void UczenieWstepne()
{
double blad = 1;
double poprzedni_blad = 1;
double odZmiany = 0;
Boolean czyUczyc = true;
int j = 0;
for (int i = 0; i < 6; i++)
{
WygenerujZestawUczacy(j % 6);
j++;
}
List <double[]> wejscie = new List <double[]>();
List <double[]> wyjscie = new List <double[]>();
foreach (ZestawDanychSieci zestawDanych in zestawyUczace)
{
zestawDanych.NormalizujWejscie();
wejscie.Add(zestawDanych.TablicaWejscia());
wyjscie.Add(zestawDanych.TablicaWyjscia());
}
double[][] wejscieTab = wejscie.ToArray();
double[][] wyjscieTab = wyjscie.ToArray();
while (czyUczyc)
{
// run epoch of learning procedure
//blad = nauczyciel.RunEpoch(wejscieTab, wyjscieTab);
blad = ewolutor.RunEpoch(wejscieTab, wyjscieTab);
if (blad <= PROG_NAUKI)
{
czyUczyc = false;
}
else if (poprzedni_blad == blad)
{
if (odZmiany >= MAX_BEZ_ZMIANY)
{
czyUczyc = false;
}
else
{
odZmiany++;
}
}
else
{
poprzedni_blad = blad;
odZmiany = 0;
}
}
zestawyUczace.Clear();
}
void backgroundWorkerTrainer_DoWork(object sender, DoWorkEventArgs e)
{
while (work)
{
// run epoch of learning procedure
double error = teacherEV.RunEpoch(input, output);
// check error value to see if we need to stop
// ...
var c = error * 10;
var d = c.ToString();
gerrror.Add(Convert.ToInt32(c));
SetText(error.ToString());
chartdata.Add(Convert.ToSingle(c));
listPointsOne.Add((double)watch.ElapsedMilliseconds, error);
axisChangeZedGraph(zedGraphControl1);
Thread.Sleep(2);
if (logger)
{
SetTextLogger(error.ToString());
}
if (watch.IsRunning)
{
SetTextTime(watch.ElapsedMilliseconds.ToString());
}
}
}
private void TrainNetworkE(TrainSet trainSet, EvolutionaryLearning teacher)
{
double[] sides = new double[4];
double[] direction = new double[2];
double[] input = new double[5];
double[] output = new double[4];
double error = 10;
int epoch = 10000;
while (epoch-- > 0)
{
for (int s = 0; s < trainSet.Situation.Count; s++)
{
sides = SimplifyEnvironment(trainSet.Situation[s].Environment);
direction = VectorFromDirection(trainSet.Decision[s].Direction);
// INPUT
input[0] = trainSet.Situation[s].ColonyPopulation;
input[1] = sides[0]; // UP
input[2] = sides[1]; // RIGHT
input[3] = sides[2]; // DOWN
input[4] = sides[3]; // LEFT
// OUTPUT
output[0] = trainSet.Decision[s].IsLeaving ? 1 : -1;
output[1] = trainSet.Decision[s].PopulationToMove;
output[2] = direction[0]; // X
output[3] = direction[1]; // Y
error = teacher.RunEpoch(new double[][] { input }, new double[][] { output });
}
Debug.Print(error.ToString());
}
}
void backgroundWorkerTrainer_DoWork(object sender, DoWorkEventArgs e)
{
while (work)
{
double error = 0;
// run epoch of learning procedure
if (selected_algorythm == 0)
{
error = teacher.RunEpoch(input, output);
listPointsOne.Add((double)watch1.ElapsedMilliseconds, error);
}
if (selected_algorythm == 1)
{
error = reprop.RunEpoch(input, output);
listPointsOne.Add((double)watch1.ElapsedMilliseconds, error);
}
if (selected_algorythm == 2)
{
error = evteacher.RunEpoch(input, output);
listPointsOne.Add((double)watch1.ElapsedMilliseconds, error);
}
if (selected_algorythm == 3)
{
error = lbteacher.RunEpoch(input, output);
listPointsOne.Add((double)watch1.ElapsedMilliseconds, error);
}
if (selected_algorythm == 4)
{
error = delta.RunEpoch(input, output);
listPointsOne.Add((double)watch1.ElapsedMilliseconds, error);
}
if (selected_algorythm == 5)
{
error = perceptron.RunEpoch(input, output);
listPointsOne.Add((double)watch1.ElapsedMilliseconds, error);
}
// check error value to see if we need to stop
// ...
var c = error * 10;
var d = c.ToString();
//gerrror.Add(Convert.ToInt32(c));
SetText(error.ToString());
chartdata.Add(Convert.ToSingle(c));
axisChangeZedGraph(zedGraphControl1);
Thread.Sleep(2);
if (logger)
{
SetTextLogger(error.ToString());
}
if (watch1.IsRunning)
{
SetTextTime(watch1.ElapsedMilliseconds.ToString());
}
Thread.Sleep(sleeptime);
}
}
static double Neural_Network(bool show)
{
double error = new double();
DataTable entireData = DataController.MakeDataTable("../../drug_consumption.txt");
Codification codebook = new Codification(entireData);
//"Alcohol", "Amfet", !!"Amyl", "Benzos", "Cofeine", "Cannabis", "Chocolate", "Coke", (1)"Crac", ///"Ecstasy", !!"Heroine",
// !!"Ketamine", //"LegalH", "LSD", !!"Meth", //"Mushrooms", "Nicotine", lol "Semeron", "VSA"
string LookingFor = "Heroine";
int good = 0;
string[][] outputs;
string[][] inputs = DataController.MakeString("../../drug_consumption_500.txt", out outputs);
string[][] testOutputs;
string[][] testInputs = DataController.MakeString("../../drug_consumption_500.txt", out testOutputs);
DataTable outputs1 = DataController.MakeDataFromString(outputs, "output");
DataTable inputs1 = DataController.MakeDataFromString(inputs, "input");
DataTable testOutputs1 = DataController.MakeDataFromString(testOutputs, "output");
DataTable testInputs1 = DataController.MakeDataFromString(testInputs, "input");
DataTable Isymbols = codebook.Apply(inputs1);
DataTable Osymbols = codebook.Apply(outputs1);
DataTable TIsymbols = codebook.Apply(testInputs1);
DataTable TOsymbols = codebook.Apply(testOutputs1);
double[][] inputsD = Isymbols.ToJagged <double>("Age", "Gender", "Education", "Country", "Eticnity", "Nscore", "Escore", "Oscore", "Ascore", "Cscore", "Impulsive", "SS");
double[][] outputsD = Osymbols.ToJagged <double>(LookingFor);
outputsD = DataController.convertDT(outputsD);
double[][] inputsT = TIsymbols.ToJagged <double>("Age", "Gender", "Education", "Country", "Eticnity", "Nscore", "Escore", "Oscore", "Ascore", "Cscore", "Impulsive", "SS");
double[][] outputsT = TOsymbols.ToJagged <double>(LookingFor);
outputsT = DataController.convertDT(outputsT);
DeepBeliefNetwork network = new DeepBeliefNetwork(inputs.First().Length, 10, 7);
new GaussianWeights(network, 0.1).Randomize();
network.UpdateVisibleWeights();
DeepBeliefNetworkLearning FirstLearner = new DeepBeliefNetworkLearning(network)
{
Algorithm = (h, v, i) => new ContrastiveDivergenceLearning(h, v)
{
LearningRate = 0.1,
Momentum = 0.5,
Decay = 0.001,
}
};
int batchCount = Math.Max(1, inputs.Length / 100);
int[] groupsNew = Accord.Statistics.Classes.Random(inputsD.Length, batchCount);
double[][][] batchesNew = Accord.Statistics.Classes.Separate(inputsD, groupsNew);
double[][][] layerData;
for (int layerIndex = 0; layerIndex < network.Machines.Count - 1; layerIndex++)
{
FirstLearner.LayerIndex = layerIndex;
layerData = FirstLearner.GetLayerInput(batchesNew);
for (int i = 0; i < 500; i++)
{
error = FirstLearner.RunEpoch(layerData) / inputsD.Length;
if (i % 10 == 0 && show == true)
{
Console.WriteLine("Error value(" + LookingFor + ", test: " + i + ") = " + error);
}
}
}
var SecondLearner = new BackPropagationLearning(network)
{
LearningRate = 0.15,
Momentum = 0.7
};
EvolutionaryLearning teacher = new EvolutionaryLearning(network, 100);
for (int i = 0; i < 800; i++)
{
error = teacher.RunEpoch(inputsD, outputsD) / inputsD.Length;
if (i % 50 == 0 && show == true)
{
Console.WriteLine("Error value(" + LookingFor + ", test: " + i + ") = " + error);
}
}
for (int i = 0; i < 800; i++)
{
error = SecondLearner.RunEpoch(inputsD, outputsD) / inputsD.Length;
if (i % 10 == 0 && show == true)
{
Console.WriteLine("Error value(" + LookingFor + ", test: " + i + ") = " + error);
}
}
for (int i = 0; i < inputsD.Length; i++)
{
double[] outputValues = network.Compute(inputsT[i]);
if (outputValues.ToList().IndexOf(outputValues.Max()) == outputsT[i].ToList().IndexOf(outputsT[i].Max()))
{
good++;
}
}
if (show == true)
{
Console.WriteLine("Poprawność - " + Math.Round(((double)good / (double)inputsD.Length * 100), 4) + "%");
Console.ReadKey();
}
return(error);
}
///<summary>
/// Trains the neural network on input training data using an evolutionary algorithm and returns the final error.
///</summary>
public double Train(double[][] trainingData, double[][] targetResults)
{
return(teacher.RunEpoch(trainingData, targetResults));
}