.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_rim.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 .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_rim.py: ================================================================= Simple logistic regression with RIM ================================================================= RIM (regularised mutual information) is a proposal of model by Krause et al. (2010) which consists in maximising for a linear model under :math:`\ell_2` penalty. In this example, we show how to do clustering of a Gaussian mixture using RIM. .. GENERATED FROM PYTHON SOURCE LINES 10-17 .. code-block:: default import numpy as np from matplotlib import pyplot as plt from sklearn import datasets from gemclus.linear import RIM .. GENERATED FROM PYTHON SOURCE LINES 18-20 Load a simple synthetic dataset -------------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 22-26 .. code-block:: default # Generate samples on that are simple to separate X, y = datasets.make_blobs(centers=3, cluster_std=0.5, n_samples=200, random_state=0) .. GENERATED FROM PYTHON SOURCE LINES 27-30 Train the model -------------------------------------------------------------- Create the RIM clustering model (just a logistic regression) and fit it to the data. .. GENERATED FROM PYTHON SOURCE LINES 32-36 .. code-block:: default clf = RIM(n_clusters=3, random_state=0) clf.fit(X) .. raw:: html

RIM(random_state=0)

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.. GENERATED FROM PYTHON SOURCE LINES 37-40 Final Clustering ----------------- Let us take a look at the decision boundaries according to the probabilities .. GENERATED FROM PYTHON SOURCE LINES 42-77 .. code-block:: default # Predict a grad of inputs x_vals = np.linspace(X[:, 0].min() - 0.5, X[:, 0].max() + 0.5, num=50) y_vals = np.linspace(X[:, 1].min() - 0.5, X[:, 1].max() + 0.5, num=50) xx, yy = np.meshgrid(x_vals, y_vals) grid_inputs = np.c_[xx.ravel(), yy.ravel()] grid_pred = clf.predict_proba(grid_inputs) # Isolate probability of the argmax zz = grid_pred.max(1) zz = zz.reshape((50, 50)) plt.contourf(xx, yy, zz, alpha=0.3, levels=10) # Now, show the cluster predictions y_pred = clf.predict(X) X_0 = X[y_pred == 0] X_1 = X[y_pred == 1] X_2 = X[y_pred == 2] ax0 = plt.scatter(X_0[:, 0], X_0[:, 1], c='crimson', s=50) ax1 = plt.scatter(X_1[:, 0], X_1[:, 1], c='deepskyblue', s=50) ax2 = plt.scatter(X_2[:, 0], X_2[:, 1], c='darkgreen', s=50) leg = plt.legend([ax0, ax1, ax2], ['Cluster 0', 'Cluster 1', 'Cluster 2'], loc='upper left', fancybox=True, scatterpoints=1) leg.get_frame().set_alpha(0.5) plt.xlabel('Feature 1') plt.ylabel('Feature 2') plt.show() print(clf.score(X)) .. image-sg:: /auto_examples/images/sphx_glr_plot_rim_001.png :alt: plot rim :srcset: /auto_examples/images/sphx_glr_plot_rim_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none 0.02554995289356299 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.322 seconds) .. _sphx_glr_download_auto_examples_plot_rim.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_rim.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_rim.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_