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.

Internal variables of interest

self.mu

Mean of the input data (available after training)

self.A

Generating weights (available after training)

self.E_y_mtx

Weights for Maximum A Posteriori inference

self.sigma

Vector of estimated variance of the noise for all input components

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

[hide private]

__init__(self, tol=0.0001, max_cycles=100, verbose=False, input_dim=None, output_dim=None, dtype=None)
If the input dimension and the output dimension are unspecified, they will be set when the train or execute method is called for the first time. If dtype is unspecified, it will be inherited from the data it receives at the first call of train or execute.

_execute(self, x)

_stop_training(self)

_train(self, x)

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

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 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.

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)

If the input dimension and the output dimension are unspecified, they will be set when the train or execute method is called for the first time. If dtype is unspecified, it will be inherited from the data it receives at the first call of train or execute.

Every subclass must take care of up- or down-casting the internal structures to match this argument (use _refcast private method when possible).

Parameters:

tol - tolerance (minimum change in log-likelihood before exiting the EM algorithm)
max_cycles - maximum number of EM cycles
verbose - if true, print log-likelihood during the EM-cycles

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 - if true, generation includes the estimated noise

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

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)

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

is_invertible()
Static Method