889 lines
33 KiB
Python
889 lines
33 KiB
Python
"""Bayesian Gaussian Mixture Model."""
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# Author: Wei Xue <xuewei4d@gmail.com>
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# Thierry Guillemot <thierry.guillemot.work@gmail.com>
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# License: BSD 3 clause
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import math
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from numbers import Real
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import numpy as np
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from scipy.special import betaln, digamma, gammaln
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from ..utils import check_array
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from ..utils._param_validation import Interval, StrOptions
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from ._base import BaseMixture, _check_shape
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from ._gaussian_mixture import (
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_check_precision_matrix,
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_check_precision_positivity,
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_compute_log_det_cholesky,
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_compute_precision_cholesky,
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_estimate_gaussian_parameters,
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_estimate_log_gaussian_prob,
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)
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def _log_dirichlet_norm(dirichlet_concentration):
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"""Compute the log of the Dirichlet distribution normalization term.
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Parameters
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----------
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dirichlet_concentration : array-like of shape (n_samples,)
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The parameters values of the Dirichlet distribution.
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Returns
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-------
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log_dirichlet_norm : float
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The log normalization of the Dirichlet distribution.
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"""
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return gammaln(np.sum(dirichlet_concentration)) - np.sum(
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gammaln(dirichlet_concentration)
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)
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def _log_wishart_norm(degrees_of_freedom, log_det_precisions_chol, n_features):
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"""Compute the log of the Wishart distribution normalization term.
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Parameters
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----------
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degrees_of_freedom : array-like of shape (n_components,)
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The number of degrees of freedom on the covariance Wishart
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distributions.
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log_det_precision_chol : array-like of shape (n_components,)
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The determinant of the precision matrix for each component.
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n_features : int
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The number of features.
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Return
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------
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log_wishart_norm : array-like of shape (n_components,)
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The log normalization of the Wishart distribution.
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"""
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# To simplify the computation we have removed the np.log(np.pi) term
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return -(
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degrees_of_freedom * log_det_precisions_chol
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+ degrees_of_freedom * n_features * 0.5 * math.log(2.0)
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+ np.sum(
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gammaln(0.5 * (degrees_of_freedom - np.arange(n_features)[:, np.newaxis])),
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0,
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)
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)
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class BayesianGaussianMixture(BaseMixture):
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"""Variational Bayesian estimation of a Gaussian mixture.
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This class allows to infer an approximate posterior distribution over the
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parameters of a Gaussian mixture distribution. The effective number of
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components can be inferred from the data.
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This class implements two types of prior for the weights distribution: a
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finite mixture model with Dirichlet distribution and an infinite mixture
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model with the Dirichlet Process. In practice Dirichlet Process inference
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algorithm is approximated and uses a truncated distribution with a fixed
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maximum number of components (called the Stick-breaking representation).
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The number of components actually used almost always depends on the data.
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.. versionadded:: 0.18
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Read more in the :ref:`User Guide <bgmm>`.
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Parameters
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----------
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n_components : int, default=1
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The number of mixture components. Depending on the data and the value
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of the `weight_concentration_prior` the model can decide to not use
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all the components by setting some component `weights_` to values very
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close to zero. The number of effective components is therefore smaller
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than n_components.
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covariance_type : {'full', 'tied', 'diag', 'spherical'}, default='full'
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String describing the type of covariance parameters to use.
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Must be one of::
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'full' (each component has its own general covariance matrix),
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'tied' (all components share the same general covariance matrix),
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'diag' (each component has its own diagonal covariance matrix),
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'spherical' (each component has its own single variance).
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tol : float, default=1e-3
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The convergence threshold. EM iterations will stop when the
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lower bound average gain on the likelihood (of the training data with
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respect to the model) is below this threshold.
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reg_covar : float, default=1e-6
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Non-negative regularization added to the diagonal of covariance.
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Allows to assure that the covariance matrices are all positive.
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max_iter : int, default=100
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The number of EM iterations to perform.
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n_init : int, default=1
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The number of initializations to perform. The result with the highest
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lower bound value on the likelihood is kept.
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init_params : {'kmeans', 'k-means++', 'random', 'random_from_data'}, \
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default='kmeans'
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The method used to initialize the weights, the means and the
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covariances.
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String must be one of:
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'kmeans' : responsibilities are initialized using kmeans.
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'k-means++' : use the k-means++ method to initialize.
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'random' : responsibilities are initialized randomly.
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'random_from_data' : initial means are randomly selected data points.
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.. versionchanged:: v1.1
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`init_params` now accepts 'random_from_data' and 'k-means++' as
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initialization methods.
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weight_concentration_prior_type : {'dirichlet_process', 'dirichlet_distribution'}, \
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default='dirichlet_process'
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String describing the type of the weight concentration prior.
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weight_concentration_prior : float or None, default=None
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The dirichlet concentration of each component on the weight
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distribution (Dirichlet). This is commonly called gamma in the
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literature. The higher concentration puts more mass in
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the center and will lead to more components being active, while a lower
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concentration parameter will lead to more mass at the edge of the
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mixture weights simplex. The value of the parameter must be greater
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than 0. If it is None, it's set to ``1. / n_components``.
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mean_precision_prior : float or None, default=None
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The precision prior on the mean distribution (Gaussian).
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Controls the extent of where means can be placed. Larger
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values concentrate the cluster means around `mean_prior`.
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The value of the parameter must be greater than 0.
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If it is None, it is set to 1.
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mean_prior : array-like, shape (n_features,), default=None
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The prior on the mean distribution (Gaussian).
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If it is None, it is set to the mean of X.
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degrees_of_freedom_prior : float or None, default=None
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The prior of the number of degrees of freedom on the covariance
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distributions (Wishart). If it is None, it's set to `n_features`.
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covariance_prior : float or array-like, default=None
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The prior on the covariance distribution (Wishart).
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If it is None, the emiprical covariance prior is initialized using the
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covariance of X. The shape depends on `covariance_type`::
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(n_features, n_features) if 'full',
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(n_features, n_features) if 'tied',
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(n_features) if 'diag',
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float if 'spherical'
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random_state : int, RandomState instance or None, default=None
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Controls the random seed given to the method chosen to initialize the
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parameters (see `init_params`).
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In addition, it controls the generation of random samples from the
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fitted distribution (see the method `sample`).
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Pass an int for reproducible output across multiple function calls.
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See :term:`Glossary <random_state>`.
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warm_start : bool, default=False
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If 'warm_start' is True, the solution of the last fitting is used as
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initialization for the next call of fit(). This can speed up
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convergence when fit is called several times on similar problems.
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See :term:`the Glossary <warm_start>`.
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verbose : int, default=0
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Enable verbose output. If 1 then it prints the current
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initialization and each iteration step. If greater than 1 then
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it prints also the log probability and the time needed
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for each step.
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verbose_interval : int, default=10
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Number of iteration done before the next print.
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Attributes
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----------
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weights_ : array-like of shape (n_components,)
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The weights of each mixture components.
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means_ : array-like of shape (n_components, n_features)
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The mean of each mixture component.
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covariances_ : array-like
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The covariance of each mixture component.
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The shape depends on `covariance_type`::
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(n_components,) if 'spherical',
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(n_features, n_features) if 'tied',
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(n_components, n_features) if 'diag',
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(n_components, n_features, n_features) if 'full'
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precisions_ : array-like
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The precision matrices for each component in the mixture. A precision
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matrix is the inverse of a covariance matrix. A covariance matrix is
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symmetric positive definite so the mixture of Gaussian can be
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equivalently parameterized by the precision matrices. Storing the
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precision matrices instead of the covariance matrices makes it more
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efficient to compute the log-likelihood of new samples at test time.
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The shape depends on ``covariance_type``::
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(n_components,) if 'spherical',
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(n_features, n_features) if 'tied',
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(n_components, n_features) if 'diag',
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(n_components, n_features, n_features) if 'full'
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precisions_cholesky_ : array-like
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The cholesky decomposition of the precision matrices of each mixture
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component. A precision matrix is the inverse of a covariance matrix.
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A covariance matrix is symmetric positive definite so the mixture of
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Gaussian can be equivalently parameterized by the precision matrices.
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Storing the precision matrices instead of the covariance matrices makes
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it more efficient to compute the log-likelihood of new samples at test
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time. The shape depends on ``covariance_type``::
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(n_components,) if 'spherical',
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(n_features, n_features) if 'tied',
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(n_components, n_features) if 'diag',
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(n_components, n_features, n_features) if 'full'
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converged_ : bool
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True when convergence was reached in fit(), False otherwise.
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n_iter_ : int
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Number of step used by the best fit of inference to reach the
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convergence.
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lower_bound_ : float
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Lower bound value on the model evidence (of the training data) of the
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best fit of inference.
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weight_concentration_prior_ : tuple or float
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The dirichlet concentration of each component on the weight
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distribution (Dirichlet). The type depends on
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``weight_concentration_prior_type``::
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(float, float) if 'dirichlet_process' (Beta parameters),
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float if 'dirichlet_distribution' (Dirichlet parameters).
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The higher concentration puts more mass in
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the center and will lead to more components being active, while a lower
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concentration parameter will lead to more mass at the edge of the
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simplex.
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weight_concentration_ : array-like of shape (n_components,)
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The dirichlet concentration of each component on the weight
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distribution (Dirichlet).
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mean_precision_prior_ : float
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The precision prior on the mean distribution (Gaussian).
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Controls the extent of where means can be placed.
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Larger values concentrate the cluster means around `mean_prior`.
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If mean_precision_prior is set to None, `mean_precision_prior_` is set
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to 1.
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mean_precision_ : array-like of shape (n_components,)
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The precision of each components on the mean distribution (Gaussian).
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mean_prior_ : array-like of shape (n_features,)
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The prior on the mean distribution (Gaussian).
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degrees_of_freedom_prior_ : float
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The prior of the number of degrees of freedom on the covariance
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distributions (Wishart).
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degrees_of_freedom_ : array-like of shape (n_components,)
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The number of degrees of freedom of each components in the model.
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covariance_prior_ : float or array-like
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The prior on the covariance distribution (Wishart).
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The shape depends on `covariance_type`::
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(n_features, n_features) if 'full',
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(n_features, n_features) if 'tied',
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(n_features) if 'diag',
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float if 'spherical'
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Names of features seen during :term:`fit`. Defined only when `X`
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has feature names that are all strings.
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.. versionadded:: 1.0
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See Also
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--------
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GaussianMixture : Finite Gaussian mixture fit with EM.
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References
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----------
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.. [1] `Bishop, Christopher M. (2006). "Pattern recognition and machine
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learning". Vol. 4 No. 4. New York: Springer.
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<https://www.springer.com/kr/book/9780387310732>`_
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.. [2] `Hagai Attias. (2000). "A Variational Bayesian Framework for
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Graphical Models". In Advances in Neural Information Processing
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Systems 12.
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<https://citeseerx.ist.psu.edu/doc_view/pid/ee844fd96db7041a9681b5a18bff008912052c7e>`_
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.. [3] `Blei, David M. and Michael I. Jordan. (2006). "Variational
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inference for Dirichlet process mixtures". Bayesian analysis 1.1
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<https://www.cs.princeton.edu/courses/archive/fall11/cos597C/reading/BleiJordan2005.pdf>`_
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.mixture import BayesianGaussianMixture
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>>> X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [12, 4], [10, 7]])
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>>> bgm = BayesianGaussianMixture(n_components=2, random_state=42).fit(X)
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>>> bgm.means_
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array([[2.49... , 2.29...],
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[8.45..., 4.52... ]])
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>>> bgm.predict([[0, 0], [9, 3]])
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array([0, 1])
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"""
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_parameter_constraints: dict = {
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**BaseMixture._parameter_constraints,
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"covariance_type": [StrOptions({"spherical", "tied", "diag", "full"})],
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"weight_concentration_prior_type": [
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StrOptions({"dirichlet_process", "dirichlet_distribution"})
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],
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"weight_concentration_prior": [
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None,
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Interval(Real, 0.0, None, closed="neither"),
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],
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"mean_precision_prior": [None, Interval(Real, 0.0, None, closed="neither")],
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"mean_prior": [None, "array-like"],
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"degrees_of_freedom_prior": [None, Interval(Real, 0.0, None, closed="neither")],
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"covariance_prior": [
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None,
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"array-like",
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Interval(Real, 0.0, None, closed="neither"),
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],
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}
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def __init__(
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self,
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*,
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n_components=1,
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covariance_type="full",
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tol=1e-3,
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reg_covar=1e-6,
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max_iter=100,
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n_init=1,
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init_params="kmeans",
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weight_concentration_prior_type="dirichlet_process",
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weight_concentration_prior=None,
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mean_precision_prior=None,
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mean_prior=None,
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degrees_of_freedom_prior=None,
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covariance_prior=None,
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random_state=None,
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warm_start=False,
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verbose=0,
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verbose_interval=10,
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):
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super().__init__(
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n_components=n_components,
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tol=tol,
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reg_covar=reg_covar,
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max_iter=max_iter,
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n_init=n_init,
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init_params=init_params,
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random_state=random_state,
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warm_start=warm_start,
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verbose=verbose,
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verbose_interval=verbose_interval,
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)
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self.covariance_type = covariance_type
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self.weight_concentration_prior_type = weight_concentration_prior_type
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self.weight_concentration_prior = weight_concentration_prior
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self.mean_precision_prior = mean_precision_prior
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self.mean_prior = mean_prior
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self.degrees_of_freedom_prior = degrees_of_freedom_prior
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self.covariance_prior = covariance_prior
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def _check_parameters(self, X):
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"""Check that the parameters are well defined.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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"""
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self._check_weights_parameters()
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self._check_means_parameters(X)
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self._check_precision_parameters(X)
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self._checkcovariance_prior_parameter(X)
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def _check_weights_parameters(self):
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"""Check the parameter of the Dirichlet distribution."""
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if self.weight_concentration_prior is None:
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self.weight_concentration_prior_ = 1.0 / self.n_components
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else:
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self.weight_concentration_prior_ = self.weight_concentration_prior
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def _check_means_parameters(self, X):
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"""Check the parameters of the Gaussian distribution.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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"""
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_, n_features = X.shape
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if self.mean_precision_prior is None:
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self.mean_precision_prior_ = 1.0
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else:
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self.mean_precision_prior_ = self.mean_precision_prior
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if self.mean_prior is None:
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self.mean_prior_ = X.mean(axis=0)
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else:
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self.mean_prior_ = check_array(
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self.mean_prior, dtype=[np.float64, np.float32], ensure_2d=False
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)
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_check_shape(self.mean_prior_, (n_features,), "means")
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def _check_precision_parameters(self, X):
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"""Check the prior parameters of the precision distribution.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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"""
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_, n_features = X.shape
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if self.degrees_of_freedom_prior is None:
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self.degrees_of_freedom_prior_ = n_features
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elif self.degrees_of_freedom_prior > n_features - 1.0:
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self.degrees_of_freedom_prior_ = self.degrees_of_freedom_prior
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else:
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raise ValueError(
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"The parameter 'degrees_of_freedom_prior' "
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"should be greater than %d, but got %.3f."
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% (n_features - 1, self.degrees_of_freedom_prior)
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)
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def _checkcovariance_prior_parameter(self, X):
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"""Check the `covariance_prior_`.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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"""
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_, n_features = X.shape
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if self.covariance_prior is None:
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self.covariance_prior_ = {
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"full": np.atleast_2d(np.cov(X.T)),
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"tied": np.atleast_2d(np.cov(X.T)),
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"diag": np.var(X, axis=0, ddof=1),
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"spherical": np.var(X, axis=0, ddof=1).mean(),
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}[self.covariance_type]
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elif self.covariance_type in ["full", "tied"]:
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self.covariance_prior_ = check_array(
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self.covariance_prior, dtype=[np.float64, np.float32], ensure_2d=False
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)
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_check_shape(
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self.covariance_prior_,
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(n_features, n_features),
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"%s covariance_prior" % self.covariance_type,
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)
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_check_precision_matrix(self.covariance_prior_, self.covariance_type)
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elif self.covariance_type == "diag":
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self.covariance_prior_ = check_array(
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self.covariance_prior, dtype=[np.float64, np.float32], ensure_2d=False
|
|
)
|
|
_check_shape(
|
|
self.covariance_prior_,
|
|
(n_features,),
|
|
"%s covariance_prior" % self.covariance_type,
|
|
)
|
|
_check_precision_positivity(self.covariance_prior_, self.covariance_type)
|
|
# spherical case
|
|
else:
|
|
self.covariance_prior_ = self.covariance_prior
|
|
|
|
def _initialize(self, X, resp):
|
|
"""Initialization of the mixture parameters.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
|
|
resp : array-like of shape (n_samples, n_components)
|
|
"""
|
|
nk, xk, sk = _estimate_gaussian_parameters(
|
|
X, resp, self.reg_covar, self.covariance_type
|
|
)
|
|
|
|
self._estimate_weights(nk)
|
|
self._estimate_means(nk, xk)
|
|
self._estimate_precisions(nk, xk, sk)
|
|
|
|
def _estimate_weights(self, nk):
|
|
"""Estimate the parameters of the Dirichlet distribution.
|
|
|
|
Parameters
|
|
----------
|
|
nk : array-like of shape (n_components,)
|
|
"""
|
|
if self.weight_concentration_prior_type == "dirichlet_process":
|
|
# For dirichlet process weight_concentration will be a tuple
|
|
# containing the two parameters of the beta distribution
|
|
self.weight_concentration_ = (
|
|
1.0 + nk,
|
|
(
|
|
self.weight_concentration_prior_
|
|
+ np.hstack((np.cumsum(nk[::-1])[-2::-1], 0))
|
|
),
|
|
)
|
|
else:
|
|
# case Variational Gaussian mixture with dirichlet distribution
|
|
self.weight_concentration_ = self.weight_concentration_prior_ + nk
|
|
|
|
def _estimate_means(self, nk, xk):
|
|
"""Estimate the parameters of the Gaussian distribution.
|
|
|
|
Parameters
|
|
----------
|
|
nk : array-like of shape (n_components,)
|
|
|
|
xk : array-like of shape (n_components, n_features)
|
|
"""
|
|
self.mean_precision_ = self.mean_precision_prior_ + nk
|
|
self.means_ = (
|
|
self.mean_precision_prior_ * self.mean_prior_ + nk[:, np.newaxis] * xk
|
|
) / self.mean_precision_[:, np.newaxis]
|
|
|
|
def _estimate_precisions(self, nk, xk, sk):
|
|
"""Estimate the precisions parameters of the precision distribution.
|
|
|
|
Parameters
|
|
----------
|
|
nk : array-like of shape (n_components,)
|
|
|
|
xk : array-like of shape (n_components, n_features)
|
|
|
|
sk : array-like
|
|
The shape depends of `covariance_type`:
|
|
'full' : (n_components, n_features, n_features)
|
|
'tied' : (n_features, n_features)
|
|
'diag' : (n_components, n_features)
|
|
'spherical' : (n_components,)
|
|
"""
|
|
{
|
|
"full": self._estimate_wishart_full,
|
|
"tied": self._estimate_wishart_tied,
|
|
"diag": self._estimate_wishart_diag,
|
|
"spherical": self._estimate_wishart_spherical,
|
|
}[self.covariance_type](nk, xk, sk)
|
|
|
|
self.precisions_cholesky_ = _compute_precision_cholesky(
|
|
self.covariances_, self.covariance_type
|
|
)
|
|
|
|
def _estimate_wishart_full(self, nk, xk, sk):
|
|
"""Estimate the full Wishart distribution parameters.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
|
|
nk : array-like of shape (n_components,)
|
|
|
|
xk : array-like of shape (n_components, n_features)
|
|
|
|
sk : array-like of shape (n_components, n_features, n_features)
|
|
"""
|
|
_, n_features = xk.shape
|
|
|
|
# Warning : in some Bishop book, there is a typo on the formula 10.63
|
|
# `degrees_of_freedom_k = degrees_of_freedom_0 + Nk` is
|
|
# the correct formula
|
|
self.degrees_of_freedom_ = self.degrees_of_freedom_prior_ + nk
|
|
|
|
self.covariances_ = np.empty((self.n_components, n_features, n_features))
|
|
|
|
for k in range(self.n_components):
|
|
diff = xk[k] - self.mean_prior_
|
|
self.covariances_[k] = (
|
|
self.covariance_prior_
|
|
+ nk[k] * sk[k]
|
|
+ nk[k]
|
|
* self.mean_precision_prior_
|
|
/ self.mean_precision_[k]
|
|
* np.outer(diff, diff)
|
|
)
|
|
|
|
# Contrary to the original bishop book, we normalize the covariances
|
|
self.covariances_ /= self.degrees_of_freedom_[:, np.newaxis, np.newaxis]
|
|
|
|
def _estimate_wishart_tied(self, nk, xk, sk):
|
|
"""Estimate the tied Wishart distribution parameters.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
|
|
nk : array-like of shape (n_components,)
|
|
|
|
xk : array-like of shape (n_components, n_features)
|
|
|
|
sk : array-like of shape (n_features, n_features)
|
|
"""
|
|
_, n_features = xk.shape
|
|
|
|
# Warning : in some Bishop book, there is a typo on the formula 10.63
|
|
# `degrees_of_freedom_k = degrees_of_freedom_0 + Nk`
|
|
# is the correct formula
|
|
self.degrees_of_freedom_ = (
|
|
self.degrees_of_freedom_prior_ + nk.sum() / self.n_components
|
|
)
|
|
|
|
diff = xk - self.mean_prior_
|
|
self.covariances_ = (
|
|
self.covariance_prior_
|
|
+ sk * nk.sum() / self.n_components
|
|
+ self.mean_precision_prior_
|
|
/ self.n_components
|
|
* np.dot((nk / self.mean_precision_) * diff.T, diff)
|
|
)
|
|
|
|
# Contrary to the original bishop book, we normalize the covariances
|
|
self.covariances_ /= self.degrees_of_freedom_
|
|
|
|
def _estimate_wishart_diag(self, nk, xk, sk):
|
|
"""Estimate the diag Wishart distribution parameters.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
|
|
nk : array-like of shape (n_components,)
|
|
|
|
xk : array-like of shape (n_components, n_features)
|
|
|
|
sk : array-like of shape (n_components, n_features)
|
|
"""
|
|
_, n_features = xk.shape
|
|
|
|
# Warning : in some Bishop book, there is a typo on the formula 10.63
|
|
# `degrees_of_freedom_k = degrees_of_freedom_0 + Nk`
|
|
# is the correct formula
|
|
self.degrees_of_freedom_ = self.degrees_of_freedom_prior_ + nk
|
|
|
|
diff = xk - self.mean_prior_
|
|
self.covariances_ = self.covariance_prior_ + nk[:, np.newaxis] * (
|
|
sk
|
|
+ (self.mean_precision_prior_ / self.mean_precision_)[:, np.newaxis]
|
|
* np.square(diff)
|
|
)
|
|
|
|
# Contrary to the original bishop book, we normalize the covariances
|
|
self.covariances_ /= self.degrees_of_freedom_[:, np.newaxis]
|
|
|
|
def _estimate_wishart_spherical(self, nk, xk, sk):
|
|
"""Estimate the spherical Wishart distribution parameters.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
|
|
nk : array-like of shape (n_components,)
|
|
|
|
xk : array-like of shape (n_components, n_features)
|
|
|
|
sk : array-like of shape (n_components,)
|
|
"""
|
|
_, n_features = xk.shape
|
|
|
|
# Warning : in some Bishop book, there is a typo on the formula 10.63
|
|
# `degrees_of_freedom_k = degrees_of_freedom_0 + Nk`
|
|
# is the correct formula
|
|
self.degrees_of_freedom_ = self.degrees_of_freedom_prior_ + nk
|
|
|
|
diff = xk - self.mean_prior_
|
|
self.covariances_ = self.covariance_prior_ + nk * (
|
|
sk
|
|
+ self.mean_precision_prior_
|
|
/ self.mean_precision_
|
|
* np.mean(np.square(diff), 1)
|
|
)
|
|
|
|
# Contrary to the original bishop book, we normalize the covariances
|
|
self.covariances_ /= self.degrees_of_freedom_
|
|
|
|
def _m_step(self, X, log_resp):
|
|
"""M step.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
|
|
log_resp : array-like of shape (n_samples, n_components)
|
|
Logarithm of the posterior probabilities (or responsibilities) of
|
|
the point of each sample in X.
|
|
"""
|
|
n_samples, _ = X.shape
|
|
|
|
nk, xk, sk = _estimate_gaussian_parameters(
|
|
X, np.exp(log_resp), self.reg_covar, self.covariance_type
|
|
)
|
|
self._estimate_weights(nk)
|
|
self._estimate_means(nk, xk)
|
|
self._estimate_precisions(nk, xk, sk)
|
|
|
|
def _estimate_log_weights(self):
|
|
if self.weight_concentration_prior_type == "dirichlet_process":
|
|
digamma_sum = digamma(
|
|
self.weight_concentration_[0] + self.weight_concentration_[1]
|
|
)
|
|
digamma_a = digamma(self.weight_concentration_[0])
|
|
digamma_b = digamma(self.weight_concentration_[1])
|
|
return (
|
|
digamma_a
|
|
- digamma_sum
|
|
+ np.hstack((0, np.cumsum(digamma_b - digamma_sum)[:-1]))
|
|
)
|
|
else:
|
|
# case Variational Gaussian mixture with dirichlet distribution
|
|
return digamma(self.weight_concentration_) - digamma(
|
|
np.sum(self.weight_concentration_)
|
|
)
|
|
|
|
def _estimate_log_prob(self, X):
|
|
_, n_features = X.shape
|
|
# We remove `n_features * np.log(self.degrees_of_freedom_)` because
|
|
# the precision matrix is normalized
|
|
log_gauss = _estimate_log_gaussian_prob(
|
|
X, self.means_, self.precisions_cholesky_, self.covariance_type
|
|
) - 0.5 * n_features * np.log(self.degrees_of_freedom_)
|
|
|
|
log_lambda = n_features * np.log(2.0) + np.sum(
|
|
digamma(
|
|
0.5
|
|
* (self.degrees_of_freedom_ - np.arange(0, n_features)[:, np.newaxis])
|
|
),
|
|
0,
|
|
)
|
|
|
|
return log_gauss + 0.5 * (log_lambda - n_features / self.mean_precision_)
|
|
|
|
def _compute_lower_bound(self, log_resp, log_prob_norm):
|
|
"""Estimate the lower bound of the model.
|
|
|
|
The lower bound on the likelihood (of the training data with respect to
|
|
the model) is used to detect the convergence and has to increase at
|
|
each iteration.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
|
|
log_resp : array, shape (n_samples, n_components)
|
|
Logarithm of the posterior probabilities (or responsibilities) of
|
|
the point of each sample in X.
|
|
|
|
log_prob_norm : float
|
|
Logarithm of the probability of each sample in X.
|
|
|
|
Returns
|
|
-------
|
|
lower_bound : float
|
|
"""
|
|
# Contrary to the original formula, we have done some simplification
|
|
# and removed all the constant terms.
|
|
(n_features,) = self.mean_prior_.shape
|
|
|
|
# We removed `.5 * n_features * np.log(self.degrees_of_freedom_)`
|
|
# because the precision matrix is normalized.
|
|
log_det_precisions_chol = _compute_log_det_cholesky(
|
|
self.precisions_cholesky_, self.covariance_type, n_features
|
|
) - 0.5 * n_features * np.log(self.degrees_of_freedom_)
|
|
|
|
if self.covariance_type == "tied":
|
|
log_wishart = self.n_components * np.float64(
|
|
_log_wishart_norm(
|
|
self.degrees_of_freedom_, log_det_precisions_chol, n_features
|
|
)
|
|
)
|
|
else:
|
|
log_wishart = np.sum(
|
|
_log_wishart_norm(
|
|
self.degrees_of_freedom_, log_det_precisions_chol, n_features
|
|
)
|
|
)
|
|
|
|
if self.weight_concentration_prior_type == "dirichlet_process":
|
|
log_norm_weight = -np.sum(
|
|
betaln(self.weight_concentration_[0], self.weight_concentration_[1])
|
|
)
|
|
else:
|
|
log_norm_weight = _log_dirichlet_norm(self.weight_concentration_)
|
|
|
|
return (
|
|
-np.sum(np.exp(log_resp) * log_resp)
|
|
- log_wishart
|
|
- log_norm_weight
|
|
- 0.5 * n_features * np.sum(np.log(self.mean_precision_))
|
|
)
|
|
|
|
def _get_parameters(self):
|
|
return (
|
|
self.weight_concentration_,
|
|
self.mean_precision_,
|
|
self.means_,
|
|
self.degrees_of_freedom_,
|
|
self.covariances_,
|
|
self.precisions_cholesky_,
|
|
)
|
|
|
|
def _set_parameters(self, params):
|
|
(
|
|
self.weight_concentration_,
|
|
self.mean_precision_,
|
|
self.means_,
|
|
self.degrees_of_freedom_,
|
|
self.covariances_,
|
|
self.precisions_cholesky_,
|
|
) = params
|
|
|
|
# Weights computation
|
|
if self.weight_concentration_prior_type == "dirichlet_process":
|
|
weight_dirichlet_sum = (
|
|
self.weight_concentration_[0] + self.weight_concentration_[1]
|
|
)
|
|
tmp = self.weight_concentration_[1] / weight_dirichlet_sum
|
|
self.weights_ = (
|
|
self.weight_concentration_[0]
|
|
/ weight_dirichlet_sum
|
|
* np.hstack((1, np.cumprod(tmp[:-1])))
|
|
)
|
|
self.weights_ /= np.sum(self.weights_)
|
|
else:
|
|
self.weights_ = self.weight_concentration_ / np.sum(
|
|
self.weight_concentration_
|
|
)
|
|
|
|
# Precisions matrices computation
|
|
if self.covariance_type == "full":
|
|
self.precisions_ = np.array(
|
|
[
|
|
np.dot(prec_chol, prec_chol.T)
|
|
for prec_chol in self.precisions_cholesky_
|
|
]
|
|
)
|
|
|
|
elif self.covariance_type == "tied":
|
|
self.precisions_ = np.dot(
|
|
self.precisions_cholesky_, self.precisions_cholesky_.T
|
|
)
|
|
else:
|
|
self.precisions_ = self.precisions_cholesky_**2
|