92 lines
2.6 KiB
Python
92 lines
2.6 KiB
Python
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# Sebastian Raschka 2014-2020
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# mlxtend Machine Learning Library Extensions
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#
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# A class to apply column-based mean centering to a dataset.
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# Author: Sebastian Raschka <sebastianraschka.com>
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#
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# License: BSD 3 clause
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import numpy as np
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class MeanCenterer(object):
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"""Column centering of vectors and matrices.
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Attributes
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-----------
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col_means : numpy.ndarray [n_columns]
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NumPy array storing the mean values for centering after fitting
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the MeanCenterer object.
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Examples
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-----------
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For usage examples, please see
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http://rasbt.github.io/mlxtend/user_guide/preprocessing/MeanCenterer/
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"""
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def __init__(self):
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pass
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def transform(self, X):
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"""Centers a NumPy array.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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Array of data vectors, where n_samples is the number of samples and
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n_features is the number of features.
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Returns
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--------
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X_tr : {array-like, sparse matrix}, shape = [n_samples, n_features]
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A copy of the input array with the columns centered.
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"""
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if not hasattr(self, "col_means"):
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raise AttributeError("MeanCenterer has not been fitted, yet.")
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X_tr = np.copy(self._get_array(X))
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X_tr = np.apply_along_axis(func1d=lambda x: x - self.col_means,
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axis=1, arr=X_tr)
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return X_tr
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def fit(self, X):
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"""Gets the column means for mean centering.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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Array of data vectors, where n_samples is the number of samples and
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n_features is the number of features.
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Returns
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--------
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self
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"""
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self.col_means = self._get_array(X).mean(axis=0)
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return self
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def fit_transform(self, X):
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"""Fits and transforms an arry.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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Array of data vectors, where n_samples is the number of samples and
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n_features is the number of features.
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Returns
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--------
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X_tr : {array-like, sparse matrix}, shape = [n_samples, n_features]
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A copy of the input array with the columns centered.
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"""
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self.fit(X)
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return self.transform(X)
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def _get_array(self, X):
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if isinstance(X, list):
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X_fl = np.asarray(X, dtype='float')[:, None]
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else:
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X_fl = X.astype('float')
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return X_fl
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