Uploaded Test files

venv/Lib/site-packages/sklearn/cluster/_feature_agglomeration.py

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+"""
+Feature agglomeration. Base classes and functions for performing feature
+agglomeration.
+"""
+# Author: V. Michel, A. Gramfort
+# License: BSD 3 clause
+
+import numpy as np
+
+from ..base import TransformerMixin
+from ..utils import check_array
+from ..utils.validation import check_is_fitted
+from scipy.sparse import issparse
+
+###############################################################################
+# Mixin class for feature agglomeration.
+
+
+class AgglomerationTransform(TransformerMixin):
+    """
+    A class for feature agglomeration via the transform interface
+    """
+
+    def transform(self, X):
+        """
+        Transform a new matrix using the built clustering
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features) or (n_samples,)
+            A M by N array of M observations in N dimensions or a length
+            M array of M one-dimensional observations.
+
+        Returns
+        -------
+        Y : array, shape = [n_samples, n_clusters] or [n_clusters]
+            The pooled values for each feature cluster.
+        """
+        check_is_fitted(self)
+
+        X = check_array(X)
+        if len(self.labels_) != X.shape[1]:
+            raise ValueError("X has a different number of features than "
+                             "during fitting.")
+        if self.pooling_func == np.mean and not issparse(X):
+            size = np.bincount(self.labels_)
+            n_samples = X.shape[0]
+            # a fast way to compute the mean of grouped features
+            nX = np.array([np.bincount(self.labels_, X[i, :]) / size
+                          for i in range(n_samples)])
+        else:
+            nX = [self.pooling_func(X[:, self.labels_ == l], axis=1)
+                  for l in np.unique(self.labels_)]
+            nX = np.array(nX).T
+        return nX
+
+    def inverse_transform(self, Xred):
+        """
+        Inverse the transformation.
+        Return a vector of size nb_features with the values of Xred assigned
+        to each group of features
+
+        Parameters
+        ----------
+        Xred : array-like of shape (n_samples, n_clusters) or (n_clusters,)
+            The values to be assigned to each cluster of samples
+
+        Returns
+        -------
+        X : array, shape=[n_samples, n_features] or [n_features]
+            A vector of size n_samples with the values of Xred assigned to
+            each of the cluster of samples.
+        """
+        check_is_fitted(self)
+
+        unil, inverse = np.unique(self.labels_, return_inverse=True)
+        return Xred[..., inverse]