Source code for metric_learn.covariance

Covariance metric (baseline method)

import numpy as np
import scipy
from sklearn.base import TransformerMixin

from .base_metric import MahalanobisMixin
from ._util import components_from_metric

[docs]class Covariance(MahalanobisMixin, TransformerMixin): """Covariance metric (baseline method) This method does not "learn" anything, rather it calculates the covariance matrix of the input data. This is a simple baseline method first introduced in On the Generalized Distance in Statistics, P.C.Mahalanobis, 1936 Read more in the :ref:`User Guide <covariance>`. Attributes ---------- components_ : `numpy.ndarray`, shape=(n_features, n_features) The linear transformation ``L`` deduced from the learned Mahalanobis metric (See function `components_from_metric`.) Examples -------- >>> from metric_learn import Covariance >>> from sklearn.datasets import load_iris >>> iris = load_iris()['data'] >>> cov = Covariance().fit(iris) >>> x = cov.transform(iris) """
[docs] def __init__(self, preprocessor=None): super(Covariance, self).__init__(preprocessor)
[docs] def fit(self, X, y=None): """ X : data matrix, (n x d) y : unused """ X = self._prepare_inputs(X, ensure_min_samples=2) M = np.atleast_2d(np.cov(X, rowvar=False)) if M.size == 1: M = 1. / M else: M = scipy.linalg.pinvh(M) self.components_ = components_from_metric(np.atleast_2d(M)) return self