Package mdp :: Package parallel :: Class ParallelSFANode

Class ParallelSFANode

Parallel version of MDP SFA node.

Instance Methods

[hide private]

_fork(self)
Hook method for forking with default implementation.

_join(self, forked_node)
Combine the covariance matrices.

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 ParallelExtensionNode

_default_fork(self)
Default implementation of _fork.

fork(self)
Return a new instance of this node class for remote training.

join(self, forked_node)
Absorb the trained node from a fork into this parent node.

Inherited from nodes.SFANode

__init__(self, input_dim=None, output_dim=None, dtype=None, include_last_sample=True, rank_deficit_method='none')
Initialize an object of type 'SFANode'.

_check_train_args(self, x, *args, **kwargs)
Raises exception if time dimension does not have enough elements.

numpy.ndarray

_execute(self, x, n=None)
Compute the output of the slowest functions.

_init_cov(self)

_inverse(self, y)

_set_range(self)

_stop_training(self, debug=False)

_train(self, x, include_last_sample=None)
Training method.

numpy.ndarray

execute(self, x, n=None)
Compute the output of the slowest functions.

get_eta_values(self, t=1)
Return the eta values of the slow components learned during the training phase. If the training phase has not been completed yet, call stop_training.

inverse(self, y)
Invert y.

set_rank_deficit_method(self, rank_deficit_method)

stop_training(self, debug=False)
Stop the training phase.

numpy.ndarray

time_derivative(self, x)
Compute the linear approximation of the time derivative

train(self, x, include_last_sample=None)
Training method.

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)

_get_supported_dtypes(self)
Return the list of dtypes supported by this node.

_get_train_seq(self)

_if_training_stop_training(self)

_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.

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]

Inherited from ParallelExtensionNode

_join_covariance(cov, forked_cov)
Helper method to join two CovarianceMatrix instances.

use_execute_fork()
Return True if node requires a fork / join even during execution.

Inherited from Node

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

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

Class Variables

[hide private]

Inherited from ParallelExtensionNode

extension_name = 'parallel'
hash(x)

Instance Variables

[hide private]

Inherited from nodes.SFANode

avg
Mean of the input data (available after training)

d
Delta values corresponding to the SFA components (generalized eigenvalues). [See the docs of the get_eta_values method for more information]

sf
Matrix of the SFA filters (available after training)

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]

_fork(self)

Hook method for forking with default implementation.

Overwrite this method for nodes that can be parallelized.
You can use _default_fork, if that is compatible with your node class,
typically the hard part is the joining.

Overrides: ParallelExtensionNode._fork: (inherited documentation)

_join(self, forked_node)

Combine the covariance matrices.

Overrides: ParallelExtensionNode._join