Class GMMScikitsLearnNode

Gaussian Mixture Model This node has been automatically generated by wrapping the scikits.learn.mixture.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.

Parameters

n_states : int, optional

Number of mixture components. Defaults to 1.

cvtype : string (read-only), optional

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

Attributes

cvtype : string (read-only)

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

n_features : int

Dimensionality of the Gaussians.

n_states : int (read-only)

Number of mixture components.

weights : array, shape (n_states,)

Mixing weights for each mixture component.

means : array, shape (n_states, n_features)

Mean parameters for each mixture component.

covars : array

Covariance parameters for each mixture component. The shape depends on cvtype:

• (n_states,) if 'spherical',
• (n_features, n_features) if 'tied',
• (n_states, n_features) if 'diag',
• (n_states, n_features, n_features) if 'full'

converged_ : bool

True when convergence was reached in fit(), False otherwise.

Methods

decode(X)

Find most likely mixture components for each point in X.

eval(X)

Compute the log likelihood of X under the model and the posterior distribution over mixture components.

fit(X)

Estimate model parameters from X using the EM algorithm.

predict(X)

Like decode, find most likely mixtures components for each observation in X.

rvs(n=1)

Generate n samples from the model.

score(X)

Compute the log likelihood of X under the model.

Examples

>>> import numpy as np
>>> from scikits.learn import mixture
>>> g = mixture.GMM(n_states=2)

>>> # Generate random observations with two modes centered on 0
>>> # and 10 to use for training.
>>> np.random.seed(0)
>>> obs = np.concatenate((np.random.randn(100, 1),
...                       10 + np.random.randn(300, 1)))
>>> g.fit(obs)
GMM(cvtype='diag', n_states=2)
>>> g.weights
array([ 0.25,  0.75])
>>> g.means
array([[ 0.05980802],
       [ 9.94199467]])
>>> g.covars
[array([[ 1.01682662]]), array([[ 0.96080513]])]
>>> np.round(g.weights, 2)
array([ 0.25,  0.75])
>>> np.round(g.means, 2)
array([[ 0.06],
       [ 9.94]])
>>> np.round(g.covars, 2)
... #doctest: +NORMALIZE_WHITESPACE
array([[[ 1.02]],
       [[ 0.96]]])
>>> g.predict([[0], [2], [9], [10]])
array([0, 0, 1, 1])
>>> np.round(g.score([[0], [2], [9], [10]]), 2)
array([-2.32, -4.16, -1.65, -1.19])

>>> # 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]])
GMM(cvtype='diag', n_states=2)
>>> 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 scikits.learn.mixture.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,)

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Returns: bool

A boolean indicating whether the node can be trained.

Overrides: Node.is_trainable

Estimate model parameters with the expectation-maximization algorithm. This node has been automatically generated by wrapping the scikits.learn.mixture.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 em algorithm. If you want to avoid this step, set the keyword argument init_params to the empty string ''. Likewise, if you would like just to do an initialization, call this method with 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.

n_iter : int, optional

Number of EM iterations to perform.

min_covar : float, optional

Floor on the diagonal of the covariance matrix to prevent overfitting. Defaults to 1e-3.

thresh : float, optional

Convergence threshold.

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'.

Overrides: Node.stop_training