Package mdp :: Package nodes :: Class WhiteningNode
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Class 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)
Stop the training phase.
 
get_eigenvectors(self)
Return the eigenvectors of the covariance matrix.
 
get_recmatrix(self, transposed=1)
Return the back-projection matrix (i.e.
 
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)
The number of principal components to be kept can be specified as 'output_dim' directly (e.g.
 
_adjust_output_dim(self)
Return the eigenvector range and set the output dim if required.
 
_check_output(self, y)
 
_execute(self, x, n=None)
Project the input on the first 'n' principal components.
 
_inverse(self, y, n=None)
Project 'y' to the input space using the first 'n' components.
 
_set_output_dim(self, n)
 
_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.
 
train(self, x)
Update the internal structures according to the input data `x`.
    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`.
 
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]

_stop_training(self, debug=False)

 
Stop the training phase.

Keyword arguments:

debug=True     if stop_training fails because of singular cov
               matrices, the singular matrices itselves are stored in
               self.cov_mtx and self.dcov_mtx to be examined.

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

stop_training(self, debug=False)

 
Stop the training phase.

Keyword arguments:

debug=True     if stop_training fails because of singular cov
               matrices, the singular matrices itselves are stored in
               self.cov_mtx and self.dcov_mtx to be examined.

Overrides: Node.stop_training