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| author | tvayer <titouan.vayer@gmail.com> | 2019-05-29 17:05:38 +0200 |
|---|---|---|
| committer | tvayer <titouan.vayer@gmail.com> | 2019-05-29 17:05:48 +0200 |
| commit | e1bd94bb7e85a0d2fd0fcd7642b06da12c1db6db (patch) | |
| tree | 1e85920b878ab715d211db56f99e25bfa2482fd3 /ot/optim.py | |
| parent | d4320382fa8873d15dcaec7adca3a4723c142515 (diff) | |
code review1
Diffstat (limited to 'ot/optim.py')
| -rw-r--r-- | ot/optim.py | 31 |
1 files changed, 16 insertions, 15 deletions
diff --git a/ot/optim.py b/ot/optim.py index 7d103e2..4d428d9 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -5,6 +5,7 @@ Optimization algorithms for OT # Author: Remi Flamary <remi.flamary@unice.fr> # Titouan Vayer <titouan.vayer@irisa.fr> +# # License: MIT License import numpy as np @@ -88,20 +89,20 @@ def do_linesearch(cost, G, deltaG, Mi, f_val, Cost matrix of the linearized transport problem. Corresponds to the gradient of the cost f_val : float Value of the cost at G - armijo : bool, optionnal + armijo : bool, optional If True the steps of the line-search is found via an armijo research. Else closed form is used. If there is convergence issues use False. - C1 : ndarray (ns,ns), optionnal + C1 : ndarray (ns,ns), optional Structure matrix in the source domain. Only used when armijo=False - C2 : ndarray (nt,nt), optionnal + C2 : ndarray (nt,nt), optional Structure matrix in the target domain. Only used when armijo=False - reg : float, optionnal + reg : float, optional Regularization parameter. Only used when armijo=False Gc : ndarray (ns,nt) Optimal map found by linearization in the FW algorithm. Only used when armijo=False constC : ndarray (ns,nt) Constant for the gromov cost. See [24]. Only used when armijo=False - M : ndarray (ns,nt), optionnal + M : ndarray (ns,nt), optional Cost matrix between the features. Only used when armijo=False Returns ------- @@ -223,9 +224,9 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, it = 0 if verbose: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) - print('{:5d}|{:8e}|{:8e}'.format(it, f_val, 0)) + print('{:5s}|{:12s}|{:8s}|{:8s}'.format( + 'It.', 'Loss', 'Relative loss', 'Absolute loss') + '\n' + '-' * 48) + print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, 0, 0)) while loop: @@ -261,8 +262,8 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, if verbose: if it % 20 == 0: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Relative variation loss', 'Absolute variation loss') + '\n' + '-' * 32) + print('{:5s}|{:12s}|{:8s}|{:8s}'.format( + 'It.', 'Loss', 'Relative loss', 'Absolute loss') + '\n' + '-' * 48) print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, relative_delta_fval, abs_delta_fval)) if log: @@ -363,9 +364,9 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, it = 0 if verbose: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) - print('{:5d}|{:8e}|{:8e}'.format(it, f_val, 0)) + print('{:5s}|{:12s}|{:8s}|{:8s}'.format( + 'It.', 'Loss', 'Relative loss', 'Absolute loss') + '\n' + '-' * 48) + print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, 0, 0)) while loop: @@ -402,8 +403,8 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, if verbose: if it % 20 == 0: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Relative variation loss', 'Absolute variation loss') + '\n' + '-' * 32) + print('{:5s}|{:12s}|{:8s}|{:8s}'.format( + 'It.', 'Loss', 'Relative loss', 'Absolute loss') + '\n' + '-' * 48) print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, relative_delta_fval, abs_delta_fval)) if log: |