Inheritance diagram for feedforward_closedloop_learning.FeedforwardClosedloopLearning:

Collaboration diagram for feedforward_closedloop_learning.FeedforwardClosedloopLearning:

Public Member Functions
def	__init__ (self, "int" num_of_inputs, "int *" num_of_neurons_per_layer_array)

"void"	doStep (self, *args)

"double"	getOutput (self, "int" index)

"void"	setLearningRate (self, "double" learningRate)

"void"	setLearningRateDiscountFactor (self, "double" _learningRateDiscountFactor)

"void"	setDecay (self, "double" decay)

"void"	setMomentum (self, "double" momentum)

"void"	setActivationFunction (self, "Neuron::ActivationFunction" _activationFunction)

"void"	initWeights (self, *args)

"void"	seedRandom (self, "int" s)

"void"	setBias (self, "double" _bias)

"int"	getNumLayers (self)

"Layer *"	getLayer (self, "int" i)

"Layer *"	getOutputLayer (self)

"int"	getNumInputs (self)

"Layer **"	getLayers (self)

"bool"	saveModel (self, "char const *" name)

"bool"	loadModel (self, "char const *" name)

Properties
	thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")

Detailed Description

Proxy of C++ FeedforwardClosedloopLearning class.

Constructor & Destructor Documentation

◆ init()

def feedforward_closedloop_learning.FeedforwardClosedloopLearning.__init__	(		self,
		"int"	num_of_inputs,
		"int *"	num_of_neurons_per_layer_array
	)

__init__(FeedforwardClosedloopLearning self, int num_of_inputs, int * num_of_neurons_per_layer_array) -> FeedforwardClosedloopLearning

Parameters
----------
num_of_inputs: int
num_of_neurons_per_layer_array: int *

Member Function Documentation

◆ doStep()

"void" feedforward_closedloop_learning.FeedforwardClosedloopLearning.doStep	(		self,
		*	args
	)

doStep(FeedforwardClosedloopLearning self, double * input, double * error)

Parameters
----------
input: double *
error: double *

doStep(FeedforwardClosedloopLearning self, double * input, double * error)

Parameters
----------
input: double *
error: double *

Reimplemented in feedforward_closedloop_learning.FeedforwardClosedloopLearningWithFilterbank.

◆ getLayer()

"Layer *" feedforward_closedloop_learning.FeedforwardClosedloopLearning.getLayer	(		self,
		"int"	i
	)

getLayer(FeedforwardClosedloopLearning self, int i) -> Layer

Parameters
----------
i: int

◆ getLayers()

"Layer **" feedforward_closedloop_learning.FeedforwardClosedloopLearning.getLayers ( self )

getLayers(FeedforwardClosedloopLearning self) -> Layer **

◆ getNumInputs()

"int" feedforward_closedloop_learning.FeedforwardClosedloopLearning.getNumInputs ( self )

getNumInputs(FeedforwardClosedloopLearning self) -> int

◆ getNumLayers()

"int" feedforward_closedloop_learning.FeedforwardClosedloopLearning.getNumLayers ( self )

getNumLayers(FeedforwardClosedloopLearning self) -> int

◆ getOutput()

"double" feedforward_closedloop_learning.FeedforwardClosedloopLearning.getOutput	(		self,
		"int"	index
	)

getOutput(FeedforwardClosedloopLearning self, int index) -> double

Parameters
----------
index: int

◆ getOutputLayer()

"Layer *" feedforward_closedloop_learning.FeedforwardClosedloopLearning.getOutputLayer ( self )

getOutputLayer(FeedforwardClosedloopLearning self) -> Layer

◆ initWeights()

"void" feedforward_closedloop_learning.FeedforwardClosedloopLearning.initWeights	(		self,
		*	args
	)

initWeights(FeedforwardClosedloopLearning self, double max=0.001, int initBias=1, Neuron::WeightInitMethod weightInitMethod=MAX_OUTPUT_RANDOM)

Parameters
----------
max: double
initBias: int
weightInitMethod: enum Neuron::WeightInitMethod

◆ loadModel()

"bool" feedforward_closedloop_learning.FeedforwardClosedloopLearning.loadModel	(		self,
		"char const *"	name
	)

loadModel(FeedforwardClosedloopLearning self, char const * name) -> bool

Parameters
----------
name: char const *

◆ saveModel()

"bool" feedforward_closedloop_learning.FeedforwardClosedloopLearning.saveModel	(		self,
		"char const *"	name
	)

saveModel(FeedforwardClosedloopLearning self, char const * name) -> bool

Parameters
----------
name: char const *

◆ seedRandom()

"void" feedforward_closedloop_learning.FeedforwardClosedloopLearning.seedRandom	(		self,
		"int"	s
	)

seedRandom(FeedforwardClosedloopLearning self, int s)

Parameters
----------
s: int

◆ setActivationFunction()

"void" feedforward_closedloop_learning.FeedforwardClosedloopLearning.setActivationFunction	(		self,
		"Neuron::ActivationFunction"	_activationFunction
	)

setActivationFunction(FeedforwardClosedloopLearning self, Neuron::ActivationFunction _activationFunction)

Parameters
----------
_activationFunction: enum Neuron::ActivationFunction

◆ setBias()

"void" feedforward_closedloop_learning.FeedforwardClosedloopLearning.setBias	(		self,
		"double"	_bias
	)

setBias(FeedforwardClosedloopLearning self, double _bias)

Parameters
----------
_bias: double

◆ setDecay()

"void" feedforward_closedloop_learning.FeedforwardClosedloopLearning.setDecay	(		self,
		"double"	decay
	)

setDecay(FeedforwardClosedloopLearning self, double decay)

Parameters
----------
decay: double

◆ setLearningRate()

"void" feedforward_closedloop_learning.FeedforwardClosedloopLearning.setLearningRate	(		self,
		"double"	learningRate
	)

setLearningRate(FeedforwardClosedloopLearning self, double learningRate)

Parameters
----------
learningRate: double

◆ setLearningRateDiscountFactor()

"void" feedforward_closedloop_learning.FeedforwardClosedloopLearning.setLearningRateDiscountFactor	(		self,
		"double"	_learningRateDiscountFactor
	)

setLearningRateDiscountFactor(FeedforwardClosedloopLearning self, double _learningRateDiscountFactor)

Parameters
----------
_learningRateDiscountFactor: double

◆ setMomentum()

"void" feedforward_closedloop_learning.FeedforwardClosedloopLearning.setMomentum	(		self,
		"double"	momentum
	)

setMomentum(FeedforwardClosedloopLearning self, double momentum)

Parameters
----------
momentum: double

The documentation for this class was generated from the following file:

feedforward_closedloop_learning.py

Public Member Functions

Properties