public Layer(Matrix weightMatrix, Vector biasVector, ActivationFunction af) {
activationFunction = af;
this.weightMatrix = weightMatrix;
lastWeightUpdateMatrix = new Matrix(weightMatrix.getRowDimension(),
weightMatrix.getColumnDimension());
penultimateWeightUpdateMatrix = new Matrix(weightMatrix
.getRowDimension(), weightMatrix.getColumnDimension());
this.biasVector = biasVector;
lastBiasUpdateVector = new Vector(biasVector.getRowDimension());
penultimateBiasUpdateVector = new Vector(biasVector.getRowDimension());
}
public Layer(int numberOfNeurons, int numberOfInputs,
double lowerLimitForWeights, double upperLimitForWeights,
ActivationFunction af) {
activationFunction = af;
this.weightMatrix = new Matrix(numberOfNeurons, numberOfInputs);
lastWeightUpdateMatrix = new Matrix(weightMatrix.getRowDimension(),
weightMatrix.getColumnDimension());
penultimateWeightUpdateMatrix = new Matrix(weightMatrix
.getRowDimension(), weightMatrix.getColumnDimension());
this.biasVector = new Vector(numberOfNeurons);
lastBiasUpdateVector = new Vector(biasVector.getRowDimension());
penultimateBiasUpdateVector = new Vector(biasVector.getRowDimension());
initializeMatrix(weightMatrix, lowerLimitForWeights,
upperLimitForWeights);
initializeVector(biasVector, lowerLimitForWeights, upperLimitForWeights);
}
/**
* Solve A*X = B
*
* @param B
* A Matrix with as many rows as A and any number of columns.
* @return X so that L*U*X = B(piv,:)
* @exception IllegalArgumentException
* Matrix row dimensions must agree.
* @exception ApplicationException
* Matrix is singular.
*/
public Matrix solve(Matrix B)
{
if (B.getRowDimension() != m)
{
throw new ArgumentException(
"Matrix row dimensions must agree.");
}
if (!this.isNonsingular())
{
throw new ApplicationException("Matrix is singular.");
}
// Copy right hand side with pivoting
int nx = B.getColumnDimension();
Matrix Xmat = B.getMatrix(piv, 0, nx - 1);
double[][] X = Xmat.getArray();
// Solve L*Y = B(piv,:)
for (int k = 0; k < n; k++)
{
for (int i = k + 1; i < n; i++)
{
for (int j = 0; j < nx; j++)
{
X[i][j] -= X[k][j] * LU[i][k];
}
}
}
// Solve U*X = Y;
for (int k = n - 1; k >= 0; k--)
{
for (int j = 0; j < nx; j++)
{
X[k][j] /= LU[k][k];
}
for (int i = 0; i < k; i++)
{
for (int j = 0; j < nx; j++)
{
X[i][j] -= X[k][j] * LU[i][k];
}
}
}
return(Xmat);
}
/*
* ------------------------ Constructor ------------------------
*/
/**
* LU Decomposition, a structure to access L, U and piv.
*
* @param A
* Rectangular matrix
*/
public LUDecomposition(Matrix A)
{
// Use a "left-looking", dot-product, Crout/Doolittle algorithm.
LU = A.getArrayCopy();
m = A.getRowDimension();
n = A.getColumnDimension();
piv = new int[m];
for (int i = 0; i < m; i++)
{
piv[i] = i;
}
pivsign = 1;
double[] LUrowi;
double[] LUcolj = new double[m];
// Outer loop.
for (int j = 0; j < n; j++)
{
// Make a copy of the j-th column to localize references.
for (int i = 0; i < m; i++)
{
LUcolj[i] = LU[i][j];
}
// Apply previous transformations.
for (int i = 0; i < m; i++)
{
LUrowi = LU[i];
// Most of the time is spent in the following dot product.
int kmax = Math.Min(i, j);
double s = 0.0;
for (int k = 0; k < kmax; k++)
{
s += LUrowi[k] * LUcolj[k];
}
LUrowi[j] = LUcolj[i] -= s;
}
// Find pivot and exchange if necessary.
int p = j;
for (int i = j + 1; i < m; i++)
{
if (Math.Abs(LUcolj[i]) > Math.Abs(LUcolj[p]))
{
p = i;
}
}
if (p != j)
{
for (int k = 0; k < n; k++)
{
double t = LU[p][k];
LU[p][k] = LU[j][k];
LU[j][k] = t;
}
int k2 = piv[p];
piv[p] = piv[j];
piv[j] = k2;
pivsign = -pivsign;
}
// Compute multipliers.
if (j < m & LU[j][j] != 0.0)
{
for (int i = j + 1; i < m; i++)
{
LU[i][j] /= LU[j][j];
}
}
}
}
/**
* Solve A*X = B
*
* @param B
* A Matrix with as many rows as A and any number of columns.
* @return X so that L*U*X = B(piv,:)
* @exception IllegalArgumentException
* Matrix row dimensions must agree.
* @exception ApplicationException
* Matrix is singular.
*/
public Matrix solve(Matrix B)
{
if (B.getRowDimension() != m)
{
throw new ArgumentException(
"Matrix row dimensions must agree.");
}
if (!this.isNonsingular())
{
throw new ApplicationException("Matrix is singular.");
}
// Copy right hand side with pivoting
int nx = B.getColumnDimension();
Matrix Xmat = B.getMatrix(piv, 0, nx - 1);
double[][] X = Xmat.getArray();
// Solve L*Y = B(piv,:)
for (int k = 0; k < n; k++)
{
for (int i = k + 1; i < n; i++)
{
for (int j = 0; j < nx; j++)
{
X[i][j] -= X[k][j] * LU[i][k];
}
}
}
// Solve U*X = Y;
for (int k = n - 1; k >= 0; k--)
{
for (int j = 0; j < nx; j++)
{
X[k][j] /= LU[k][k];
}
for (int i = 0; i < k; i++)
{
for (int j = 0; j < nx; j++)
{
X[i][j] -= X[k][j] * LU[i][k];
}
}
}
return Xmat;
}
/*
* ------------------------ Constructor ------------------------
*/
/**
* LU Decomposition, a structure to access L, U and piv.
*
* @param A
* Rectangular matrix
*/
public LUDecomposition(Matrix A)
{
// Use a "left-looking", dot-product, Crout/Doolittle algorithm.
LU = A.getArrayCopy();
m = A.getRowDimension();
n = A.getColumnDimension();
piv = new int[m];
for (int i = 0; i < m; i++)
{
piv[i] = i;
}
pivsign = 1;
double[] LUrowi;
double[] LUcolj = new double[m];
// Outer loop.
for (int j = 0; j < n; j++)
{
// Make a copy of the j-th column to localize references.
for (int i = 0; i < m; i++)
{
LUcolj[i] = LU[i][j];
}
// Apply previous transformations.
for (int i = 0; i < m; i++)
{
LUrowi = LU[i];
// Most of the time is spent in the following dot product.
int kmax = Math.Min(i, j);
double s = 0.0;
for (int k = 0; k < kmax; k++)
{
s += LUrowi[k] * LUcolj[k];
}
LUrowi[j] = LUcolj[i] -= s;
}
// Find pivot and exchange if necessary.
int p = j;
for (int i = j + 1; i < m; i++)
{
if (Math.Abs(LUcolj[i]) > Math.Abs(LUcolj[p]))
{
p = i;
}
}
if (p != j)
{
for (int k = 0; k < n; k++)
{
double t = LU[p][k];
LU[p][k] = LU[j][k];
LU[j][k] = t;
}
int k2 = piv[p];
piv[p] = piv[j];
piv[j] = k2;
pivsign = -pivsign;
}
// Compute multipliers.
if (j < m & LU[j][j] != 0.0)
{
for (int i = j + 1; i < m; i++)
{
LU[i][j] /= LU[j][j];
}
}
}
}
//
// PRIVATE METHODS
//
private static void initializeMatrix(Matrix aMatrix, double lowerLimit,
double upperLimit) {
for (int i = 0; i < aMatrix.getRowDimension(); i++) {
for (int j = 0; j < aMatrix.getColumnDimension(); j++) {
double random = Util.generateRandomDoubleBetween(lowerLimit,
upperLimit);
aMatrix.set(i, j, random);
}
}
}
