<jupyter_start><jupyter_text>Linear OT mapping estimation<jupyter_code># Author: <NAME> <<EMAIL>>
#
# License: MIT License

# sphinx_gallery_thumbnail_number = 2
import os
from pathlib import Path

import numpy as np
from matplotlib import pyplot as plt
import ot<jupyter_output><empty_output><jupyter_text>Generate data<jupyter_code>n = 1000
d = 2
sigma = .1

rng = np.random.RandomState(42)

# source samples
angles = rng.rand(n, 1) * 2 * np.pi
xs = np.concatenate((np.sin(angles), np.cos(angles)),
                    axis=1) + sigma * rng.randn(n, 2)
xs[:n // 2, 1] += 2


# target samples
anglet = rng.rand(n, 1) * 2 * np.pi
xt = np.concatenate((np.sin(anglet), np.cos(anglet)),
                    axis=1) + sigma * rng.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<jupyter_output><empty_output><jupyter_text>Plot data<jupyter_code>plt.figure(1, (5, 5))
plt.plot(xs[:, 0], xs[:, 1], '+')
plt.plot(xt[:, 0], xt[:, 1], 'o')<jupyter_output><empty_output><jupyter_text>Estimate linear mapping and transport<jupyter_code>Ae, be = ot.da.OT_mapping_linear(xs, xt)

xst = xs.dot(Ae) + be<jupyter_output><empty_output><jupyter_text>Plot transported samples<jupyter_code>plt.figure(1, (5, 5))
plt.clf()
plt.plot(xs[:, 0], xs[:, 1], '+')
plt.plot(xt[:, 0], xt[:, 1], 'o')
plt.plot(xst[:, 0], xst[:, 1], '+')

plt.show()<jupyter_output><empty_output><jupyter_text>Load image data<jupyter_code>def im2mat(img):
    """Converts and image to matrix (one pixel per line)"""
    return img.reshape((img.shape[0] * img.shape[1], img.shape[2]))


def mat2im(X, shape):
    """Converts back a matrix to an image"""
    return X.reshape(shape)


def minmax(img):
    return np.clip(img, 0, 1)


# Loading images
this_file = os.path.realpath('__file__')
data_path = os.path.join(Path(this_file).parent.parent.parent, 'data')

I1 = plt.imread(os.path.join(data_path, 'ocean_day.jpg')).astype(np.float64) / 256
I2 = plt.imread(os.path.join(data_path, 'ocean_sunset.jpg')).astype(np.float64) / 256


X1 = im2mat(I1)
X2 = im2mat(I2)<jupyter_output><empty_output><jupyter_text>Estimate mapping and adapt<jupyter_code>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))<jupyter_output><empty_output><jupyter_text>Plot transformed images<jupyter_code>plt.figure(2, figsize=(10, 7))

plt.subplot(2, 2, 1)
plt.imshow(I1)
plt.axis('off')
plt.title('Im. 1')

plt.subplot(2, 2, 2)
plt.imshow(I2)
plt.axis('off')
plt.title('Im. 2')

plt.subplot(2, 2, 3)
plt.imshow(I1t)
plt.axis('off')
plt.title('Mapping Im. 1')

plt.subplot(2, 2, 4)
plt.imshow(I2t)
plt.axis('off')
plt.title('Inverse mapping Im. 2')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18880623450230252, 'fraction_numerical': 0.0375487070492384, 'mean_word_length': 5.125813449023862, 'pattern_counts': {'":': 0, '<': 36, '<?xml version=': 0, '>': 36, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '8', 'dir': 'jupyter-structured-clean-dedup', 'id': '534941', 'license': 'MIT', 'n_tokens_mistral': 1316, 'n_tokens_neox': 1222, 'n_words': 230, 'path': 'master/_downloads/3ccc3ac2fee6df48b3a08497133a1354/plot_otda_linear_mapping.ipynb', 'repo_name': 'PythonOT/pythonot.github.io', 'size': '2843'}