Node Class

This class is responsible for managing all hidden nodes and most of the functionality behind other node types that inherit it.

Node .new ( string activeName, number bias = 0, number learningRate = 0.1 )

Creates and returns the Node with the given activation function, bias, and learning rate.

void : ( )

Fires the node and fires any node that is at it's output, potentially causing a chain reaction.

This function is fired when the Node object is called, such as:

node:()

number :GetValue ( )

Returns the node's output value if calculated. If not, calculates it and returns the new output value.

void :SetValue ( number value )

Sets the node's output value to value.

number :CalculateValue ( )

Calculates and sets the node's new output value while also returning it.

void :ClearValue ( )

Resets the node's output value.

void :AddInputSynapse ( Synapse inputSynapse )

Adds the synapse inputSynapse as an input synapse.

void :AddOutputSynapse ( Synapse outputSynapse )

Adds the synapse outputSynapse as an output synapse.

array :GetInputSynapses ( )

Returns the input synapses.

array :GetOutputSynapses ( )

Returns the output synapses.

void :RemoveInputSynapse ( Synapse inputSynapse )

Removes the input synapse inputSynapse.

void :RemoveOutputSynapse ( Synapse outputSynapse )

Removes the output synapse !inputSynapse!.

void :ClearInputSynapses ( )

Removes all input synapses.

void :ClearOutputSynapses ( )

Removes all output synapses.

void :SetBias ( number bias )

Sets the node's bias to bias.

number :GetBias ( )

Returns the node's bias.

void :AddBias ( number biasDelta )

Adds biasDelta to the noder's bias.

void :SetLearningRate ( number learningRate )

Sets the node's learning rate to learningRate.

number :GetLearningRate ( )

Returns the node's learning rate.

ActivationFunction :GetActivationFunction ( )

Returns the node's ActivationFunction object.

void :AddRandomNoise ( number min, number max )

Adds random noise to the node's bias and input weights with the given minimum min and maximum max.