internal static float[] GetOutputSignalOfSample(float[] inputSignal, float[][] neuronWeights, ActivationFunc activationFunc, float[] isBiasNeuron)
{
int _synapsesCount = neuronWeights.Length;
int _neuronsCount = neuronWeights[0].Length;
float[] _outputSignals = new float[_neuronsCount];
float[][] _neuronWeightsT = TransposeArray(neuronWeights);
for (int neuron = 0; neuron < _neuronsCount; neuron++)
{
float _inputSignalForNeuron = 0;
for (int synapse = 0; synapse < _synapsesCount; synapse++)
{
_inputSignalForNeuron += inputSignal[synapse] * _neuronWeightsT[neuron][synapse] + (isBiasNeuron != null ? isBiasNeuron[neuron] : 0);
}
_outputSignals[neuron] = NeuralMath.GetApproximateByFunction(_inputSignalForNeuron, activationFunc);
}
return(_outputSignals);
}
internal static float[][] GetOutputSignalsOfAllSamples(float[][] inputSignals, float[][] neuronWeights, ActivationFunc activationFunc, float[] isBiasNeuron)
{
int _synapsesCount = neuronWeights.Length;
int _neuronsCount = neuronWeights[0].Length;
float[][] _outputSignals = new float[inputSignals.Length][];
float[][] _neuronWeightsT = TransposeArray(neuronWeights);
Parallel.For(0, inputSignals.Length, numberOfActiveDataset =>
{
float _inputSignalForNeuron;
if (_outputSignals[numberOfActiveDataset] == null)
{
_outputSignals[numberOfActiveDataset] = new float[_neuronsCount];
}
for (int neuron = 0; neuron < _neuronsCount; neuron++)
{
_inputSignalForNeuron = SumAllSynapseSignals(numberOfActiveDataset, neuron, _synapsesCount);
_outputSignals[numberOfActiveDataset][neuron] = NeuralMath.GetApproximateByFunction(_inputSignalForNeuron, activationFunc);
}
});
return(_outputSignals);
float SumAllSynapseSignals(int currentSet, int neuron, int synapsesOfNeuron)
{
float _inputSignal = 0;
for (int synapse = 0; synapse < synapsesOfNeuron; synapse++)
{
_inputSignal += inputSignals[currentSet][synapse] * _neuronWeightsT[neuron][synapse] + (isBiasNeuron != null ? isBiasNeuron[neuron] : 0);
}
return(_inputSignal);
}
}
