/// <summary>
/// Construct a FastAcyclicNetwork with provided network definition data structures.
/// </summary>
/// <param name="nodeActivationFnArr">Array of neuron activation functions.</param>
/// <param name="nodeAuxArgsArr">Array of neuron activation function arguments.</param>
/// <param name="connectionArr">Array of connections.</param>
/// <param name="layerInfoArr">Array of layer information.</param>
/// <param name="outputNodeIdxArr">An array that specifies the index of each output neuron within _activationArr.
/// This is necessary because the neurons have been sorted by their depth in the network structure and are therefore
/// no longer in their original positions. Note however that the bias and input neurons *are* in their original
/// positions as they are defined as being at depth zero.</param>
/// <param name="nodeCount">Number of nodes in the network.</param>
/// <param name="inputNodeCount">Number of input nodes in the network.</param>
/// <param name="outputNodeCount">Number of output nodes in the network.</param>
/// <param name="boundedOutput">Indicates that the output values at the output nodes should be bounded to the interval [0,1]</param>
public FastAcyclicNetwork(IActivationFunction[] nodeActivationFnArr,
double[][] nodeAuxArgsArr,
FastConnection[] connectionArr,
LayerInfo[] layerInfoArr,
int[] outputNodeIdxArr,
int nodeCount,
int inputNodeCount,
int outputNodeCount,
bool boundedOutput)
{
// Store refs to network structure data.
_nodeActivationFnArr = nodeActivationFnArr;
_nodeAuxArgsArr = nodeAuxArgsArr;
_connectionArr = connectionArr;
_layerInfoArr = layerInfoArr;
// Create working array for node activation signals.
_activationArr = new double[nodeCount];
// Wrap a sub-range of the _activationArr that holds the activation values for the input nodes.
// Offset is 1 to skip bias neuron (The value at index 1 is the first black box input).
_inputSignalArrayWrapper = new SignalArray(_activationArr, 1, inputNodeCount);
// Wrap the output nodes. Nodes have been sorted by depth within the network therefore the output
// nodes can no longer be guaranteed to be in a contiguous segment at a fixed location. As such their
// positions are indicated by outputNodeIdxArr, and so we package up this array with the node signal
// array to abstract away the level of indirection described by outputNodeIdxArr.
if (boundedOutput)
{
_outputSignalArrayWrapper = new OutputMappingSignalArray(_activationArr, outputNodeIdxArr);
}
else
{
_outputSignalArrayWrapper = new MappingSignalArray(_activationArr, outputNodeIdxArr);
}
// Store counts for use during activation.
_inputNodeCount = inputNodeCount;
_inputAndBiasNodeCount = inputNodeCount + 1;
_outputNodeCount = outputNodeCount;
// Initialise the bias neuron's fixed output value.
_activationArr[0] = 1.0;
}
/// <summary>
/// Construct a FastAcyclicNetwork with provided network definition data structures.
/// </summary>
/// <param name="nodeActivationFnArr">Array of neuron activation functions.</param>
/// <param name="nodeAuxArgsArr">Array of neuron activation function arguments.</param>
/// <param name="connectionArr">Array of connections.</param>
/// <param name="layerInfoArr">Array of layer information.</param>
/// <param name="outputNodeIdxArr">An array that specifies the index of each output neuron within _activationArr.
/// This is necessary because the neurons have been sorted by their depth in the network structure and are therefore
/// no longer in their original positions. Note however that the bias and input neurons *are* in their original
/// positions as they are defined as being at depth zero.</param>
/// <param name="nodeCount">Number of nodes in the network.</param>
/// <param name="inputNodeCount">Number of input nodes in the network.</param>
/// <param name="outputNodeCount">Number of output nodes in the network.</param>
public FastAcyclicNetwork(IActivationFunction[] nodeActivationFnArr,
double[][] nodeAuxArgsArr,
FastConnection[] connectionArr,
LayerInfo[] layerInfoArr,
int[] outputNodeIdxArr,
int nodeCount,
int inputNodeCount,
int outputNodeCount)
{
// Store refs to network structrue data.
_nodeActivationFnArr = nodeActivationFnArr;
_nodeAuxArgsArr = nodeAuxArgsArr;
_connectionArr = connectionArr;
_layerInfoArr = layerInfoArr;
// Create working array for node activation signals.
_activationArr = new double[nodeCount];
// Wrap a sub-range of the _activationArr that holds the activation values for the input nodes.
// Offset is 1 to skip bias neuron (The value at index 1 is the first black box input).
_inputSignalArrayWrapper = new SignalArray(_activationArr, 1, inputNodeCount);
// Wrap the output nodes. Nodes have been sorted by depth within the network therefore the output
// nodes can no longer be guaranteed to be in a contiguous segment at a fixed location. As such their
// positions are indicated by outputNodeIdxArr, and so we package up this array with the node signal
// array to abstract away the level of indirection described by outputNodeIdxArr.
_outputSignalArrayWrapper = new MappingSignalArray(_activationArr, outputNodeIdxArr);
// Store counts for use during activation.
_inputNodeCount = inputNodeCount;
_inputAndBiasNodeCount = inputNodeCount+1;
_outputNodeCount = outputNodeCount;
// Initialise the bias neuron's fixed output value.
_activationArr[0] = 1.0;
}
