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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 array2d
###############################################################################
# Mixin class for feature agglomeration.
class AgglomerationTransform(TransformerMixin):
"""
A class for feature agglomeration via the transform interface
"""
def transform(self, X, pooling_func=np.mean):
"""
Transform a new matrix using the built clustering
Parameters
---------
X : array-like, shape = [n_samples, n_features]
A M by N array of M observations in N dimensions or a length
M array of M one-dimensional observations.
pooling_func : a function that takes an array of shape = [M, N] and
return an array of value of size M.
Defaut is np.mean
"""
X = np.asarray(X)
nX = []
for l in np.unique(self.labels_):
nX.append(pooling_func(X[:, self.labels_ == l], axis=1))
return np.array(nX).T
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 of size k
The values to be assigned to each cluster of samples
Returns
-------
X : array of size nb_samples
A vector of size nb_samples with the values of Xred assigned to
each of the cluster of samples.
"""
if np.size((Xred.shape)) == 1:
X = np.zeros([self.labels_.shape[0]])
else:
X = np.zeros([Xred.shape[0], self.labels_.shape[0]])
unil = np.unique(self.labels_)
for i in range(len(unil)):
if np.size((Xred.shape)) == 1:
X[self.labels_ == unil[i]] = Xred[i]
else:
ncol = np.sum(self.labels_ == unil[i])
X[:, self.labels_ == unil[i]] = np.tile(array2d(Xred[:, i]).T,
ncol)
return X
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