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`` 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)
:Parameters:...
 
_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 :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`.
 
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)

 

: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)

 
Overrides: Node._execute

_stop_training(self)

 
Overrides: Node._stop_training

_train(self, x)

 
Overrides: Node._train

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.

: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()
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