From 06fab4c1e5efbe79f91589917fba01c3fb300a87 Mon Sep 17 00:00:00 2001
From: Alexandre Gramfort <alexandre.gramfort@m4x.org>
Date: Tue, 9 Jul 2019 17:20:02 +0200
Subject: more

---
 ot/datasets.py | 32 ++++++++++++--------------------
 1 file changed, 12 insertions(+), 20 deletions(-)

(limited to 'ot/datasets.py')

diff --git a/ot/datasets.py b/ot/datasets.py
index e76e75d..ba0cfd9 100644
--- a/ot/datasets.py
+++ b/ot/datasets.py
@@ -17,7 +17,6 @@ def make_1D_gauss(n, m, s):
 
     Parameters
     ----------
-
     n : int
         number of bins in the histogram
     m : float
@@ -25,12 +24,10 @@ def make_1D_gauss(n, m, s):
     s : float
         standard deviaton of the gaussian distribution
 
-
     Returns
     -------
-    h : np.array (n,)
-          1D histogram for a gaussian distribution
-
+    h : ndarray (n,)
+        1D histogram for a gaussian distribution
     """
     x = np.arange(n, dtype=np.float64)
     h = np.exp(-(x - m)**2 / (2 * s**2))
@@ -44,16 +41,15 @@ def get_1D_gauss(n, m, sigma):
 
 
 def make_2D_samples_gauss(n, m, sigma, random_state=None):
-    """return n samples drawn from 2D gaussian N(m,sigma)
+    """Return n samples drawn from 2D gaussian N(m,sigma)
 
     Parameters
     ----------
-
     n : int
         number of samples to make
-    m : np.array (2,)
+    m : ndarray, shape (2,)
         mean value of the gaussian distribution
-    sigma : np.array (2,2)
+    sigma : ndarray, shape (2, 2)
         covariance matrix of the gaussian distribution
     random_state : int, RandomState instance or None, optional (default=None)
         If int, random_state is the seed used by the random number generator;
@@ -63,9 +59,8 @@ def make_2D_samples_gauss(n, m, sigma, random_state=None):
 
     Returns
     -------
-    X : np.array (n,2)
-          n samples drawn from  N(m,sigma)
-
+    X : ndarray, shape (n, 2)
+        n samples drawn from N(m, sigma).
     """
 
     generator = check_random_state(random_state)
@@ -86,11 +81,10 @@ def get_2D_samples_gauss(n, m, sigma, random_state=None):
 
 
 def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs):
-    """ dataset generation for classification problems
+    """Dataset generation for classification problems
 
     Parameters
     ----------
-
     dataset : str
         type of classification problem (see code)
     n : int
@@ -105,13 +99,11 @@ def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs):
 
     Returns
     -------
-    X : np.array (n,d)
-          n observation of size d
-    y : np.array (n,)
-          labels of the samples
-
+    X : ndarray, shape (n, d)
+        n observation of size d
+    y : ndarray, shape (n,)
+        labels of the samples.
     """
-
     generator = check_random_state(random_state)
 
     if dataset.lower() == '3gauss':
-- 
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