Package mdp :: Package nodes :: Class AdaptiveCutoffNode

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.

Instance Methods

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__init__(self, lower_cutoff_fraction=None, upper_cutoff_fraction=None, hist_fraction=1.0, hist_filename=None, input_dim=None, output_dim=None, dtype=None)
Initialize the node.

_execute(self, x)
Return the clipped data.

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

_stop_training(self)
Calculate the cutoff bounds based on collected histogram data.

execute(self, x)
Return the clipped data.

stop_training(self)
Calculate the cutoff bounds based on collected histogram data.

Inherited from PreserveDimNode (private): _set_input_dim, _set_output_dim

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 HistogramNode

_train(self, x)
Store the history data.

train(self, x)
Store the history data.

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_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)

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.

inverse(self, y, *args, **kwargs)
Invert y.

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

Initialize the node.

Parameters:

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__