// bpnn Training
public void training(int epoch, string path, int Znode, int Ynode, double learningRate, double numberOfClasses)
{
Console.WriteLine("Training Result :");
bpnn bp = new bpnn();
bp.num = 1;
bp.inputNode = 0;
bp.totalData = 0;
//read data from txt file
using (StreamReader myReader = new StreamReader(path))
{
string line = " ";
string l = "";
while (line != null)
{
line = myReader.ReadLine();
if (line != null)
{
l = line;
//Split data by comma
String[] value = l.Split(null);
//to get the number of input node
bp.inputNode = value.Length;
//to get the number of total iteration/ line of data(150 data)
bp.totalData = bp.totalData + 1;
}
}
myReader.Close();
}
//Read Input data
bp.Inode = bp.inputNode - 1;
bp.data = new double[bp.totalData, bp.Inode];
bp.target = new double[bp.totalData];
using (StreamReader myReader = new StreamReader(path))
{
string l = "";
int a = 0;
string line = myReader.ReadLine();
while (line != null)
{
l = line;
//Split data by space
String[] value = l.Split(null);
for (int i = 0; i < bp.inputNode; i++)
{
if (i == bp.inputNode - 1)
{
bp.target[a] = Convert.ToDouble(value[i]);
}
else
{
bp.data[a, i] = Convert.ToDouble(value[i]);
// Console.WriteLine(bp.data[a, i]);
}
}
a++;
line = myReader.ReadLine();
}
myReader.Close();
}
bp.Vweight = new double[bp.Inode, Znode];
bp.Wweight = new double[Znode, Ynode];
//Step 0 Initilize random weight
initializeWeight(bp.Inode, bp.Wweight, bp.Vweight, bp.inputNode, Znode, Ynode);
bp.x = new double[bp.Inode];
bp.deltaKError = new double[Ynode];
bp.Z_in = new double[Znode];
bp.Z = new double[Znode];
bp.Y_in = new double[Ynode];
bp.Y = new double[Ynode];
bp.weightChangeV = new double[bp.Inode, Znode];
bp.weightChangeW = new double[Znode, Ynode];
bp.sum2 = 0.0;
double benchmark = (1.0 / numberOfClasses) / 2.0;
int[,] confusionMatrix = new int[(int)numberOfClasses, (int)numberOfClasses];
while (bp.num <= epoch)
{
correctnessIrisSetosa = 0;
correctnessIrisVersicolour = 0;
correctnessIrisVirginica = 0;
incorrectnessIrisSetosa = 0;
incorrectnessIrisVersicolour = 0;
incorrectnessIrisVirginica = 0;
for (int p = 0; p < bp.totalData; p++)
{
for (int i = 0; i < bp.Inode; i++)
{
bp.x[i] = bp.data[p, i];
}
feedForward(bp.Inode, Znode, Ynode, bp.x, bp.Vweight, bp.Wweight, bp.Z_in, bp.Z, bp.Y_in, bp.Y, bp.target, p, numberOfClasses, benchmark, confusionMatrix);
bp.sum2 = bp.sum2 + backpropagationOfError(bp.Inode, Znode, Ynode, bp.deltaKError, bp.Vweight, bp.Wweight, bp.Y, bp.target, p, learningRate, bp.Z, bp.weightChangeW, bp.weightChangeV, bp.x);
updateWeight(bp.weightChangeV, bp.weightChangeW, bp.Vweight, bp.Wweight, bp.Inode, Znode, Ynode);
}
/* if (bp.meanSquareError < 0.1)
* {
* break;
* }*/
bp.num++;
}
File.Create("C:/Users/Firdaus Samad/Documents/Visual Studio 2015/Projects/backpropagation2/backpropagation2/WeightV.txt").Close();
File.Create("C:/Users/Firdaus Samad/Documents/Visual Studio 2015/Projects/backpropagation2/backpropagation2/WeightW.txt").Close();
WriteTextWeight(bp.Vweight, bp.Inode, Znode, "C:/Users/Firdaus Samad/Documents/Visual Studio 2015/Projects/backpropagation2/backpropagation2/WeightV.txt");
WriteTextWeight(bp.Wweight, Znode, Ynode, "C:/Users/Firdaus Samad/Documents/Visual Studio 2015/Projects/backpropagation2/backpropagation2/WeightW.txt");
Console.WriteLine("===============================================");
Console.WriteLine("Confusion Matrix");
Console.WriteLine("===============================================");
Console.WriteLine("a\tb\tc");
for (int i = 0; i < ((int)numberOfClasses); i++)
{
for (int y = 0; y < numberOfClasses; y++)
{
Console.Write(confusionMatrix[i, y] + "\t");
}
switch (i)
{
case 0:
Console.WriteLine("a - Iris-Setosa");
break;
case 1:
Console.WriteLine("b - Iris Versicolour");
break;
case 2:
Console.WriteLine("c - Iris-Virginica");
break;
}
}
Console.WriteLine();
Console.WriteLine("===============================================");
//for()
double correctness = correctnessIrisVirginica + correctnessIrisVersicolour + correctnessIrisSetosa;
double CorrectnessPercent = (correctness / bp.totalData) * 100;
double NotCorrectnessPercent = 100 - CorrectnessPercent;
Console.WriteLine("Correctly Classified Instances\t\t:" + (int)correctness + "\t" + CorrectnessPercent + "%");
Console.WriteLine("Incorrectly Classified Instances\t:" + (bp.totalData - (int)correctness) + "\t" + NotCorrectnessPercent + "%");
// calAccuracy(numberOfClasses, bp.Y, Ynode, bp.target, bp.totalData);
bp.meanSquareError = bp.sum2 / epoch;
Console.WriteLine("MSE :" + bp.meanSquareError);
}
//Testing
public void testing(string path, int Znode, int Ynode, double learningRate, double numberOfClasses)
{
Console.WriteLine("\n\nTesting Result :");
bpnn bp = new bpnn();
bp.num = 1;
bp.inputNode = 0;
bp.totalData = 0;
//read data from txt file
using (StreamReader myReader = new StreamReader(path))
{
string line = " ";
string l = "";
while (line != null)
{
line = myReader.ReadLine();
if (line != null)
{
l = line;
//Split data by comma
String[] value = l.Split(null);
//to get the number of input node
bp.inputNode = value.Length;
//to get the number of total iteration/ line of data(150 data)
bp.totalData = bp.totalData + 1;
}
}
myReader.Close();
}
//Read Input data
bp.Inode = bp.inputNode - 1;
bp.data = new double[bp.totalData, bp.Inode];
bp.target = new double[bp.totalData];
using (StreamReader myReader = new StreamReader(path))
{
string l = "";
int a = 0;
string line = myReader.ReadLine();
while (line != null)
{
l = line;
//Split data by space
String[] value = l.Split(null);
for (int i = 0; i < bp.inputNode; i++)
{
if (i == bp.inputNode - 1)
{
bp.target[a] = Convert.ToDouble(value[i]);
}
else
{
bp.data[a, i] = Convert.ToDouble(value[i]);
}
}
a++;
line = myReader.ReadLine();
}
myReader.Close();
}
bp.Vweight = new double[bp.Inode, Znode];
bp.Wweight = new double[Znode, Ynode];
bp.x = new double[bp.Inode];
bp.deltaKError = new double[Ynode];
bp.Z_in = new double[Znode];
bp.Z = new double[Znode];
bp.Y_in = new double[Ynode];
bp.Y = new double[Ynode];
bp.weightChangeV = new double[bp.Inode, Znode];
bp.weightChangeW = new double[Znode, Ynode];
bp.sum2 = 0.0;
double benchmark = (1.0 / numberOfClasses) / 2.0;
int[,] confusionMatrix = new int[(int)numberOfClasses, (int)numberOfClasses];
readWeight(Znode, bp.Inode, Ynode, bp.Wweight, bp.Vweight);
correctnessIrisSetosa = 0;
correctnessIrisVersicolour = 0;
correctnessIrisVirginica = 0;
incorrectnessIrisSetosa = 0;
incorrectnessIrisVersicolour = 0;
incorrectnessIrisVirginica = 0;
for (int p = 0; p < bp.totalData; p++)
{
for (int i = 0; i < bp.Inode; i++)
{
bp.x[i] = bp.data[p, i];
}
feedForward(bp.Inode, Znode, Ynode, bp.x, bp.Vweight, bp.Wweight, bp.Z_in, bp.Z, bp.Y_in, bp.Y, bp.target, p, numberOfClasses, benchmark, confusionMatrix);
}
/* if (bp.meanSquareError < 0.1)
* {
* break;
* }*/
bp.num++;
Console.WriteLine("===============================================");
Console.WriteLine("Confusion Matrix");
Console.WriteLine("===============================================");
Console.WriteLine("a\tb\tc");
for (int i = 0; i < ((int)numberOfClasses); i++)
{
for (int y = 0; y < numberOfClasses; y++)
{
Console.Write(confusionMatrix[i, y] + "\t");
}
switch (i)
{
case 0:
Console.WriteLine("a - Iris-Setosa");
break;
case 1:
Console.WriteLine("b - Iris Versicolour");
break;
case 2:
Console.WriteLine("c - Iris-Virginica");
break;
}
}
Console.WriteLine();
Console.WriteLine("===============================================");
//for()
double correctness = correctnessIrisVirginica + correctnessIrisVersicolour + correctnessIrisSetosa;
double CorrectnessPercent = (correctness / bp.totalData) * 100;
double NotCorrectnessPercent = 100 - CorrectnessPercent;
Console.WriteLine("Correctly Classified Instances\t\t:" + (int)correctness + "\t" + CorrectnessPercent + "%");
Console.WriteLine("Incorrectly Classified Instances\t:" + (bp.totalData - (int)correctness) + "\t" + NotCorrectnessPercent + "%");
}
