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| author | Rémi Flamary <remi.flamary@gmail.com> | 2018-11-19 11:17:07 +0100 |
|---|---|---|
| committer | Rémi Flamary <remi.flamary@gmail.com> | 2018-11-19 11:17:07 +0100 |
| commit | 93db239e1156ad1db8edbb13c1ecde973ce009c0 (patch) | |
| tree | 14101caa2699d0b90d165303658f1540d1e87a5c /ot/bregman.py | |
| parent | 87930c4bcddfded480983343ecc68c6b94bcce14 (diff) | |
remove W605 errors
Diffstat (limited to 'ot/bregman.py')
| -rw-r--r-- | ot/bregman.py | 18 |
1 files changed, 9 insertions, 9 deletions
diff --git a/ot/bregman.py b/ot/bregman.py index d1057ff..43340f7 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -370,9 +370,9 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, v = np.divide(b, KtransposeU) u = 1. / np.dot(Kp, v) - if (np.any(KtransposeU == 0) or - np.any(np.isnan(u)) or np.any(np.isnan(v)) or - np.any(np.isinf(u)) or np.any(np.isinf(v))): + if (np.any(KtransposeU == 0) + or np.any(np.isnan(u)) or np.any(np.isnan(v)) + or np.any(np.isinf(u)) or np.any(np.isinf(v))): # we have reached the machine precision # come back to previous solution and quit loop print('Warning: numerical errors at iteration', cpt) @@ -683,13 +683,13 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, def get_K(alpha, beta): """log space computation""" - return np.exp(-(M - alpha.reshape((na, 1)) - - beta.reshape((1, nb))) / reg) + return np.exp(-(M - alpha.reshape((na, 1)) + - beta.reshape((1, nb))) / reg) def get_Gamma(alpha, beta, u, v): """log space gamma computation""" - return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) / - reg + np.log(u.reshape((na, 1))) + np.log(v.reshape((1, nb)))) + return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) + / reg + np.log(u.reshape((na, 1))) + np.log(v.reshape((1, nb)))) # print(np.min(K)) @@ -899,8 +899,8 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne def get_K(alpha, beta): """log space computation""" - return np.exp(-(M - alpha.reshape((na, 1)) - - beta.reshape((1, nb))) / reg) + return np.exp(-(M - alpha.reshape((na, 1)) + - beta.reshape((1, nb))) / reg) # print(np.min(K)) def get_reg(n): # exponential decreasing |