Package mdp :: Package nodes :: Class FANode
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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' function returns the Maximum A Posteriori estimate
of the latent variables. The 'generate_input' function generates
observations from the prior distribution.

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

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'.

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, 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.
 
_execute(self, x)
 
_get_supported_dtypes(self)
Return the list of dtypes supported by this node.
 
_stop_training(self)
 
_train(self, x)
 
generate_input(self, len_or_y=1, noise=False)
Generate data from the prior distribution.
 
is_invertible(self)
Return True if the node can be inverted, False otherwise.

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

    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_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.
 
execute(self, x, *args, **kargs)
Process the data contained in 'x'.
 
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_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.
 
stop_training(self, *args, **kwargs)
Stop the training phase.
 
train(self, x, *args, **kwargs)
Update the internal structures according to the input data 'x'.
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, 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).

Overrides: object.__init__
(inherited documentation)

_execute(self, x)

 
Overrides: Node._execute

_get_supported_dtypes(self)

 
Return the list of dtypes supported by this node.

Overrides: Node._get_supported_dtypes

_stop_training(self)

 
Overrides: Node._stop_training

_train(self, x)

 
Overrides: Node._train

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.

Input arguments:
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(self)

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

Overrides: Node.is_invertible
(inherited documentation)