Package mdp :: Package nodes :: Class AdaptiveCutoffNode
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Class AdaptiveCutoffNode


Node which uses the data history during training to learn cutoff values.

As opposed to the simple CutoffNode, a different cutoff value is learned
for each data coordinate. For example if an upper cutoff fraction of
0.05 is specified, then the upper cutoff bound is set so that the upper
5% of the training data would have been clipped (in each dimension).
The cutoff bounds are then applied during execution.
This Node also works as a HistogramNode, so the histogram data is stored. 

When stop_training is called the cutoff values for each coordinate are
calculated based on the collected histogram data.

Nested Classes [hide private]
    Inherited from Node
  __metaclass__
This Metaclass is meant to overwrite doc strings of methods like execute, stop_training, inverse with the ones defined in the corresponding private methods _execute, _stop_training, _inverse, etc...
Instance Methods [hide private]
 
__init__(self, lower_cutoff_fraction=None, upper_cutoff_fraction=None, hist_fraction=1.0, hist_filename=None, input_dim=None, dtype=None)
Initialize the node.
 
_execute(self, x)
Return the clipped data.
 
_stop_training(self)
Calculate the cutoff bounds based on collected histogram data.
 
execute(self, x, *args, **kargs)
Return the clipped data.
 
stop_training(self, *args, **kwargs)
Calculate the cutoff bounds based on collected histogram data.

Inherited from object: __delattr__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__

    Inherited from HistogramNode
 
_train(self, x)
Store the history data.
 
train(self, x, *args, **kwargs)
Store the history data.
    Inherited from Node
 
__add__(self, other)
 
__call__(self, x, *args, **kargs)
Calling an instance of Node is equivalent to call 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)
 
_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=-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.
 
inverse(self, y, *args, **kargs)
Invert 'y'.
 
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 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.
Properties [hide private]

Inherited from object: __class__

    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
Method Details [hide private]

__init__(self, lower_cutoff_fraction=None, upper_cutoff_fraction=None, hist_fraction=1.0, hist_filename=None, input_dim=None, dtype=None)
(Constructor)

 
Initialize the node.

lower_cutoff_fraction -- Fraction of data that will be cut off after
    the training phase (assuming the data distribution does not
    change). If set to None (default value) no cutoff is performed.
upper_cutoff_fraction -- Works like lower_cutoff_fraction.
hist_fraction -- Defines the fraction of the data that is stored for the
    histogram.
hist_filename -- Filename for the file to which the data history will
    be pickled after training. The data is pickled when stop_training
    is called and data_hist is then cleared (to free memory).
    If filename is None (default value) then data_hist is not cleared
    and can be directly used after training.

Overrides: object.__init__

_execute(self, x)

 
Return the clipped data.

Overrides: Node._execute

_stop_training(self)

 
Calculate the cutoff bounds based on collected histogram data.

Overrides: Node._stop_training

execute(self, x, *args, **kargs)

 
Return the clipped data.

Overrides: Node.execute

stop_training(self, *args, **kwargs)

 
Calculate the cutoff bounds based on collected histogram data.

Overrides: Node.stop_training