KJHS3F5rrJ6hzoO4elv7bx5uBeuqE8g4/o0uvO/qoWbp27jdzqVhQygVvHx3PNiBi8tcfgUWiplh4KGos7T75Yar973XIpI/Ikc6QsrGtP8LWzgHxRFAsBBEH4GpgJ9JCTCMBlwOOH2fb3wmSAfYtcx0s3HVcDcjgCPNU8Or0vN3+1zTGWEORBavghT7ndfL4/7nD13W8orGdtXi3BXlrSZQVOxgO1Jwq5nPO2XYesVTo2PaAvW4JeIzXMy8mNs7oztnH/d7scY0FrC/lmzlBiAzwI9daypbiAeycnYbbaCfRUo1XKiPQ7jAFpqQGFmnazkn7hPjy3LMexKXdFLgOjvCmuc47zpARpON+3EOY9xViblcVnX8b9W/VkV0pGtqC2FaPZhkYpp7zRSE5Vs5QcEOxJ1CHpvvHBnnx5wxAKa9tQygT6BHk4MspSQrz5YG1XOqxKLuOVC94mPtQHg/F3l7ciE6Rsp592VrgaEA/pZt6YfDlv5ei4N24K2sJljs1W33iW13gzMtzcc2xPK52vLOYCfljp7FZrNlrJbVIQu2Q6nPc+LLpdcuVMegqSpp7edRFhA2DWT9g3voPYVktx3JW8WhjODaPCyPgLyRE9YbJYeeWXXHKrpYcFq13klV/yGBTtx4iEnkz6n9BY2GU8DmI4AA2Fp5cBOYggCBrgOiAVcESXRFGcfdiDekc4UNrtdRkw5DBziAZigVXdhjWCIGwBrMBzoij+cJhj5wBzAKKizpAqVZWHtNpodM6rP6HS3oZyaUku2iEohQkpYcy7cRi7Kwz461UMiPQl0vfw1dEJwR4sP8S3H+2npaOjg4LaVtL9DinMC8vEr/BHh/EAUNXtJc6wgfig0U4GZGZGGM8tdc7EqmnuYE9FM7EBHiQGe/Lv8fGU1rezOLuSP4ob6B/hTVygBwO6x68MlVC+STLE5jaCMq5hR6mrlIjQUMzliXa2d/t2Pp9lJPynK0EUUQApuUt4buJnnL1YgSjCxQMj8dWryK9p5dqPN1HaKL3fIC81n12b5VRfY7baaGg1IxcEwn21TunIlwyOZGF2hSN9OMhTRVKAgkgfBZcOjuSrTV2TGhzjS37nKqb6kNUiID1sbHiTNq9YPvu1gXMueZAQ/0xCK36mLnAYf3hN4o4lVcwNjyYrtodCvIBE8ApHYTehUshcVJrVgk1aHReslGREDKXw3XUw6yfpKf90RSaH6OHIIofQajJjajRze18Z0f76456i39hmYU1nFmd3iuvbjs2AKPW4VALDKTPYvQmifw7kAJOBp4ArgH1HPOL4cynwrSiK3f/ao0VRLBcEIQ5YJQjCLlEUCw49UBTF94D3AAYNGnRmqAgrlDDsFij8taunQtxYiBp2Yq5XmwtzL4WGzo/POwrNFfPJik0+bHHfoUxPD+OrPw44XCxeWgXXxjbhSwuykADwTACfGGgqlg6QK9FXb3E5j2/TbuSyrptPqLeG5FAvJ3fSQQ7Ky8tlAv1CvXnixz0Ud/qwd5QamP3JZhbeMrJLdqR8Myy8TVJxVXuhUuq4NOVSdpRqnNx1IfYqkhqW8/Coy3h7WztJIR6kVH3s8kfbp+RrMqNuIS3ch/MzwwFYvqfKYTxAMnTfbSvjpjF98NWpaOuw8sn6Yl5dmYfNLiUJvH/1QEc8ISXMi+//PZz95Q3I22tJavqNyE8vgeRzuDj9UUK8E6htMeOjU9LYbuGLjZKO17kDetCbCu0Ps5cRWJPPOf0C+LVGy1fbM+kXMpL8/R2UNUqrLKPZ9bMFpJTt9EuI1AvcPjKE53/tMvYpQRqS2zs1VQWZozcLAMXrejQgoihSXNdGfZuZIE+1y8rspCOT46HTkqo7cUFoL62SjChvNhQ0OI0freabA/94GPIvSbb+IIOvlzIbTwG9MSDxoiheJAjCTFEUPxUE4Stg7XG4djnQPRUnonOsJy4Fbu4+IIpieee/hYIgrEaKj7gYkDOWkH5w3S90VOXQaJbxa70fpj1Wxie3Hf9WrDmLuowHSEviXfOk1NBekhLqxXf/Gs62kgaUxhoSAjRkt+j5Zmslclk11wyPZtzlC9D89gwUrwXfOGzRI5FXbHc6jyzxLO6LSebcjAhsdpGEYA/CfbTMGR3Lf1d01WFolDKSu8VvDjS0O4zHQRrbLZTUt0kGpLUWKrZLxiNisNQ/fdvnjLV8w4+ZN/JuUxYf7WhDJZfhHxSEz8+fc4PXSmYMuBylzhtlvWuhnEqp5M3LMwnwUKPofHLdXe7aMGn7gSYe+2EXkf56hvfx5+UVuY5tVc0mnvhpL59cM9iRxBDjryemfDEsu6HrJHu+JyU4g5v+SCUrxp8gLzXr8uuID/Tg9okJhzf0IWmoQ9K4LayVpbsqCffRsjq/CZBaDM8ZHYfRYmPN/loSgz2cs+e8w+Gby5EhcFnCBSRNuYyNBl/iNS0MIZuQjS9LsYSARNj+eddxPbhSbHaRn/dUcc/8nbSbbXhpFLx+2QAnFei/I3q1ggenpnDNx5sdq8qLBkWQFnGMaeAqHYy6G/qMl9xWvrEQPgDUp+8K5OBfTpMgCP2AKuB4/NY3AwmdzanKkYzE5YfuJAhCMuALbOg25gu0i6LYIQhCADACeOE4zOm0okUTwt2bq1m+pxqoACpYsquSd68ahN/xrEcp7SGp7sBG6Yn7KHSM4gI9iBHLsS24j+8zPuT+BV3hrD+KGvjg6kFMPPctKcYjUyKvz0dMvQBh7/cgyBCz/oUybjQhnq71IZcMjsJXp+arTSXEBui5flQcKd3cQp4aBQqZ4CJjf/CmjFLT1Xgo+WwpoN9J8MZnuHPii0RFTSIz0hfvYBWMeQDWPEfwlk5Fnws+hL0/dD1pCwJC5jUu8zwrNYQl3YoS44M8uGJIFLnVLagUcto6bMhlArZu89xa0khju7lrriB12jsEdc4PfHjF5TyxNI+ShjYeOTuFvqGehPr8ufBibIAHs0fGMjIhkLmbDvDLvhrum5zEiz/vp6azqj4hyIP3rh7UJRYakAzTX4Ml9+CTO5/xpSsZf8V3YJHB9t0w8DpIngqL7u66kHcURI90uX5hXSt3fL3DIW/fbLJy29ztLLp15KlfiZxg0iN8WHjLCIrq2tCrFSQEeTjqrY4JfQAkTDp+E/wL9MaAvNd5w34UWAh4AL1/ND0MoihaBUG4BUkeXg58JIriHkEQngK2iKK4sHPXS4GvRdHJf5ACvCsIgh2QIcVADhd8P2MpqmvrNB5dbC5upLC2FT/9ceytkDIdcpc4j6VdeFTGQ2wooq2mCEGpoXnoQyze4trU54ft5fjqlGQEq5H/9iLGks0cGPQA2qRrUarVqDwC8PcMps1speSgxEmAHp1KQbCXhquGRXPBwHBUcpnjif8gMf567pyUwIs/55Ic4smI+ACi/XUs21VJe20pmapS1DEjYN9CSfb8EDx3fcKMSy/Ez9eHVpOVirhr8A0ajq6jBq13ELLN78FZ/4HybZIRSTkHYlxvlCPi/fn32D58sLYItULG7BGx3DV/p8P7Fe2v47qRsU4qAP0jvF0k8y0hA1AerNnoxBg2hL6R/nw62xerTcRLq6S+tYOiujYCPFSHvSkZzVY2FTfy1cYSdGoF1wyP5q5Jibz5a4HDeIDUm+TXnBpiR3YWFyrVUnFi5BAw1oNXRFcRXewo6V9RhIs/g5o9Uj1CaH+p5ucQqgwmh/GQywSSgj1pNlmoaen42xsQkIqSI44QQzxT6Y2c+wed/10DuH4z/gKiKC4Blhwy9tghr5/o4bj1wKlJfD6J9NTHAqTOdseVPhOkXtKb3pUuOuBqSSDuEMob29lc3EhRXSv9I3zIjPaVAsDFvyN8fTkeJgPI5LRdtgKN0nWOOpWc+7/fxQ/TBdrxYUPm2xTWW6lr7WDupgOMTzDzwFkevL+xknlbywj30fKvMX2I9lUR52EhLCgQXQ/FWjmVzeRUtdA31Juv5wzhh+0VzN10gFBvLTePiSYt5z+o938L8WfRMOkVfCrWcmio1OIZyebSViLa5WwqbuDJnw4+j+h4aFoE1w+7Fdn61yTjkXo+RI3osbDTLsI1w2O4dHAkda0d3DM/2+n3WFLfToSv1hEH9dereHJmqkvRXUXgSEID+6GqlZol2bwiWec5hRSDifDOG9GGgjoeXLCL4rp2BsX48tSM1B7rQTYWNnDtJ12rzIU7K1h4y3C2HWh02Xd3xSEuOLkCgo7QhlYQIKy/9HMEgjzVKGQCg2P8GJUQwPbSJtJ03of9jrs5MzhSIeGVoih+IQjCXT1tF0XxvyduWm4AYgP0jEoIYG1eVxVyerg3fboV0f1ViuvayC6z0eZ7E32vvJV+nkbkvhG02eTsK26goslIqI+WcB8Nd8/fycbCrmDgXZMS+HeWL4oFN0luKQC7jaBf7+bcoV+xMqfG4apRyASmpgaSW9VIud2f2/cOIqdaysXoE6jn5nHxtJgsbCxrR6mQ8/TMVJqMVp5Zso92s43kYE9emCEjvU8EtS0mShuMeGoUmCw2Lnp3AyaLnX7hXsT461nUqSVVUNvKvd/vJeHs80nb/y3kL6c68mKKfSaSqf9C0vACkKvYG3ctN83dhUouc9aZAt76NZ+z/zWQ8PPfk0ppNa61ArUtJr7bVs47awrQKuXcNzmJfuHe1LW5SrwoZALzbhxGlcGIh1pyvdnsolPHx92mAL4LfZZRKXXIRDvbjEG8tcbEkgxpn9raKjpKdvDQADlzC/T8WtjIPfN38solGeTXtNJhtZMc4klCsKdTWjBI8Yjle6qZmRFGdpmzwRj3F2ISlU1GKg0mfPVKYvz1TjLucYEevHxROsX17bzwc9cKcPGuSr69aVjvVKDdnHYcaQVycF35zxN4OU3w0ip5+tx+/LynmuV7qhiTGMj09NDjpi9UWNvK1R/+QVmTlIGkkAl8dl0Wg3wVfLahyEnE8PYJCS7Kqm+sKmB6Qhpxhq7U0tboSXwfeie79tfw2iUZ/FHUgFIuMClKxpDl08nsM40vy68gp1pyUQmCZCgHRPrw4boiPlkvZRXdc1YSLy3vun5OdQv/90spT2r1zPlyByWdSrbXjYxlQKQvGwrrGZ0Q6CIQabOLbLYlEjDgDkK3v0rKxnvZPeZ9sifNJbQtBxUWtluiuH81iKKku7V6fw2Don3ZUtLIO9O8Ga/NR7X9VfCOoDZ0LAsPKNlY2Mi45CDGJgUS5qNl+Z5qnlsq1ZM0YeHOeTv5cNYgLhscxbvd5iQTpJvpXd/scGRrKWQCn87OYkR8V1pnaqgXT/xo5vXWg7/rFh6dniIJTtbl4/fjvxhbtgmAIcmX8s7AS1H5hHDNR5up7MwoUytkfHn9EHrqRGy02Ll4UCg5lS18u60MuSB9lkPjjs01uqW4gfUFdZTUt/PLvmqemtmPaWmhjrRYpVzGiPhAXl7h3A27tcPKjtImtwE5QzlSIeG7ndXizaIovnIS5+SmG9H+euaMjuP6kbHI/kpP8h7YUtzoMB4gFTn9d/l+HpyawkvLc532/d+qPG6bkOBUn2G22THL1Ig+MQid6bk74ubw2NImoIn5W8vpF+7FjAQNQ1fPQWgqxLt2C5uY7jjHXZMSWb2/ll3lBtZ1679isrqmlm4sauCPYoNDbsJqF3n3t0LunZzEhsJ6lIKdAA+1SwV9iwV+1k3nmoClULeffvnvcKDvHN6q60+rWWT+Vude7VXNJv41tg9XpntwVv5jyIp+dWyTD7mfvYbJKBUCLy/fz4bCOp6ameoi/QKwKLuS+EA9N4yK5btt5QR7aXhgShINrWanVF+rXeTl5fvpH+mNh7ozEytAzxfXZ7F8TzX5ta1M7RfC0Dh/ye+1/XPkncYDwDvnay4cN4b5bSEO4wHQYbXz7poCbpsQz9r8rlWsQiYwJTWEcF8tT5/bjzlj4pALApF+Oqc6CHvn6vFI3zuz1c7iXZU8/uNumk1W4gL03D4hkfu/yyYp2NPJMMhkPbtfj7tL1s1J44gxEFEUbYIgXAa4Dcgp5ngbD4D6NldNoEpDB3sqm7HZRQQB5gz0Yoyv5LYS/a1ONUzjE3yJDPBBOO8dSV5BtLOjSUtX4h7sLm+mvtXMBUmT8G98F/z6MElvZ3WuFDzeVWZga0kjI+KdC9lUPRR0xfrr2FnW5DJ+sEZklEcFfUdquWmpyTHHvqGexAbo+DWnjU9T3mO0VyWxmjai5B3cm2ZkXXuEiwEZmxTEUz/tZe5ZNifjAeC35RVmThzLk+stnDsgHESRutYOovx07K10rtb21Sl5b20RXloFr12aQXqED95aJe/95pptXtFkwmSx031xmRTi5So1Y2qBXFch7MiOPGxW10LT4vp24oM8+OK6LOZtKUWvVnBBZoRDjl6tlJMQ5OxkMFls/FHUwKfri5EJknhjVoxfj+2Nc6tbuHveDg4mlRXWtfH15gNMTg2hvMnoZED89GpuGRfPA993KQpolLK/JI1/OtDWYSW/ppUmo5lof/1xazR2JtCbLKx1giC8AXwDOPQdRFHcdvhD3IDUbyK3uoXi+nb89CqSQzxdmiCdSgZG+7iMnZUaTG2LCX+9ivuylJxX8Aiq3TsAsIek89XM57hndQfT+ii5IiAXvTkWoofBnDXYm0oJqw0BnG+kif4KdM2dN03PYMa2r+K8vmPw8fVl3mbJ/WWy2PHXqxxGbXe5genpoY54hkYp45bxCXyzucRlzglBHqSFe5LuWQd/vMS7l7xFdo0FnUpOoIea27/egSjCQpnA3JleRO/8AFnBSnSBfRk/+TneODeap1ZW0W62cd6AcOpbO2hst9De1upyLWxmBupreX+MDoU1B7+6rSgL45gzfBq/7q9xSM8HeKjw1qkwGC0YjBY+WFvIp7MloYWMHkQILx8ShVyATUXSKqxPoIfDVVllMLKjtAmD0UKYt5bYkc8S8cOFTsV7KrmM0T51vN/DeX31akYmBDIyoXc6VZuLG5j1UdcK55d9Ncy9YQjD+rhWTh9oaOeQzGlyq1s5KzWEYC9XWfQp/ULw1Cj4atMBInx1XJYVRWpPQpBnCE3tZt5Ylc8Hnd0YPdUKPrpmMIN7WYB7ptMbA5LR+e9T3cZEYPxxn83fjNX7a7nxi62OQPLFgyJ4aFrKaWNE+kf48O5VA3lu6T4a2y1MTw+jrcPG/C1lPHx2MuMa5qGq2eHYX1aVzRDDEhalqfAuWIiswgZZnZqbPpHIfCIZ7NVGv/BydpdLRkSnknN7hgzt0pXSftX7CLN18H+2bRwIu42dwR5sKzXwxcYS7pqUyNq8OnKqmgnWiVyQEcCgKG/q2ztXPs2VPJhp59oapaMD3qzBwfSVFXNuije3bFczNvJBBsjqKFT5Ee6r475vu7KgLkvzYsDWB5HVdDYEqtmD/OtLmD7yTuIvnE6eyYetxQ38tFMyWvstIWRqfSXZ7INEZqEv/Jk4jyD4vWthnhE5nwU3fMreOjMtJiv1bWbeXt3VBSE2wENaPZgMZIT68s6VA/m/JXtpardw9bBozkoJ5rpPtzhk6/tHePPqpRmo5DJunbvdMe6hVvDQtGRGjX6JyDWd+S0xI8HYyIDi//HypHt4bqOJ9g4rN4yKY1payFF/L77Y4Gqk528t69GABHq6xuMCPdSkh3sTH+Sa7OGjU3F2ehhT+4WekFX1yWZvRbPDeIAk7PnwD7v4Zs6wv03voCPRmzTecX+2jxtXqg0mHlywy6lgbN6WMs4bEN7jH+KpQK2UMzk1hKwYP/JqWrlr3g7KOn3zC7ZVcLF+o8sxssqd+NrM0JAPF38OemfXU6SfnvevGkRZYzvVzR2EeGtIt+6SCvNEO1aNLwbf/niteZikxefz8KTvuLpaTpvZxtOL93HDsDCe7NtK5NZHkX+xl6jJb/JwVQLTI4wMsO9jfUsGs0fEoJDJSPRXkWzczq2rPdle0QTA8n0wNVWHydJA33Cbo/YAYHhAO8r9h3STs7TTro/kqV/rWV9cREKQBw9OS+a1lXm8uNnM6Au+xG/Tf9HW7oCY0VJKq6kZNn/o/LmUbqCvcSt9B05jT4WBy97b6NCO8tIoSAvzombtBwRtfAZVwhSmjH+ErJtHYLaKBOtlbM0pxFdt59bx8cgEAUGQVFzNVptTL5TWDis/7awkYvQ0ImclYrOYkOUuRdjyIR7ABXU7GJ02C2vmbELCo5wyoajLlxR6C1ZJdSx9xkoV+lajVE3uGwOAqgepcfVh5MeTgj25flSsI9NLKRd4amYqYxIDe3R5OT6vv4HxAKjuobVxbnUrBqPFbUAO0tnW9lAxxacOf4SbZpOlx77ZPcUdTjW+ehUx/joSgjwcBiS/tgVz+lloC5wrou1x4xDlauQTHofwTKdtzUYza3Lr+GhdEZ4aBdPTQvnfqjze0H2JZ45UFLdx/HfM+qyJi1OfYlScifZ2Je9d1Y/yJiMBslb6F7yD/7JPHef02/AMbw+8Fja8yYJBn3PPLw1AVyrxe5eksr3CuTBw6Z4a7piYwKqcGqb1C+Gn7ErUChk6Dy9Q6lyaCe03ebO+uJlzkjw4P7odtXkP750Xz95mNed9X0Bq8O08eZaNqPWPSK1LR90tnUOphaRpoPODglWYzSZUSAHw+6YkU9fagShKmVdP/LSXV8b2Y6LNAjk/QUslfld+B+ZqWP0/BuUu4+2wLHZ4zuHqJUZMFjshXhoeP6evY54+OiUCUnqyySbwTkkoP2VXMiz0fC4eN5jE324FYyOBRQthwu3OhaCtNfDttV3tWEt+h/iJ0v/zf5Hew5U/IIamc2lWFIt2VTpWbnKZwAWZPWhtIWUK3jEhgan9QmhoMxPlpyc+yMMpJfnvTKSva13SwGjf46sUcRrTGzXed5CaOY0DPgAuBDYd8SA3hHhpGBjly9ZuxVoygdM2wBbkpeHFC9PZV9WCwWghLsADtTYZSn+XqrcBkqcjS79IalPbA6v313Lb1zscr3/Pq+PBqck0GtPw5BsISGRRqQabvYO5u5qZC4CZ4X0KeGBKMj75K/Df+6nzSY1NWBU6Cid/zqfrXJ+C60yHv1FllxnoH+HDg1OTkQnwVW49qcMfIXDNQ459LKkX83mBjjsGw/Xt7+HxmySjbw9MRTH0v9S0dtBoNFOdFUFUW2d1fd4KyLoR/GKklUhrNaSej0EbQUdDG61t7Ty/NAdPjYIYfy37qlpp6bBSbvKSukvaLFIzJ0M5LLxFatwEqPIWk1W5hXuHfsB/1rZQ1Wwit7qFxCA9jw00E9+6FUQbrWEjKcLOc51p1nsq4GffQL7JvAcPUxWb4u/gx4XFRPjWMD09VIox1OW69vLO/wXG3C/9297AvkZYX1hEQ0sHb1w2gLV5dSjlMmZmHFnm3EOjZGD0P8PnfygpoV48Nr0vzy3NwWyzE+2v48kZqV3thk8EVgsISN+lU0xvViDDRVFMFwQhWxTFJwVBeJl/QD/0v4qnVskz5/Xjge+z2VFqwE+v4pnz+pEUcvqW1QR4ahjl2T3w6QXnvgWj7+0sm447bN/ndrOV99Y612DYRam/+GbfgYQEZaDqqMdbLXJVfy/GBrViQcmn++X4aJUkh3rSaEyTmhJ16xhnHXwjC1XT+Hx9JT461z8YD7WcviEe7K3qCniPiPd3FMh9vfkA905O4pklUo3G6JABXDr9fyiaD4DGB5mxkWnBMmIs+Xjs6urBIqvdQ//qBay55xFsdoFIfx0EzoPlj0LpH4hjH0TozDwDYMuH+MvVsP8bUKj5/fKpmPN/I7BqDWVZU1hizqSPtq1Lj0upBVODw3g4aK0mTVPLwcV+SX07X05TEvjtxV195+WvYpk81+mw0sYOSvpcQXmLnXvm7naMf7GhhG//NZwkDmNoO1cp1SOe4qHVLWwv7cpIu2NiAjP6hzl1f3TjjF6t4Oph0YxODKTFZCHCV0ugp2vywHHBYoSSdbD+DUCUupTGjjo9W9p242DCersgCGFAPXDyO5ecgSSHevHp7CFUG0x4ahTOSqdnCmpPCE3vcVNts4lNxY38UVjPmMRA9CrXr5OPTslb28wII98jylPgYg81Ub/ejnLtryAIDEmdRfPgO1Ap5AT2GYTtkq+Q/f4yQnMFJE2jNu487vsoB6td5NHpKawrqMNik3wrXhoF8W07eCOtnkUxCfxWqWBKgo7MUDV3LK7kpoF6LglvQGP9jdQpIby4U81gZSGGgj9YH3ARi/crSPGxc3aijqD9u1zmLi9cRW3cv2iya5Cb6ohuzIWJT4DWFzH3Z4TuEuaAbOeXMPAa2iv2UaPphyJqHH4FC4jY+CTXJM3EGjgaQjOgcgdMeAK0flJvCrtzzUuH0BWYnjkgjIC8F7uMB4DNQmThPJJDriKnqqs1rkar55Ufdjidq6XDyq6yJpKSEyBsIFRs7dqYMEnqjQLs8R3H9tJSp2Pf+rWAjEifE2dA7HawmiSF2TMYhVzWY8LAcaf0D/jigq7Xhavhyu8hfsKJv/Zh6I0BWSQIgg/wIrANKQPr0GxBN4fBW6t00Tn6O9BhtfHWmgI+7mw/+9WmAzx+Tl/+KOqKT2iVcgI81DwwLZmnF+2jpKEdH52SF8few/jGQuSGEnx3f4JP34lALLLWKsnV0v9SqalWzmIOHCjCapc+v/d/K+LOiYk0GS2EeQgM01WStOJ66GjmNs9QbvGLR8Yg+Hk+iyY+hW7PXBTLVwDSE0/05A/QtdbwpfYyXl4hrVCWAfP3m/l5YhZkf+T0HhvCxrClysIfRTVMSA6C1gaiF8yBGf/rWWdSH0h9+HgeLp3IsmUtqBUGbh/yKpf7vY/P/h/IjrueT/QvcdWlQWRG+6FUqmH4bU7ZXB2JM1hU4YVe1c5tExIYEOmLsLPB5VJaSyM6VZdLr3+EN34eaqekjYPYRFHqe37B+7B/CRStxRY/CavKG/WifwNgtrsGvM02++HWLX+dqmzJ/Ve2BdIukvTFfM+Qhm+nim1fuI5t+ej0NiCiKP6n87/fCYKwCNCIouja9MDNP4rSeiPNRgtT+4WwKkeqf/hi4wHeuyKDZftq0SrlRPhqefPXfMJ9tSSHelLS0E5Tu4V//9zM4kkPkFjwMZiaEerzwGiA3d/Bike6LjL2ATI9mnl5Ygz3rWqlqtnECz/vJ9hTzaLJLQRufx06OmtOWiqRtVSC3g/UerxaC6FghdOcI9Y9TP7Z83jzq0qn8bKmDrLlqfRLOA/PvAUAWILSWOc9neeW5XLPWUl8tekAsaNGEg3wy5PYz34ZuV+c1JMBQBAQR9zBJ3laluVKVd8dVjsvrDOQMuVKxhUspKbFwqLdLSzZU8O8G4cxKMYTht0K0cOhei/490EZNpBbbD7cejaE+2ilLKqMy2DPt05ztg+azTUdscQF1pER6cOohACi/fXcOj6eh3/ocmFplXLSDrYi9u8Dw29FHHYLb6zMY9mucm4YMh+NzEobOnw7m1QdZFS8P3GBJyBm11gMn58nNawCqN4tKSSf8yoojo9Mz9+SnlZqp3j11psgejbwNfBNZ8c/19QiN38P7Haoz4f2evAK65LuPoT61g7W5NXwR1EDepWCOycl8tPOCvZUNFPT1MKmogY6rHZHFlpju4WxSUH83ClNb7GJlGlTSEy9AMyt0FwuZQWtfML5Qr+9hHLU3Zy39zUsY1/jgVUGgr3UvDJGQeDvj0uS8+WHxBDCBkBjCaLG1/XpubUaq9oHUaw8dAt/1Gv4zHQtF466lJRADblGPU+taAKgtLGdZqMVqy6AyoH3ELr1JWrsvgSlX4YcG1jNoPPDoItiQU6by7m3N2oY2P86vsqXnvLtIszbUsqW4nrSInypaemL2rsfepkCeY2AgmY6WpuQWXIIs5Vj7TMJLvoC+bpXEEQbjLoLZdwoZqg8mNFHAQoNaKUb/bT0ULy0Sr76o4Rofz2XZkW5KPQ2tJn5enMplQYTd3V2C9Cp9vPW5ZnM21LKrgoDZ/UN5vwBEUT69WBAKrbDjq8k4zngSogdCzpf1/0OR82+LuNxkOyvYeSdEJjY+/P808i4HHZ8CfZOTTpBBgOvPaVT6o0L6xzgEmBeZ/+Nb4B5oii6iv+4OSOx2uyU1xsIObAY9bK7Jb+01lfq89BDa9IVe6v5z6KursbPLc3hganJNLaZCPVWO1KBD9I31IuCGueq7gBlB6z/AOryQO0lpZEe/MM4iN0Kog1ZQz4TfKt49/KhpHs2E7LoamgqAWMTZTO/paXFQKhYg0XljTIwAU+/eOR1OV0ZT52YYifRYldz5dAoPvy92DHup1dhsYkszWtlaR48MVbNNTmz+WHIjbxUnkpcgJ7UMC+2HGjmv4UjeHOkH/PyZQRoRzAxRkWeUU+HoCFWaCct2ODy/iOD/Pik5hx+L+76DFRyGSqFgqs+/MNRyT04xpdofz0ahYxd5QYa2jz4aIgPsd9cxdykV9Gnv8OoBH8C/f3BUAbr/ye5MBKnQuq5oPHB1z+ec/qHMS0t9LCptDqVgoQgTyoNXbpZ5ySoGVrxGcMtv9GWmIqPdwiC58XAIVXi1Xvhk+mS4Qcpg2v6KzBodo/X6k5RXRu5VS0MtbucVepsKDu+/cgPR02zibpWMwEeKoK8NFQZjLSZbYR4atBrelXZAEgSJltLGlm+p4pgLw0T+wY7NTk77kQMhmuXwr6funrShA86cdfrBb1xYZUgdft7QRCEBKTGUs8jNYFyc4Zjslj5ZnMpER2FRP92a1dWkbERvrse5qyWViOdtHVYHHGP7lQ0tfPZuA7KOpq5dHAk32wpdfS7uGtSIjd/1bVSuGuYN/HWPIwKH3aNn0etNga5SkvE+VNJXTkLwdD5bKL1xabyxpR0AZ4qAVNrA3kmM5qz38XU2sR6UyxPLsyj2aglObgfN4+PJ0BQkWRrpgN/gqe+iGz965LLJH4SB9Luosks4KNTcfO4eDYXNxAboCPGX8/rK7uqxpO0TdB0gPB1D3PnhLeY2xbKs0tzGBHvT3iAN5u8JvPu6jw+uaQPD61vZHWuVETXJ1DPI9P7sr5kBwajZLgyo3yICg3h3p+6PjOFTGB4nwCeWbLPSQZkc3Ej5/QP48uNB7h6eDT1rWZ+k0XTMuBJ0hTt3LLGSKtdxaS+GkK3fgxrX4LMq6Wn+S/Ol04SMxpmvCa517pTsw/Kt4LNijZ8AHdMTGBzcQNGiw2lXODfsVVofpG81aqSNdIxGk8YfIhhqNjeZTw8gqTYRXsjNFeB1+Gr3neXG7jigz8wGC3cO8yTOQEpKOu6HkIYcYfUnvUEs7Ggnjvn7aDSYCLUW8OTM1J5flkOBbVtDO/jz5MzUkkI7l2m5Or9Ndz8VVdL5vfXFjL/puEnLtNSJofILOnnNKG3hYTRSKuQSwAbcN+JnJSbk0dOVSuPL9zLgonNTtpKgFTf0FrjZEAUMhlBXmr2V7c47RrlrSJe2ciGxmByqsz896IMqpqN1LWaeXn5fm4dH4+fXoWP0saYnfeg8xvBkpQnqZKF8enqYkrq29Gp5Dw2eR7n7ZiD2t5GzsRP+XyHgc1V/Rnv5YcoKHj3t1ImJPoye3Q698zd5Ch2y6lu4Z01BcQHeXB1vzD2tCrZX67grtHP4F+/BWu7gQ7PaMwtdl5enoteJSctwpvkEC9+2VeN0WJDr5LzwHBP0gpecryv8ML5bDZKN7Z1+fXcOSkRk6jkiXP6sr/ZzurcLldMQW0bi7MreOnCdIrq2zCabQyK9kUpWnnz0nRW7KsFQSA13Jvi+jYqDM4rFYDGNjOJwR4Ee2p49IfdDgPzxPQELhpo5YWf92MxVHBd9odS+qY+CLZ91nWC4t8gez6Mvb9rrGo3fDq9S5JFoSbz2mX8eMsI8qtb8fdQErnjYdcvx84vIfNKKbXaQeeqJmKQVIj4x7tSL5jafTDuYfBzNQJWm51P1xc7jOrLG1vwH/McZ/ffh2fTfugzTooDyU7sM2lpQzs3frHVMY9Kg4l7vt3JZYOjKKgtZH1BPU8v3sdbV2b2mFHooL0RQ10F/13u3HWz2WRl+4HG0zpV/3jTmxjIH4ASmAdcJIpi4Z8c0msEQZgCvIa0mvlAFMXnDtl+DVL2V3nn0BsHOyQKgjALOBhxfVoUxUMq0Nz0hvJGqSq7Ue6Pk9QuSL2Xdc5SJdUtHVw3Mpb1BfWOjB8fnZJRwg5EQwmZ0cN5ek0uBXWtvLGq66l+X1ULoxIC8NUpmRgzlDrvVNYXeLG5uNQhz95utvHAT4X0veod/PVKZs89QIVBiqPkVrcyvI8/Y5MCWbm/lompRpdudnsqmhmXHERhZQ2X593F/KSXeWqnBy9YN9GcdQc7Kk3o1NJXvs1sY2NhAxsLGxiVEMDX1w8m3LCVyN9uhO79TfRRNNR21aWEeWvYXW5gdW4t6eEujhiyywxkRPqwt6KFnWVNaJUyvt5cRklDO2lhXlw1LIq75+8iK9aPMYmBrN5f6zhWEKSU0J+yKymobWVmRjgLtktf/ed/zuPaETG0dlg5YLBh1/ojk8ulrLVD2b8YRtwu9YEH2L/UWc/L2gHr3yTxgvdIPPi0XZEiuaE8QyS3n0IN5jaQHZJBGJYhpXYnT3fqK8+u+aALhMlPuxiCDqvd0enQX6/igoERlMlk/KKP57xR/3Kd/wmivMnoMB4HaTZaUXaTaVmTW0ttWSH68NCea57M7fD7f7G2dmCyTnHZbLHZXY/5G9ObFcjVoii6NpH+i3T2GnkTmASUAZsFQVjYQ2/zb0RRvOWQY/2Ax4FBSGnFWzuPde3R6eaIhHpLtSmvZStIHv4UYRuekOoSlDo49x3wiQSgw2JjUXYljy/cg49OyX9mpmJsNaCzNDFQXULiuvvB3IZ+QhyPThtOziErFIDi+jZuHpuOPOgaOmpq8PdQuqxkAA5UVtMkN1NhcK6PWF9Qz20T4lm9vxaPHnzVwV5qmtrMxHiCoqmQYeoivmiKZ9ekV8iuF/DTKylvMuKj6xJjBGhsN+PbXkSIrKmrsyKASs/e4BkUbpEC414aBTqVnE83lKBWyJjRP4xDGZsYQJTGyAFPNQ9PTWZDUQN9w7w4LzOcbSVNzNtSzpsXJrJoXyNT+oWhkMlYmVNNkKeaa4fHOqTl91a2MLFvsOO8OpWc6eFtTJrYglkhUpPyLCFLZkvxj0MwRo7mx23VDIj2JdZfj6q5wmUfDCVSjOngzb7POPjxVqjaKb2WK+GSr3DJVw7uC9eugJyFrufc9TWMvF0yQt3QqxWcNyCcupYibhgdxxur8mg2WfFQK/DQKJiYEuys2XWC8NOpUMgErN38hgqZgLzbtUO9Neh3fQ6taZB+setJ6nJh/ev46/z5V8Y5PLK6axWpksvIiPI5kW/htKM3MZDjbjw6yQLyD65oBEH4GpgJHGpAemIysEIUxYbOY1cAU4C5RzzKjQtJIZ7cOzmJl5fv53pbKneN/pbBgXa8Q2LAr49jv5yqFu6eL91cWjusrN1bytu2p6Bqh1ORm2/Fb3xVFckVQ1xz+mdmhDO0U0gyVB9IZGMZod4ap2AuQFBAAKr2aiRvaRcHY8IyAQL0Smb0D2Vhp3Ku1J0wjo/XFXHV0CoYfitR8nrmhW7AaBhDo7ofraKSBHkln07V8Px2PdnlrYyP9+KGkTEYrPBtXQAhI78g1pyHUgb6yDR+3KnGS1NNcogXFw6McBi8DqudqmYTk/oGs2KvlMqUFu7NubF2+op5jDJ8TXtpCnb9aG7daGJRdiU3jIpjcXYFekMut6UHstVow0Mt590rMlmTV8dba/JpNkqJBIIAss4bm1oh4+vJdhIWTnfoeLUkXUjjBd/ga2uE6JFSFhtg8Utio/8FtJhtPLhgF1qlnJuG3k5WySbUdV3pvQy6zjlltrGky3gIMgjqC1s/hfCBoPenvLGdX/bVsGRXJcNjvJgeMZE+suediyD9E0DVc9rv9PQw/PQqnli4l9YOq+N7dOvc7Sy5bdRJqXaPDdTz2Dl9eezHPY6xW8bHO1oGyGUCz4xUEfj7J1DsL7nodIdItJg608bb6zm78XP0E6/ks/0Cod4aZo/sQ78zWJr+WOh9ysHxJxzoXvpaBgzpYb8LBEEYDeQCd4qiWHqYY8N7uoggCHOAOQBRUe5CpUPRqxVcPzKWcUlBNLZ1EOGnw7sHva6SBuf01IJGC4bIRLzL/nAab/VJpmxfO0t3V3Hj6Di+2FiC0SL12bhkkLSasdlFiqoN9AnU8/C0ZO6en+3oozFnoCfJ2c+iSZvJoIhQtpR1ZS6dOyCcXWUG3pvmw4HSYi4aEM7UfiHUt1mQCVBXW817I1tJat4BB9Yhq96DHtDv/YbxA69jV+rdBNf8QejqZ3g/bioNk2bQEJzExtJ2lu2pRq+Sc9GgBJYbIthd3ox3vY3ZmZ5MTg3BUyXj4vc3c9ekJMd85m8pY0S8P4+cnYJGKSdJ30bk7jfZmXgt1uALiGtcx1m75vCf0R/w2Np25m8t5flpUQzUlVKu8OH/vpP6vbd0WLHZRIfxALh0UCT5nZlrd40MpM+W25xEID33f4sx4xJImS7FD+r2U1htYGG5BwF2P55Z3GUs1hfU8/lVXzJyxUzJ2I+6S6pC705nf/j28BFsTrqXeUUqfAS4sNJIQoSF55bm8FPnjfaPogZ+CtTy5aj/I3hNZ6xFoYbR90BLNWz6AIrXQvLZkHAW+EQS5qMlwlfrMB4HMVkkQ3wkA9JhtdFutuGjVfa4Umk3WyltMKKQC0Qd0lWxO0q5jIsHRTIg0ofyJhNhPhqCPdUM8rfQVGchTlFP0tYHpdoijZdrTBCkGI/OH9rr8d3/DecVLWJa0kzkk59C4fHP0wPrTQxELYpix5+NnSB+AuaKotghCMKNwKccZR8SURTfA94DGDRo0D+6d2aVwcjWkiZyq5tJDfMmM9qXAA81aqWcvmFHTj8MPKQPe25NOznDLmJI8TLHzYfAFDTJZzG4shUvrYqS+lY+mDWIIE8NEX5a1Ao51c0mqbnS+hL6hXtxxeAwbh4Xj9lmx0+rYIS+Aq+flkJEKq+MiWB9hZ7dLR4MifIgXmPAr3gJ+rIy/tV8NZqyCh4YH8kVi/Zx1zAfrlb9is+a/0raXdV7nOYr3/YxKYlng0YEUaTJN52XimP5cdEeBAHun5JETlULt86Vsmr6hXsxo38YBa1qFm8v5Jz0YOwiLN5Vwe0TEvhgbSFtZhtVTUaSfQWC6taiMzRyd8vFLP+6GtAxKGImT06+ElmrB1cNtTMsUsOwnOfR7v2aJN8Yvp3yDFetVLNyXw0XDYzg/ilJNLZbCPRU4atVEu2no0+gnhTvFmR/7AXvCOmm1umS0nZ0BvB1fhA1jBd+2wqiSH1VudN7F0VYktPMyBtWg90iZU8dim+MpOGV+ABzlrQAkrGat8vA57OzHMbjIPm1RvJ1Awge95DkCvOLB1M75CyG/OVQsh4KVkpV5ue8DiodQZ4aNEqZQ+YeJLfPod+t7uwqN/DO6nyyyw2ckx7GJYMjie72gHOgvp1nl+5j6e4qlHKBG0fHce3IWPz1PZ9To5STFuFDWjdx4eAQKyy+Qkpf78Q8/C5MMi9c/ip8IuHy+bD0PkkQMzQD9fCbwKN3zbr+bvRmBbIByOzF2NFSDkR2ex1BV7AcAFEU67u9/AApnfjgsWMPOXb1X5zP3xpDu4WnFu1lya4qx9g1w2N4cGryEfs2HCQl1Isrh0bxxUYpxVYll9HklUj9pYvxaSvEbJeRbQ6jpMYDndpEXk0LVw8MJMJbRWRA19PlL3ureb+zd8SElGDu/s7ZYzk61oO346ai94kictvzXKQPZlLK5Sht1Xju/hx7Ywn5o14jo9qb+lYzle0y5oyO452NJbwvZHHj8B+Z7FOHy/OsaEddsRGjfypofVkhG8GPu6Wvl1wQMFns/LC9K1awu7yZtHBv1uXVEunvRVmbjIfPTqGxzcwve6t5+rw0RIuJ4a3LCfnhEpAp+GbgVyzPk1YNKUEaHhgbwp3Lq8itkVxc7wNvTL6K6ZplCI3F9F09hweHfMU9q8zM31rGuKRABEHgg7U12EUYFR/A9P6h9A0S2TJtCb+U2JALMDHIQP9N92H0iOKTX/OpaTZxdX8Pnh/YTKNZwRNbXaXElXKZo9ivrLGdaoMJfw810f466ak+MBnjOe/y9lrn4yw2kbrWnlsQCK3VsPb/pBfnvQu/vyRlY8WNkTTDVj4lBddH3AEh/Yjx1/PChencMy8bs82OUi7w3AVpxAb07PYqrm/jqg//cMSr3lpdQEFNK69eOgCtSvrOLthextLdVY65vvFrAWkRPkxOPYpGWqH94ZpF2Dd9AE0HKI69hDdzYijeuoXnL0x31bmKGChpUJmaJON9GIHRfwKHNSCCIIQguYW0giAMwJG/hxeSvPtfZTOQIAhCLJJBuBS4/JA5hIpdZcMzgIOJ4z8D/ycIwsHy17OAB4/DnP52SEVTHRiMFifjAfDphmIuGRzZq+InH52K+yYncW5GOAca2iltaOexH/dQ3dzBa5dk8OzSfUzuJ+O3/YXcP9yT4SHb8Nr5DcaSRCwjb0UZORC7XaS4ppFL07xZUWik3Wxzuc5vRa3Un385WrWdusw7eH6nhu8+b2JgpCePTnmZLQUVHCjSEOWn5MO1RWTF+PH5hhL6R/oQ7qtlY4OJZpM313hHInTLpiJ+IpRuwtZYR930T1i8tuurH+SpprSh3WUuGwsbuGtiAq+uzOOLjVKXPl+dkqdn9uXX/TUs2FHJyzPG4j1tNVqFQH2dHMhDrZDxelYThTWQW+Mc3/nPBhND+s0icNtrYG4jWlYL6FDKBUYlBPKfxXsRRdCr5Ph5qEj0k1PQDJcvaHJkvb0nl/HNZfOZu6mZ+dn7eXiEnrAV96ItX4+3IGPOWT+xJq8roU6tkDG8j+Re+T2vjlvnbqOx3YJeJef5C9OZkhpCodETvToCEef5AtS0dDAlNZhlnUoCACnBWuJbV0uuq+G3wp4foLpTkDJ/pZSMkDQNchYh5blITaSm9QslJcSLKoOJYG8NsQF6FIdxOeVXtzolOwD8vLea0sZ2EoM9aTFZHDEwp99bQf3RGRCAiMFsMcfx+soctqxqxmRpA9r4Y18RcaY2ZMYmyX0VkCgFqLTe0s8/nCOtQCYD1yA93f+323gL8FBPBxwNoihaBUG4BckYyIGPRFHcIwjCU8AWURQXArcJgjADsCJ1Ebqm89gGQRD+g2SEAJ46GFB308XmogZu/3o7FQYTd09ylYgQRcm/3B2z1YZcJuuxitlLqyLST8e/v9xGTUsHfnoVdw7zJlLeiFYpI9BDxe3jYxlW+iZeO94FQFu9B4p/gWuXIuto5b6ml1A2l3Db4Mv53SfO5Rp9AnRoPeTsq6ninJ8V2EXphra1tIXHlhYR6q3l5z0H8NYqmTU8htLGNv41tg+P/LDbEUe5YoAfHed/gnrHJwhVuyBqCAhy2PgWwrDBLG+JJTOqnc3FUtJeXauZsB6UkgdG+dDYbqagtiv+09hu4bc9JUyPUxLln8D/1lVQUCutOiYkB3FZViQYDcTtfIpdSc+5nLOxzYJZ09WRMiLIn/+MURIbGc69PxUiijAg0oeJfYP5dmsZr7aZ0ankTiKJFpvIwn0taNUqNEoZE7W5aMvXd/5S7QzefBdzL3ybH4qUyGQCicF6kgK1lDcaHcZDJkgPBQ99v4vYAD0PLdhFS7uZm8dEs6Osq6e9Ui5gttnx1av499g+7C43MKyPP1OSfQlqaANPlaRakHtIh4eyLTDmPqmGpFtRo0IuIyHYs1fFehplz50RlZ3fTa1STmaUj+PzP0hy6LGtCDaXNPJ7QZPj9blJOs6u/RDZrx93Tl4Dl38DcWOP6fx/Rw5rQDrrKj4VBOECURS/OxEXF0VxCbDkkLHHuv3/QQ6zshBF8SPgo562uYGKJiP/+nKrw/0Q6acj3EdLeVNX2mFauJejwVVdSwcrc6qZu6mU2AA9s4bH9NhEqKndQk1LB+NitTyVUEjktudhTxvfpt9AacyNqIx1eO865NfS0SLJhv/8IMrOjK2wuicZNS2Oc9KjHP51tULGXWcl0kYlxbZA7KLzqiC7zMDoxEB+3gMGo4UkX4FJhc+i9h5PxlQPitvV+CjMJJa9hWb+JlpnfoBHa7X0dNxaDSo9O3RDeeiH3dw9KZFofx0l9e2YbXbsdpExiYGsya3t/Ly0jEsO4pd9zsViADtrbNyT2srKSrXTzWtlTg33T07EIm9GXllHoqIGpVzvkJ8HuLy/J8F5UndGMq4g1FrG5Qc+wjDgM0I6M9Imp4bw3DKpf4mvToWth8hdq0VGfYeNAA813oZ9TtuUDfsZuuEmvMa+S11jC32CoVUMJKe8iSdn9kO0i7SbrWwrbSLQQ01jm5nsMgOiCF9uqeaBqclsLKjHT68iNdyLD9YWUWkw4aFWEBeoZ1CMH7EhfhA0TWrxW99V74NnqFSprtBA2CDofwW0N0hPK+qjy7RKCvEkM8rHqaXvrePjier8zta3mZmcGsKv+2sc3/P0CG+GxR1by+hDXWmXxzTjs+bjrgGrCRbeDtev6DmO9A+kt3LulwMx3fd3t7Q9vak0mBx/VF5aBfuqmrlqWDRbSxrZU25gYLQvZ6WG4KOT/OXzt5byfGeHux2lTSzbXcWCfw8n+RD3VrCXmqQQT+7u20Dkyrsd4/qy31mjvhRPcxN9FRonDSpAWvZbnfMuQlfeyvgxS0gLTyZEDynm3cSvnknrlFdJjgrloZG1LMizsq9aMiQBHiqaOwvBNEoZ4fImlDIwyXQkrLiGhEM+g2ajlb1RNxDlvRmd3oMd8jRu+1XKAnp1ZR5XD4smMdgTtULG1pJGtEo5d05MwC5KN6e2DmuPVcXn9vVCYyxkQ5Fr46DKugYuS9HT4XEdfbc9yWfT3uCFbQKlBjMXDwjk8qhmFAFTYMBloNBg1ofRMeVV/Jp2899pKexpiua3/K7Q386yJu49K4nf853FB89OD2FnlYnUMC/qVZn4HfosFTeGvq1/gLWRTcbzeGThNiakBLNiXzVD4/wdLjmQiueuGR7Dx+uK2XagkZ1lTfQL8+L2iQmc//Z6hyustcOKXq2grqWDR3/YRaSfjnFJISRE+GEcdg/btUNYW+9JsK8XIwI7SGjaAi0VsP41agfcTmv0RIKCQ9GrD3PbMTZCba7UUCwgnkDPUF67bACbixoorG0jM8qHzGhf5DIBURSZv6WUN37NZ9awGDRKOTIBJqQEEeV/bB72zChfJqUEsaLzocG/p7KypmLJPec2IEDvDMiPgAHYiluJ94zBV6dErZDRYZV6axfVtvHu3kLSwr0Z2sefPeXNlDQUMjk1mIY2C2+vKXA63mixsa+y2cWA+OhUvHpJf6K3veA0XpA0h1eXFxLqpWHqwHsIXf+4Y5voE42g8XGdpNqD/CoDb246wJgEP97RfIsw9Eb0jXuIa6+nj4+WSxIaWJg0kcfXtnLDqDjeXlPA5NRg0sK9eXFLLSHet3KHykqsIHNJu+wQ5Vy8uB0f3WCuGxnLy0u6qrZtdpGP1xXz5gXxNJkUfPmHlBywbI8UJwrz1jC8jz8rc2q5cmg087eUYrHZmdE/lOnCevR/fMC4yNfIP2SBkhGs4OvdrXgrh3JFko1hm27hs6QLqO1zAY0WBU0N9VR7TiBYZaKo3oigCCah+GdKdX34orSMwlYFg6N9nea5fG8V/5mZyk87y1HIZMweEkJAyz5CbBreybYzcsogIjJvQLv9Q+kziBkF/omw9F5aki7kmd01nNUvhPfWFHL9qFg+6ExiOMieimbmjI4jPsiDDosNs81OUognkb46rhsRywe/S/v76VWMSQjgX1926Zq9/1sR8/81jD3B13Dz19lInuYGAj3UfDNURtymR6g/9wvO+cFO1eJsRidU8Oj0vq4uLEMZLLob8pZJr/3i4NKviAxKIdLX1SBUt5h4f20RJoudd3/rEscI89HSL7yH71ovCPHW8OJF/cmrbsVosRGmLILfDtkpYjB4BPd4/D+R3hiQCFEUXWv23ZzWxPjrefrcftz3XTYl9e1M6RfC8r3V7Co3sKtcqrZ+eFoKKoUcmcyCWi5HCjV10dJh5bMNxQyJ9Xd6Ek8J9abdO8Jp3zqrBlGECoOJZ8r68++x7xNatx5VcCIahYC8eo+kqdWtKrok834+XyPFFzYWNSFeMQt+nI3sYDW4QoP3hMe4tPpbEmfdw84aK3dPSsRDreDOeTu7rm3w5IPBt6A15EFYJtg6aA8eRK0yjqSgAvoGCJzlV0v0RD1vZYvk1EgrGrVCRrLhd7SYGJMwkDV5nVlZMoGHpiaxM6+ECSFmitrt3DAqjmFx/vgbdhGxSPKqXppWwKbQaHZWSuc7N8WTMC8Vdy2pwlOtgBEXMnjsedhkKnYUWxme4M/yCjljQ9Rc9UM+Uu+tAyQFD+TFib7MsJXx7n4dKmWAU7X87vJm7piYyEcXxSBseAv9j++A3Uqa1pfYkR/yW4XAJ/Xncc7IqYyNkKEXLFLnwTH30xE8EvEXgWBPNWabHblMwGJ3rW/Q29t4Z4ydXZYwZGo9YT56NCo5/x7Xh+HxARTUtpAe7uMkiglQ29pBWUM7zy/PdxnPJoE4Sxvk/YJMkLLvKw0mfs+vk/rF+HUzDCXruowHSFLxmz+AqS/0qJGllMnwUCtcpEnUir+mp+WjUzE4trOew+IJM9+SUnbNrRCYDGe/ItWIuAF6Z0DWC4KQJoqia89PN6ctMpnAzIww+oZ5Ud5oJMhLQ3O7lc82FmMXYUZGGFP7SZkqQZ4a7jorkQe/7/oV++tV1LR08MaqfHx0SubNGUZiNyPSHDYCjWc4shYp8zpS3oBGGYLJYmdRbjtL8vSkR1zE/yKqiVx2rRRMHXknJrU/dkM5+fpMXtjtRbNRismMSwpEUbbEWUrEaoKS9Siby8nVlPJ/q6VtYxMDOatvMMs7K8AjA33YGzuLfvteQf37f9kz5j3e3aJnW1Uub03xou+2J1D8sJYkuZJhmbfzjO9IlhWaeHlKEP4+CopqW7ksw585o2JoqS4hQqwgQbGNaYZ3MdGH3aEXUowPeysNTArqWh30WXsHH/ebTXHacJQBcQQ3bGK3dSS+OiW3jk/g7dUFvN7agV4l5/4pyeRWGojz6GDBXjNFDV2L+f3V7WwolnNjzu3EpN7AzXvlXDM8Br1KgdUuIooiD36XzeIJNfhseaPr8zE20q/gfbaFP8bumg589V5MSRRgzbOSzLogo2lyOukRkVQ1dxDipWFzcQPjk4NY2S2246tTEipv4ofKEKpbOli8qxg/vYo7JiZwTnoY45ODGJ8cRGWT0amGwzENi422QwoEAYw2GQgy1MYaPDQKrkyOwmS18+ySHP63Kp+HJsczVZONvmoLBCZJKrOdLXYBKFoDHa09Zjv5e6h5YGqyo24HJPdqWsRxzIxSaqQeHNHDpQp07wjQ+//5cf8gemNARgLXCIJQhOTCEgBRFMWeG2W7OW1QKeSkhnmT2imv0DfUkyuGRmEXRaL89I5cemxWzo4WCb6iH8tyGvHUKvHRqXjzV+mpsqndwqbiBicDYvOLZ0nm+wzWlmIzmygR4rh/SiRvrS6gtqWDcF8tD05NJsQjGM5+WZK8iBpClSKevJpWfsqu5PcD0mrEX6/ilhHB2LaUub6JtlrwjaOqWzx9dW4td05KZPneai4ZHElOVQsW32LU2V9QNuwpZq3WUtfaQkqwjqjcz1CUdBY32CwEbn6Jp2ckc1tkE75Vy3ml8nI+3WIGctEoZXx0fjj9GvZQYhpEadab7K618tyP+5GSDyG3fyCPDL4T782vgCjit+tD2s8aykaTL0/mpGOxNvHihel8vrGEa0bE0G62oVHK2FBYz6iEQML9tXyZXeLyNrfUyrjRO5KQLS9y7fB53L26gPeuzOSVlXnsKDXgo1NiqclzOU5fl82ATDmmzAguygxFvvttyXgAjamzuHuTnp0VZWiVcu6bksSXfxwgNcybWcOj+T2vnowIL2ZHVVPXbqe+3eLQ4jJajPy0tYgBgQJ9oqTVZoi3hjmj4/jvii5XoFYp50CDkRkZYU4y/0q5QD9NDdhtHAibSkuRFblcxredrsKGNjP3fL+X0KlaRqx/XTpo3MNSDMTUJL1OnHrEGosJKUF8df0Q1uXXEdzpcjxcTckxIwg9KgyfKTS0dbC3opnqlg6i/XT0DfNCdySl4aOkN2dyVWtzc0aiUshdfc9GA2z5EK/VzzLebmVc6nlsDL6Jy74rdtqtxeTsKgj30dERH8PK8nAeWrIHaMFLm8sFA8IZEudPbYuJB77PZkisP7NHXuJQfY2yi+TXttInUM8rF/enrcNKYogn/fzM1PSZjnbvN87z6zOOSl0ircVy/PUq6tukxABbpxsmzEfLN5tL8UgygNaXfO9h1LVKmVTjoxT4FC3jUDyqNuGR/Q0bhrzFpz93PYmbLHYeWFHHm5fM4dnlhWREmvhonXO8YP7OWi646kqG6jWIzZXUxF/AJmMsK/fV0CfQg9gAPaWNRtIifHjx5y4ZucmpwQR6qrhvwV4uHhzpSCE+yKRwK2zNBbsVD9q5bVwi/mID/fxgX6WMpnYL1R4pHKx3Fj3DKE67nRqf/qg9A7mznx+01Tt1aCzxHcrOLZLlNVpsPLskh6lpIYxKCABEIn20hHmp8C/9ifqoy1i+R/qdB3upeX+slcT9r6D58QAMnA39zkfwCuXSrEgCPFR8+ccB4gI9GJcUwCsr8hgY7cddE/uwMLuacC8FN6eJ9Nv7FraZ77CqMoqsWDmrcqo5lE0NWkboA6SeJts+lZokbf8cIrKkXidHaDKlUykYHh/A8Phjy7r6u9NsNPP80v18s6WrHuo/M1O5Ykg0ssM0GztaetVQShCEkUCCKIofC4IQCK6Fvm7OUCq2wconHS+F3d8xUFQwKPIKtpRKKaoyAbIO+oW74a0SqegUQgzwUPFglpKBXoVUNlbx2S4ZRXVGiura2V7axFfXD8HfQ41MJjCxbwgJwZ7Ut5oJ9FQT2ekLN4ZlUTr2VSJ3vgo2M9bBN0J4JgHZ3/JkzXpuyrqC3T7j2N2iY3icP+WNRjxV0h+CQRcDI25H3dGVwbSrzk57YAa61p+d5m3zjUNubqVG9AaanbYdaDCyoaSF9QX1DIz27dFlk10HsW02glV6NhijuWt+tmObTiXnjcsG8EJnRptaISPcR8tvuXVM6xfK8PhA/HVKJqd2ueAu6efFaONyST7dK4ykmAiG7f8U9Y9f8KRHKFdPfZB/rffkoxI9z416AHldLssi7+DeZZW0mxvw0m7m/85NY1CUF77RY1B31mSohK65x/lreHiQSJh9F77GWl7dpeLrPW346pRc2P8cZuoVBHtpqG8z8+xwSF95lZQNBbD8ITC30DjoTjzUCi4fEs3I+ACW7ali2e5qzk4PxWIT+Wj9AZ4/J45xQe2oOhpg2vPIA5O5NMFKSV0biFKf932VLY7YRZjOLrmpQHr/g2+AgddIQp5H0ya3J+w2aCwCmxV8okHlWutzRDpapHbFZ6jbKrem1cl4ADyzZB8jEwKIDTg+t/DeaGEdlE1PAj5G6g3yBTDiuMzAzVFT2SRVcYd4aw6fEtlb6lzdIqqCZTxx7l3cvljET6/m9onx9A93DRzWi57oVCb0Kjmfj+8gZfUsMLcRA/QZcDsPKsaystDI/qoWiuvb8O+meRTtryfaX09Tu5n8mlZ8dEqiQ0PYYTmXQp/hyLARpWoj+stpjhtZaN0TKAe38uTu4Xz1xwHunJhAqqcRuUxAJZpg5ZMkpl7LiKgZrDvQzm9Frew650aGVG+WahGAtqhxrBcHITtnAxarikPFnzMifdha0gTAgYZ2kkM8yanqkpzXqeREB+jJtaaj9bPy9upip+PbzTb2VkpG6ZZBes71LSSofgv1vukYFGHk6dUoBJHbRoRwXkY4frJWUve9in7HF1hCB9A+6b8E7v0KxZb3AJCbDCT+ci3PTpjHHiEOW6BIiddA7vyhAnNn74lmo5WHf9jNc+enUd3Ul4ujxqM7sIrY4q+5POMBFu1v4f2h9fRZdaMjU+2hxPOZcsn9/Fpqw2gXMaLj5nF9uO3rHcTZiruMx0E2vsUS8xgWF9u5fagvP+5v46utXau3zCgfRsUHoNB4ogqPdzo0wENOU7uZMB8NFQYjFw+KxGSx8XtuFVmyfV0aVMNvhbD+Lt+zY6KtHja/D2tflvS/+p4HEx4Hv5g/P9ZmlcQgVz0NbTWQdQOknAvNpVJLX48gKVnDJ/JPT3Uq6S7OeRCTxU5bh6sCxLHSm7vPecAAYBuAKIoVgiD8c8VfTiFmq41f9tXw6A+7qW8zMyLenydmpJIQ9Bd+HYdkUwEQOoB+cZF8/+84NBYD6pLf4ItPJF/wwFmSxDegUynx16t474IoUlZdLD1BdhK0/TWuG5PFykJphaDpITtmZ2kTD3yfzb7KFqL8tDw1sx/B3mryWpT839J8Vk2ocLmRBex6nytSx/H8OgP/tySHz2f15/2L4wlu/B1EEf/dH/HCwEA2Jw6joFlOvS6K0gsWY6zcR4NZwfI6P4LMfjy3MId+4V7cOj6eD9YWYbTYiA3Qc9OYOO7qzPDaWFjP/WclMm9rORuLGkgI8uDyIVGs2V/HzDAfROqdigQP0mG1c1VmAHNM7+K1dh4AXnyJNXYsqpGvM29nPYv22KhuNpFX3col/S4lfcQlbKkWuKRVQebOL6QTyRTUp82h2GsAKu8QxuosaL+/gcqMl/DReZAY7ElBbSuVBhMGo4WShna21uvZq7yTaaNuINJHTUaHH+emeNFnxa1Oac5eud/jGXYun66XbgFfbzrAR7MG8eSMVLxlzYe+JVB5UNZiY32hga0HmrllfDzQZUC2HWjihtFxDIl1fVqvbDJyw2dbKaqTvh+bixs5Oy2E/53fh5D170NQCrbMa5Gnnut63WOldCOsfrbr9Z7vpSyq7p0aD0fldqlF8MHPa/mj0ipp++ddWYQRWXDxZ+AVevzmfJyJ8dehU8mdJINSQj0J70F14VjpTRd7syiKIp2CNoIgHOcolZveklPVws1fbXPEAdbl1/Ps4n20m12fNHpNeCb0ndn1WusLEx8DlR5vrQr1/p/gu9lSq9Rtn8In06UWqUgy4fd/v4u2xlqnLn4H8bFJ7qTLsiJdgps1zSZu+mIr+yqlp/sDDUZu/nIbP+2oZE+5gbk3DCXQp4eMGrUnBrN0027tsLIy38ADiw6g8+lSQw3f+jznbriIu+seY7RlHc+urmaltT9XrVRS3qFl4U7pJrC7vJkF28u5d3ISj5/Tl4HRvryyIpfLh0Tx0HA9CwZs4/ytV/NW390sndOPtHBvnluaQ2O7mZggL7y3v8s1w5yfQlVyGckhnlyXKuK1f57TNkXRajRN+WwqbaO62USknw6zzc7nO5u5d2ULvxSZsMm12PVSnUHu2Le5rHASF6zQc95XB/hwRxt1UVPwCollcmoIrR1WxiUFcev4eDw1CpQyAaPZxrw9Ldz2u5xvqkKpbJPhITNJlfiHoLd2xWGsdpGvt5SSEuJJqSYR0cu5O0Jx5v18uVsyAB1WO54apUuvKT+dqsdGX/m1rQ7jcZAlu6tYXmTmuvabmdvvQ+yDbgDPY6ivsFmgLl9aSVu7PWwUr3Pdd/d8Keb3Z5Rvc5Vy3/YpxHeTwC/bBFWnd2JqXKAHn1w72JGCPzI+gFcuzsBX7yq2eaz0ZgUyTxCEdwEfQRBuAGYjKeO6OckU1ra5tHFdtb+WmuYOYgK6/SotJqjNkZ6WvMMhIBmUh5HM9gyB6a/CkJukFYR/fFfWSWsNrH3ReX9LO1Rsp8krkTc6s7TWV8kYH5CCos5ZUkMTGMtTM6PIiPR2tJI9SFmj0aWRVJvZhkwmsGBHBdP6KNB5eSOOfxShowU2vQeWdkoG3M9Xq6XA8MFucjWtHXyQ78mdMeNQF/8qnUy0Q78L8PjlPq4Z/jb/za1lYLQvSrnMoZl1cB6/5dXS0CnnATA61otZ5i9Qb5sPgF/ldnw8Qunf9z2+t9q5OM2H4B+m037xtyQY9dw1KZG1ebX46FQMi/Pj7dUFfD6l53qE6qZW/j1mILd+s5NzB4SzPr+OZpP0AHBZVhS3LzzA56MeJnLve7xbHEJurWRgRRG+2FzB9Etu4LXfm9hQIsUNdpQ2kRTsyWuXZFDVbGJDYT3XDIsm1FPBkr11xAXoyfENoE/sBNQ12VKzqNZqEGRUyMOBrpuuwWhhV3kT+TUKCtLeZKC4G19rHSVeg/i/bL0j5Rqk1fDI+AB0KjmJwZ7olDL6BPb8bClw+IBtdZudzMQop7ayvaalGja8AX+8Lf2+B86Wep14hUkSK4cSNhBUvahS7ynzS+sr9QnpjrnVdb9TiaFcWrF7hYNCMhJZsf58M2cozUYr/h6qv+7yPoTeBNFfEgRhElK0MQl4TBTFFcd1Fm56hV8PTw5h3ho8un8pbBbY8RUsuUu66wgCnP0qDLgS5If5dev8pFz3QxGkPP6exgWkVE2Ar3a1cs7ZTzPwj9ulimKFBobfQkjtOn7OF3lxmYnv/j2cRG8RGotBoSJQF+qolO+OUi7joeF6Ru24B6F8Y+f8/LHMeJtamT+/GcK4ergVhUxAq5Axb6tUh/LO1jZmXvEo/gNvQ2Frx6+tEDZ/COZWfK11aJUhGIwWfsut5bpRsbyyoiv2s7W4kfumJDkMSJpHI+p13zrNS9ZayUjvOj6/IIas2vmQdSO7i8rZLWooqWtDIZNRaTDy3NL9PDQtGZvWijVqJIoD/9/eeYdHWWV//HOnZJJMJr33HgKh9yagiCiKdRUXRWyoWFZ3rWtZ11VX15+u7uqu67r2XlBRKQKiKNJ7CzUhJKT3npnJ/f3xTspkAoSQhtzP88yTmTvv+86Zycx73nvPOd/zc/MxbP5JrKv0Z1xYHZ9eaOJ3P2Xw1xmxxJnrKbR5Uqcz8/LKOu5c78+TU5/nx4XOswZfTyMFjT6sOeyc7rw3v5KKOismg44B4RZ02Pjrd1qq8LbscrbnlBM44xU278ui0d7A5JBaEr0lTy9xPrGPTwhk5d4i5o6LZVOWiVy3KErq64nWe/JLVkusKDbAk7zyOm6dGMv/LTvA0l35eLrp8fZ045IhES4nqeQQL/qFepGe13LCvXhwOOf2C+baMTEEWVwlYTrEoR+gKQUYtJhHSH+tt3vsWRA2VFuOAu07PvZ2rVXviYgcpWl6VTqUfoWAUbfAkgdbtjGYtNqVvkB9Fez6ApY9qjm5wbO15l5+MYBWHNkkWdTVCNn2krbtBkI8K6V84ERjpwMjRoyQGzdu7G0zOk1xVT2PfLmzuf+BXid4fc4IpvRrpctTsAdenaA1+WlCb4Rbfm7/quxEbHkPvrq95bHJG25YCiH9+Xrb0eZCLg+jnrtGWbgmughL0TbYuQDKMvly3BfcvaKGVTdGEf3LI1pxmM5A/ag7WB96Ndd+1CKhMn98GONDGwmv3E7cqnuczLAPuJwlSX/irk92YW+UGPWCv8xMY1duOV6mJtkWG59szMHL3cADI42cvfcJ3HPXsXbyB2RbBuOus2GuLyQ2Oo4tuXW8ty4Lbw8DE5MC2ZZVRkKIhe925XH/CB2Tls90/gwBed7TiGWPatk9bl7kXvQ+H+aFsTWrjBGx/jTYGzHoBB9vOMKLo8pJiEuEXZ8RmL2C4pBxrPOdwQEZztBgHcbivQyODcF0dB26+jIaTT5Uhozm0fU6Fm7LY2SMH35mI9/tLmBsfAATkgLJLa9jXEIA81tJiTTx2EX98TYZSAsxMfPVTc0BdoCHZ6Ty3JK9zWMGneAfs4ZQa7XzzprD2KXk/LQw1meUcNGgMP78zW5GxPiTEOTJ+IRAyuus2Bu1JcPCqnqstkbyy2tpsDeyaKezk/v8trEM96vT0nK9gpv7o2/IKGbRzjwyCqvpH+5NUVUDfp5GHpjer/MppR9dA+lfO49Fj4G5i7X038o8Lehtq9e++ydTz1G4D46sg9oSiBoN5mBY+29tGcw/Ec79s3bR1QO93E/IoR/hnZnOYxN+D+c81mX2CSE2SSlHtB3vyHzmXKCtszi/nTFFNxPgZeLJS9KYPSaashor8YFmUkLbZEdVF7mc+LBbocZZjK/D9JsJHv5aYyC/GBhwqXaVB0zpF8ybc0fy9fajhJp1TLAcwrLkbk0UD0DvRkOjICHQg5C972vOA6DRhmnti0SeO5BPrxlE3tHDhLrVkpr5V7wyM6lPnOZihv7oJjY0HmmWNfc3u1Frs7POIbR3TmowaRE+lNQ0UFzdwPwl8OGMe0lN2kWxVzIvf7+fzOIaXr9mMEerJfZGiaebnrIaK3/5Rlt68zAW8OeL+1PnJigbfDO+W/7d/PoyqB+ysoA9E//NQVsg3voGYuxg0utYtb+IVfudP1+zaEBm/MTPEfOo8rmatVnVTIgIYu+BIl5Ylsenl/vjsfwhbS0d0Ln74nP2Izw8YRJuOh2XRFYQYKyhqNKbodEtNSXZpTVMTgnih72Fza81LNoPg9B0vEZFxGhS/I64aWyAJ7tyKrA2NvLbUdGEeJuwS0lZrZWUEAtpEd7kVdTzz+/3c93YWN5Yncnvz01m4dajLNhSRkm1lVAfd/71w0HGJwQQ4evBV9uO8v71Q7j6jS1O7znIy42EivXw6Z3a8qdPpCYFEhBPbqnkq61HifD14K1fMqlpsOPnaeTGCXEEe3dyBhIx3NWBRI5uqR2xhDY7sJMmKFm7tWb6X7UlMjdz35IzyXG9oGDbhzBmfrd3SjxeQ6nbgPlAvBBie6unLEA7ESpFTxDgZWJC4nG+FD6R2iyh9Xqtuw94t5Nt1RE8vKHfBdqtDV4mA1P6BWszoMp8eO/WFucBFA6ez+u7JNPi3TEdWOSyf0DZdqIOvIchY2XLoNChD77LZduGlJl8v71lvf63o6L566L05qvqpbvyKa+1clZSiyT7DxVhvF/ixzfftVROf7uriB055QyK9CGvoq657zho9S7+ZhMVtVZerDmP2Wf1J7TgZ2whafgZG/lZDOP6b6uwNWqxm3FxBn5/nj9Xj4riw/UtSQRzh/oQn/EfPI/8SPwVk9iqdyfM1x0djXy7Iw+9TtC/cX+z8wC06uv0b2hwT+bBgQL/9c+hO7KOpy9dwhWftlSu/7C3kGtGR/PojFTWHComIcgLd6OekhorA0PcifIS3DXGj2d/0hyau1FPrdXGDePjWH2gqDkl2dvDwGuzh3Br8C52RQ/j4kFhhPp44Omm59kl6c31Lwu25HBOajCj4vxZfbCYF68azLyUGoJ2vkhK8HR25rZIBNw/0ojvwjktvdvLs7UEjEFXMb3BSsHQi3h7d0PzRUB0gGeLGkJnSJ2hBbdLHcWeljAYcnXnj3ci9Ia+mXXlE+E6FpjcsXjPKXK8GcgHwGLgr0CrxT8qVfOmPox/HFz1LiyYpwVLvULgsv92LP/9VLCEwJXvwaEfaMzfSXXEBJaURHN2qpkrh4VA40goy3Lapc43Ee/MNpXispEqq8A+4m4CtrwCdiv2pOnUDbyG/J9bVFdtjdJpmQa0DoJ3np3Y7EA83Iys3O/cH9zXw0h5jZWvtx3l/un9+G5XHhsyS+kXauGucxLJLKomPsjMtwftfLAziCj/2dRl2nh3up7Hl9uwtWrs9EtGOZOzypg+IJS4ADPF1Q2EeLszJNSI5wda7GPwgkkkXLMIt8AK/pmhOXGzmx5dRTuyLYXpGAxu5NndCczU5FcapN4ly+69dVk8d8UgLkgLI6+ijoLKerZmFvDCgIOIQ97MGjYSX/8gfjxYSlKQF/3DLew8WulUz1JRa+OtX7J4NNnAdMthSJ5GcVU9azNKXIonv08v4PYpiazPKKG2rp7EZReBgMfOOY85i1t6nKd5lbc4jyaqi6gL6I/7N7dz/XhPfpOmp8gtjGU1yQwe0A+LewdiEsciMBmu+1pbtpWNEJzavO7fK9RXgdGjXfHHbiVqFIQObMkKM7jDlD9qM6Vu5ngNpcrRZNyvBhBCBAPugJcQwktKmXWsfRW9TPxkuHmlYx06SMtK6QkC4iEgHh3aNPXa1s+Nv1srzqrSagdqoiaxsDSWmf1vIPjHVquhOj3pDUH8eX8EN4+ZgknYiE7oz4CwMP55tQ9/+GQblfU23Nvp4+7jYWzOeQ+ymBgfaeQ/rUJ8fp5GYgM8KazShAyfXrSHs5KC+PfsYQyK8mHNwWKEgAZrI8/9ZhDz39/MwcIqdAKOmgaTVbrd5TWFEMx7dxP1Nq3Ht9UuSYvw5t2R9+K38QUYcRNepekcsgVgcmgQVdTZyPFMJbHtwRLPpcFu56dsK/0DU9AV78dsgAsHhTenHoNWzBjl78EAMjhQlYPVbOfOyHUE/PwaBCRimLOCt9Zto7FRsu5QMYMifYkLdL0a3ZNfxcvmFGZHRZOGNrttL5PKYjKQEqLJtMT5GgAJdhujVt/M15MfIVOGovOLRehLtaSL1imwBnfKfPoT6mZGv+NTfOIn4bP+eWITpiFCX3N5rZPGN+q4BX2Hi6tZta+Q7dnlTEgKZGxCAMGdDdofi9IsbYl352cQMQJGz9NO6D2FXyxc/ZHmQBpqNEfqWGbubjoSRL8IraVtOFrlUAywR0o54JRfXIjpwEtoLW1fl1I+0+b53wM3oemMFwI3SCkPO56zA02J2FlSyjZRJFdO9yD6aU/ZEcqP7CKrws5XR705WmfiT4MrCSlaA3sXIT2DKBxyO1ct1ZFRUo+3h4EJCYE8dEE/ovy1E9vh4mqKqxrw9jDw5Dd7+GFfSyzgTxf1p7bBjreHASkh1FBNgLcnu4ps2DBgb5SYDDoe/WpX8z56neCZy9LYX1DNa636Stx/XgoDwr3JKKqhrLaBQ4VV2KXk2+3OfeVfmjWE33201eWt/nhTLDFl67UCNjcvDgy+l+c2SSzuRhZszmZGkidPRa3De90LWpA3fjKkzuRIeQMvFo/h9oQCRHk287fFMT4llMo6G6v2FRIfZOa6sbHsPlrO+owSLompZ1LVIkJ2aC2ECe5P/eVvc/vX+Sw/oC1jCgH3TkvmuaX7nGz87ahoymobGBnjx6VDI/E1u5GeW8EDC7az7UhLvcQdZyfy1ZYcrhsTyRX2JfjW58C6V5ufb7RE8G7qqwyKDych9xu8f3T0ghGCIxOf45FDaTwfs4bArCXg5gUHV2jP3/Cd1m74eFhrIXcb1BRrdR6VudosO3zYCetGCirruPGtjc3tCwCuHx/Lg+f3O2XZdyf7Ft4FO1rV/JgD4cYV3T/r70GOFUTviAPZBpwNLJdSDhVCTAGukVLeeIoG6YF9aEH6bLT+5ldLKXe32mYKsE5KWeOIyUyWUl7leK5KSnlSgi7KgfQ+DTY7u49WcKiomgCzG5v37CXZx069zsLiA7WMTgrjUFE1tkZJmI8HK9LzSQjy4vrxcS4tdrOKq1m2J5/SGiseRj2Ld+ZSU2fjuvGxLNyWS1mNtbnl7Og4fyTgphdcNDicBZtzMJsMjE8MQEp48ts2NSxGHTeMjyOvvI79BVXsyCnnkRmprD1UzIr0AgLMbswdF4e3u4HHFu5y2jfIYuKXqVkYF7dkktVHTeBxy+PsKWxgUnIQEombTnJ90D7MxTvg6BbYv4w957zBj/Yh1NvsCGnjhRXa+n60vycjY/24cFAYD3y+g4LKFjn4W0b6cF/e/RgKd8HVH0PuNjYZh/HbxbbmNOl/XhJLdnEVL64pocHeyDn9grl8WCQfbTjC5sOlDI32Y/6UBHZkl3GktJa4QDN55XX4eBhZkV7ApsOlCAGLLqgnNSYc6sqRW97H6pdAccQ5lGAhsXI9a8znQNF+/GxFFBlCeGGrYFd+HZ+cZ2eURw5y9T9JH/4Y+2wheAVGkxob3m4/+mY2v6ul54YPhU1vtYwPmgUX/E2L7x2D1QeKmP36OqcxnYCld59FUrDj1HGqWUqF6fCvMbgUaF39MaT8etoonUoWllVKWSyE0AkhdFLKlUKIF7vAplHAASnlIYeBHwEX00qcSErZKrrKWuCaLnhdRS/iZtAzJNqPIdF+vLbqIC+tbQr2a5XKe4oa+Gr+ON5ec5iXVmi1GjtzKli2O58v5o93amwVHWDm7JRgPtuUzYLNOYxPCGD2mBgOFFSSV17H9LRQJiQFsmhHLiPj/PmfQ7LkqpFReHsY8HE3UmdtJMTbtciyztqITif4attRbp+SwI6ccp5bupf3bxjOZcMiWHuohP/9fIhRjpP6N46+7gAvXhyHcdWjTsczHfmZOy/KZ7nFl68OFDE0wouJSV6Yv723uUq8YcCVZLkl8/ev9/HIhams2F3WvH9WSQ1ZJTUkh1icnAfAm5srufrSx4j10WuppXu+Zvj+//Ll1CfZYwvDXTQyxT0d04GnGHrVh6zO1Rzp/323l4OF2ue+an8he/IqePiCVJ5alM4dZyfy9ppMp3jI5FgPYoz5sOBPYG9ATPg9buYgwso2E7ZMe7/ul/7MrKU2wJfWDUz1AXGw4iHWj32Fa7+to8FeA6STFnGUV68ZTmQ7XQcpPQxLH4Ixt2maVq3Z/hGMuknrEHgMbG1iZACNEmxVhfDjHZoG18ibIXa8FrvoDMIAOqOrdpi+e+oujkVhZT3puRVU1ttICPIiOcQL0QMpxh1xIGVCCC+05o7vCyEKaPq1nxoRQGv9i2zgePPZG9GC+k24CyE2oi1vPSOl/LK9nYQQ84B5ANHR0adir6KLsbQje+FvdqOq3t7cRrWJmgY76XkVLQ6kPJus/GIOFlsZGhbA+QMG4mN2J9LPAw+jjsuHRfDm6kwMesGNE+JobJTUWu0MjfYlKcgLN72OEB8Pnlu6l0dnpOJh1FNrbdEMivD1oKy6gUYpGRTuzXsXuBNiqCTeXEpt+Q4i/Y1MHqsnzLcCm18i0/qHUFlno85mZ/vRcsbpjC711+GFP3Nt7nZmT3sI3c9/Iiv3QgrO+zfe9bmY6os4bBnDLR9oocUGq40B4d5OS3QAFg/XoLMQIOImQoAjftF/Jmx9j9QfbiG1aaMr3oTSg/Tb/gyrPW/E6hXU7DyaKKysp8HWJNBoxWIyUmfVnIBOwH2ppXguaVWfs/h+rdfLjk+bh9K2P8OF/e/gm90teTZj4wMot0LuqD/yzCYdDXYtXnTtIAsj/GuxFmWCXztr9g1VmiIuaLU3bamvch1rRVKIRRNwLGtRPLhgQDAxqx+EQw6F5n1LYPbnkDT1uMc6Jn6xMP53sKqVYkPoQC0O0UPklddx36fb+OmAlnlnMuh454ZRjI7vfhXhjjiQi4E64B5gNuADPNGdRrVFCHENmiLwpFbDMVLKHCFEPPC9EGKHlPJg232llK8Br4G2hNUjBp+JWGu1bnL7lmoFZIlTITTtuLuMiPF36vEhBPz+3GTc3fSYDDonETgAQ1N+f10Few4cYs6SBgor64F8YgI8uXRoBGHe7nh7GvnH9y0tVt9YnckTMwfwl4sHMC4xsHlJbM4bWhrtv388yP/9ZhBPL0onp6yWxGAvZo+O5tkl6ZybGkKEzCfVp0hLjT5ciFddGYPSF2lS+EDpkFv5d+UVzB4by+zX12My6Dh32nwS825pMd7oQX7END6zzeTSej1BJgsxax+F/peAJYxGn0j8PPR8Oqcf5O8kwLeQHV7RXDAwlMU785ASzk8LxdfDQIi3ifyKlqv7myfGOV/BR4/V6i9WPqVdGU/4vXal3u8ifNMXcFfIIbaNf5mXhXZF3hopJWclBfLllhx+NzWJvy/bT1W9jZsnxpFY9DIubP9E61+epyUYeB38hoeHJnL2xbP4KaueMB93Kuts3PDpYeZPHkZm6REMOsE7F3gwcst9GPbsgU0+miNKvbhZggMAnyiIGgNlRyBqFHWmQIxVR9HnbdW+YwEuaQhOhPt68ObckXy84QjrMkq4cFA4F1oO4Pm1s7w/616FhCmdy57SGzQZoNBBWp1TSBrET+rRdN/tOWXNzgM0rbKnF+3h3RtH493OBUdX0hEpk9aXKW934WvnAK3TJyIdY04IIaYCDwOTpJTNvxopZY7j7yEhxA9oisEuDkTRQxxYDh+3WmH8+e9w/eLjZoMkhVj4aN4YNmSWUF5rY2SsH4MifXEz6LjvvBT++MXO5m2DvU30D9dmH7L0MJ8c0Dmch8bh4hoqaq3oa4sZF2vj7+d48sp2iA+ykBjsxV8Xp+PupsPf7EaYQwa/yUFV1tkorbHy0AX98DIZyCmt5YstOfzh3GSGRXmTWVzKUWs8qSUrCN/4rJZlNPEPWs1LaQZ+B7/iqTm/Q5h9uGRIOF9uPcojO4J57Jy3SMxbTI3Rjz0BU7njSzvF1aVsLgriiXOeIGLQFchljyFKM7AmnMfm6AQeXnWIRRd74GHLJbi6hnljU5k+IBSdTrD2YDF3f7SNO89JpKbezt78Si5KC2JCnI9WPOigVudJZuiFNF5xLrHeOsy+joZL5/8N0i7DmLeDgR4l3HpWHP/6sWWmd8WwCNZlFOPhpuemiXH4ebrx4c2jqbPaeXvNYcosIbTSPNDwjdEc1u4vm4fCtv2T6dfOwGYI4+lFeyhz9P5YukvrHWKvLmPE9odbtNPqymHBzXBzEoQPaTm2uzdc9BL56WtY5nc7H+0oI8lXMHemnsFhXh2SU08J9ebRC/tTb2vUMvcWt5P5pTtFfShzoDbr63/CPJ5uobiq3mVsf0EV1fW23nMgQohKHAq8bZ9Ca2l7qqWYG4AkIUQcmuOYBfy2jQ1Dgf8A06WUBa3G/YAaKWW9ECIQrTfJ307RHkVnqSuH759qM1amzUhOkE6YFGJx7ZKIlroa6uPBij35xASYmZAYQIijYtkq9GwudNmFwspaHml8Ad3n3xGld2Ps6D+y2nIBf/iqZTZy+wdbeO/GUQyM9GFwlA/bjpQzOSWY99Yebq6TiPb3JNrfk3g/I9e9tZkqR7/vlKCRvDb2SWJ++SOseUXTVvrpeQhJw9fHF6tOzyMz+nPJ0AgKKuup9PdkVcBYHv96N9mlLUKEGw+X4WU3wuc3IOzaydV0cCmTGmr47/kPEfDTvegKdzMWsA2cxWL7LP6zuaW+4qXl+9lwSzR+fA5L3wK/BJjxHMSMI7e8lue/28dnjta001JDePQiT61pl084+FwGaZdhAuZFNDAmIYiMomqi/D0ZFOkDUou3eLkbiAswYzToWLY7n2+253LZ+RM52/2NlpazBncYfYsm3tdQpQleWsLg7IfZUBfFs0t2cuukBEprGqiotRIbaKbB1sjocD3GRduc/3lSQkkGhA/hcHE1JdVas7HwwBTer4N/fK9dG+7Mge/26/ny9jSSjvvNakEI0ZL2PeAyLSjfekls9K09X7vRhSQEtff7CSPQyzW219Ucrw6kW3t+SCltQog7gKVoabxvSCl3CSGeADZKKRcCz6F1P/zUERBqStdNBf4jhGhEk6R/pnX2lqKHsdtcC8gAbLWuYx3E28PI2f2CGR7jy497C7n74634eBi5Y0oi4yNDuWVkI/fkVTqJMZ4bbkP30w8OmxoI/eVxkqc5r0V7GPVkFlXTL9SbF64cwl8X7cHD6NwzIaukhkAvEx9tzmt2HgB7C+tYK9OIMXpoS3ZCByYLNePv47klGWzOKmXmkAjOTwtlcop2rb4yvcDJeQAMjfalMicdH7tzm2BTQwn9sz9CV9jyVTbs+Ii5503m9a0+zRXc80b44bv41pbCsYKd8P4VMO8nVmUYm50HwHd78hke68ctkxJcPmNfTzfOSg7irAQ/LVU2/VswWQgMH6bV9Dgoclzh3rmigWcnvUUaB9FjIyhlPO6RjnqH8XfBkNmayKDJi4bdeRRXN/DMknTun57Cl1tzyN+gHeeZaUGMai1W6ECag/huVx73Omp9fD2NvHDlYD5p9X5AU23ek1vR7oXHCYkcgf3G78kqKMUqBZFB/niGdUIjrg+RFuHNs5cP4qlvd1NRZ+OcfsHcNjmhcwrHJ0nXavueJFLKRcCiNmOPtbrfbmRLSvkL0IOVOorjYg6AcXfBoj+0jOmNWoXsKbJ8dwF/+FS7WvX1NOJeuA32fMX5ZRmMvGAW/3cwii/Sa7l2TCTjCv7n2oDKngeOTuIDwr25aFA4b/6SyT++P8DNE+N4YuYAJFq1dev6kAhfd3YedW2sdKhSBx5+SK9QaiMnQMrlvLpbcKiogh055WzLLmd/fiV/vngAJoOewVE+XDcuhnfWHEZKiA/wZP7oAMqKc3ERl4kYgduhH1xeM6h0K386/wY+3lLA4Chfbh1cj3inTS+KhmrsVfmsTHe96lyyM48bJsRh1LecUBpsdhpsjXi5G7UCz/cvb74qt8ZMYt+UV8ms0IoLox0th6sb7NyxrAaTIZwnLxlIeYaVQxt2MCUliJGx/vi2av2aGuZNXKCZjKJqKmttVNW1OOKnV5Uy/NynSfp+nqbTBtQPu5kCUzx3/W9L80VBWY2Vez7exjVjonllpfPqtEDrKXOyOlpldXbe22vmn9/nUm9rZPoACw9dYCUmoGezproSTzcDV42MYnxiAPXWRsJ83fF065lTe686EMWviAGXaNo76/6jLWOMv0uT0z4FquttvN5KvuRvE/SMXnVtc+fDoMyfeXLas8w5exZGezX+X69zOYZXQCRGfQNWu+SSIeE8tSi9+bmnFqWj1+m4YUIcMwZp8vJv/pJJsAfc2t/O9wEBPL/SOVNpTGAdDUXhbB74CLd/bOX/fuPPO2u2kRDkxaMX9ufF5fv5ZOMRbpoYT0yAJ7UNdm4e6s0svzpqGhqJqVpD4Of/IfeST7GnXIh+7zfNxy6IOIdA2Yhuy7tOr2nw9GNOwD6uuPV83A16dKUHtbRTq/PMpq5RNPeXb83EpECKq+opqKzH38NASVUdL/+YSUZRNb8dFcUF9bsIaVrS8Qxgefz93P6fLc0B9t+fm8zTl6bx10XpVNbbePD8VF5avo9sR3bTB+uyeOzCVG6Y0DJrifTz5I25I1m9L5+C0jKevCiZPy7cR63VTr2tkZLQCaye+iVe1VlU6X1496An3r8Ukhbhw6bDLXpq5bVWktvMNIItJvYVVPH417u597wULhwY1m52Wntszirl/1ppoy3ZlUd8kJn7zkvpkbTX7qTdVOhuRjkQRddgDoQhv9WyinQG52yaTmLQCfwcfQyEgGSZ4dQ2F8D4y/MMGHgpe6st7Br2OAN/uKlZSLI47QbqfFP5/DYz6bkVZBS5LrO9v+4wlwwNx99s4srhkcyIbEC3/UN26K/Az8vIJUPCWb6ngMuGhjM8xg93dztPFTzJ+9+WYmuUlNZYqam3s+lwKXvzKrl+fCyv/niQ2gYbj321iwWbswnxNvGnscGctftejMWaAwve8hJc/AqMuB5qCmnwTcAzcAC6iATIWgPFjrhN8nQoOwyJU1uuKv3i4dy/wKJ7W97IkGsodo9BiHKuHBLI2UGVuMtacgghLNKXmS+vpqCyHl9PI/MnJ7A9u5y8ijr+/M0eysYO527fOERZBkcH3MLDq2qdsrNeWLaPT24Zw6vXDKOwqp7GRpqdR8s2+zkvLcypXWpcoJm48iOw/g5kuo3Bk+4hnwCCPfXk2yWzvypHS+oEqEGIGu48O9HJgXgY9aSGWvjvnBF8n57fHAf718qDNNgbeWjBDkK93Z1bGhyHLY5+9635evtRbp4Y36Wd+s4UlANRdC1dqABqMuqZPyWRtYeK0Xoqt3OFqDOAEFTW2bhpmeCBUe+RYCigRmfhnQMmbqoxMS7Rl0GRvvznR9ckvUCLibJqK/5Uwob/YV79EhsvXMKsDw7RKGF0nB/PXj6QZxan887aLAK93Jh3VgKBXjUYDQJbYyNuBq22oarehhCaDMpH6w/z4Xpt7T6rpJabF9WyYPofGbJyjmZ2QyXC07+5/sDNccOcpvXaPrhS85p5OyFuMoSkYbU3sjevkoyiauKDZpA8pz/G8kxNsjxsMD46HyyN2dzptRDLz6+AlEi/WNaZ/9FcfFhWY+X57/Yx76x4/ulIdf7vpnJmjbuWsLVPUGkKpqS6TVEcWjHnE99osZnfneMavm6wNWJvp3CPiiNQloUA4n++j3jQuiFe6doOVkpIC9cyypp6vvztikEkhVhIDvXmrKRA5r61njUHnbVcV+zJ77ADiW9H62tghA+eptM3iN6bKAei6NOMjvPn01vGsfpgETIsBNx9W7KAACb/EbyCiWqsw9Oo56EfqgBPwI6/2UZ0QItDGxjhQ6CXG0VV2gnSoBOc1z+Ul384wB+HNhD4w9PgF8vifZXNV+BJId488c3u5rqLoqoGnv9uL/+8eigVdVZKqxucAu1JwRZS/OD5Zc66U40S9jf4M8TxWIy9HYztr983BPRjS1UQK9MLsHhPZVJgKGlGd5bvzGX++5ubVTMuHx7JozN+09xtzge4ObEc949a6jVEaSaD9r3MlIQ7WXlQyzKrtzU6pVea3QzowweDpz+hDVkMikhke05L/EevE1S3eo8SicVkoLLV2NyxUUTU7AVdgLMirm876rjxU0gM8SY2wJPM4pZZ4fS0UMbE+/PNnRMoqKgjzMeD+CBz89KSXicI8nL9zFr/j0/EyDh/hkX7sjmrDNAEOG+bnNh12lhnGMqBnCkUH9CydhobtQK/vtKO8wQY9TqGx/oxPNZPG/D7Wqs5KMmEgVdoMhRAiLc7r80ZwZ+/3sWGzFKGRPny+Mz+TuvCcUFmHr4gleyyWi1H3t3IG6szyC6tZWZ0MJMBpMQgJLNGRhFkMREXaOa9tYedbKq3NbI9p5y4ADOLdrSIK05MDGRcrAW2vEuYdz8yip1jFBYPoyYCOO5OiD/nmO957aESrntzQ7OjeOXHDN67cTSPfLHTSXLp803ZXDEsgrEJgc1j7pVHaItnzmrGDrqLpji0ToCxVd3IA9NTCB4YCXFr8NGbeHaongcdgopBXib+NLM/f1uyt3n7t1ZnctfUJHbllLO/oIrfDPJjesUC9P97XnPwV76tiUMChA2GqY87ihqtEJAE5z5BmL83/50zggWbc/jlYBEzBoVxfloY3h5ueHu4kRrmWiVg0Ou4YXwsy3bnN6sGBHmZmJLSsdkHaHGCV68dzr68SuqsjSQGexEb2P2y579WlAM5E8jfrbW8rHYUT5i8tT4KrYu2WlNTquXF96Wua02EDdJu7ZAW4cObc0dSUmPF18PoUkQV5uNBapg3f/xiJ41SOqUA51kda/flWZx9Xjj3f3OYw8U1zJ+cgKebc5qvEJpj+9vSdP41exjbjpQT5uPOkChfAiiBDS/w6Jg3uGlRS6X3wHAvUpJTYfS3YDz2FXOd1c6/Vh5wchQ1DXZWHyzC3o7waWmNcyowfq5yPfbIMWwuaLH9oXNj8NdXcPOYUM7qH8XQGH/tCUf3vlRPeOeG0eRX1OHtbsDsZuD9dYfJKtFmCxV1Nt5ancGC28bhXZ+Lx1tTNbVc0GaHn98E837Qmpu5e8PYOyDlfE16xDemuUteUoiFB87vR4Pdjpu+YzOAIdF+fHn7OPbkVmDQ6RgQ4UPcSTqAYIt710u6n6EoB3ImkP5ti/MALci88Q246CVnNdKaUq1F6M9/B71Ja0qTeM5JN6ZpsDWyL7+Sw8XVBHqZ6BfmjU83V8Q24eVu1FJTj0FMgJmJiYF8t8e5l3dssC/4xlDj149Xf87msGNp5cstOdw6KYEXl+9rdgZzxsayYk8+BZX1BHqZuGFCq17btkBk7EQmbrmXL6Y/xP6GACy6BlJig4kNPrZybBONUjotDTVRb7UzItqP5enN9bTodYLYtss3YUM0gcAN/9UeW8IQU//E3SKOK0eVEayvIqlkFabgBBg1Ejx827XDx8Po9D97+pKBfLThCN/tzmdUnD/Xj4slxMcD9u5rcR5NVBe2tLQFLaU76Ni1Fh11Hk2khHq7tnJW9ArKgZwJFO93HSvco/VO17c62R5YDgvvbHn8ybVw7ZeaTtBJsHRXHnd9tKX5Kvq6sbH84bxkvE+l+1wX4eGm5w/npXCkrIY9uZW46XX8YVoyaTEhMPdbSkurWP16ZvP2R8vr+GprDm/MHcmmw6W4GXSsTC9ge3Y5Fw4KI7RtHYLBhJj0AIYFNzF45VwG6wzYJ96PPtxFCdsJm72RA4VV5JfXcd24WO7/rKV5lU7A5JRgLhocTlmdlY2ZpQRbTDx96UCXFFfMgXDu4zB0tnbF7x+PzieCfoCf2Y09ue5kWmaSaPEixd3SXlpCu8QFefHg+f24fUoinm56DE01Jd7h2kVI69mRpz+Yu7cXt6JvoBzImUDqRbD9Y+exoXOcnYetHtb/x3XfPV+flAPJLq3h4S93OJ1P3l6TyYWDwxgZ63+ShncPKaEWPrhpDEdKazC7GYgJ8NROiO5R+LhbGRpdxrqMlkyfg4XVmAx6hsf48aeFu8gureXSoeHcdU4ypnY6IxLcDzFnIZRmgpsnev8E58+6DVJKluzK4+6PtmJrlExKDuKJiwfw6cYj+Hi4cevkhGaNsDfnjiS/oh6Lu6E5pdUFNy+tf4aDOqud/Ipa7v10OxsytRTZzii2CiFctZUCU2DG37WU4kabNlu99LUO6VT1RYqr6tmXX0lNg52EIBUfORHKgZwJxEyA85+FlX+FRiuMvweSpzlvI/Rgaaf1rdfxu761pbLORkWt6xJMaTupob2Jn9mt3bx/L3cjD89I5fo3NzSrBM8ZG0NKqBf+ZhNf3OZDjdVOkMV0/MwdT3/t1gGyimt44LPtzf3Wf9xXyMbMEr64fRyxAWbcWr2OyaDJzqfnVRBg1oLNTVlYbbHaG1mXUcIr3x9gUkpQs/MAh2Lr4j28d8PoDhfhtUtDpbZUdcm/NOW8oBQtcN4D1NvsVNTa8PUwdolsR255LQ9+tp0f92vKtt4eBt69YTSD2zQyU7SgHMiZgKefJhjXbybQqInfta261Rtg7HzYt6hZXgKTtxb8PAnCfNzpF2ppFiYELV32ZFIte5tBkb58dcd4DhfX4GUykBDshZdJ+6n4e5no6nlUSU0D1W2k66sb7OSW1ZEc4rzW33Z58Lejonng/H7txpi2HSljzv/WoRPCpZsjwKGCaqob7J13IHYrrPkX/Nyq2VPECJj1wQnbzZ4q6bkVvPLDAdYeLGFSciDzJiW4LuedJFuzypqdB0BFrY0Xlu3j1WuG4+Gm0nzbQzmQMwmfdmYYrYkcpfWpzlqrieJFj4GQASf1Er6ebrxw5WAeXLCd7dkVBFtMPHP5QJKCu1Wbs8uJ9PM8ZWmI/Io6DhZUodcJEoO9CDiGOmqot7tTfQqAm17n0uo1u7SGR750TuX9YH0WlwwNZ1Sc61LUmoPFNEotMO/VTvOumUPCCdBXw6F1WoV/YDIEtt9jo7FRotO1uegoyYBfXnIey9moxde60YHkl9dx0zsbm0UqP9ucw7accj68ecwpKdAeKXVVKtiZU05lnVU5kGOgHIiiBZ0OIoZpt1Ogf7gP7944mvyKenzcjYT4nHkpkwcKKrnl3U3NXf+Gx/jywpVDiAlwXVMP8/Xg9Tkj+OlAEYcKq/lpfyFPXzqQhCAvp+0q62yU11pd9m+vchzAbGr5eS/bnc/vzknirV8yqaizckFaKDePCcW44CY49L22kcmiJU1EtgT8j5RUs3RXPkt25jEhKZCZg8OJb7Kr0arFPdpi697lyoziaheF4/35Vc1Zf50ltZ3MrulpofgriZNjohyIolvw8XDDx+PM/eF9vinHqWXspsNlrNpXyLVjXR3IlqxS/vjFDvbkVpIU7MU/rh7KuFbFgU0EWdzoH2Zhd67z8mDoMRz02IQAvN0NVNTZ2HqkjJyyWv577XCCLCbCfN1x37+oxXmA1j52xRNw9YfgZqaqzspfvt7TnPK88XApS3fm8faNo7Q6Cr9Y6HeRlvrdhDmo24tUPdpJXBCClp4fnWRwlA8PTE/hxeX7qbc1Mj4xgBsnxLVknClcUA5EoehiGmxa4V9bNh0u5dqxsU5juWW1zHt3U3N3xf0FVcx/fzNf3zHBRVm3rMbGZcMj0W3JYWdOBSHeJm6cEE9tfTv9wtEk1T+6ZSyr9xdRUWtlQnIgQ6J8W4Ly5a5V6+Tv0OqE3MxkFte41MvsyavkYEG15kDczDDtSa1p2K4F2hLomNucpUy6gYQgM1cMj+CzTS0NTK8dHUP8KWZMeXtoOmfnDQil3tZIlL8HXqaOxYfqrHa2ZJWxan8hgV5uTEgMPCNqVZQDUSi6GDeDnukDQtmeXe40PjHJdVaRXVrr1JoXNMHDI6U1Lg5ESskzi9M5p18Ik1OCKatu4OXv9/PSrGPL5vcP86Z/O7IgAAS3E99KvRg8NTuPpW7uFArxj9UKTsfeoVXY67v/lOLlbuT+6f2Y1j+U/QVVJIdYGBrti0cX9MDQ60TLEt1JsGpfIfPe3dT82M/TyCe3jO1c06vTCOVAFAoHdVY76bkVHC6pIchiov9xUmRPxIWDwtmYWcL3ezUFgN8Mj2R8omtxnbeHoVl9tgkhwLed5b9of08uGRLBZ5uyWerofRXh69757KOIYXDe0/D9X7TeIgnnwLg7mmtWYgPNzBwSzsKtR5t3GRLlS2JwOyfYHpa9Cba4M21AKNNOLsejWyivbeD/vtvrNFZaY2VzVqlyIN2JEGI68BJaS9vXpZTPtHneBLwDDAeKgauklJmO5x4CbgTswF1SyqU9aLriV4aUkq+3HeW+VhXg14yJ5v7p/TpVQR8d4Mk/rh5KZnENeh3EBpjbvUKOD/TivvNSeGZxS6Ore6Ymtys7bjLquWdqMmkR3izcepSRsf5cPiySCD8Pl207hLs3jL4Nks8Hex34RGmBdAdmNwMPnNePsfEB/LC3gFFxAUztF3zMbLIzFZtdOnVcbKLW2o68/a8MIdsRaOuRFxZCD+wDzgWygQ3A1a17mwsh5gODpJS3CiFmAZdKKa8SQvQHPgRGAeHAciBZStn+YrCDESNGyI0bN3bPG1Kc1mQWVzPjpZ9c6jE+v20sw2O6t4K+ut7GntwKjpbVEurjQf8wy3H1vECTPlHB3b7DW6szePxr7dR1YbKZWbHVDAkx4BWeoiUbnOYIITZJKV30eHpzBjIKOCClPAQghPgIuBjY3Wqbi4HHHfc/A14WWnOAi4GPpJT1QIYQ4oDjeGt6yHbFSSCl7JJ2ocVV9RRXNxBgduvyq+CqOpuL8wAtHtHdmE0GRpykzItyHn2LCweHYzLqKSnIYW71m5hXOaSDPPzgmgWnnBrfV+nNb2EE0DoNJNsx1u42UkobUA4EdHBfAIQQ84QQG4UQGwsLC9vbRNFNlFY3sHBrDrNfX8fDX+xge3ZZp4+1MbOE37z6C9P+vorL/v0LGzJKTrzTSRDu60FKqPN6tcmgI+Y0qqBXdDGNjVqxZNGBE9a2BHqZuHpUNLcmVWDe00p3rrYUlv1JS5H+FfKrv4yRUr4mpRwhpRwRFKQUQnuSr7bmcNdHW/nlYDHvr8vi6tfWsjev4sQ7tiG7tIZ5727ikKOn+eHiGkclsmvlcGfxN7vx9ysHMyJGa1wV7e/B/+aOdCnmU5wh1JTA6r/Dv8fCv0bBovugrJ205zboK4+6DuZsgLpy1/FfAb25hJUDtJbsjHSMtbdNthDCgNa1s7iD+yp6kYLKOl5eecBprLrBzq6jFSedH59dWutSbV1ea+VISc0py420pn+4D29eP5LCynq8PYynVNXsRMVRyNms9cgIStEaeZ1kjxVFD3P4F62osonNb0FAPIz/3fH3849zHUs8Fzw7rnp8OtGbM5ANQJIQIk4I4QbMAha22WYhcJ3j/hXA91KL+i8EZgkhTEKIOCAJWN9Ddis6gAB07cQ92hs7EX6eRgxtdJh0gk6n2B4Pi7uR+CCvrnMeVQXwxa3w8Wz49h546wLYuaBrjq3oPg6tdB3b/rGmGXY8wobC2Y+0yPcHD9DqZIydzJTr4/SaA3HENO4AlgJ7gE+klLuEEE8IIWY6NvsfEOAIkv8eeNCx7y7gE7SA+xLg9hNlYCl6liCLO3dPTXIa8/YwMCD85OsF4gO1ZkateWB6PxLaSXXtc+TtgIwfnce+e7hDyyFnDJUFkL9L+9tXaK+DYtgQMJxA183DB8bfDbf+DDd/r7WODk7tDgv7BL1aByKlXAQsajP2WKv7dcBvjrHvU8BT3Wqg4pSYMSgMf7OJhVtziAkwc+HgsE4VVhkNOn47OprhMX4cLasl3NeD5BCLU5+MPkt9letYXTnY6nrelr5I5mr48jYoO6z1S7/k3xA7vret0pqoBSZD0T7tsYcfjL4FdB34zp2ghe+viV6rA+kNVB2Ioscp2A2vTXF2GCkz4PLXwe00zvCqLtKq171CwdDJfiKlh+G1SVqmUhMefjDvx27X0+oQ5dnazMhu1WYRAQm9bVGv0RfrQBSKXz9BqXDNF/DdI1C0FwZcpgViT1fnYbfCwe9h8QNQkQ2DroaJ94B//MkfqzzL2XmA9rg8q284EJ9I7aY4JsqBKBTdiRAQOw7mfKnVApiDO3/F3hfI2wEfzgLpkOnY8o72d8YLJ/++PAJAZ3DuKaIzaOOK04JffR2IQtEncPcGn4jT23kAFO5tcR5NbPsAqnJP/lgBSZocfGumPamNK04L1AxEoVB0HHcf1zHvSDC0n6baYGtkf34l2aW1BHubSA6xtHRKNBhh2HVaH5GKHPCO0GINp7uTPYNQDkShUHScsMHaCT/bUXYlBJz/DHi1r/KweGcu93y8lSa1+runJjHvrHg8m5SJ3Twhcjia4LbidEM5EIVC0XF8IuA3b0Ludqgt0yrrwwa1u+nh4moe/mInrVqd8OLy/ZzdL5hBkb49Yq6ie1EORKFQnBwdzE4qq7FSVe/aJ6OoTQdGxemLCqIrFIpuIczHnQhf59iIm17n0qpXcfqiHIhCoegWgr3defm3Q5sl8QPMbrx67TClcPwrQi1hKRSKbmNotB+f3zaOgop6fD2NhPv+OkUFz1SUA1EoFN1KoJep69SNFX0KtYSlUCgUik6hZiCK05qK2gb25VdRXmslNsBMQrBaX1coegrlQBSnLaXVDfxtaTofrtd6a7gbdbw1dxRjEpSWkkLRE6glLMVpy+7cimbnAVBnbeThL3dQUq3qDBSKnqBXHIgQwl8IsUwIsd/x16+dbYYIIdYIIXYJIbYLIa5q9dxbQogMIcRWx21Ij74BRZ+goNK1KdPBwmoqa12L1xQKRdfTWzOQB4EVUsokYIXjcVtqgDlSygHAdOBFIYRvq+fvk1IOcdy2drfBir5HjL9rS9uxCf4q40eh6CF6y4FcDLztuP82cEnbDaSU+6SU+x33jwIFQPuKbYozktQwC09fOhAPo9ZmtF+ohccuHIDZXYX2FIqeoLd+aSFSyqYGAnlAyPE2FkKMAtyAg62GnxJCPIZjBiOlVAvfZxgebgauHhXFuIQAquptRPh64Gd2622zOoWUksPFNRwtryXA7EZ8kBdGvQpRKvo23eZAhBDLgdB2nnq49QMppRRCHLMxuxAiDHgXuE7K5k42D6E5HjfgNeAB4Ilj7D8PmAcQHR19ku9C0dcRQhAb6LqUdbrx475C5r+/mZoGOwad4ImLB3D5sEhMjtmVQtEX6TYHIqWceqznhBD5QogwKWWuw0EUHGM7b+Bb4GEp5dpWx26avdQLId4E7j2OHa+hORlGjBhxTEelUPQWOaW13PPxVmoa7ADYGiWPfLmTwVG+DAhvp4GTQtFH6K058kLgOsf964Cv2m4ghHADvgDekVJ+1ua5MMdfgRY/2dmdxioU3UlRVT2lNVansUYJ+eWuWWYKRV+itxzIM8C5Qoj9wFTHY4QQI4QQrzu2uRI4C5jbTrru+0KIHcAOIBBo01hZoTh9CLKYCGgTu9EJCFPCg4o+jpDyzFnVGTFihNy4cWNvm6FQuPDzfi0GUlFnw2TQ8dSlA5k5OBw3gwqkK3ofIcQmKeWItuMq31Gh6Aaq6224G/XodaJD209ICuKbuyaSW15LoNlEbKC5w/sqFL2FciAKRReSVVzDV1tz+GZ7LiNi/bh2TAz9wrw7tG+0vyfRqluf4jRCORCFoouobrDx9OI9LNmZB8De/EpWpOfz2a3jiPRTjkHx60MtsCoUXURWcU2z82gir7yeAwVVvWSRQtG9KAeiUHQRBr1oN27hpirKFb9S1DdboegiYvw9uWF8nNPYkChfkkIsvWSRQtG9qBiIQtFFuBn03HJWPEOiffnlQBEDwn2YkBhIkEWpAyt+nSgHolB0IYEWEzMGhjFjYFhvm6JQdDtqCUuhUCgUnUI5EIVCoVB0CuVAFAqFQtEplANRKBQKRadQDkShUCgUnUI5EIVCoVB0ijNKzl0IUQgcdjwMBIp60ZzjoWzrPH3ZPmVb5+jLtkHftq+rbIuRUga1HTyjHEhrhBAb29O37wso2zpPX7ZP2dY5+rJt0Lft627b1BKWQqFQKDqFciAKhUKh6BRnsgN5rbcNOA7Kts7Tl+1TtnWOvmwb9G37utW2MzYGolAoFIpT40yegSgUCoXiFFAORKFQKBSd4oxxIEIIfyHEMiHEfsdfv+Ns6y2EyBZCvNxXbBNCDBFCrBFC7BJCbBdCXNXNNk0XQuwVQhwQQjzYzvMmIcTHjufXCSFiu9Oek7Tt90KI3Y7PaYUQIqanbOuIfa22u1wIIYUQPZYC2hHbhBBXOj6/XUKID/qKbUKIaCHESiHEFsf/9oIetO0NIUSBEGLnMZ4XQoh/OGzfLoQY1odsm+2waYcQ4hchxOAue3Ep5RlxA/4GPOi4/yDw7HG2fQn4AHi5r9gGJANJjvvhQC7g20326IGDQDzgBmwD+rfZZj7wquP+LODjHvqsOmLbFMDTcf+2nrKto/Y5trMAq4C1wIi+YhuQBGwB/ByPg/uQba8Btznu9wcye/D/ehYwDNh5jOcvABYDAhgDrOtDto1r9f88vyttO2NmIMDFwNuO+28Dl7S3kRBiOBACfNczZgEdsE1KuU9Kud9x/yhQALhUhnYRo4ADUspDUsoG4COHjcey+TPgHCGEa0PwXrBNSrlSSlnjeLgWiOwBuzpsn4O/AM8CdX3MtpuBV6SUpQBSyoI+ZJsEvB33fYCjPWQbUspVQMlxNrkYeEdqrAV8hRA90lXsRLZJKX9p+n/Sxb+HM8mBhEgpcx3389CchBNCCB3wPHBvTxpGB2xrjRBiFNpV2sFusicCONLqcbZjrN1tpJQ2oBwI6CZ7Tta21tyIdmXYU5zQPsfyRpSU8tsetAs69tklA8lCiNVCiLVCiOl9yLbHgWuEENnAIuDOnjGtQ5zs97K36NLfw6+qpa0QYjkQ2s5TD7d+IKWUQoj28pfnA4uklNldfTHdBbY1HScMeBe4TkrZ2KVG/soQQlwDjAAm9bYtTTguUl4A5vayKcfCgLaMNRntSnWVEGKglLKsN41ycDXwlpTyeSHEWOBdIUSa+h10DCHEFDQHMqGrjvmrciBSyqnHek4IkS+ECJNS5jpOwu1NzccCE4UQ8wEvwE0IUSWlPGYgtAdtQwjhDXwLPOyYJncXOUBUq8eRjrH2tskWQhjQlhSKu9Gmk7ENIcRUNOc8SUpZ3wN2NXEi+yxAGvCD4yIlFFgohJgppdzYy7aBduW8TkppBTKEEPvQHMqGPmDbjcB0ACnlGiGEO5pYYE8tsx2PDn0vewshxCDgdeB8KWXX/U57KtDT2zfgOZwD1X87wfZz6bkg+gltQ1uyWgHc3QP2GIBDQBwtAc0Bbba5Hecg+ic99Fl1xLahaMt7Sb3wPTuhfW22/4GeC6J35LObDrztuB+ItiwT0EdsWwzMddxPRYuBiB7838Zy7ED1DJyD6Ot7+Ht3PNuigQPAuC5/3Z58k715Q1ufXwHsB5YD/o7xEcDr7Wzfkw7khLYB1wBWYGur25ButOkCYJ/jRPywY+wJYKbjvjvwqeOLuR6I78H/5YlsWw7kt/qcFvbwd+249rXZtsccSAc/O4G2xLYb2AHM6kO29QdWO5zLVmBaD9r2IVrmoxVtlnYjcCtwa6vP7RWH7Tt6+H96ItteB0pb/R42dtVrKykThUKhUHSKMykLS6FQKBRdiHIgCoVCoegUyoEoFAqFolMoB6JQKBSKTqEciEKhUCg6hXIgijMCIUTVCZ73dRSQnsprzBVChJ/KMY5z7FuFEHNOsM1bQogr2hmfLIT45iRfb4kQouxk91OcWSgHolBo+KJJ2ZwKc9GUkrsUIYRBSvmqlPKdrj72cXgOuLYHX09xGqIciOKMQgjh5egRstnRH6FJ8fUZIEEIsVUI8Zxj2/uEEBscvRT+7BiLFULsEUL819Ev4zshhIfjyn8E8L7jGB6tXrOfEGJ9q8exQogdjvuPOV5jpxDitSZFYyHED0KIF4UQG4HfCSEeF0Lc63juZsc+24QQnwshPFu9xalCiI1CiH1CiAvbef9mR/+I9Y6+Gu0pBSOlXAFUdvqDVpwRKAeiONOoAy6VUg5D6xvyvOOk/SBwUEo5REp5nxBiGpoG1ChgCDBcCHGW4xhJaJLnA4Ay4HIp5WfARmC24xi1TS8opUxH01WLcwxdBXzsuP+ylHKklDIN8ABan/TdpJQjpJTPt3kPCxz7DAb2oFUeNxHrsHkG8KpDL6o1DwPfSylHOd7/c0IIc0c+OIWiLcqBKM40BPC0EGI7muRJBO3L509z3LYAm4F+aI4DIENKudVxfxPaSftEfILmOMDZgUwRWkfHHcDZwIBW+3xM+6QJIX5y7DO7zT6fSCkbpdY75pDD7rbv60EhxFY0GRV3NK0kheKk+VWp8SoUHWA2WiOu4VJKqxAiE+0k2hYB/FVK+R+nQa11b2t1XzvazOFEfAx8KoRYgKbav98xO/gXmm7SESHE421sqT7Gsd4CLpFSbhNCzEWTXm+irTZR28cCbca0twM2KxTHRc1AFGcaPkCBw3lMAZr6pVeiSa03sRS4QQjhBSCEiBBCBJ/g2G2P0YyU8iCas3mUlplFk7MocryOSwbVMbAAuUIII5pDbM1vhBA6IUQCWnvYto5iKXBnq1jL0A6+pkLhgpqBKM403ge+diz/bATSAaSUxULrwrcTWOyIg6QCaxzn2io0RWT7cY79FlrcoRYY2zoO4uBjtOymOMdrlgkh/gvsROtE2dGeG48C64BCx9/WTisLTR3ZG02NtU44N0f7C/AisN3R3CoD57gLAEKIn9CWv7yE1gHwRinl0g7apzhDUGq8CoVCoegUaglLoVAoFJ1CORCFQqFQdArlQBQKhULRKZQDUSgUCkWnUA5EoVAoFJ1CORCFQqFQdArlQBQKhULRKf4fV1seoweeMN4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.2792302, 0.005119955844567481)\n",
      "Ttest_indResult(statistic=2.4274745862576173, pvalue=0.015394313694188878)\n"
     ]
    }
   ],
   "source": [
    "import scipy\n",
    "het_vec = logit_model.person_het_vector.clone()\n",
    "x = het_vec[:,0].detach().numpy()\n",
    "y = het_vec[:,1].detach().numpy()\n",
    "data = pd.read_excel(\"responses.xlsx\")\n",
    "sns.scatterplot(x,y, hue=data[\"Q1.1\"])\n",
    "plt.xlabel(\"latent variable 1\")\n",
    "plt.ylabel(\"latent variable 2\")\n",
    "plt.legend(title=\"Gender\")\n",
    "plt.show()\n",
    "\n",
    "group1 = x[data[\"Q1.1\"] == \"Male\"]\n",
    "group2 = x[data[\"Q1.1\"] == \"Female\"]\n",
    "\n",
    "sns.kdeplot(group1, label=\"Male\")\n",
    "sns.kdeplot(group2, label=\"Female\")\n",
    "plt.xlabel(\"Latent Parameter for Respondent\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# from https://www.geeksforgeeks.org/how-to-perform-an-f-test-in-python/\n",
    "import scipy.stats as stats\n",
    "  \n",
    "# Create data\n",
    "\n",
    "  \n",
    "# # converting the list to array\n",
    "# x = np.array(group1)\n",
    "# y = np.array(group2)\n",
    "  \n",
    "# calculate variance of each group\n",
    "# print(np.var(group1), np.var(group2))\n",
    "  \n",
    "def f_test(group1, group2):\n",
    "    f = np.var(group1, ddof=1)/np.var(group2, ddof=1)\n",
    "    nun = group1.size-1\n",
    "    dun = group2.size-1\n",
    "    p_value = 1-scipy.stats.f.cdf(f, nun, dun)\n",
    "    return f, p_value\n",
    "print(f_test(group1, group2))\n",
    "print(stats.ttest_ind(group1, group2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neuron Examination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('adj/english-adjectives.txt') as f:\n",
    "    adjatives = [line.strip() for line in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = list(logit_model.sentence_to_neurons(\"slow\").detach().numpy())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj = pd.DataFrame.from_dict({\"adj\": adjatives})\n",
    "for nur_num, nur_act in enumerate(a):\n",
    "    adj[nur_num] = np.NAN\n",
    "    \n",
    "adj = adj.set_index(\"adj\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj.loc[\"abandoned\"] = a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj.loc[\"abandoned\", 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#list(logit_model.sentence_to_neurons(\"word\").detach().numpy())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = list(logit_model.sentence_to_neurons(\"slow\").detach().numpy())[0]\n",
    "\n",
    "for word in tqdm(adjatives):\n",
    "    neurons = list(logit_model.sentence_to_neurons(word).detach().numpy())[0]\n",
    "    \n",
    "    for index, n_weight in enumerate(neurons):\n",
    "        adj.loc[word, index] = n_weight\n",
    "    \n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj[0].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# person activations\n",
    "personal_activations = logit_model.get_specific_activations(x_all, one_hot_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fit neurons to questions\n",
    "interpretter = PCA(n_components=40)\n",
    "interpretter.fit(x_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_dict = dict(list(enumerate(adjatives)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_embeds, _ = proces_sent(adj_dict, int(NUM_PEOPLE/3), get_strange_quest=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_embeds.shape\n",
    "NUM_ROWS = int(2679030/3)\n",
    "adj_embed_short = adj_embeds[:NUM_ROWS]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2679030/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#usual function for this broke, make a new one that doesnt run out of memory\n",
    "to_be_concat = []\n",
    "np_height = len(adj_dict)\n",
    "for i in range(int(NUM_PEOPLE/3)):\n",
    "    temp_array = torch.zeros((np_height, NUM_PEOPLE))\n",
    "    temp_array[:, i] = 1\n",
    "    to_be_concat.append(temp_array)\n",
    "    \n",
    "one_hot_adj = torch.concat(to_be_concat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "activations = logit_model.get_specific_activations(adj_embed_short, one_hot_adj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#re orent activations into orthogonal dimensions\n",
    "activation_interpreter = PCA(n_components=35)\n",
    "activation_interpreter.fit(activations.detach().numpy())\n",
    "re_oriented_act = activation_interpreter.transform(activations.detach().numpy())\n",
    "\n",
    "# average each word:\n",
    "to_be_concat = []\n",
    "for index, word in adj_dict.items():\n",
    "    import_indexes = list(range(index, len(activations), len(adj_dict)))\n",
    "    \n",
    "    to_be_concat.append(np.sum(re_oriented_act[import_indexes], axis=0).reshape(1, -1))\n",
    "average_for_word = np.concatenate(to_be_concat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# look at the words that maximise each activation\n",
    "to_be_df = {\"word\": adj_dict.values()}\n",
    "for i in range(35):\n",
    "    to_be_df[i] = average_for_word[:, i]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_act_2 = pd.DataFrame.from_dict(to_be_df).set_index(\"word\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index=2\n",
    "print(adj_act_2[index].sort_values().head(10))\n",
    "print(adj_act_2[index].sort_values().tail(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(adj_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_for_word.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupe questions \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "50/2.5*3.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_array.shape[0] * NUM_PEOPLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_embeds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 75+72+65+50\n",
    "a/4\n",
    "3600*4/20\n",
    "8*4/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2679030 / 39861"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpretter.transform(x_all)\n",
    "\n",
    "questions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO\n",
    "# auto confusion matrix (for scenario)\n",
    "\n",
    "# ordered logit model\n",
    "\n",
    "# extract actual values (language model backwards)\n",
    "\n",
    "# kick out covariances with people socieal data\n",
    "#     - IE men care about speed\n",
    "#     - rich people care about status\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = go.Figure(data=[go.Sankey(\n",
    "    node = dict(\n",
    "      pad = 15,\n",
    "      thickness = 20,\n",
    "      line = dict(color = \"black\", width = 0.5),\n",
    "      label = [\"Model Predicts Agree\", \"Model Predicts Disagree\", \"Survey Agrees\", \"Survey Disagrees\"],\n",
    "      color = \"blue\"\n",
    "    ),\n",
    "    link = dict(\n",
    "      source = [0, 0, 1, 1], # indices correspond to labels, eg A1, A2, A1, B1, ...\n",
    "      target = [2, 3, 2, 3],\n",
    "      value = [8, 4, 2, 8]\n",
    "  ))])\n",
    "\n",
    "fig.update_layout(title_text=\"Basic Sankey Diagram\", font_size=10)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "removed_quest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "_, working_sample = remove_quest(\"Q22.4\", y_all)\n",
    "_, broken_sample = remove_quest(removed_quest, y_all)\n",
    "\n",
    "working_sample = working_sample[:100]\n",
    "broken_sample = broken_sample[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "working_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_sample_people = 100\n",
    "sent_to_model = 'I would consider buying an automated vehicle.'\n",
    "sent_to_model = 'I trust automated vehicles to operate safely.'\n",
    "sent_to_model = 'I would consider buying a vehicle.'# (0.7697223874366013)\n",
    "sent_to_model = 'I trust buying an automated vehicle.' #(-0.705674437480165)\n",
    "sent_to_model = 'never gonna give you up'# -0.6907486661293478\n",
    "sent_to_model ='I trust automated vehicles to operate safely.'\n",
    "new_pred = [float(logit_model.get_individ_utility(sent_to_model, ID)[0,0]) for ID in range(num_sample_people)]\n",
    "\n",
    "new_pred = np.array(new_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "\n",
    "print(scipy.stats.pearsonr(working_sample, new_pred))\n",
    "print(scipy.stats.pearsonr(broken_sample, new_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "$$ $$\n",
    "$$ $$$$ $$$$ $$$$ $$$$ $$$$ $$$$ $$$$ $$$$ $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Out of bound Question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 random guys on the internet is better then 1\n",
    "\n",
    "# is it overfitting\n",
    "def get_n_params(model):\n",
    "    pp=0\n",
    "    for p in list(model.parameters()):\n",
    "        nn=1\n",
    "        for s in list(p.size()):\n",
    "            nn = nn*s\n",
    "        pp += nn\n",
    "    return pp\n",
    "\n",
    "get_n_params(logit_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_parameters = filter(lambda p: p.requires_grad, logit_model.parameters())\n",
    "sum([np.prod(p.size()) for p in model_parameters])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(removed_quest)\n",
    "_, reomved_quest_results = remove_quest(removed_quest, y_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "questions.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = logit_model.poll_question(questions['Q16.2']).detach().numpy()\n",
    "x = reomved_quest_results\n",
    "\n",
    "import scipy\n",
    "\n",
    "print(scipy.stats.pearsonr(x, y))\n",
    "\n",
    "def jitter(values):\n",
    "    return values + np.random.normal(0,0.05,values.shape)\n",
    "\n",
    "sns.scatterplot(x=jitter(x), y=jitter(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# dont redo PCA - fix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unkown_sent = questions[removed_quest]\n",
    "print(unkown_sent)\n",
    "logit_model.get_individ_utility(unkown_sent, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "#new_pred = [logit_model.get_individ_utility(unkown_sent, ID) for ID in tqdm(range(NUM_PEOPLE))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logit_model(x_test, one_hot_people_test)\n",
    "y_pred = y_pred.detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(y_test)\n",
    "len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(sum(y_test))\n",
    "print(sum(y_pred >= 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def jitter(values):\n",
    "    return values + np.random.normal(0,0.05,values.shape)\n",
    "sns.scatterplot(x=jitter(y_test), y=jitter(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(y_test) - sum(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import random \n",
    "\n",
    "# rand_list = [0]*591 + [1]*336\n",
    "# random.shuffle(rand_list)\n",
    "# rand_list = np.array(rand_list)\n",
    "\n",
    "# pool_1 = y_pred[rand_list==1]\n",
    "# pool_2 = y_pred[rand_list==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool_1 = y_pred[y_test==1]\n",
    "pool_2 = y_pred[y_test==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "\n",
    "t_value,p_value=stats.ttest_ind(pool_1, pool_2)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "\n",
    "scipy.stats.pearsonr(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum(pool_1)/len(pool_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum(pool_2)/len(pool_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = torch.nn.Linear(1,6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a(torch.FloatTensor([0.5]))"
   ]
  }
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