private double[,] error(TrainingData tmp)
{
double[,] y = VecToMatrix(tmp.output);
var ysim = ForwardPropagation(tmp.input);
return(Substract(y, ysim));
}
public double[,] ForwardPropagation(params double[] data)
{
training_data = new TrainingData(data, new double[] { });
double[,] input_data = VecToMatrix(training_data.input);
layers[0].values = input_data;
for (int i = 1; i < layers.Length; i++)
{
input_data = Multiply(input_data, layers[i].perceptron.scales);
input_data = activate(input_data, layers[i].perceptron.activationFunction);
layers[i].values = input_data;
}
return(input_data); //wynik propagacji w przod
}
public static List <TrainingData> CSVReadXO()
{
Console.WriteLine("Wczytanie danych z pliku CSV");
var list_of_data = new List <TrainingData>();
var column1 = new List <string>();
var column2 = new List <string>();
var column3 = new List <string>();
var column4 = new List <string>();
var column5 = new List <string>();
var column6 = new List <string>();
var column7 = new List <string>();
var column8 = new List <string>();
var column9 = new List <string>();
var column10 = new List <string>();
using (var rd = new StreamReader(path))
{
while (!rd.EndOfStream)
{
var splits = rd.ReadLine().Split(';');
column1.Add(splits[0]);
column2.Add(splits[1]);
column3.Add(splits[2]);
column4.Add(splits[3]);
column5.Add(splits[4]);
column6.Add(splits[5]);
column7.Add(splits[6]);
column8.Add(splits[7]);
column9.Add(splits[8]);
column10.Add(splits[9]);
}
}
for (int i = 0; i < column1.Count(); i++)
{
var tr_data = new TrainingData(new double[] { double.Parse(column1[i]), double.Parse(column2[i]), double.Parse(column3[i]),
double.Parse(column4[i]), double.Parse(column5[i]), double.Parse(column6[i]),
double.Parse(column7[i]), double.Parse(column8[i]), double.Parse(column9[i]) },
new double[] { double.Parse(column10[i]) });
list_of_data.Add(tr_data);
}
return(list_of_data);
}
