.. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_otda_linear_mapping.py: ============================ Linear OT mapping estimation ============================ .. code-block:: default # Author: Remi Flamary # # License: MIT License import numpy as np import pylab as pl import ot Generate data ------------- .. code-block:: default 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 --------- .. code-block:: default pl.figure(1, (5, 5)) pl.plot(xs[:, 0], xs[:, 1], '+') pl.plot(xt[:, 0], xt[:, 1], 'o') .. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none [] Estimate linear mapping and transport ------------------------------------- .. code-block:: default Ae, be = ot.da.OT_mapping_linear(xs, xt) xst = xs.dot(Ae) + be Plot transported samples ------------------------ .. code-block:: default 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() .. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none /home/circleci/project/examples/plot_otda_linear_mapping.py:73: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. pl.show() Load image data --------------- .. code-block:: default 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 ---------------------------- .. code-block:: default 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 ----------------------- .. code-block:: default 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') .. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Text(0.5, 1.0, 'Inverse mapping Im. 2') .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 3.549 seconds) .. _sphx_glr_download_auto_examples_plot_otda_linear_mapping.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_otda_linear_mapping.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_otda_linear_mapping.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_