74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
"""
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Feature agglomeration. Base classes and functions for performing feature
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agglomeration.
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"""
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# Author: V. Michel, A. Gramfort
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# License: BSD 3 clause
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import numpy as np
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from ..base import TransformerMixin
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from ..utils import array2d
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###############################################################################
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# Mixin class for feature agglomeration.
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class AgglomerationTransform(TransformerMixin):
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"""
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A class for feature agglomeration via the transform interface
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"""
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def transform(self, X, pooling_func=np.mean):
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"""
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Transform a new matrix using the built clustering
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Parameters
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---------
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X : array-like, shape = [n_samples, n_features]
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A M by N array of M observations in N dimensions or a length
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M array of M one-dimensional observations.
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pooling_func : a function that takes an array of shape = [M, N] and
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return an array of value of size M.
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Defaut is np.mean
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"""
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X = array2d(X)
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nX = []
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if len(self.labels_) != X.shape[1]:
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raise ValueError("X has a different number of features than "
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"during fitting.")
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for l in np.unique(self.labels_):
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nX.append(pooling_func(X[:, self.labels_ == l], axis=1))
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return np.array(nX).T
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def inverse_transform(self, Xred):
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"""
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Inverse the transformation.
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Return a vector of size nb_features with the values of Xred assigned
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to each group of features
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Parameters
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----------
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Xred : array of size k
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The values to be assigned to each cluster of samples
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Returns
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-------
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X : array of size nb_samples
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A vector of size nb_samples with the values of Xred assigned to
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each of the cluster of samples.
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"""
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if np.size((Xred.shape)) == 1:
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X = np.zeros([self.labels_.shape[0]])
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else:
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X = np.zeros([Xred.shape[0], self.labels_.shape[0]])
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unil = np.unique(self.labels_)
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for i in range(len(unil)):
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if np.size((Xred.shape)) == 1:
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X[self.labels_ == unil[i]] = Xred[i]
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else:
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X[:, self.labels_ == unil[i]] = array2d(Xred[:, i]).T
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return X
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