Package mdp :: Package nodes :: Class WhiteningNode

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Class WhiteningNode

Whiten the input data by filtering it through the most significant of its principal components.

All output signals have zero mean, unit variance and are decorrelated.

Instance Methods

[hide private]

_stop_training(self, debug=False)
Stop the training phase.

numpy.ndarray

get_eigenvectors(self)
Return the eigenvectors of the covariance matrix.

numpy.ndarray

get_recmatrix(self, transposed=1)
Returns the the back-projection matrix (i.e. the reconstruction matrix).

stop_training(self, debug=False)
Stop the training phase.

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 PCANode

__init__(self, input_dim=None, output_dim=None, dtype=None, svd=False, reduce=False, var_rel=1e-12, var_abs=1e-15, var_part=None)
Initializes an object of type 'PCANode'.

tuple

_adjust_output_dim(self)
This function is used if the output dimensions is smaller than the input dimension (so only the larger eigenvectors have to be kept). If required it sets the output dim.

_check_output(self, y)

numpy.ndarray

_execute(self, x, n=None)
Project the input on the first 'n' principal components.

numpy.ndarray

_inverse(self, y, n=None)
Project data from the output to the input space using the first 'n' components.

_set_output_dim(self, n)

_train(self, x)
Update the covariance matrix.

numpy.ndarray

execute(self, x, n=None)
Project the input on the first 'n' principal components.

float

get_explained_variance(self)
The explained variance is the fraction of the original variance that can be explained by self._output_dim PCA components. If for example output_dim has been set to 0.95, the explained variance could be something like 0.958...

numpy.ndarray

get_projmatrix(self, transposed=1)
Returns the projection matrix.

numpy.ndarray

inverse(self, y, n=None)
Project data from the output to the input space using the first 'n' components.

train(self, x)
Update the covariance matrix.

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_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_input_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.

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.

Instance Variables

[hide private]

avg
Mean of the input data (available after training).

d
Variance corresponding to the PCA components (eigenvalues of the covariance matrix).

explained_variance
When output_dim has been specified as a fraction of the total variance, this is the fraction of the total variance that is actually explained.

v
Transposed of the projection matrix (available after training).

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]

_stop_training(self, debug=False)

Stop the training phase.

Parameters:

debug (bool) - Determines if singular matrices itself are stored in self.cov_mtx and self.dcov_mtx to be examined, given that stop_training fails because of singular covmatrices. Default is False.

Raises:

mdp.NodeException - If negative eigenvalues occur, the covariance matrix may be singular or no component amounts to variation exceeding var_abs.

Overrides: Node._stop_training

get_eigenvectors(self)

Return the eigenvectors of the covariance matrix.

Returns: numpy.ndarray: The eigenvectors of the covariance matrix.

get_recmatrix(self, transposed=1)

Returns the the back-projection matrix (i.e. the reconstruction matrix).

Parameters:

transposed (bool) - Determines whether the transposed back-projection matrix (i.e. the reconstruction matrix) is returned. Default is True.

Returns: numpy.ndarray

The back-projection matrix (i.e. the reconstruction matrix).

Overrides: PCANode.get_recmatrix

stop_training(self, debug=False)

Stop the training phase.

Parameters:

debug (bool) - Determines if singular matrices itself are stored in self.cov_mtx and self.dcov_mtx to be examined, given that stop_training fails because of singular covmatrices. Default is False.

Raises:

mdp.NodeException - If negative eigenvalues occur, the covariance matrix may be singular or no component amounts to variation exceeding var_abs.

Overrides: Node.stop_training

Instance Variable Details

[hide private]

avg

Mean of the input data (available after training).

d

Variance corresponding to the PCA components (eigenvalues of the covariance matrix).

explained_variance

When output_dim has been specified as a fraction of the total variance, this is the fraction of the total variance that is actually explained.

v

Transposed of the projection matrix (available after training).