diff options
author | aje <leo_g_autheron@hotmail.fr> | 2017-08-29 15:38:11 +0200 |
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committer | aje <leo_g_autheron@hotmail.fr> | 2017-08-29 15:38:11 +0200 |
commit | 3cecc18f53453e79ccc00484e6cbfaa5643f269c (patch) | |
tree | 1d9879cb0ed12d280b14121ccad90946682465f1 /ot/lp/__init__.py | |
parent | a2ec6e55e458c719484e86a4e6a6e764c2e38dc8 (diff) |
Changes to LP solver:
- Allow to modify the maximal number of iterations
- Display an error message in the python console if the solver encountered an issue
Diffstat (limited to 'ot/lp/__init__.py')
-rw-r--r-- | ot/lp/__init__.py | 16 |
1 files changed, 10 insertions, 6 deletions
diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 6e0bdb8..62afe76 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -15,7 +15,7 @@ import multiprocessing -def emd(a, b, M): +def emd(a, b, M, numItermax=10000): """Solves the Earth Movers distance problem and returns the OT matrix @@ -40,6 +40,8 @@ def emd(a, b, M): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix + numItermax : int + Maximum number of iterations made by the LP solver. Returns ------- @@ -84,9 +86,9 @@ def emd(a, b, M): if len(b) == 0: b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] - return emd_c(a, b, M) + return emd_c(a, b, M, numItermax) -def emd2(a, b, M,processes=multiprocessing.cpu_count()): +def emd2(a, b, M, numItermax=10000, processes=multiprocessing.cpu_count()): """Solves the Earth Movers distance problem and returns the loss .. math:: @@ -110,6 +112,8 @@ def emd2(a, b, M,processes=multiprocessing.cpu_count()): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix + numItermax : int + Maximum number of iterations made by the LP solver. Returns ------- @@ -155,12 +159,12 @@ def emd2(a, b, M,processes=multiprocessing.cpu_count()): b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] if len(b.shape)==1: - return emd2_c(a, b, M) + return emd2_c(a, b, M, numItermax) else: nb=b.shape[1] - #res=[emd2_c(a,b[:,i].copy(),M) 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) + return emd2_c(a,b,M, numItermax) res= parmap(f, [b[:,i] for i in range(nb)],processes) return np.array(res) |