Hyperlink Update in Tutorial Notebooks (#271)

* Update hyperlinks in tutorial_distance_based.ipynb
* Update hyperlinks in tutorial_partition_based.ipynb
* Update hyperlink in tutorial_similarity_based.ipynb

The hyperlinks related to their respective URLs on the package documentation page have been updated.

notebooks/tutorial_distance_based.ipynb

@@ -254,9 +254,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Example 1: [MaxMin Selector](https://selector.qcdevs.org/dissimilarity.html#selector.distance.MaxMin)\n",
+    "### Example 1: [MaxMin Selector](https://selector.qcdevs.org/api_methods_distance.html#selector.methods.distance.MaxMin)\n",
     "\n",
-    "The `MaxMin` method selects the subset that maximizes the minimum distance to all previously selected points. This method requires the pairwise distance between all points in the dataset, so it is not recommended for large datasets. To select subsets of points, either the dataset's pairwise distance matrix `X_dist` or the feature matrix `X` should be provided. The latter requires specification of `fun_dist(X) -> X_dist` function for computing the pairwise distance between points. This can be a user-defined function or a `sklearn.metrics.pairwise_distances` function, as shown below. Check [MaxMin Documentation](https://selector.qcdevs.org/dissimilarity.html#selector.distance.MaxMin) for more details.\n"
+    "The `MaxMin` method selects the subset that maximizes the minimum distance to all previously selected points. This method requires the pairwise distance between all points in the dataset, so it is not recommended for large datasets. To select subsets of points, either the dataset's pairwise distance matrix `X_dist` or the feature matrix `X` should be provided. The latter requires specification of `fun_dist(X) -> X_dist` function for computing the pairwise distance between points. This can be a user-defined function or a `sklearn.metrics.pairwise_distances` function, as shown below. Check [MaxMin Documentation](https://selector.qcdevs.org/api_methods_distance.html#selector.methods.distance.MaxMin) for more details.\n"
    ]
   },
   {
@@ -303,10 +303,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Example 2: [Directed Sphere Exclusion (DISE) Selector](https://selector.qcdevs.org/dissimilarity.html#selector.distance.DISE)\n",
+    "### Example 2: [Directed Sphere Exclusion (DISE) Selector](https://selector.qcdevs.org/api_methods_distance.html#selector.methods.distance.DISE)\n",
     "\n",
     "This selector can be used in a similar fashion, however, there are user-defined parameters that can influence the selected subset.\n",
-    "For example, `ref_index` specifies the index of the reference point which is the first point sampled, and `p` denotes which Minkowski p-norm to use for computing the distance between points. The default value of `p=2` corresponds to the Euclidean distance. Check [DISE Documentation](https://selector.qcdevs.org/dissimilarity.html#selector.distance.DISE) for more details.\n"
+    "For example, `ref_index` specifies the index of the reference point which is the first point sampled, and `p` denotes which Minkowski p-norm to use for computing the distance between points. The default value of `p=2` corresponds to the Euclidean distance. Check [DISE Documentation](https://selector.qcdevs.org/api_methods_distance.html#selector.methods.distance.DISE) for more details.\n"
    ]
   },
   {
@@ -352,7 +352,7 @@
     "\n",
     "### Distance-Based Methods\n",
     "\n",
-    "Check Documentation: **[MaxMin](https://selector.qcdevs.org/dissimilarity.html#selector.distance.MaxMin) | [MaxSum](https://selector.qcdevs.org/dissimilarity.html#selector.distance.MaxSum) | [OptiSim](https://selector.qcdevs.org/dissimilarity.html#selector.distance.OptiSim) | [DISE](https://selector.qcdevs.org/dissimilarity.html#selector.distance.DISE)**\n"
+    "Check Documentation: **[MaxMin](https://selector.qcdevs.org/api_methods_distance.html#selector.methods.distance.MaxMin) | [MaxSum](https://selector.qcdevs.org/api_methods_distance.html#selector.methods.distance.MaxSum) | [OptiSim](https://selector.qcdevs.org/api_methods_distance.html#selector.methods.distance.OptiSim) | [DISE](https://selector.qcdevs.org/api_methods_distance.html#selector.methods.distance.DISE)**\n"
    ]
   },
   {
@@ -497,7 +497,7 @@
    "metadata": {},
    "source": [
     "To select from multiple clusters provide the `labels` argument to the `select` method.\n",
-    "Check Documentation: **[MaxMin](https://selector.qcdevs.org/dissimilarity.html#selector.distance.MaxMin) | [MaxSum](https://selector.qcdevs.org/dissimilarity.html#selector.distance.MaxSum) | [OptiSim](https://selector.qcdevs.org/dissimilarity.html#selector.distance.OptiSim) | [DISE](https://selector.qcdevs.org/dissimilarity.html#selector.distance.DISE)**\n"
+    "Check Documentation: **[MaxMin](https://selector.qcdevs.org/api_methods_distance.html#selector.methods.distance.MaxMin) | [MaxSum](https://selector.qcdevs.org/api_methods_distance.html#selector.methods.distance.MaxSum) | [OptiSim](https://selector.qcdevs.org/api_methods_distance.html#selector.methods.distance.OptiSim) | [DISE](https://selector.qcdevs.org/api_methods_distance.html#selector.methods.distance.DISE)**\n"
    ]
   },
   {

notebooks/tutorial_partition_based.ipynb

@@ -260,7 +260,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Check Documentation: **[Medoid](https://selector.qcdevs.org/partition.html#selector.partition.Medoid) | [GridParition](https://selector.qcdevs.org/partition.html#selector.partition.GridPartition)**\n"
+    "Check Documentation: **[Medoid](https://selector.qcdevs.org/api_methods_partition.html#selector.methods.partition.Medoid) | [GridParition](https://selector.qcdevs.org/api_methods_partition.html#selector.methods.partition.GridPartition)**\n"
    ]
   },
   {
@@ -416,7 +416,7 @@
    "metadata": {},
    "source": [
     "To select from multiple clusters provide the `labels` argument to the `select` method.\n",
-    "Check Documentation: **[Medoid](https://selector.qcdevs.org/partition.html#selector.partition.Medoid) | [GridParition](https://selector.qcdevs.org/partition.html#selector.partition.GridPartition)**\n"
+    "Check Documentation: **[Medoid](https://selector.qcdevs.org/api_methods_partition.html#selector.methods.partition.Medoid) | [GridParition](https://selector.qcdevs.org/api_methods_partition.html#selector.methods.partition.GridPartition)**\n"
    ]
   },
   {

notebooks/tutorial_similarity_based.ipynb

@@ -258,7 +258,7 @@
    "source": [
     "### NSimilarity based methods\n",
     "\n",
-    "Check Documentation: <span style=\"color:red;\">**Missing Documentation**</span>"
+    "Check Documentation: **[NSimilarity](https://selector.qcdevs.org/api_methods_similarity.html#selector.methods.similarity.NSimilarity)**\n"
    ]
   },
   {
@@ -326,7 +326,7 @@
    "source": [
     "### Compute diversity of selected points\n",
     "\n",
-    "The similarity of a set of points can be computed using the `SimilarityIndex` class and an appropriate similarity index (Check Documentation: <span style=\"color:red;\">**Missing Documentation**</span>). The diversity of the set can then be computed as `1 - similarity_index`."
+    "The similarity of a set of points can be computed using the `SimilarityIndex` class and an appropriate similarity index (Check Documentation: **[SimilarityIndex](https://selector.qcdevs.org/api_methods_similarity.html#selector.methods.similarity.SimilarityIndex)**). The diversity of the set can then be computed as `1 - similarity_index`."
    ]
   },
   {