class sklearn.covariance.MinCovDet(store_precision=True, assume_centered=False, support_fraction=None, random_state=None)
[source]
Minimum Covariance Determinant (MCD): robust estimator of covariance.
The Minimum Covariance Determinant covariance estimator is to be applied on Gaussian-distributed data, but could still be relevant on data drawn from a unimodal, symmetric distribution. It is not meant to be used with multi-modal data (the algorithm used to fit a MinCovDet object is likely to fail in such a case). One should consider projection pursuit methods to deal with multi-modal datasets.
Read more in the User Guide.
Parameters: |
|
---|---|
Attributes: |
|
[Rouseeuw1984] | P. J. Rousseeuw. Least median of squares regression. J. Am Stat Ass, 79:871, 1984. |
[Rousseeuw] | A Fast Algorithm for the Minimum Covariance Determinant Estimator, 1999, American Statistical Association and the American Society for Quality, TECHNOMETRICS |
[ButlerDavies] | R. W. Butler, P. L. Davies and M. Jhun, Asymptotics For The Minimum Covariance Determinant Estimator, The Annals of Statistics, 1993, Vol. 21, No. 3, 1385-1400 |
>>> import numpy as np >>> from sklearn.covariance import MinCovDet >>> 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 = MinCovDet(random_state=0).fit(X) >>> cov.covariance_ array([[0.7411..., 0.2535...], [0.2535..., 0.3053...]]) >>> cov.location_ array([0.0813... , 0.0427...])
correct_covariance (data) | Apply a correction to raw Minimum Covariance Determinant estimates. |
error_norm (comp_cov[, norm, scaling, squared]) | Computes the Mean Squared Error between two covariance estimators. |
fit (X[, y]) | Fits a Minimum Covariance Determinant with the FastMCD algorithm. |
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. |
reweight_covariance (data) | Re-weight raw Minimum Covariance Determinant estimates. |
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, support_fraction=None, random_state=None)
[source]
correct_covariance(data)
[source]
Apply a correction to raw Minimum Covariance Determinant estimates.
Correction using the empirical correction factor suggested by Rousseeuw and Van Driessen in [RVD].
Parameters: |
|
---|---|
Returns: |
|
[RVD] |
(1, 2) A Fast Algorithm for the Minimum Covariance Determinant Estimator, 1999, American Statistical Association and the American Society for Quality, TECHNOMETRICS
|
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: |
|
---|---|
Returns: |
|
fit(X, y=None)
[source]
Fits a Minimum Covariance Determinant with the FastMCD algorithm.
Parameters: |
|
---|---|
Returns: |
|
get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: |
|
---|---|
Returns: |
|
get_precision()
[source]
Getter for the precision matrix.
Returns: |
|
---|
mahalanobis(X)
[source]
Computes the squared Mahalanobis distances of given observations.
Parameters: |
|
---|---|
Returns: |
|
reweight_covariance(data)
[source]
Re-weight raw Minimum Covariance Determinant estimates.
Re-weight observations using Rousseeuw’s method (equivalent to deleting outlying observations from the data set before computing location and covariance estimates) described in [RVDriessen].
Parameters: |
|
---|---|
Returns: |
|
[RVDriessen] |
(1, 2) A Fast Algorithm for the Minimum Covariance Determinant Estimator, 1999, American Statistical Association and the American Society for Quality, TECHNOMETRICS
|
score(X_test, y=None)
[source]
Computes the log-likelihood of a Gaussian data set with self.covariance_
as an estimator of its covariance matrix.
Parameters: |
|
---|---|
Returns: |
|
set_params(**params)
[source]
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: |
|
---|
sklearn.covariance.MinCovDet
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.covariance.MinCovDet.html