Package mdp :: Package nodes :: Class ICANode

Class ICANode

ICANode is a general class to handle different batch-mode algorithm for Independent Component Analysis. More information about ICA can be found among others in Hyvarinen A., Karhunen J., Oja E. (2001). Independent Component Analysis, Wiley.

Instance Methods

[hide private]

__init__(self, limit=0.001, telescope=False, verbose=False, whitened=False, white_comp=None, white_parm=None, input_dim=None, dtype=None)
Input arguments:

_execute(self, x)

_inverse(self, y)

_set_input_dim(self, n)

_stop_training(self)
Whiten data if needed and call the 'core' routine to perform ICA. Take care of telescope-mode if needed.

core(self, data)
This is the core routine of the ICANode. Each subclass must define this function to return the achieved convergence value. This function is also responsible for setting the ICA filters matrix self.filters. Note that the matrix self.filters is applied to the right of the matrix containing input data. This is the transposed of the matrix defining the linear transformation.

execute(self, x)
Process the data contained in x.

inverse(self, y)
Invert y.

stop_training(self)
Whiten data if needed and call the 'core' routine to perform ICA. Take care of telescope-mode if needed.

Inherited from unreachable.ProjectMatrixMixin: get_projmatrix, get_recmatrix

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_supported_dtypes(self)
Return the list of dtypes supported by this node.

_get_train_seq(self)

_if_training_stop_training(self)

_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_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.

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]

Inherited from Node

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, limit=0.001, telescope=False, verbose=False, whitened=False, white_comp=None, white_parm=None, input_dim=None, dtype=None)
(Constructor)

Input arguments:

whitened -- Set whitened is True if input data are already whitened.: Otherwise the node will whiten the data itself.
white_comp -- If whitened is False, you can set 'white_comp' to the: number of whitened components to keep during the calculation (i.e., the input dimensions are reduced to white_comp by keeping the components of largest variance).
white_parm -- a dictionary with additional parameters for whitening.: It is passed directly to the WhiteningNode constructor. Ex: white_parm = { 'svd' : True }

limit -- convergence threshold.

telescope -- If telescope == True, use Telescope mode: Instead of: using all input data in a single batch try larger and larger chunks of the input data until convergence is achieved. This should lead to significantly faster convergence for stationary statistics. This mode has not been thoroughly tested and must be considered beta.

Overrides: object.__init__

_stop_training(self)

Whiten data if needed and call the 'core' routine to perform ICA. Take care of telescope-mode if needed.

Overrides: Node._stop_training

core(self, data)

This is the core routine of the ICANode. Each subclass must define this function to return the achieved convergence value. This function is also responsible for setting the ICA filters matrix self.filters. Note that the matrix self.filters is applied to the right of the matrix containing input data. This is the transposed of the matrix defining the linear transformation.

execute(self, x)

Process the data contained in x.

If the object is still in the training phase, the function stop_training will be called. x is a matrix having different variables on different columns and observations on the rows.

By default, subclasses should overwrite _execute to implement their execution phase. The docstring of the _execute method overwrites this docstring.

Overrides: Node.execute

inverse(self, y)

Invert y.

If the node is invertible, compute the input x such that y = execute(x).

By default, subclasses should overwrite _inverse to implement their inverse function. The docstring of the inverse method overwrites this docstring.

Overrides: Node.inverse

stop_training(self)

Whiten data if needed and call the 'core' routine to perform ICA. Take care of telescope-mode if needed.

Overrides: Node.stop_training

Class ICANode

__init__(self, limit=0.001, telescope=False, verbose=False, whitened=False, white_comp=None, white_parm=None, input_dim=None, dtype=None) (Constructor)

_execute(self, x)

_inverse(self, y)

_set_input_dim(self, n)

_stop_training(self)

core(self, data)

execute(self, x)

inverse(self, y)

stop_training(self)

init(self, limit=0.001, telescope=False, verbose=False, whitened=False, white_comp=None, white_parm=None, input_dim=None, dtype=None)
(Constructor)