sklearn.covariance.EmpiricalCovariance — scikit-learn 0.20.4 documentation (original) (raw)
class sklearn.covariance. EmpiricalCovariance(store_precision=True, assume_centered=False)[source]¶
Maximum likelihood covariance estimator
Read more in the User Guide.
| Parameters: | store_precision : bool Specifies if the estimated precision is stored. assume_centered : bool If True, data are not centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If False (default), data are centered before computation. |
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| Attributes: | location_ : array-like, shape (n_features,) Estimated location, i.e. the estimated mean. covariance_ : 2D ndarray, shape (n_features, n_features) Estimated covariance matrix precision_ : 2D ndarray, shape (n_features, n_features) Estimated pseudo-inverse matrix. (stored only if store_precision is True) |
Examples
import numpy as np from sklearn.covariance import EmpiricalCovariance from sklearn.datasets import make_gaussian_quantiles real_cov = np.array([[.8, .3], ... [.3, .4]]) np.random.seed(0) X = np.random.multivariate_normal(mean=[0, 0], ... cov=real_cov, ... size=500) cov = EmpiricalCovariance().fit(X) cov.covariance_ array([[0.7569..., 0.2818...], [0.2818..., 0.3928...]]) cov.location_ array([0.0622..., 0.0193...])
Methods
| error_norm(comp_cov[, norm, scaling, squared]) | Computes the Mean Squared Error between two covariance estimators. |
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| fit(X[, y]) | Fits the Maximum Likelihood Estimator covariance model according to the given training data and parameters. |
| get_params([deep]) | Get parameters for this estimator. |
| get_precision() | Getter for the precision matrix. |
| mahalanobis(X) | Computes the squared Mahalanobis distances of given observations. |
| score(X_test[, y]) | Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix. |
| set_params(**params) | Set the parameters of this estimator. |
__init__(store_precision=True, assume_centered=False)[source]¶
error_norm(comp_cov, norm='frobenius', scaling=True, squared=True)[source]¶
Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm).
| Parameters: | comp_cov : array-like, shape = [n_features, n_features] The covariance to compare with. norm : str The type of norm used to compute the error. Available error types: - ‘frobenius’ (default): sqrt(tr(A^t.A)) - ‘spectral’: sqrt(max(eigenvalues(A^t.A)) where A is the error (comp_cov - self.covariance_). scaling : bool If True (default), the squared error norm is divided by n_features. If False, the squared error norm is not rescaled. squared : bool Whether to compute the squared error norm or the error norm. If True (default), the squared error norm is returned. If False, the error norm is returned. |
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| Returns: | The Mean Squared Error (in the sense of the Frobenius norm) between `self` and `comp_cov` covariance estimators. |
Fits the Maximum Likelihood Estimator covariance model according to the given training data and parameters.
| Parameters: | X : array-like, shape = [n_samples, n_features] Training data, where n_samples is the number of samples and n_features is the number of features. y not used, present for API consistence purpose. |
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| Returns: | self : object |
get_params(deep=True)[source]¶
Get parameters for this estimator.
| Parameters: | deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. |
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| Returns: | params : mapping of string to any Parameter names mapped to their values. |
Getter for the precision matrix.
| Returns: | precision_ : array-like The precision matrix associated to the current covariance object. |
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Computes the squared Mahalanobis distances of given observations.
| Parameters: | X : array-like, shape = [n_samples, n_features] The observations, the Mahalanobis distances of the which we compute. Observations are assumed to be drawn from the same distribution than the data used in fit. |
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| Returns: | dist : array, shape = [n_samples,] Squared Mahalanobis distances of the observations. |
score(X_test, y=None)[source]¶
Computes the log-likelihood of a Gaussian data set withself.covariance_ as an estimator of its covariance matrix.
| Parameters: | X_test : array-like, shape = [n_samples, n_features] Test data of which we compute the likelihood, where n_samples is the number of samples and n_features is the number of features. X_test is assumed to be drawn from the same distribution than the data used in fit (including centering). y not used, present for API consistence purpose. |
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| Returns: | res : float The likelihood of the data set with self.covariance_ as an estimator of its covariance matrix. |
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form<component>__<parameter> so that it’s possible to update each component of a nested object.
| Returns: | self |
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