diff options
Diffstat (limited to 'ot/utils.py')
| -rw-r--r-- | ot/utils.py | 56 |
1 files changed, 26 insertions, 30 deletions
diff --git a/ot/utils.py b/ot/utils.py index 5707d9b..b71458b 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': @@ -169,33 +164,34 @@ 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": + if norm is None: + pass + elif norm == "median": C /= float(np.median(C)) elif norm == "max": C /= float(np.max(C)) 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)) + else: + raise ValueError('Norm %s is not a valid option.\n' + 'Valid options are:\n' + 'median, max, log, loglog' % norm) return C @@ -261,6 +257,7 @@ def check_params(**kwargs): def check_random_state(seed): """Turn seed into a np.random.RandomState instance + Parameters ---------- seed : None | int | instance of RandomState @@ -280,7 +277,6 @@ def check_random_state(seed): class deprecated(object): - """Decorator to mark a function or class as deprecated. deprecated class from scikit-learn package @@ -296,8 +292,8 @@ 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, @@ -378,9 +374,9 @@ 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 ----- @@ -422,7 +418,7 @@ 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. |