1 files changed, 0 insertions, 144 deletions
diff --git a/examples/plot_otda_linear_mapping.py b/examples/plot_otda_linear_mapping.py
deleted file mode 100644
index c65bd4f..0000000
--- a/examples/plot_otda_linear_mapping.py
+++ /dev/null
@@ -1,144 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-============================
-Linear OT mapping estimation
-============================
-
-
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#
-# License: MIT License
-
-import numpy as np
-import pylab as pl
-import ot
-
-##############################################################################
-# Generate data
-# -------------
-
-n = 1000
-d = 2
-sigma = .1
-
-# source samples
-angles = np.random.rand(n, 1) * 2 * np.pi
-xs = np.concatenate((np.sin(angles), np.cos(angles)),
-                    axis=1) + sigma * np.random.randn(n, 2)
-xs[:n // 2, 1] += 2
-
-
-# target samples
-anglet = np.random.rand(n, 1) * 2 * np.pi
-xt = np.concatenate((np.sin(anglet), np.cos(anglet)),
-                    axis=1) + sigma * np.random.randn(n, 2)
-xt[:n // 2, 1] += 2
-
-
-A = np.array([[1.5, .7], [.7, 1.5]])
-b = np.array([[4, 2]])
-xt = xt.dot(A) + b
-
-##############################################################################
-# Plot data
-# ---------
-
-pl.figure(1, (5, 5))
-pl.plot(xs[:, 0], xs[:, 1], '+')
-pl.plot(xt[:, 0], xt[:, 1], 'o')
-
-
-##############################################################################
-# Estimate linear mapping and transport
-# -------------------------------------
-
-Ae, be = ot.da.OT_mapping_linear(xs, xt)
-
-xst = xs.dot(Ae) + be
-
-
-##############################################################################
-# Plot transported samples
-# ------------------------
-
-pl.figure(1, (5, 5))
-pl.clf()
-pl.plot(xs[:, 0], xs[:, 1], '+')
-pl.plot(xt[:, 0], xt[:, 1], 'o')
-pl.plot(xst[:, 0], xst[:, 1], '+')
-
-pl.show()
-
-##############################################################################
-# Load image data
-# ---------------
-
-
-def im2mat(I):
-    """Converts and image to matrix (one pixel per line)"""
-    return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
-
-
-def mat2im(X, shape):
-    """Converts back a matrix to an image"""
-    return X.reshape(shape)
-
-
-def minmax(I):
-    return np.clip(I, 0, 1)
-
-
-# Loading images
-I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256
-I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
-
-
-X1 = im2mat(I1)
-X2 = im2mat(I2)
-
-##############################################################################
-# Estimate mapping and adapt
-# ----------------------------
-
-mapping = ot.da.LinearTransport()
-
-mapping.fit(Xs=X1, Xt=X2)
-
-
-xst = mapping.transform(Xs=X1)
-xts = mapping.inverse_transform(Xt=X2)
-
-I1t = minmax(mat2im(xst, I1.shape))
-I2t = minmax(mat2im(xts, I2.shape))
-
-# %%
-
-
-##############################################################################
-# Plot transformed images
-# -----------------------
-
-pl.figure(2, figsize=(10, 7))
-
-pl.subplot(2, 2, 1)
-pl.imshow(I1)
-pl.axis('off')
-pl.title('Im. 1')
-
-pl.subplot(2, 2, 2)
-pl.imshow(I2)
-pl.axis('off')
-pl.title('Im. 2')
-
-pl.subplot(2, 2, 3)
-pl.imshow(I1t)
-pl.axis('off')
-pl.title('Mapping Im. 1')
-
-pl.subplot(2, 2, 4)
-pl.imshow(I2t)
-pl.axis('off')
-pl.title('Inverse mapping Im. 2')