mdp.parallel.ParallelFDANode

Class ParallelFDANode

Instance Methods

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

_join(self, forked_node)
Hook method for joining, to be overridden.

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.FDANode

__init__(self, 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.

_check_train_args(self, x, labels)

_execute(self, x, n=None)
Compute the output of the FDA projection.

_get_train_seq(self)

_inverse(self, y)

_stop_fda(self)
Solve the eigenvalue problem for the total covariance.

_stop_means(self)
Calculate the class means.

_train(self, x, label)
Update the internal structures according to the input data 'x'.

_train_fda(self, x, labels)
Gather data for the overall and within-class covariance

_train_means(self, x, labels)
Gather data to compute the means and number of elements.

_update_SW(self, x, label)
Update the covariance matrix of the class means.

_update_means(self, x, label)
Update the internal variables that store the data for the means.

execute(self, x, n=None)
Compute the output of the FDA projection.

inverse(self, y)
Invert y.

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

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.

_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)

_stop_training(self, *args, **kwargs)

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.

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.

stop_training(self, *args, **kwargs)
Stop the training phase.

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.

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

_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)

Hook method for joining, to be overridden.

Overrides: ParallelExtensionNode._join: (inherited documentation)