correct import in doctests of sliced

Author: lchapel

ot/sliced/_sliced_distances.py

@@ -69,6 +69,7 @@ def sliced_wasserstein_distance(
     --------
 
     >>> import ot
+    >>> import numpy as np
     >>> n_samples_a = 20
     >>> X = np.random.normal(0., 1., (n_samples_a, 5))
     >>> ot.sliced_wasserstein_distance(X, X, seed=0)  # doctest: +NORMALIZE_WHITESPACE
@@ -176,6 +177,7 @@ def max_sliced_wasserstein_distance(
     --------
 
     >>> import ot
+    >>> import numpy as np
     >>> n_samples_a = 20
     >>> X = np.random.normal(0., 1., (n_samples_a, 5))
     >>> ot.sliced_wasserstein_distance(X, X, seed=0)  # doctest: +NORMALIZE_WHITESPACE

ot/sliced/_sliced_plans.py

@@ -250,6 +250,7 @@ def min_pivot_sliced(
     Examples
     --------
     >>> import ot
+    >>> import numpy as np
     >>> x=np.array([[3.,3.], [1.,1.]])
     >>> y=np.array([[2.,2.5], [3.,2.]])
     >>> thetas=np.array([[1, 0], [0, 1]])
@@ -411,6 +412,7 @@ def expected_sliced(
     Examples
     --------
     >>> import ot
+    >>> import numpy as np
     >>> x=np.array([[3.,3.], [1.,1.]])
     >>> y=np.array([[2.,2.5], [3.,2.]])
     >>> thetas=np.array([[1, 0], [0, 1]])
@@ -448,7 +450,7 @@ def expected_sliced(
 
     log_dict = {}
     G, costs, log_dict_plans = sliced_plans(
-        X_s, X_t, a, b, metric, p, thetas, n_proj=n_proj, log=True
+        X_s, X_t, a, b, metric, p, thetas, n_proj=n_proj, log=log
     )
 
     if beta != 0.0:  # computing the temperature weighting

ot/sliced/_spherical_sliced.py

@@ -71,6 +71,7 @@ def sliced_wasserstein_sphere(
     Examples
     --------
     >>> import ot
+    >>> import numpy as np
     >>> n_samples_a = 20
     >>> X = np.random.normal(0., 1., (n_samples_a, 5))
     >>> X = X / np.sqrt(np.sum(X**2, -1, keepdims=True))
@@ -157,6 +158,7 @@ def sliced_wasserstein_sphere_unif(
     Examples
     ---------
     >>> import ot
+    >>> import numpy as np
     >>> np.random.seed(42)
     >>> x0 = np.random.randn(500,3)
     >>> x0 = x0 / np.sqrt(np.sum(x0**2, -1, keepdims=True))
@@ -243,6 +245,7 @@ def linear_sliced_wasserstein_sphere(
     Examples
     ---------
     >>> import ot
+    >>> import numpy as np
     >>> n_samples_a = 20
     >>> X = np.random.normal(0., 1., (n_samples_a, 5))
     >>> X = X / np.sqrt(np.sum(X**2, -1, keepdims=True))