static void Main(string[] args)
{
Neural neural = new Neural(1, 2, 1);
double learningRate = 0.7;
double moment = 0.4;
double maxEpouch = 10000;
double expectError = 0.00001;
string fileName = @"E:\PROJECT\FINAL PROJECT\Other\Test\fuel.txt";
List<double> sample = new List<double>();
System.IO.StreamReader file = null;
string line = null;
int counter = 0;
bool isFormatFileRight = true;
int beginRow = 1;
int endRow = 71;
int columnSelected = 1;
int idxRow = 0;
try
{
file = new System.IO.StreamReader(fileName);
while ((line = file.ReadLine()) != null)
{
idxRow++;
if (idxRow < beginRow || idxRow > endRow)
continue;
char[] delimiterChars = { ' ', ',' };
string[] words = line.Split(delimiterChars);
if (columnSelected <= words.Length)
{
sample.Add(Double.Parse(words[columnSelected - 1]));
}
else
{
isFormatFileRight = false;
break;
}
}
}
catch (System.OutOfMemoryException outOfMemory)
{
sample = null;
}
double max = sample.Max();
double min = sample.Min();
int count = sample.Count;
double[] series = new double[count];
List<double> sample2 = new List<double>();
for (int i = 0; i < count; i++)
{
double a = sample.ElementAt(i);
double b = (a - min) / (max - min) * (0.99 - 0.01) + 0.01;
series[i] = b;
sample2.Add(b);
}
NeuralTraining training = new NeuralTraining();
training.s_Network = neural;
//training.Rprop_Run(sample2, null);
training.Bp_Run(sample2, null, 0.7, 0.4);
int x = 0;
}
static void Main(string[] args)
{
Neural neural = new Neural(1, 2, 1);
double learningRate = 0.7;
double moment = 0.4;
double maxEpouch = 10000;
double expectError = 0.00001;
string fileName = @"E:\PROJECT\FINAL PROJECT\Other\Test\fuel.txt";
List <double> sample = new List <double>();
System.IO.StreamReader file = null;
string line = null;
int counter = 0;
bool isFormatFileRight = true;
int beginRow = 1;
int endRow = 71;
int columnSelected = 1;
int idxRow = 0;
try
{
file = new System.IO.StreamReader(fileName);
while ((line = file.ReadLine()) != null)
{
idxRow++;
if (idxRow < beginRow || idxRow > endRow)
{
continue;
}
char[] delimiterChars = { ' ', ',' };
string[] words = line.Split(delimiterChars);
if (columnSelected <= words.Length)
{
sample.Add(Double.Parse(words[columnSelected - 1]));
}
else
{
isFormatFileRight = false;
break;
}
}
}
catch (System.OutOfMemoryException outOfMemory)
{
sample = null;
}
double max = sample.Max();
double min = sample.Min();
int count = sample.Count;
double[] series = new double[count];
List <double> sample2 = new List <double>();
for (int i = 0; i < count; i++)
{
double a = sample.ElementAt(i);
double b = (a - min) / (max - min) * (0.99 - 0.01) + 0.01;
series[i] = b;
sample2.Add(b);
}
NeuralTraining training = new NeuralTraining();
training.s_Network = neural;
//training.Rprop_Run(sample2, null);
training.Bp_Run(sample2, null, 0.7, 0.4);
int x = 0;
}
