Package mdp :: Package nodes :: Class GMMScikitsLearnNode
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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])

Instance Methods [hide private]
 
__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Gaussian Mixture Model
 
_execute(self, x)
 
_get_supported_dtypes(self)
Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.
 
_stop_training(self, **kwargs)
Concatenate the collected data in a single array.
 
execute(self, x)
Predict label for data.
 
stop_training(self, **kwargs)
Estimate model parameters with the EM algorithm.

Inherited from unreachable.newobject: __long__, __native__, __nonzero__, __unicode__, next

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __subclasshook__

    Inherited from Cumulator
 
_train(self, *args)
Collect all input data in a list.
 
train(self, *args)
Collect all input data in a list.
    Inherited from Node
 
__add__(self, other)
 
__call__(self, x, *args, **kwargs)
Calling an instance of Node is equivalent to calling its execute method.
 
__repr__(self)
repr(x)
 
__str__(self)
str(x)
 
_check_input(self, x)
 
_check_output(self, y)
 
_check_train_args(self, x, *args, **kwargs)
 
_get_train_seq(self)
 
_if_training_stop_training(self)
 
_inverse(self, x)
 
_pre_execution_checks(self, x)
This method contains all pre-execution checks.
 
_pre_inversion_checks(self, y)
This method contains all pre-inversion checks.
 
_refcast(self, x)
Helper function to cast arrays to the internal dtype.
 
_set_dtype(self, t)
 
_set_input_dim(self, n)
 
_set_output_dim(self, n)
 
copy(self, protocol=None)
Return a deep copy of the node.
 
get_current_train_phase(self)
Return the index of the current training phase.
 
get_dtype(self)
Return dtype.
 
get_input_dim(self)
Return input dimensions.
 
get_output_dim(self)
Return output dimensions.
 
get_remaining_train_phase(self)
Return the number of training phases still to accomplish.
 
get_supported_dtypes(self)
Return dtypes supported by the node as a list of dtype objects.
 
has_multiple_training_phases(self)
Return True if the node has multiple training phases.
 
inverse(self, y, *args, **kwargs)
Invert y.
 
is_training(self)
Return True if the node is in the training phase, False otherwise.
 
save(self, filename, protocol=-1)
Save a pickled serialization of the node to filename. If filename is None, return a string.
 
set_dtype(self, t)
Set internal structures' dtype.
 
set_input_dim(self, n)
Set input dimensions.
 
set_output_dim(self, n)
Set output dimensions.
Static Methods [hide private]
 
is_invertible()
Return True if the node can be inverted, False otherwise.
 
is_trainable()
Return True if the node can be trained, False otherwise.
Properties [hide private]

Inherited from object: __class__

    Inherited from Node
  _train_seq
List of tuples:
  dtype
dtype
  input_dim
Input dimensions
  output_dim
Output dimensions
  supported_dtypes
Supported dtypes
Method Details [hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
(Constructor)

 

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__

_execute(self, x)

 
Overrides: Node._execute

_get_supported_dtypes(self)

 
Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.
Overrides: Node._get_supported_dtypes

_stop_training(self, **kwargs)

 
Concatenate the collected data in a single array.
Overrides: Node._stop_training

execute(self, x)

 

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

is_invertible()
Static Method

 
Return True if the node can be inverted, False otherwise.
Overrides: Node.is_invertible
(inherited documentation)

is_trainable()
Static Method

 
Return True if the node can be trained, False otherwise.
Overrides: Node.is_trainable

stop_training(self, **kwargs)

 

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