.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/manifold/plot_mds.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_manifold_plot_mds.py: ========================= Multi-dimensional scaling ========================= An illustration of the metric and non-metric MDS on generated noisy data. The reconstructed points using the metric MDS and non metric MDS are slightly shifted to avoid overlapping. .. GENERATED FROM PYTHON SOURCE LINES 12-101 .. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_mds_001.png :alt: plot mds :srcset: /auto_examples/manifold/images/sphx_glr_plot_mds_001.png :class: sphx-glr-single-img .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import numpy as np from matplotlib import pyplot as plt from matplotlib.collections import LineCollection from sklearn import manifold from sklearn.decomposition import PCA from sklearn.metrics import euclidean_distances EPSILON = np.finfo(np.float32).eps n_samples = 20 seed = np.random.RandomState(seed=3) X_true = seed.randint(0, 20, 2 * n_samples).astype(float) X_true = X_true.reshape((n_samples, 2)) # Center the data X_true -= X_true.mean() similarities = euclidean_distances(X_true) # Add noise to the similarities noise = np.random.rand(n_samples, n_samples) noise = noise + noise.T noise[np.arange(noise.shape[0]), np.arange(noise.shape[0])] = 0 similarities += noise mds = manifold.MDS( n_components=2, max_iter=3000, eps=1e-9, random_state=seed, dissimilarity="precomputed", n_jobs=1, ) pos = mds.fit(similarities).embedding_ nmds = manifold.MDS( n_components=2, metric=False, max_iter=3000, eps=1e-12, dissimilarity="precomputed", random_state=seed, n_jobs=1, n_init=1, ) npos = nmds.fit_transform(similarities, init=pos) # Rescale the data pos *= np.sqrt((X_true**2).sum()) / np.sqrt((pos**2).sum()) npos *= np.sqrt((X_true**2).sum()) / np.sqrt((npos**2).sum()) # Rotate the data clf = PCA(n_components=2) X_true = clf.fit_transform(X_true) pos = clf.fit_transform(pos) npos = clf.fit_transform(npos) fig = plt.figure(1) ax = plt.axes([0.0, 0.0, 1.0, 1.0]) s = 100 plt.scatter(X_true[:, 0], X_true[:, 1], color="navy", s=s, lw=0, label="True Position") plt.scatter(pos[:, 0], pos[:, 1], color="turquoise", s=s, lw=0, label="MDS") plt.scatter(npos[:, 0], npos[:, 1], color="darkorange", s=s, lw=0, label="NMDS") plt.legend(scatterpoints=1, loc="best", shadow=False) similarities = similarities.max() / (similarities + EPSILON) * 100 np.fill_diagonal(similarities, 0) # Plot the edges start_idx, end_idx = np.where(pos) # a sequence of (*line0*, *line1*, *line2*), where:: # linen = (x0, y0), (x1, y1), ... (xm, ym) segments = [ [X_true[i, :], X_true[j, :]] for i in range(len(pos)) for j in range(len(pos)) ] values = np.abs(similarities) lc = LineCollection( segments, zorder=0, cmap=plt.cm.Blues, norm=plt.Normalize(0, values.max()) ) lc.set_array(similarities.flatten()) lc.set_linewidths(np.full(len(segments), 0.5)) ax.add_collection(lc) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.212 seconds) .. _sphx_glr_download_auto_examples_manifold_plot_mds.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/manifold/plot_mds.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/manifold/plot_mds.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_mds.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_mds.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_mds.zip ` .. include:: plot_mds.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_