/// <summary>
/// Returns the difference for each neuron bewteen the expected values and actual values
/// </summary>
/// <param name="IEO"></param>
/// <returns></returns>
private double[] CalculateDifferencePerNeuron(InputExpectedOutputPair IEO)
{
Double[] Result = neuralNetwork.Predict(IEO.InputArray);
int iterator = 0;
foreach (double D in IEO.ExpectedOutput)
{
Result[iterator] = D - Result[iterator];
iterator++;
}
return(Result);
}
/// <summary>
/// Trains the neural net by simulating an input and comparing the expected output with the actual output
/// </summary>
/// <param name="InputExpectedOutputList">Pairs of input data and their respective expected outputs</param>
/// <returns></returns>
///
/*
* public Double Train(List<InputExpectedOutputPair> InputExpectedOutputList)
* {
* //Calculate total cost of the network over all examples given
* double CumulativeCost = 0;
* foreach (InputExpectedOutputPair IEO in InputExpectedOutputList)
* {
* //Run number through neural net
* //Compare output with expected output
* CumulativeCost += CalculateCost(IEO);
* }
* Double AverageCost = (CumulativeCost / InputExpectedOutputList.Count);
*
*
* //create an array that has enough indexes for the number of neurons from the previous layer
* int PlaceHolderNumberOfNeurons = 20;
* Double[] Nudges = new Double[PlaceHolderNumberOfNeurons];
*
* foreach (InputExpectedOutputPair IEO in InputExpectedOutputList)
* {
* //Now focus on ONE training example
* //nudge each neuron in approparite direciton
* //magnitude of nudge is proportional to distance from ideal value
* Double[] Difference = CalculateDifferencePerNeuron(InputExpectedOutputList.First());
*
* //now we can nudge the neuron by either changing bias, changing weighting, change activation from previous layer
* //Focus on neurons from previous layers with stronger activations as they are more relevant to this single example
* //compute this for all outputs, even if they are outputs we do not want as we can aim to reduce them to 0
*
* //keep track of what neuron we're on
* int iterator = 0;
* foreach (Double D in Difference)
* {
* //look at all inputs and weights from previous layer
* //nudge appropriately by the difference
* //add nudge to the Double[] Nudges
* iterator++;
* }
* }
*
* //apply result of nudges to neural network weightings
*
* }
*/
/// <summary>
/// Calculates the cost of one training example for all the whole network
/// </summary>
/// <param name="IEO"></param>
/// <returns></returns>
private double CalculateCost(InputExpectedOutputPair IEO)
{
Double[] Result = neuralNetwork.Predict(IEO.InputArray);
//square of the difference between the expected and actual results
int iterator = 0;
double difference = 0;
foreach (double D in IEO.ExpectedOutput)
{
difference += Math.Pow(D - IEO.ExpectedOutput[iterator], 2);
iterator++;
}
return(difference);
}
