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| author | Rémi Flamary <remi.flamary@gmail.com> | 2019-12-18 10:15:30 +0100 |
|---|---|---|
| committer | Rémi Flamary <remi.flamary@gmail.com> | 2019-12-18 10:15:30 +0100 |
| commit | 3cb03158c42dde141d6f33973ea6e3394b9dc3d4 (patch) | |
| tree | 272412760f0e55b9a354b7c43ddc71a2a6320a69 /ot/lp/__init__.py | |
| parent | a4afee871d8e9d5db68228d1ed5bf4853eedc294 (diff) | |
cleanup variable name dense
Diffstat (limited to 'ot/lp/__init__.py')
| -rw-r--r-- | ot/lp/__init__.py | 30 |
1 files changed, 14 insertions, 16 deletions
diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index d476071..bb9829a 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -107,7 +107,6 @@ def emd(a, b, M, numItermax=100000, log=False, dense=True): 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: @@ -115,11 +114,11 @@ def emd(a, b, M, numItermax=100000, log=False, dense=True): if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] - if sparse: - Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) - G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + if dense: + G, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) else: - G, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) + Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) + G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) result_code_string = check_result(result_code) if log: @@ -217,8 +216,6 @@ 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 @@ -231,12 +228,11 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), if log or return_matrix: def f(b): - - if sparse: - Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) - G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + if dense: + G, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) else: - G, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) + Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) + G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) result_code_string = check_result(result_code) log = {} @@ -249,11 +245,13 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), return [cost, log] else: def f(b): - if sparse: - Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) - G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + if dense: + G, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) else: - G, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) + Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) + G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + + result_code_string = check_result(result_code) check_result(result_code) return cost |