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Modular toolkit for Data Processing MDP
Package mdp :: Package nodes :: Class FastICAScikitsLearnNode
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Class FastICAScikitsLearnNode

FastICA; a fast algorithm for Independent Component Analysis This node has been automatically generated by wrapping the scikits.learn.decomposition.fastica_.FastICA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

n_components : int, optional
Number of components to use. If none is passed, all are used.
algorithm: {'parallel', 'deflation'}
Apply parallel or deflational algorithm for FastICA
whiten: boolean, optional
If whiten is false, the data is already considered to be whitened, and no whitening is performed.
fun: {'logcosh', 'exp', or 'cube'}, or a callable
The non-linear function used in the FastICA loop to approximate negentropy. If a function is passed, it derivative should be passed as the 'fun_prime' argument.
fun_prime: None or a callable
The derivative of the non-linearity used.
max_iter : int, optional
Maximum number of iterations during fit
tol : float, optional
Tolerance on update at each iteration
w_init: None of an (n_components, n_components) ndarray
The mixing matrix to be used to initialize the algorithm.

Attributes

unmixing_matrix_ : 2D array, [n_components, n_samples]
The unmixing matrix

Methods

get_mixing_matrix() :

Notes

Implementation based on :

  1. Hyvarinen and E. Oja, Independent Component Analysis:

Algorithms and Applications, Neural Networks, 13(4-5), 2000, pp. 411-430

Instance Methods [hide private]
 
__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
FastICA; a fast algorithm for Independent Component Analysis This node has been automatically generated by wrapping the scikits.learn.decomposition.fastica_.FastICA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
 
_execute(self, x)
list
_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)
Apply un-mixing matrix "W" to X to recover the sources This node has been automatically generated by wrapping the scikits.learn.decomposition.fastica_.FastICA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. S = W * X
 
stop_training(self, **kwargs)
Concatenate the collected data in a single array.

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 numpy.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.
bool
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)

 

FastICA; a fast algorithm for Independent Component Analysis This node has been automatically generated by wrapping the scikits.learn.decomposition.fastica_.FastICA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

n_components : int, optional
Number of components to use. If none is passed, all are used.
algorithm: {'parallel', 'deflation'}
Apply parallel or deflational algorithm for FastICA
whiten: boolean, optional
If whiten is false, the data is already considered to be whitened, and no whitening is performed.
fun: {'logcosh', 'exp', or 'cube'}, or a callable
The non-linear function used in the FastICA loop to approximate negentropy. If a function is passed, it derivative should be passed as the 'fun_prime' argument.
fun_prime: None or a callable
The derivative of the non-linearity used.
max_iter : int, optional
Maximum number of iterations during fit
tol : float, optional
Tolerance on update at each iteration
w_init: None of an (n_components, n_components) ndarray
The mixing matrix to be used to initialize the algorithm.

Attributes

unmixing_matrix_ : 2D array, [n_components, n_samples]
The unmixing matrix

Methods

get_mixing_matrix() :

  • Returns an estimate of the mixing matrix

Notes

Implementation based on :

  1. Hyvarinen and E. Oja, Independent Component Analysis:

Algorithms and Applications, Neural Networks, 13(4-5), 2000, pp. 411-430

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.
Returns: list
The list of dtypes supported by this node.
Overrides: Node._get_supported_dtypes

_stop_training(self, **kwargs)

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

execute(self, x)

 
Apply un-mixing matrix "W" to X to recover the sources This node has been automatically generated by wrapping the scikits.learn.decomposition.fastica_.FastICA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. S = W * X
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.
Returns: bool
A boolean indication whether the node can be trained.
Overrides: Node.is_trainable

stop_training(self, **kwargs)

 
Concatenate the collected data in a single array.
Overrides: Node.stop_training
(inherited documentation)

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Modular toolkit for Data Processing MDP
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