.. _example_mixture_plot_gmm_sin.py: ================================= Gaussian Mixture Model Sine Curve ================================= This example highlights the advantages of the Dirichlet Process: complexity control and dealing with sparse data. The dataset is formed by 100 points loosely spaced following a noisy sine curve. The fit by the GMM class, using the expectation-maximization algorithm to fit a mixture of 10 Gaussian components, finds too-small components and very little structure. The fits by the Dirichlet process, however, show that the model can either learn a global structure for the data (small alpha) or easily interpolate to finding relevant local structure (large alpha), never falling into the problems shown by the GMM class. .. image:: images/plot_gmm_sin_001.png :align: center **Python source code:** :download:`plot_gmm_sin.py ` .. literalinclude:: plot_gmm_sin.py :lines: 16- **Total running time of the example:** 0.44 seconds ( 0 minutes 0.44 seconds)