Class GMMScikitsLearnNode

Gaussian Mixture Model

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

Representation of a Gaussian mixture model probability distribution.
This class allows for easy evaluation of, sampling from, and
maximum-likelihood estimation of the parameters of a GMM distribution.

Initializes parameters such that every mixture component has zero
mean and identity covariance.

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

**Parameters**

n_components : int, optional
    Number of mixture components. Defaults to 1.

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

random_state: RandomState or an int seed (None by default)
    A random number generator instance

min_covar : float, optional
    Floor on the diagonal of the covariance matrix to prevent
    overfitting.  Defaults to 1e-3.

tol : float, optional
    Convergence threshold. EM iterations will stop when average
    gain in log-likelihood is below this threshold.  Defaults to 1e-3.

n_iter : int, optional
    Number of EM iterations to perform.

n_init : int, optional
    Number of initializations to perform. the best results is kept

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

init_params : string, optional
    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
    Enable verbose output. If 1 then it always prints the current
    initialization and iteration step. If greater than 1 then
    it prints additionally the change and time needed for each step.

**Attributes**

``weights_`` : array, shape (`n_components`,)
    This attribute stores the mixing weights for each mixture component.

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

``covars_`` : array
    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


DPGMM : Infinite gaussian mixture model, using the dirichlet
    process, fit with a variational algorithm


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.

**Examples**


>>> import numpy as np
>>> from sklearn import mixture
>>> np.random.seed(1)
>>> g = mixture.GMM(n_components=2)
>>> # Generate random observations with two modes centered on 0
>>> # and 10 to use for training.
>>> obs = np.concatenate((np.random.randn(100, 1),
...                       10 + np.random.randn(300, 1)))
>>> g.fit(obs) # doctest: +NORMALIZE_WHITESPACE
GMM(covariance_type='diag', init_params='wmc', min_covar=0.001,
        n_components=2, n_init=1, n_iter=100, params='wmc',
        random_state=None, thresh=None, tol=0.001, verbose=0)
>>> np.round(g.weights_, 2)
array([ 0.75,  0.25])
>>> np.round(g.means_, 2)
array([[ 10.05],
       [  0.06]])
>>> np.round(g.covars_, 2) #doctest: +SKIP
array([[[ 1.02]],
       [[ 0.96]]])
>>> g.predict([[0], [2], [9], [10]]) #doctest: +ELLIPSIS
array([1, 1, 0, 0]...)
>>> np.round(g.score([[0], [2], [9], [10]]), 2)
array([-2.19, -4.58, -1.75, -1.21])
>>> # Refit the model on new data (initial parameters remain the
>>> # same), this time with an even split between the two modes.
>>> g.fit(20 * [[0]] +  20 * [[10]]) # doctest: +NORMALIZE_WHITESPACE
GMM(covariance_type='diag', init_params='wmc', min_covar=0.001,
        n_components=2, n_init=1, n_iter=100, params='wmc',
        random_state=None, thresh=None, tol=0.001, verbose=0)
>>> np.round(g.weights_, 2)
array([ 0.5,  0.5])

Overrides: object.__init__

Predict label for data.

This node has been automatically generated by wrapping the sklearn.mixture.gmm.GMM 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.gmm.GMM 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