From a303cc6b483d3cd958c399621e22e40574bcbbc8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 21 Apr 2020 17:48:37 +0200 Subject: [MRG] Actually run sphinx-gallery (#146) * generate gallery * remove mock * add sklearn to requirermnt?txt for example * remove latex from fgw example * add networks for graph example * remove all * add requirement.txt rtd * rtd debug * update readme * eradthedoc with redirection * add conf rtd --- docs/source/auto_examples/plot_stochastic.py | 208 --------------------------- 1 file changed, 208 deletions(-) delete mode 100644 docs/source/auto_examples/plot_stochastic.py (limited to 'docs/source/auto_examples/plot_stochastic.py') diff --git a/docs/source/auto_examples/plot_stochastic.py b/docs/source/auto_examples/plot_stochastic.py deleted file mode 100644 index 742f8d9..0000000 --- a/docs/source/auto_examples/plot_stochastic.py +++ /dev/null @@ -1,208 +0,0 @@ -""" -========================== -Stochastic examples -========================== - -This example is designed to show how to use the stochatic optimization -algorithms for descrete and semicontinous measures from the POT library. - -""" - -# Author: Kilian Fatras -# -# License: MIT License - -import matplotlib.pylab as pl -import numpy as np -import ot -import ot.plot - - -############################################################################# -# COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM -############################################################################# -############################################################################# -# DISCRETE CASE: -# -# Sample two discrete measures for the discrete case -# --------------------------------------------- -# -# Define 2 discrete measures a and b, the points where are defined the source -# and the target measures and finally the cost matrix c. - -n_source = 7 -n_target = 4 -reg = 1 -numItermax = 1000 - -a = ot.utils.unif(n_source) -b = ot.utils.unif(n_target) - -rng = np.random.RandomState(0) -X_source = rng.randn(n_source, 2) -Y_target = rng.randn(n_target, 2) -M = ot.dist(X_source, Y_target) - -############################################################################# -# -# Call the "SAG" method to find the transportation matrix in the discrete case -# --------------------------------------------- -# -# Define the method "SAG", call ot.solve_semi_dual_entropic and plot the -# results. - -method = "SAG" -sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, - numItermax) -print(sag_pi) - -############################################################################# -# SEMICONTINOUS CASE: -# -# Sample one general measure a, one discrete measures b for the semicontinous -# case -# --------------------------------------------- -# -# Define one general measure a, one discrete measures b, the points where -# are defined the source and the target measures and finally the cost matrix c. - -n_source = 7 -n_target = 4 -reg = 1 -numItermax = 1000 -log = True - -a = ot.utils.unif(n_source) -b = ot.utils.unif(n_target) - -rng = np.random.RandomState(0) -X_source = rng.randn(n_source, 2) -Y_target = rng.randn(n_target, 2) -M = ot.dist(X_source, Y_target) - -############################################################################# -# -# Call the "ASGD" method to find the transportation matrix in the semicontinous -# case -# --------------------------------------------- -# -# Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the -# results. - -method = "ASGD" -asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, - numItermax, log=log) -print(log_asgd['alpha'], log_asgd['beta']) -print(asgd_pi) - -############################################################################# -# -# Compare the results with the Sinkhorn algorithm -# --------------------------------------------- -# -# Call the Sinkhorn algorithm from POT - -sinkhorn_pi = ot.sinkhorn(a, b, M, reg) -print(sinkhorn_pi) - - -############################################################################## -# PLOT TRANSPORTATION MATRIX -############################################################################## - -############################################################################## -# Plot SAG results -# ---------------- - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG') -pl.show() - - -############################################################################## -# Plot ASGD results -# ----------------- - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD') -pl.show() - - -############################################################################## -# Plot Sinkhorn results -# --------------------- - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') -pl.show() - - -############################################################################# -# COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM -############################################################################# -############################################################################# -# SEMICONTINOUS CASE: -# -# Sample one general measure a, one discrete measures b for the semicontinous -# case -# --------------------------------------------- -# -# Define one general measure a, one discrete measures b, the points where -# are defined the source and the target measures and finally the cost matrix c. - -n_source = 7 -n_target = 4 -reg = 1 -numItermax = 100000 -lr = 0.1 -batch_size = 3 -log = True - -a = ot.utils.unif(n_source) -b = ot.utils.unif(n_target) - -rng = np.random.RandomState(0) -X_source = rng.randn(n_source, 2) -Y_target = rng.randn(n_target, 2) -M = ot.dist(X_source, Y_target) - -############################################################################# -# -# Call the "SGD" dual method to find the transportation matrix in the -# semicontinous case -# --------------------------------------------- -# -# Call ot.solve_dual_entropic and plot the results. - -sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, - batch_size, numItermax, - lr, log=log) -print(log_sgd['alpha'], log_sgd['beta']) -print(sgd_dual_pi) - -############################################################################# -# -# Compare the results with the Sinkhorn algorithm -# --------------------------------------------- -# -# Call the Sinkhorn algorithm from POT - -sinkhorn_pi = ot.sinkhorn(a, b, M, reg) -print(sinkhorn_pi) - -############################################################################## -# Plot SGD results -# ----------------- - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD') -pl.show() - - -############################################################################## -# Plot Sinkhorn results -# --------------------- - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') -pl.show() -- cgit v1.2.3