diff options
Diffstat (limited to 'examples/others')
| -rw-r--r-- | examples/others/plot_WeakOT_VS_OT.py | 2 | ||||
| -rw-r--r-- | examples/others/plot_factored_coupling.py | 6 | ||||
| -rw-r--r-- | examples/others/plot_logo.py | 2 | ||||
| -rw-r--r-- | examples/others/plot_screenkhorn_1D.py | 4 | ||||
| -rw-r--r-- | examples/others/plot_stochastic.py | 6 |
5 files changed, 10 insertions, 10 deletions
diff --git a/examples/others/plot_WeakOT_VS_OT.py b/examples/others/plot_WeakOT_VS_OT.py index a29c875..e3164ba 100644 --- a/examples/others/plot_WeakOT_VS_OT.py +++ b/examples/others/plot_WeakOT_VS_OT.py @@ -5,7 +5,7 @@ Weak Optimal Transport VS exact Optimal Transport ==================================================== Illustration of 2D optimal transport between distributions that are weighted -sum of diracs. The OT matrix is plotted with the samples. +sum of Diracs. The OT matrix is plotted with the samples. """ diff --git a/examples/others/plot_factored_coupling.py b/examples/others/plot_factored_coupling.py index b5b1c9f..02074d7 100644 --- a/examples/others/plot_factored_coupling.py +++ b/examples/others/plot_factored_coupling.py @@ -47,8 +47,8 @@ pl.title('Source and target distributions') # %% -# Compute Factore OT and exact OT solutions -# -------------------------------------- +# Compute Factored OT and exact OT solutions +# ------------------------------------------ #%% EMD M = ot.dist(xs, xt) @@ -61,7 +61,7 @@ Ga, Gb, xb = ot.factored_optimal_transport(xs, xt, a, b, r=4) # %% # Plot factored OT and exact OT solutions -# -------------------------------------- +# --------------------------------------- pl.figure(2, (14, 4)) diff --git a/examples/others/plot_logo.py b/examples/others/plot_logo.py index bb4f640..b032801 100644 --- a/examples/others/plot_logo.py +++ b/examples/others/plot_logo.py @@ -8,7 +8,7 @@ Logo of the POT toolbox In this example we plot the logo of the POT toolbox. This logo is that it is done 100% in Python and generated using -matplotlib and ploting teh solution of the EMD solver from POT. +matplotlib and plotting the solution of the EMD solver from POT. """ diff --git a/examples/others/plot_screenkhorn_1D.py b/examples/others/plot_screenkhorn_1D.py index 2023649..3640b88 100644 --- a/examples/others/plot_screenkhorn_1D.py +++ b/examples/others/plot_screenkhorn_1D.py @@ -62,8 +62,8 @@ ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') # Screenkhorn lambd = 2e-03 # entropy parameter -ns_budget = 30 # budget number of points to be keeped in the source distribution -nt_budget = 30 # budget number of points to be keeped in the target distribution +ns_budget = 30 # budget number of points to be kept in the source distribution +nt_budget = 30 # budget number of points to be kept in the target distribution G_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True) pl.figure(4, figsize=(5, 5)) diff --git a/examples/others/plot_stochastic.py b/examples/others/plot_stochastic.py index 3a1ef31..f3afb0b 100644 --- a/examples/others/plot_stochastic.py +++ b/examples/others/plot_stochastic.py @@ -3,7 +3,7 @@ Stochastic examples =================== -This example is designed to show how to use the stochatic optimization +This example is designed to show how to use the stochastic optimization algorithms for discrete and semi-continuous measures from the POT library. [18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. @@ -61,7 +61,7 @@ print(sag_pi) # Semi-Continuous Case # ```````````````````` # -# Sample one general measure a, one discrete measures b for the semicontinous +# Sample one general measure a, one discrete measures b for the semicontinuous # case, the points where source and target measures are defined and compute the # cost matrix. @@ -80,7 +80,7 @@ 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 +# Call the "ASGD" method to find the transportation matrix in the semicontinuous # case. method = "ASGD" |