static void DisplayFullNetworkResponse()
{
DataTable dt = new DataTable();
dt.Columns.Add("y1");
dt.Columns.Add("y2");
dt.Columns.Add("y3");
for (int l = 0; l < X_train.Length; l++)
{
double[][] x = Matrix.Create(1, N);
x[0] = X_train[l];
var y = CalculateReponse(x);
DataRow row = dt.NewRow();
row[0] = "PD";
row[1] = "PI";
row[2] = "PID";
row[3] = y[0];
row[4] = y[1];
row[5] = y[2];
dt.Rows.Add(row);
}
ConsoleTableBuilder builder = ConsoleTableBuilder.From(dt);
builder.ExportAndWrite();
}
static void DisplayQuickNetworkResponse()
{
DataTable dt = new DataTable();
dt.Columns.Add("EXPECTED");
dt.Columns.Add("ACTUAL");
dt.Columns.Add("PROBABILITY [%]");
for (int l = 0; l < Y_validation.Length; l++)
{
int expectedResponse = Y_validation[l].ToList().IndexOf(Y_validation[l].Max());
var y = Validate(l);
double actualMaxValue = y.Max();
int actualResponse = y.ToList().IndexOf(actualMaxValue);
DataRow row = dt.NewRow();
row[0] = (PID)expectedResponse;
row[1] = (PID)actualResponse;
row[2] = actualMaxValue;
dt.Rows.Add(row);
}
ConsoleTableBuilder builder = ConsoleTableBuilder.From(dt);
builder.ExportAndWrite();
}
static void DisplayQuickNetworkResponse()
{
DataTable dt = new DataTable();
dt.Columns.Add("EXPECTED");
dt.Columns.Add("ACTUAL");
dt.Columns.Add("PROBABILITY [%]");
for (int l = 0; l < X.Length; l++)
{
double[][] x = Matrix.Create(1, N);
x[0] = X[l];
int expectedResponse = Y[l].ToList().IndexOf(Y[l].Max());
var y = CalculateReponse(x);
double actualMaxValue = y.Max();
int actualResponse = y.ToList().IndexOf(actualMaxValue);
DataRow row = dt.NewRow();
row[0] = (Animals)expectedResponse;
row[1] = (Animals)actualResponse;
row[2] = actualMaxValue;
dt.Rows.Add(row);
}
ConsoleTableBuilder builder = ConsoleTableBuilder.From(dt);
builder.ExportAndWrite();
}
static void Check()
{
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~CHECK~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
DataTable dt = new DataTable();
dt.Columns.Add("x1");
dt.Columns.Add("x2");
dt.Columns.Add("true");
dt.Columns.Add("false");
for (int l = 0; l < X[0].Length; l++)
{
double[][] x = Matrix.Create(1, N);
x[0] = X.GetColumn(l);
double[][] u1 = Matrix.Multiple(W.Transpose(), x.Transpose());
double[][] y1 = Matrix.Create(K, 1);
for (int i = 0; i < u1.Length; i++)
{
y1[i][0] = 1 / (1 + Math.Exp(-Beta * u1[i][0]));
}
//2nd layer
double[][] u2 = Matrix.Multiple(W2.Transpose(), y1);
double[] y2 = new double[M];
for (int i = 0; i < u1.Length; i++)
{
y2[i] = 1 / (1 + Math.Exp(-Beta * u2[i][0]));
}
DataRow row = dt.NewRow();
row[0] = x[0][0];
row[1] = x[0][1];
row[2] = y2[0];
row[3] = y2[1];
dt.Rows.Add(row);
}
ConsoleTableBuilder builder = ConsoleTableBuilder.From(dt);
builder.ExportAndWrite();
}
public void Print()
{
DataTable dt = new DataTable();
dt.Columns.Add("Name");
dt.Columns.Add("Neurons");
dt.Columns.Add("Weight");
foreach (var element in Layers)
{
DataRow row = dt.NewRow();
row[0] = element.Name;
row[1] = element.Neurons.Count;
row[2] = element.Weight;
dt.Rows.Add(row);
}
ConsoleTableBuilder builder = ConsoleTableBuilder.From(dt);
builder.ExportAndWrite();
}
