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author | Rémi Flamary <remi.flamary@gmail.com> | 2019-12-02 11:31:32 +0100 |
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committer | Rémi Flamary <remi.flamary@gmail.com> | 2019-12-02 11:31:32 +0100 |
commit | a6a654de5e78dd388a793fbd26f60045b05d519c (patch) | |
tree | a8e3049507db770892d05c7747b2bf083c2d9af8 /ot/lp/__init__.py | |
parent | 57321bd0172c97b77dfc8b14972c18d063b6dda8 (diff) |
proper documentation and parameter
Diffstat (limited to 'ot/lp/__init__.py')
-rw-r--r-- | ot/lp/__init__.py | 16 |
1 files changed, 14 insertions, 2 deletions
diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 4fec7d9..d476071 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -27,7 +27,7 @@ __all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx', 'emd_1d', 'emd2_1d', 'wasserstein_1d'] -def emd(a, b, M, numItermax=100000, log=False, sparse=False): +def emd(a, b, M, numItermax=100000, log=False, dense=True): r"""Solves the Earth Movers distance problem and returns the OT matrix @@ -62,6 +62,10 @@ def emd(a, b, M, numItermax=100000, log=False, sparse=False): log: bool, optional (default=False) If True, returns a dictionary containing the cost and dual variables. Otherwise returns only the optimal transportation matrix. + dense: boolean, optional (default=True) + If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). + Otherwise returns a sparse representation using scipy's `coo_matrix` + format. Returns ------- @@ -103,6 +107,8 @@ def emd(a, b, M, numItermax=100000, log=False, sparse=False): b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) + sparse= not dense + # if empty array given then use uniform distributions if len(a) == 0: a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] @@ -128,7 +134,7 @@ def emd(a, b, M, numItermax=100000, log=False, sparse=False): def emd2(a, b, M, processes=multiprocessing.cpu_count(), - numItermax=100000, log=False, sparse=False, return_matrix=False): + numItermax=100000, log=False, dense=True, return_matrix=False): r"""Solves the Earth Movers distance problem and returns the loss .. math:: @@ -166,6 +172,10 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), variables. Otherwise returns only the optimal transportation cost. return_matrix: boolean, optional (default=False) If True, returns the optimal transportation matrix in the log. + dense: boolean, optional (default=True) + If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). + Otherwise returns a sparse representation using scipy's `coo_matrix` + format. Returns ------- @@ -207,6 +217,8 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) + sparse=not dense + # problem with pikling Forks if sys.platform.endswith('win32'): processes=1 |