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author | Antoine Rolet <antoine.rolet@gmail.com> | 2017-09-09 17:38:31 +0900 |
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committer | Antoine Rolet <antoine.rolet@gmail.com> | 2017-09-09 17:38:31 +0900 |
commit | cd8c04246b6d1f15b68d6433741e8c808fd517d8 (patch) | |
tree | af45a723fb29644b7be75b20db48bf238cdf6296 /ot/lp/__init__.py | |
parent | 1ba2c837d54ce963ad63ddf8df2e47230800b747 (diff) |
Renamed variable
Diffstat (limited to 'ot/lp/__init__.py')
-rw-r--r-- | ot/lp/__init__.py | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 1238cdb..9a0cb1c 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -16,7 +16,7 @@ from .emd_wrap import emd_c, check_result from ..utils import parmap -def emd(a, b, M, num_iter_max=100000, log=False): +def emd(a, b, M, max_iter=100000, log=False): """Solves the Earth Movers distance problem and returns the OT matrix @@ -41,7 +41,7 @@ def emd(a, b, M, num_iter_max=100000, log=False): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - num_iter_max : int, optional (default=100000) + max_iter : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. log: boolean, optional (default=False) @@ -94,7 +94,7 @@ def emd(a, b, M, num_iter_max=100000, log=False): if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] - G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max) + G, cost, u, v, result_code = emd_c(a, b, M, max_iter) result_code_string = check_result(result_code) if log: log = {} @@ -107,7 +107,7 @@ def emd(a, b, M, num_iter_max=100000, log=False): return G -def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, log=False): +def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000, log=False): """Solves the Earth Movers distance problem and returns the loss .. math:: @@ -131,7 +131,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - num_iter_max : int, optional (default=100000) + max_iter : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -183,7 +183,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo if log: def f(b): - G, cost, u, v, resultCode = emd_c(a, b, M, num_iter_max) + G, cost, u, v, resultCode = emd_c(a, b, M, max_iter) result_code_string = check_result(resultCode) log = {} log['G'] = G @@ -194,7 +194,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo return [cost, log] else: def f(b): - G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max) + G, cost, u, v, result_code = emd_c(a, b, M, max_iter) check_result(result_code) return cost |