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; }