diff options
author | aje <leo_g_autheron@hotmail.fr> | 2017-08-30 10:06:41 +0200 |
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committer | Nicolas Courty <Nico@MacBook-Pro-de-Nicolas.local> | 2017-09-01 11:09:13 +0200 |
commit | 8875f653e57aa11c8d62d291abb16fdbeff65511 (patch) | |
tree | f82bb152b7d51a48615a2b553a32d1b8afd790b2 /ot/lp/__init__.py | |
parent | ceeb063541fa71d4ddd7b13b043f985dc5bcab14 (diff) |
Rename for emd and emd2
Diffstat (limited to 'ot/lp/__init__.py')
-rw-r--r-- | ot/lp/__init__.py | 16 |
1 files changed, 8 insertions, 8 deletions
diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 7bef648..de91e74 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -14,7 +14,7 @@ from ..utils import parmap import multiprocessing -def emd(a, b, M, max_iter=100000): +def emd(a, b, M, numItermax=100000): """Solves the Earth Movers distance problem and returns the OT matrix @@ -39,7 +39,7 @@ def emd(a, b, M, max_iter=100000): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - max_iter : int, optional (default=100000) + numItermax : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -86,10 +86,10 @@ def emd(a, b, M, max_iter=100000): if len(b) == 0: b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] - return emd_c(a, b, M, max_iter) + return emd_c(a, b, M, numItermax) -def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000): +def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000): """Solves the Earth Movers distance problem and returns the loss .. math:: @@ -113,7 +113,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - max_iter : int, optional (default=100000) + numItermax : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -161,12 +161,12 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000): b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] if len(b.shape) == 1: - return emd2_c(a, b, M, max_iter) + return emd2_c(a, b, M, numItermax) else: nb = b.shape[1] - # res = [emd2_c(a, b[:, i].copy(), M, max_iter) for i in range(nb)] + # res = [emd2_c(a, b[:, i].copy(), M, numItermax) for i in range(nb)] def f(b): - return emd2_c(a, b, M, max_iter) + return emd2_c(a, b, M, numItermax) res = parmap(f, [b[:, i] for i in range(nb)], processes) return np.array(res) |