Package mdp :: Package nodes :: Class FANode

Class FANode

Perform Factor Analysis.

The current implementation should be most efficient for long data sets: the sufficient statistics are collected in the training phase, and all EM-cycles are performed at its end.

The execute method returns the Maximum A Posteriori estimate of the latent variables. The generate_input method generates observations from the prior distribution.

Reference

More information about Factor Analysis can be found in Max Welling's classnotes: http://www.ics.uci.edu/~welling/classnotes/classnotes.html , in the chapter 'Linear Models'.

Instance Methods

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__init__(self, tol=0.0001, max_cycles=100, verbose=False, input_dim=None, output_dim=None, dtype=None)
Initializes an object of type 'FANode'.

_execute(self, x)

_stop_training(self)

_train(self, x)

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

numpy.ndarray

generate_input(self, len_or_y=1, noise=False)
Generate data from the prior distribution.

stop_training(self)
Stop the training phase.

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

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 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_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=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.

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]

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

Inherited from Node

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

Instance Variables

[hide private]

A
Generating weights (available after training).

E_y_mtx
Weights for Maximum A Posteriori inference.

mu
Mean of the input data (available after training).

sigma
Vector of estimated variance of the noise for all input components.

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, tol=0.0001, max_cycles=100, verbose=False, input_dim=None, output_dim=None, dtype=None)
(Constructor)

Initializes an object of type 'FANode'.

Parameters:

tol (float) - Tolerance (minimum change in log-likelihood before exiting the EM algorithm).
max_cycles (int) - Maximum number of EM cycles/
verbose (bool) - If true, print log-likelihood during the EM-cycles.
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)

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

generate_input(self, len_or_y=1, noise=False)

Generate data from the prior distribution.

If the training phase has not been completed yet, call stop_training.

Parameters:

len_or_y - If integer, it specified the number of observation to generate. If array, it is used as a set of samples of the latent variables
noise (bool) - If true, generation includes the estimated noise

Returns: numpy.ndarray

The generated data.

is_invertible()
Static Method

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

Overrides: Node.is_invertible: (inherited documentation)

stop_training(self)

Stop the training phase.

By default, subclasses should overwrite _stop_training to implement this functionality. The docstring of the _stop_training method overwrites this docstring.

Overrides: Node.stop_training

train(self, x)

Update the internal structures according to the input data x.

x is a matrix having different variables on different columns and observations on the rows.

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

Note: a subclass supporting multiple training phases should implement the same signature for all the training phases and document the meaning of the arguments in the _train method doc-string. Having consistent signatures is a requirement to use the node in a flow.

Overrides: Node.train

Instance Variable Details

[hide private]

A

Generating weights (available after training).

E_y_mtx

Weights for Maximum A Posteriori inference.

mu

Mean of the input data (available after training).

sigma

Vector of estimated variance of the noise for all input components.

Class FANode

__init__(self, tol=0.0001, max_cycles=100, verbose=False, input_dim=None, output_dim=None, dtype=None) (Constructor)

_execute(self, x)

_stop_training(self)

_train(self, x)

execute(self, x)

generate_input(self, len_or_y=1, noise=False)

is_invertible() Static Method

stop_training(self)

train(self, x)

A

E_y_mtx

mu

sigma

init(self, tol=0.0001, max_cycles=100, verbose=False, input_dim=None, output_dim=None, dtype=None)
(Constructor)

is_invertible()
Static Method