Class DPGMMScikitsLearnNode

Variational Inference for the Infinite Gaussian Mixture Model.

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

DPGMM stands for Dirichlet Process Gaussian Mixture Model, and it
is an infinite mixture model with the Dirichlet Process as a prior
distribution on the number of clusters. In practice the
approximate inference algorithm uses a truncated distribution with
a fixed maximum number of components, but almost always the number
of components actually used depends on the data.

Stick-breaking Representation of 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 variable number of
components (smaller than the truncation parameter n_components).

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

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

**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 process. Intuitively, the Dirichlet Process
    is as likely to start a new cluster for a point as it is
    to add that point to a cluster with alpha elements. A
    higher alpha means more clusters, as the expected number
    of clusters is ``alpha*log(N)``.

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_components : int
    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

VBGMM : Finite Gaussian mixture model fit with a variational
    algorithm, better for situations where there might be too little
    data to get a good estimate of the covariance matrix.

Overrides: object.__init__

Predict label for data.

This node has been automatically generated by wrapping the sklearn.mixture.dpgmm.DPGMM 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.DPGMM 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