mdp.nodes.WhiteningNode

type WhiteningNode

'Whiten' the input data by filtering it through the most significatives of its principal components. All output signals have zero mean, unit variance and are decorrelated. Internal variables of interest: self.avg -- Mean of the input data (available after training) self.v -- Transpose of the projection matrix (available after training) self.d -- Variance corresponding to the PCA components (eigenvalues of the covariance matrix). self.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

Instance Methods

[hide private]

_stop_training(self, debug=False)

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

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

Inherited from PCANode

__init__(self, input_dim=None, output_dim=None, dtype=None, svd=False, reduce=False, var_rel=1e-15, var_abs=1e-15)
The number of principal components to be kept can be specified as 'output_dim' directly (e.g.

_adjust_output_dim(self)

_check_output(self, y)

_execute(self, x, n=None)

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

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

_train(self, x)

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

get_explained_variance(self)
Return the fraction of the original variance that can be explained by self._output_dim PCA components.

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

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

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

Inherited from Node

__call__(self, x)
Calling an instance if Node is equivalent to call its 'execute' method.

__repr__(self)

__str__(self)

_check_input(self, x)

_check_train_args(self, x, *args, **kwargs)

_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)

_set_output_dim(self, n)

copy(self, protocol=-1)
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.

is_invertible(self)
Return True if the node can be inverted, False otherwise.

is_trainable(self)
Return True if the node can be trained, False otherwise.

is_training(self)
Return True if the node is in the training phase, False otherwise.

save(self, filename, protocol=-1)
Save a pickled representation of the node to 'filename'.

set_dtype(self, t)
Set Node's internal structures dtype.

set_input_dim(self, n)
Set input dimensions.

set_output_dim(self, n)
Set output dimensions.

train(self, x, *args, **kwargs)
Update the internal structures according to the input data 'x'.

Properties

[hide private]

Inherited from Node

_train_seq
List of tuples: [(training-phase1, stop-training-phase1), (training-phase2, stop_training-phase2), ...

dtype
dtype

input_dim
Input dimensions

output_dim
Output dimensions

supported_dtypes
Supported dtypes

_stop_training(self, debug=False)

Overrides: Node._stop_training

get_eigenvectors(self)

Return the eigenvectors of the covariance matrix.

get_recmatrix(self, transposed=1)

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

Overrides: PCANode.get_recmatrix