Pushing the docs to dev/ for branch: master, commit a1aaccd8099affa968fe9d3bdffd4081e26fdbb7

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture\n\n\nThis example plots the ellipsoids obtained from a toy dataset (mixture of three\nGaussians) fitted by the ``BayesianGaussianMixture`` class models with a\nDirichlet distribution prior\n(``weight_concentration_prior_type='dirichlet_distribution'``) and a Dirichlet\nprocess prior (``weight_concentration_prior_type='dirichlet_process'``). On\neach figure, we plot the results for three different values of the weight\nconcentration prior.\n\nThe ``BayesianGaussianMixture`` class can adapt its number of mixture\ncomponentsautomatically. The parameter ``weight_concentration_prior`` has a\ndirect link with the resulting number of components with non-zero weights.\nSpecifying a low value for the concentration prior will make the model put most\nof the weight on few components set the remaining components weights very close\nto zero. High values of the concentration prior will allow a larger number of\ncomponents to be active in the mixture.\n\nThe Dirichlet process prior allows to define an infinite number of components\nand automatically selects the correct number of components: it activates a\ncomponent only if it is necessary.\n\nOn the contrary the classical finite mixture model with a Dirichlet\ndistribution prior will favor more uniformly weighted components and therefore\ntends to divide natural clusters into unnecessary sub-components.\n\n"
+        "\n# Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture\n\n\nThis example plots the ellipsoids obtained from a toy dataset (mixture of three\nGaussians) fitted by the ``BayesianGaussianMixture`` class models with a\nDirichlet distribution prior\n(``weight_concentration_prior_type='dirichlet_distribution'``) and a Dirichlet\nprocess prior (``weight_concentration_prior_type='dirichlet_process'``). On\neach figure, we plot the results for three different values of the weight\nconcentration prior.\n\nThe ``BayesianGaussianMixture`` class can adapt its number of mixture\ncomponents automatically. The parameter ``weight_concentration_prior`` has a\ndirect link with the resulting number of components with non-zero weights.\nSpecifying a low value for the concentration prior will make the model put most\nof the weight on few components set the remaining components weights very close\nto zero. High values of the concentration prior will allow a larger number of\ncomponents to be active in the mixture.\n\nThe Dirichlet process prior allows to define an infinite number of components\nand automatically selects the correct number of components: it activates a\ncomponent only if it is necessary.\n\nOn the contrary the classical finite mixture model with a Dirichlet\ndistribution prior will favor more uniformly weighted components and therefore\ntends to divide natural clusters into unnecessary sub-components.\n\n"
       ]
     },
     {

dev/_downloads/auto_examples_python.zip

@@ -12,7 +12,7 @@
 concentration prior.
 
 The ``BayesianGaussianMixture`` class can adapt its number of mixture
-componentsautomatically. The parameter ``weight_concentration_prior`` has a
+components automatically. The parameter ``weight_concentration_prior`` has a
 direct link with the resulting number of components with non-zero weights.
 Specifying a low value for the concentration prior will make the model put most
 of the weight on few components set the remaining components weights very close