scikit-learn · jnothman · Mar 1, 2019 · Aug 1, 2018 · Aug 2, 2018 · Aug 3, 2018
diff --git a/sklearn/covariance/elliptic_envelope.py b/sklearn/covariance/elliptic_envelope.py
@@ -68,6 +68,26 @@ class EllipticEnvelope(MinCovDet, OutlierMixin):
         such a way we obtain the expected number of outliers (samples with
         decision function < 0) in training.
 
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.covariance import EllipticEnvelope
+    >>> true_cov = np.array([[.8, .3],
+    ...                      [.3, .4]])
+    >>> X = np.random.RandomState(0).multivariate_normal(mean=[0, 0],
+    ...                                                  cov=true_cov,
+    ...                                                  size=500)
+    >>> cov = EllipticEnvelope(random_state=0).fit(X)
+    >>> # predict returns 1 for an inlier and -1 for an outlier
+    >>> cov.predict([[0, 0],
+    ...              [3, 3]])
+    array([ 1, -1])
+    >>> cov.covariance_ # doctest: +ELLIPSIS
+    array([[0.7411..., 0.2535...],
+           [0.2535..., 0.3053...]])
+    >>> cov.location_
+    array([0.0813... , 0.0427...])
+
     See Also
     --------
     EmpiricalCovariance, MinCovDet

diff --git a/sklearn/covariance/graph_lasso_.py b/sklearn/covariance/graph_lasso_.py
@@ -321,6 +321,9 @@ class GraphicalLasso(EmpiricalCovariance):
 
     Attributes
     ----------
+    location_ : array-like, shape (n_features,)
+        Estimated location, i.e. the estimated mean.
+
     covariance_ : array-like, shape (n_features, n_features)
         Estimated covariance matrix
 
@@ -330,6 +333,27 @@ class GraphicalLasso(EmpiricalCovariance):
     n_iter_ : int
         Number of iterations run.
 
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.covariance import GraphicalLasso
+    >>> true_cov = np.array([[.8, 0., .2, 0.],
+    ...                      [0., .4, 0., 0.],
+    ...                      [.2, 0., .3, .1],
+    ...                      [0., 0., .1, .7]])
+    >>> np.random.seed(0)
+    >>> X = np.random.multivariate_normal(mean=[0, 0, 0, 0],
+    ...                                   cov=true_cov,
+    ...                                   size=200)
+    >>> cov = GraphicalLasso().fit(X)
+    >>> np.around(cov.covariance_, decimals=3)
+    array([[0.816, 0.049, 0.218, 0.019],
+           [0.049, 0.364, 0.017, 0.034],
+           [0.218, 0.017, 0.322, 0.093],
+           [0.019, 0.034, 0.093, 0.69 ]])
+    >>> np.around(cov.location_, decimals=3)
+    array([0.073, 0.04 , 0.038, 0.143])
+
     See Also
     --------
     graphical_lasso, GraphicalLassoCV
@@ -543,6 +567,9 @@ class GraphicalLassoCV(GraphicalLasso):
 
     Attributes
     ----------
+    location_ : array-like, shape (n_features,)
+        Estimated location, i.e. the estimated mean.
+
     covariance_ : numpy.ndarray, shape (n_features, n_features)
         Estimated covariance matrix.
 
@@ -561,6 +588,27 @@ class GraphicalLassoCV(GraphicalLasso):
     n_iter_ : int
         Number of iterations run for the optimal alpha.
 
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.covariance import GraphicalLassoCV
+    >>> true_cov = np.array([[.8, 0., .2, 0.],
+    ...                      [0., .4, 0., 0.],
+    ...                      [.2, 0., .3, .1],
+    ...                      [0., 0., .1, .7]])
+    >>> np.random.seed(0)
+    >>> X = np.random.multivariate_normal(mean=[0, 0, 0, 0],
+    ...                                   cov=true_cov,
+    ...                                   size=200)
+    >>> cov = GraphicalLassoCV(cv=5).fit(X)
+    >>> np.around(cov.covariance_, decimals=3)
+    array([[0.816, 0.051, 0.22 , 0.017],
+           [0.051, 0.364, 0.018, 0.036],
+           [0.22 , 0.018, 0.322, 0.094],
+           [0.017, 0.036, 0.094, 0.69 ]])
+    >>> np.around(cov.location_, decimals=3)
+    array([0.073, 0.04 , 0.038, 0.143])
+
     See Also
     --------
     graphical_lasso, GraphicalLasso