.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/neighbors/plot_digits_kde_sampling.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_neighbors_plot_digits_kde_sampling.py: ========================= Kernel Density Estimation ========================= This example shows how kernel density estimation (KDE), a powerful non-parametric density estimation technique, can be used to learn a generative model for a dataset. With this generative model in place, new samples can be drawn. These new samples reflect the underlying model of the data. .. GENERATED FROM PYTHON SOURCE LINES 12-62 .. image:: /auto_examples/neighbors/images/sphx_glr_plot_digits_kde_sampling_001.png :alt: Selection from the input data, "New" digits drawn from the kernel density model :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none best bandwidth: 3.79269019073225 | .. code-block:: default import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_digits from sklearn.neighbors import KernelDensity from sklearn.decomposition import PCA from sklearn.model_selection import GridSearchCV # load the data digits = load_digits() # project the 64-dimensional data to a lower dimension pca = PCA(n_components=15, whiten=False) data = pca.fit_transform(digits.data) # use grid search cross-validation to optimize the bandwidth params = {'bandwidth': np.logspace(-1, 1, 20)} grid = GridSearchCV(KernelDensity(), params) grid.fit(data) print("best bandwidth: {0}".format(grid.best_estimator_.bandwidth)) # use the best estimator to compute the kernel density estimate kde = grid.best_estimator_ # sample 44 new points from the data new_data = kde.sample(44, random_state=0) new_data = pca.inverse_transform(new_data) # turn data into a 4x11 grid new_data = new_data.reshape((4, 11, -1)) real_data = digits.data[:44].reshape((4, 11, -1)) # plot real digits and resampled digits fig, ax = plt.subplots(9, 11, subplot_kw=dict(xticks=[], yticks=[])) for j in range(11): ax[4, j].set_visible(False) for i in range(4): im = ax[i, j].imshow(real_data[i, j].reshape((8, 8)), cmap=plt.cm.binary, interpolation='nearest') im.set_clim(0, 16) im = ax[i + 5, j].imshow(new_data[i, j].reshape((8, 8)), cmap=plt.cm.binary, interpolation='nearest') im.set_clim(0, 16) ax[0, 5].set_title('Selection from the input data') ax[5, 5].set_title('"New" digits drawn from the kernel density model') plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 7.113 seconds) .. _sphx_glr_download_auto_examples_neighbors_plot_digits_kde_sampling.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/neighbors/plot_digits_kde_sampling.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_digits_kde_sampling.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_digits_kde_sampling.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_