.. _sphx_glr_auto_examples_mixture_plot_gmm_pdf.py: ========================================= Density Estimation for a Gaussian mixture ========================================= Plot the density estimation of a mixture of two Gaussians. Data is generated from two Gaussians with different centers and covariance matrices. .. image:: /auto_examples/mixture/images/sphx_glr_plot_gmm_pdf_001.png :align: center .. code-block:: python import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import LogNorm from sklearn import mixture n_samples = 300 # generate random sample, two components np.random.seed(0) # generate spherical data centered on (20, 20) shifted_gaussian = np.random.randn(n_samples, 2) + np.array([20, 20]) # generate zero centered stretched Gaussian data C = np.array([[0., -0.7], [3.5, .7]]) stretched_gaussian = np.dot(np.random.randn(n_samples, 2), C) # concatenate the two datasets into the final training set X_train = np.vstack([shifted_gaussian, stretched_gaussian]) # fit a Gaussian Mixture Model with two components clf = mixture.GaussianMixture(n_components=2, covariance_type='full') clf.fit(X_train) # display predicted scores by the model as a contour plot x = np.linspace(-20., 30.) y = np.linspace(-20., 40.) X, Y = np.meshgrid(x, y) XX = np.array([X.ravel(), Y.ravel()]).T Z = -clf.score_samples(XX) Z = Z.reshape(X.shape) CS = plt.contour(X, Y, Z, norm=LogNorm(vmin=1.0, vmax=1000.0), levels=np.logspace(0, 3, 10)) CB = plt.colorbar(CS, shrink=0.8, extend='both') plt.scatter(X_train[:, 0], X_train[:, 1], .8) plt.title('Negative log-likelihood predicted by a GMM') plt.axis('tight') plt.show() **Total running time of the script:** (0 minutes 0.213 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_gmm_pdf.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_gmm_pdf.ipynb `