Uses of Class
org.knime.base.data.neural.Perceptron

Packages that use Perceptron
org.knime.base.data.neural This package contains all the necessary classes to build your own neural network. 
 

Uses of Perceptron in org.knime.base.data.neural
 

Subclasses of Perceptron in org.knime.base.data.neural
 class InputPerceptron
          Class representing an input perceptron.
 class SigmoidPerceptron
          A hidden layer perceptron with a sigmoid activation function.
 

Methods in org.knime.base.data.neural that return Perceptron
 Perceptron Layer.getPerceptron(int i)
          Returns perceptron at a given position.
 Perceptron[] Layer.getPerceptrons()
          Returns all perceptrons in the layer.
 Perceptron SigmoidPerceptron.getPredecessor(int i)
          Returns the predecessor at a given position.
 Perceptron[] SigmoidPerceptron.getPredecessors()
          Returns the predecessors.
 

Methods in org.knime.base.data.neural with parameters of type Perceptron
 void Layer.setPerceptron(int i, Perceptron perceptron)
          Sets perceptron at a given position.
 void Layer.setPerceptrons(Perceptron[] perceptrons)
          Sets all perceptrons.
 void SigmoidPerceptron.setPredecessor(int i, Perceptron predecessor)
          Set predecessor at a given position.
 void SigmoidPerceptron.setPredecessors(Perceptron[] predecessors)
          Sets the predecessors.
 

Constructors in org.knime.base.data.neural with parameters of type Perceptron
HiddenLayer(Layer predLayer, Perceptron[] neurons)
          Constructs a hidden layer with the given predecessor layer and the given neurons.
InputLayer(Perceptron[] inputs)
          Constructs an input layer with given input perceptrons.
Layer(Perceptron[] perceptrons)
          Constructs a layer with given perceptrons.
SigmoidPerceptron(double[] weights, Perceptron[] predecessors)
          Constructs a perceptron with given weights and predecessors.
SigmoidPerceptron(Perceptron[] predecessors)
          Constructs a perceptron with given predecessors, weights initialized randomly.
 



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