Feedforward Closedloop Learning
feedforward_closedloop_learning.FeedforwardClosedloopLearning Class Reference
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: