1 files changed, 0 insertions, 101 deletions
diff --git a/ot/gpu/utils.py b/ot/gpu/utils.py
deleted file mode 100644
index 41e168a..0000000
--- a/ot/gpu/utils.py
+++ /dev/null
@@ -1,101 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Utility functions for GPU
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#         Nicolas Courty <ncourty@irisa.fr>
-#         Leo Gautheron <https://github.com/aje>
-#
-# License: MIT License
-
-import cupy as np  # np used for matrix computation
-import cupy as cp  # cp used for cupy specific operations
-
-
-def euclidean_distances(a, b, squared=False, to_numpy=True):
-    """
-    Compute the pairwise euclidean distance between matrices a and b.
-
-    If the input matrix are in numpy format, they will be uploaded to the
-    GPU first which can incur significant time overhead.
-
-    Parameters
-    ----------
-    a : np.ndarray (n, f)
-        first matrix
-    b : np.ndarray (m, f)
-        second matrix
-    to_numpy : boolean, optional (default True)
-        If true convert back the GPU array result to numpy format.
-    squared : boolean, optional (default False)
-        if True, return squared euclidean distance matrix
-
-    Returns
-    -------
-    c : (n x m) np.ndarray or cupy.ndarray
-        pairwise euclidean distance distance matrix
-    """
-
-    a, b = to_gpu(a, b)
-
-    a2 = np.sum(np.square(a), 1)
-    b2 = np.sum(np.square(b), 1)
-
-    c = -2 * np.dot(a, b.T)
-    c += a2[:, None]
-    c += b2[None, :]
-
-    if not squared:
-        np.sqrt(c, out=c)
-    if to_numpy:
-        return to_np(c)
-    else:
-        return c
-
-
-def dist(x1, x2=None, metric='sqeuclidean', to_numpy=True):
-    """Compute distance between samples in x1 and x2 on gpu
-
-    Parameters
-    ----------
-
-    x1 : np.array (n1,d)
-        matrix with n1 samples of size d
-    x2 : np.array (n2,d), optional
-        matrix with n2 samples of size d (if None then x2=x1)
-    metric : str
-        Metric from 'sqeuclidean', 'euclidean',
-
-
-    Returns
-    -------
-
-    M : np.array (n1,n2)
-        distance matrix computed with given metric
-
-    """
-    if x2 is None:
-        x2 = x1
-    if metric == "sqeuclidean":
-        return euclidean_distances(x1, x2, squared=True, to_numpy=to_numpy)
-    elif metric == "euclidean":
-        return euclidean_distances(x1, x2, squared=False, to_numpy=to_numpy)
-    else:
-        raise NotImplementedError
-
-
-def to_gpu(*args):
-    """ Upload numpy arrays to GPU and return them"""
-    if len(args) > 1:
-        return (cp.asarray(x) for x in args)
-    else:
-        return cp.asarray(args[0])
-
-
-def to_np(*args):
-    """ convert GPU arras to numpy and return them"""
-    if len(args) > 1:
-        return (cp.asnumpy(x) for x in args)
-    else:
-        return cp.asnumpy(args[0])