Class VBGMMScikitsLearnNode

Variational Inference for the Gaussian Mixture Model

This node has been automatically generated by wrapping the ``sklearn.mixture.dpgmm.VBGMM`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Variational inference for a Gaussian mixture model probability
distribution. This class allows for easy and efficient inference
of an approximate posterior distribution over the parameters of a
Gaussian mixture model with a fixed number of components.

Initialization is with normally-distributed means and identity
covariance, for proper convergence.

Read more in the :ref:`User Guide <vbgmm>`.

**Parameters**

n_components: int, default 1
    Number of mixture components.

covariance_type: string, default 'diag'
    String describing the type of covariance parameters to
    use.  Must be one of 'spherical', 'tied', 'diag', 'full'.

alpha: float, default 1
    Real number representing the concentration parameter of
    the dirichlet distribution. Intuitively, the higher the
    value of alpha the more likely the variational mixture of
    Gaussians model will use all components it can.

tol : float, default 1e-3
    Convergence threshold.

n_iter : int, default 10
    Maximum number of iterations to perform before convergence.

params : string, default 'wmc'
    Controls which parameters are updated in the training
    process.  Can contain any combination of 'w' for weights,
    'm' for means, and 'c' for covars.

init_params : string, default 'wmc'
    Controls which parameters are updated in the initialization
    process.  Can contain any combination of 'w' for weights,
    'm' for means, and 'c' for covars.  Defaults to 'wmc'.

verbose : int, default 0
    Controls output verbosity.

**Attributes**

covariance_type : string
    String describing the type of covariance parameters used by
    the DP-GMM.  Must be one of 'spherical', 'tied', 'diag', 'full'.

n_features : int
    Dimensionality of the Gaussians.

n_components : int (read-only)
    Number of mixture components.

``weights_`` : array, shape (`n_components`,)
    Mixing weights for each mixture component.

``means_`` : array, shape (`n_components`, `n_features`)
    Mean parameters for each mixture component.

``precs_`` : array
    Precision (inverse covariance) parameters for each mixture
    component.  The shape depends on `covariance_type`::


        (`n_components`, 'n_features')                if 'spherical',
        (`n_features`, `n_features`)                  if 'tied',
        (`n_components`, `n_features`)                if 'diag',
        (`n_components`, `n_features`, `n_features`)  if 'full'

``converged_`` : bool
    True when convergence was reached in fit(), False
    otherwise.

See Also

GMM : Finite Gaussian mixture model fit with EM
DPGMM : Infinite Gaussian mixture model, using the dirichlet
    process, fit with a variational algorithm

Overrides: object.__init__

Predict label for data.

This node has been automatically generated by wrapping the sklearn.mixture.dpgmm.VBGMM class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like, shape = [n_samples, n_features]

Returns

C : array, shape = (n_samples,) component memberships

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Estimate model parameters with the EM algorithm.

This node has been automatically generated by wrapping the sklearn.mixture.dpgmm.VBGMM class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

A initialization step is performed before entering the expectation-maximization (EM) algorithm. If you want to avoid this step, set the keyword argument init_params to the empty string '' when creating the GMM object. Likewise, if you would like just to do an initialization, set n_iter=0.

Parameters

X : array_like, shape (n, n_features)

List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

self

Overrides: Node.stop_training