From de04afc0f9f01fc09a3a8138865eacc0b6f4415d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 19 Nov 2018 11:18:29 +0100 Subject: update flake8 parameters --- ot/gpu/bregman.py | 6 +++--- ot/stochastic.py | 12 ++++++------ setup.cfg | 2 +- 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 3031ed9..978b307 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -146,9 +146,9 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, 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) diff --git a/ot/stochastic.py b/ot/stochastic.py index 1376884..959c6fa 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -418,8 +418,8 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, return None opt_alpha = c_transform_entropic(b, M, reg, opt_beta) - pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) - * a[:, None] * b[None, :]) + pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) * + a[:, None] * b[None, :]) if log: log = {} @@ -520,8 +520,8 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, arXiv preprint arxiv:1711.02283. ''' - G = - (np.exp((alpha[batch_alpha, None] + beta[None, batch_beta] - - M[batch_alpha, :][:, batch_beta]) / reg) * + G = - (np.exp((alpha[batch_alpha, None] + beta[None, batch_beta] - + M[batch_alpha, :][:, batch_beta]) / reg) * a[batch_alpha, None] * b[None, batch_beta]) grad_beta = np.zeros(np.shape(M)[1]) grad_alpha = np.zeros(np.shape(M)[0]) @@ -702,8 +702,8 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, opt_alpha, opt_beta = sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr) - pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) - * a[:, None] * b[None, :]) + pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) * + a[:, None] * b[None, :]) if log: log = {} log['alpha'] = opt_alpha diff --git a/setup.cfg b/setup.cfg index 24512d2..aa0ff62 100644 --- a/setup.cfg +++ b/setup.cfg @@ -3,4 +3,4 @@ description-file = README.md [flake8] exclude = __init__.py -ignore = E265,E501,W605 +ignore = E265,E501,W605,W503,W504 -- cgit v1.2.3