Fix failing test caused by a change in multivariate_normal sequence.

skfda/ml/classification/_logistic_regression.py

@@ -58,32 +58,39 @@ class LogisticRegression(
         intercept\_: Independent term.
 
     Examples:
-        >>> from numpy import array
+        >>> import skfda
         >>> from skfda.datasets import make_gaussian_process
         >>> from skfda.ml.classification import LogisticRegression
-        >>> fd1 = make_gaussian_process(
-        ...         n_samples=50,
-        ...         n_features=100,
-        ...         noise=0.7,
-        ...         random_state=0,
+        >>>
+        >>> n_samples = 10000
+        >>> n_features = 200
+        >>>
+        >>> def mean_1(t):
+        ...     return (np.abs(t - 0.25)
+        ...             - 2 * np.abs(t - 0.5)
+        ...             + np.abs(t - 0.75))
+        >>>
+        >>> X_0 = make_gaussian_process(
+        ...     n_samples=n_samples // 2,
+        ...     n_features=n_features,
+        ...     random_state=0,
         ... )
-        >>> fd2 = make_gaussian_process(
-        ...         n_samples=50,
-        ...         n_features = 100,
-        ...         mean = array([1]*100),
-        ...         noise = 0.7,
-        ...         random_state=0
+        >>> X_1 = make_gaussian_process(
+        ...     n_samples=n_samples // 2,
+        ...     n_features=n_features,
+        ...     mean=mean_1,
+        ...     random_state=1,
         ... )
-        >>> fd = fd1.concatenate(fd2)
-        >>> y = 50*[0] + 50*[1]
-        >>> lr = LogisticRegression()
-        >>> _ = lr.fit(fd[::2], y[::2])
-        >>> lr.coef_.round(2)
-        array([[ 18.91,  19.69,  19.9 ,   6.09,  12.49]])
-        >>> lr.points_.round(2)
-        array([ 0.11,  0.06,  0.07,  0.02,  0.03])
-        >>> lr.score(fd[1::2],y[1::2])
-        0.92
+        >>> X = skfda.concatenate((X_0, X_1))
+        >>>
+        >>> y = np.zeros(n_samples)
+        >>> y [n_samples // 2:] = 1
+        >>> lr = LogisticRegression(max_features=3)
+        >>> _ = lr.fit(X[::2], y[::2])
+        >>> np.allclose(sorted(lr.points_), [0.25, 0.5, 0.75], rtol=1e-2)
+        True
+        >>> lr.score(X[1::2],y[1::2])
+        0.7498
 
         References:
             .. footbibliography::