public void pushDataToFile(String fileLocation, KFoldData dataPoint)
{
string output = JsonConvert.SerializeObject(dataPoint);
using (StreamWriter sw = new StreamWriter(fileLocation))
{
sw.WriteLine(output);
}
}
public async void Validate(int inputSize, int outputSize)
{
List <KFoldData> inputsList = new List <KFoldData>();
for (double trainingweights = 0.01; trainingweights <= 1.6; trainingweights += 0.1)
{
for (int breadth = 10; breadth <= 1000; breadth += 50)
{
for (int depth = 1; depth < 5; depth++)
{
inputsList.Add(new KFoldData(breadth, depth, trainingweights, 0, 0));
}
}
}
var kFoldList = await Task.WhenAll(inputsList.Select(i => kfold(inputSize, outputSize, i.Breadth, i.Depth, i.TrainingSpeed)));
KFoldData returnedKFoldData = new KFoldData(0, 0, 0, 0, double.MaxValue);
KFoldData[] KFoldArray = kFoldList;
foreach (var tempKFoldData in KFoldArray)
{
if (tempKFoldData.K < returnedKFoldData.K)
{
returnedKFoldData = tempKFoldData;
}
}
Console.WriteLine("Best value is:");
Console.WriteLine("Depth: " + returnedKFoldData.Depth);
Console.WriteLine("Breadth: " + returnedKFoldData.Breadth);
Console.WriteLine("Training Speed: " + returnedKFoldData.TrainingSpeed);
Console.WriteLine("Avg K Value: " + returnedKFoldData.K);
int[] returnArray = new int[returnedKFoldData.Depth];
for (int fillVal = 0; fillVal < returnedKFoldData.Depth; fillVal++)
{
if (fillVal == returnedKFoldData.Depth - 1)
{
returnArray[fillVal] = outputSize;
}
else
{
returnArray[fillVal] = returnedKFoldData.Breadth;
}
}
topResults = returnedKFoldData;
network = new ActivationNetwork(new SigmoidFunction(), inputSize, returnArray);
network.Randomize();
Save();
Console.WriteLine("Done!");
}
//private CrossValidation<ActivationNetwork, double, double> validator;
public NeuralNet(int inputs, int outputs, string ANNfilename, string KFoldFilename)
{
NodeSavingReading reader = new NodeSavingReading();
this.ANNfilename = ANNfilename;
this.KFoldFilename = KFoldFilename;
try
{
network = (ActivationNetwork)Network.Load(ANNfilename);
topResults = reader.GetKFoldDataFromFile(KFoldFilename);
}
catch (FileNotFoundException e)
{
Console.WriteLine("Could not find file, generating new one");
network = new ActivationNetwork(new SigmoidFunction(), inputs, new int[4] {
10, 10, 10, outputs
});
}
}
async Task <KFoldData> kfold(int inputSize, int outputSize, int breadth, int depth, double trainingweights)
{
await Task.Delay(1).ConfigureAwait(false);
double bestKVal = double.MaxValue;
KFoldData bestVal = new KFoldData(0, 0, 0, 0, 0);
for (int iterations = 10; iterations < 10000; iterations = iterations * 10)
{
int[] nodeArray = new int[depth + 1];
for (int fillVal = 0; fillVal < depth; fillVal++)
{
if (fillVal == 0) // depth - 1)
{
nodeArray[0] = outputSize;
}
else
{
nodeArray[fillVal] = breadth;
}
}
double kSumAvg = 0;
for (int i = 0; i < 5; i++)
{
var testNet = new ActivationNetwork(new SigmoidFunction(), inputSize, nodeArray);
var testLearner = new ResilientBackpropagationLearning(testNet);
testLearner.LearningRate = trainingweights;
int length = dataset_in.GetLength(0) / 5;
var trainingArrayIn = new double[dataset_in.GetLength(0) * 4 / 5][];
var trainingArrayOut = new double[dataset_out.GetLength(0) * 4 / 5][];
var testingArrayIn = new double[dataset_in.GetLength(0) / 5][];
var testingArrayOut = new double[dataset_out.GetLength(0) / 5][];
dataset_in.Take(i * length).ToArray().CopyTo(trainingArrayIn, 0);
dataset_in.Skip((i * length) + length).Take((length * 5) - (i * length + length)).ToArray().CopyTo(trainingArrayIn, i * length);
testingArrayIn = dataset_in.Skip(i * length).Take(length).ToArray();
dataset_out.Take(i * length).ToArray().CopyTo(trainingArrayOut, 0);
dataset_out.Skip((i * length) + length).Take((length * 5) - (i * length + length)).ToArray().CopyTo(trainingArrayOut, i * length);
testingArrayOut = dataset_out.Skip(i * length).Take(length).ToArray();
for (int iteration = 0; iteration < iterations; iteration++)
{
testLearner.RunEpoch(trainingArrayIn, trainingArrayOut);
}
double kSum = 0;
for (int k = 0; k < testingArrayIn.GetLength(0); k++)
{
var testResults = testNet.Compute(testingArrayIn[k]);
for (int j = 0; j < testResults.Length; j++)
{
kSum += Math.Abs(testResults[j] - testingArrayOut[k][j]);
}
kSumAvg += kSum;
}
}
kSumAvg = kSumAvg / dataset_in.GetLength(0);
if (kSumAvg == 0)
{
bestKVal = kSumAvg;
bestVal = new KFoldData(breadth, depth, trainingweights, iterations, bestKVal);
Console.WriteLine("Thread Complete " + breadth + " " + depth + " " + trainingweights + " " + iterations + " " + bestKVal);
return(bestVal);
}
if (kSumAvg < bestKVal)
{
bestKVal = kSumAvg;
bestVal = new KFoldData(breadth, depth, trainingweights, iterations, bestKVal);
}
}
Console.WriteLine("Thread Complete " + breadth + " " + depth + " " + trainingweights + " " + bestVal.Iterations + " " + bestKVal);
return(bestVal);
//return new Task<KFoldData>(() => helperFunction(inputSize, outputSize, breadth,depth,trainingweights));
}
