Package mdp :: Package nodes :: Class NoiseNode

Class NoiseNode

Inject multiplicative or additive noise into the input data.

Original code contributed by Mathias Franzius.

Instance Methods

__init__(self, noise_func=<built-in method normal of mtrand.RandomState object at 0x7fb8..., noise_args=(0, 1), noise_type='additive', input_dim=None, output_dim=None, dtype=None)
Initializes an object of type 'NoiseNode'.

_execute(self, x)

list

_get_supported_dtypes(self)
Return the data types supported by this node.

execute(self, x)
Process the data contained in x.

save(self, filename, protocol=-1)
Save a pickled serialization of the node to 'filename'. If 'filename' is None, return a string.

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 PreserveDimNode

_set_input_dim(self, n)

_set_output_dim(self, n)

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)

_stop_training(self, *args, **kwargs)

_train(self, x)

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.

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

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

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

set_input_dim(self, n)
Set input dimensions.

set_output_dim(self, n)
Set output dimensions.

stop_training(self, *args, **kwargs)
Stop the training phase.

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

Static Methods

[hide private]

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, noise_func=<built-in method normal of mtrand.RandomState object at 0x7fb8`...`, noise_args=`(`0`,` 1`)`, noise_type=`'additive'`, input_dim=None, output_dim=None, dtype=None)
(Constructor)

Initializes an object of type 'NoiseNode'.

Parameters:

noise_func (function) - A function that generates noise. It must take a size keyword argument and return a random array of that size. Default is normal noise.
noise_args (tuple) - Tuple of additional arguments passed to noise_func. Default is (0,1) for (mean, standard deviation) of the normal distribution.
noise_type (str) - Either 'additive' or 'multiplicative'.
input_dim (int) - The input dimensionality.
output_dim (int) - The output dimensionality.
dtype (numpy.dtype or str) - The datatype.

Overrides: object.__init__

_execute(self, x)

Overrides: Node._execute

_get_supported_dtypes(self)

Return the data types supported by this node.

Returns: list: The list of numpy.dtypes that this node supports.
Overrides: Node._get_supported_dtypes

execute(self, x)

Process the data contained in x.

If the object is still in the training phase, the function stop_training will be called. x is a matrix having different variables on different columns and observations on the rows.

By default, subclasses should overwrite _execute to implement their execution phase. The docstring of the _execute method overwrites this docstring.

Overrides: Node.execute

is_invertible()
Static Method

Return True if the node can be inverted, False otherwise.

Overrides: Node.is_invertible: (inherited documentation)

is_trainable()
Static Method

Return True if the node can be trained, False otherwise.

Overrides: Node.is_trainable: (inherited documentation)

save(self, filename, protocol=-1)

Save a pickled serialization of the node to 'filename'. If 'filename' is None, return a string.

Note: the pickled Node is not guaranteed to be upward or backward compatible.

Parameters:

filename (str) - The name of the file to save to.
protocol - Whether to open the file in binary mode (protocol != 0). Default is -1.

Overrides: Node.save

Class NoiseNode

__init__(self, noise_func=<built-in method normal of mtrand.RandomState object at 0x7fb8..., noise_args=(0, 1), noise_type='additive', input_dim=None, output_dim=None, dtype=None) (Constructor)

_execute(self, x)

_get_supported_dtypes(self)

execute(self, x)

is_invertible() Static Method

is_trainable() Static Method

save(self, filename, protocol=-1)

init(self, noise_func=<built-in method normal of mtrand.RandomState object at 0x7fb8`...`, noise_args=`(`0`,` 1`)`, noise_type=`'additive'`, input_dim=None, output_dim=None, dtype=None)
(Constructor)

is_invertible()
Static Method

is_trainable()
Static Method