public static void DActivIdentity(Vector x, Vector v, ref Vector derivatives)
{
if (v == null || derivatives == null || v.Dimension < x.Dimension || derivatives.Dimension < x.Dimension)
throw new Exception("Neural.MLPerceptron2.ALayer.DActivIdentity()");
derivatives.FillWith(1);
}
public void Build(int in_dim, int[] layer_size, CellType[] types)
{
#region validating arguments
if (in_dim < 0 || layer_size.Length < 1 || layer_size.Length != types.Length)
{
throw new Exception(this.GetType().ToString() + ".Build: wrong arguments");
}
if (layer_size.Any(t => t < 1))
{
throw new Exception(this.GetType().ToString() + ".Build: nonpositive layer size");
}
#endregion
#region initializing structures
InputAverage = new Vector(in_dim);
InputAverage.FillWith(0);
InputInvStddev = new Vector(in_dim);
InputInvStddev.FillWith(1);
Input = new Vector(in_dim);
Layer = new ALayer[layer_size.Length];
int layer_input_dim = in_dim;
for (int l = 0; l < Layer.Length; l++)
{
Layer[l] = new ALayer();
Layer[l].Build(layer_input_dim, types[l], layer_size[l]);
layer_input_dim = Layer[l].Output.Dimension;
}
int out_dim = layer_size[layer_size.Length - 1];
dL_dOutput = new Vector(out_dim);
#endregion
AfterConstruction();
}
// product of vector and matrix
public static Vector operator *(Vector v, Matrix m)
{
int rows = m.Height;
int cols = m.Width;
if (v.Dimension != rows)
throw new Exception("Computing.Vector.operator*(Vector,Matrix)");
var ret = new Vector(cols);
ret.FillWith(0);
for (int j = 0; j < cols; j++)
for (int i = 0; i < rows; i++)
ret[j] += m[i, j] * v[i];
return (ret);
}
public static Vector operator *(Matrix m, Vector v)
{
int ret_dim=m.Height;
int v_dim=v.Dimension;
if (v_dim!=m.Width)
throw (new System.Exception("Multiplication of matrix and vector impossible due to dimensions"));
Vector ret = new Vector(ret_dim);
ret.FillWith(0);
for (int i=0; i<ret_dim; i++)
for (int j=0; j<v_dim; j++)
ret[i] += m[i, j] * v[j];
return (ret);
}
public static void Inverse(Matrix A, ref Matrix invA)
{
int i,j,dim = A.Height;
if (dim<1 || dim!=A.Width)
throw new Exception("Dimensions do not fit");
Matrix.AssureDimensions(ref invA, dim,dim);
Vector b = new Vector(dim), x = null;
b.FillWith(0);
for (j=0; j<dim; j++)
{
b[j] = 1;
LinearEquationGaussElimination(A, ref x, b);
for (i=0; i<dim; i++)
invA[i,j] = x[i];
b[j] = 0;
}
}