From b6fb14861accd20a323bfc5ef96c20883e4f6ce1 Mon Sep 17 00:00:00 2001
From: Alexandre Gramfort <alexandre.gramfort@m4x.org>
Date: Tue, 9 Jul 2019 17:08:58 +0200
Subject: more

---
 ot/utils.py | 47 +++++++++++++++++++----------------------------
 1 file changed, 19 insertions(+), 28 deletions(-)

(limited to 'ot/utils.py')

diff --git a/ot/utils.py b/ot/utils.py
index e8249ef..8419c83 100644
--- a/ot/utils.py
+++ b/ot/utils.py
@@ -111,12 +111,12 @@ def dist(x1, x2=None, metric='sqeuclidean'):
     Parameters
     ----------
 
-    x1 : np.array (n1,d)
+    x1 : ndarray, shape (n1,d)
         matrix with n1 samples of size d
-    x2 : np.array (n2,d), optional
+    x2 : array, shape (n2,d), optional
         matrix with n2 samples of size d (if None then x2=x1)
-    metric : str, fun, optional
-        name of the metric to be computed (full list in the doc of scipy),  If a string,
+    metric : str | callable, optional
+        Name of the metric to be computed (full list in the doc of scipy),  If a string,
         the distance function can be 'braycurtis', 'canberra', 'chebyshev', 'cityblock',
         'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'kulsinski',
         'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean',
@@ -138,26 +138,21 @@ def dist(x1, x2=None, metric='sqeuclidean'):
 
 
 def dist0(n, method='lin_square'):
-    """Compute standard cost matrices of size (n,n) for OT problems
+    """Compute standard cost matrices of size (n, n) for OT problems
 
     Parameters
     ----------
-
     n : int
-        size of the cost matrix
+        Size of the cost matrix.
     method : str, optional
         Type of loss matrix chosen from:
 
         * 'lin_square' : linear sampling between 0 and n-1, quadratic loss
 
-
     Returns
     -------
-
-    M : np.array (n1,n2)
-        distance matrix computed with given metric
-
-
+    M : ndarray, shape (n1,n2)
+        Distance matrix computed with given metric.
     """
     res = 0
     if method == 'lin_square':
@@ -169,22 +164,18 @@ def dist0(n, method='lin_square'):
 def cost_normalization(C, norm=None):
     """ Apply normalization to the loss matrix
 
-
     Parameters
     ----------
-    C : np.array (n1, n2)
+    C : ndarray, shape (n1, n2)
         The cost matrix to normalize.
     norm : str
-        type of normalization from 'median','max','log','loglog'. Any other
-        value do not normalize.
-
+        Type of normalization from 'median', 'max', 'log', 'loglog'. Any
+        other value do not normalize.
 
     Returns
     -------
-
-    C : np.array (n1, n2)
+    C : ndarray, shape (n1, n2)
         The input cost matrix normalized according to given norm.
-
     """
 
     if norm == "median":
@@ -194,7 +185,7 @@ def cost_normalization(C, norm=None):
     elif norm == "log":
         C = np.log(1 + C)
     elif norm == "loglog":
-        C = np.log(1 + np.log(1 + C))
+        C = np.log1p(np.log1p(C))
 
     return C
 
@@ -256,6 +247,7 @@ def check_params(**kwargs):
 
 def check_random_state(seed):
     """Turn seed into a np.random.RandomState instance
+
     Parameters
     ----------
     seed : None | int | instance of RandomState
@@ -275,7 +267,6 @@ def check_random_state(seed):
 
 
 class deprecated(object):
-
     """Decorator to mark a function or class as deprecated.
 
     deprecated class from scikit-learn package
@@ -291,8 +282,8 @@ class deprecated(object):
 
     Parameters
     ----------
-    extra : string
-          to be added to the deprecation messages
+    extra : str
+        To be added to the deprecation messages.
     """
 
     # Adapted from http://wiki.python.org/moin/PythonDecoratorLibrary,
@@ -373,9 +364,9 @@ def _is_deprecated(func):
 
 
 class BaseEstimator(object):
-
     """Base class for most objects in POT
-    adapted from sklearn BaseEstimator class
+
+    Code adapted from sklearn BaseEstimator class
 
     Notes
     -----
@@ -417,7 +408,7 @@ class BaseEstimator(object):
 
         Parameters
         ----------
-        deep : boolean, optional
+        deep : bool, optional
             If True, will return the parameters for this estimator and
             contained subobjects that are estimators.
 
-- 
cgit v1.2.3