Feedforward Closedloop Learning
feedforward_closedloop_learning.FeedforwardClosedloopLearningWithFilterbank Class Reference
Inheritance diagram for feedforward_closedloop_learning.FeedforwardClosedloopLearningWithFilterbank:
Collaboration diagram for feedforward_closedloop_learning.FeedforwardClosedloopLearningWithFilterbank:

Public Member Functions

def __init__ (self, "int" num_of_inputs, "int *" num_of_neurons_per_layer_array, "int" num_filtersInput, "double" minT, "double" maxT)
 
"void" doStep (self, *args)
 
"double" getFilterOutput (self, "int" inputIdx, "int" filterIdx)
 
"int" getNFiltersPerInput (self)
 
- Public Member Functions inherited from feedforward_closedloop_learning.FeedforwardClosedloopLearning
def __init__ (self, "int" num_of_inputs, "int *" num_of_neurons_per_layer_array)
 
"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")
 
- Properties inherited from feedforward_closedloop_learning.FeedforwardClosedloopLearning
 thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
 

Detailed Description

Proxy of C++ FeedforwardClosedloopLearningWithFilterbank class.

Constructor & Destructor Documentation

◆ __init__()

def feedforward_closedloop_learning.FeedforwardClosedloopLearningWithFilterbank.__init__ (   self,
"int"  num_of_inputs,
"int *"  num_of_neurons_per_layer_array,
"int"  num_filtersInput,
"double"  minT,
"double"  maxT 
)
__init__(FeedforwardClosedloopLearningWithFilterbank self, int num_of_inputs, int * num_of_neurons_per_layer_array, int num_filtersInput, double minT, double maxT) -> FeedforwardClosedloopLearningWithFilterbank

Parameters
----------
num_of_inputs: int
num_of_neurons_per_layer_array: int *
num_filtersInput: int
minT: double
maxT: double

Member Function Documentation

◆ doStep()

"void" feedforward_closedloop_learning.FeedforwardClosedloopLearningWithFilterbank.doStep (   self,
args 
)
doStep(FeedforwardClosedloopLearningWithFilterbank self, double * input, double * error)

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

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

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

Reimplemented from feedforward_closedloop_learning.FeedforwardClosedloopLearning.

◆ getFilterOutput()

"double" feedforward_closedloop_learning.FeedforwardClosedloopLearningWithFilterbank.getFilterOutput (   self,
"int"  inputIdx,
"int"  filterIdx 
)
getFilterOutput(FeedforwardClosedloopLearningWithFilterbank self, int inputIdx, int filterIdx) -> double

Parameters
----------
inputIdx: int
filterIdx: int

◆ getNFiltersPerInput()

"int" feedforward_closedloop_learning.FeedforwardClosedloopLearningWithFilterbank.getNFiltersPerInput (   self)
getNFiltersPerInput(FeedforwardClosedloopLearningWithFilterbank self) -> int

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