/**
* <p>
* Computes the householder vector "u" for the first column of submatrix j. Note this is
* a specialized householder for this problem. There is some protection against
* overflow and underflow.
* </p>
* <p>
* Q = I - γuu<sup>T</sup>
* </p>
* <p>
* This function finds the values of 'u' and 'γ'.
* </p>
*
* @param j Which submatrix to work off of.
*/
protected void householder(int j)
{
int startQR = j * numRows;
int endQR = startQR + numRows;
startQR += j;
float max = QrHelperFunctions_FDRM.findMax(QR.data, startQR, numRows - j);
if (max == 0.0f)
{
gamma = 0;
error = true;
}
else
{
// computes tau and normalizes u by max
tau = QrHelperFunctions_FDRM.computeTauAndDivide(startQR, endQR, QR.data, max);
// divide u by u_0
float u_0 = QR.data[startQR] + tau;
QrHelperFunctions_FDRM.divideElements(startQR + 1, endQR, QR.data, u_0);
gamma = u_0 / tau;
tau *= max;
QR.data[startQR] = -tau;
}
gammas[j] = gamma;
}
/**
* <p>
* Computes the householder vector "u" for the first column of submatrix j. Note this is
* a specialized householder for this problem. There is some protection against
* overfloaw and underflow.
* </p>
* <p>
* Q = I - γuu<sup>T</sup>
* </p>
* <p>
* This function finds the values of 'u' and 'γ'.
* </p>
*
* @param j Which submatrix to work off of.
*/
protected void householder(int j)
{
float[] u = dataQR[j];
// find the largest value in this column
// this is used to normalize the column and mitigate overflow/underflow
float max = QrHelperFunctions_FDRM.findMax(u, j, numRows - j);
if (max == 0.0f)
{
gamma = 0;
error = true;
}
else
{
// computes tau and normalizes u by max
tau = QrHelperFunctions_FDRM.computeTauAndDivide(j, numRows, u, max);
// divide u by u_0
float u_0 = u[j] + tau;
QrHelperFunctions_FDRM.divideElements(j + 1, numRows, u, u_0);
gamma = u_0 / tau;
tau *= max;
u[j] = -tau;
}
gammas[j] = gamma;
}
/**
* A = Q<sup>T</sup>*A
*
* @param A Matrix that is being multiplied by Q<sup>T</sup>. Is modified.
*/
public void applyTranQ(FMatrixRMaj A)
{
for (int j = 0; j < minLength; j++)
{
int diagIndex = j * numRows + j;
float before = QR.data[diagIndex];
QR.data[diagIndex] = 1;
QrHelperFunctions_FDRM.rank1UpdateMultR(A, QR.data, j * numRows, gammas[j], 0, j, numRows, v);
QR.data[diagIndex] = before;
}
}
/**
* Computes the Q matrix from the information stored in the QR matrix. This
* operation requires about 4(m<sup>2</sup>n-mn<sup>2</sup>+n<sup>3</sup>/3) flops.
*
* @param Q The orthogonal Q matrix.
*/
public virtual FMatrixRMaj getQ(FMatrixRMaj Q, bool compact)
{
if (compact)
{
if (Q == null)
{
Q = CommonOps_FDRM.identity(numRows, minLength);
}
else
{
if (Q.numRows != numRows || Q.numCols != minLength)
{
throw new ArgumentException("Unexpected matrix dimension.");
}
else
{
CommonOps_FDRM.setIdentity(Q);
}
}
}
else
{
if (Q == null)
{
Q = CommonOps_FDRM.identity(numRows);
}
else
{
if (Q.numRows != numRows || Q.numCols != numRows)
{
throw new ArgumentException("Unexpected matrix dimension.");
}
else
{
CommonOps_FDRM.setIdentity(Q);
}
}
}
// Unlike applyQ() this takes advantage of zeros in the identity matrix
// by not multiplying across all rows.
for (int j = minLength - 1; j >= 0; j--)
{
int diagIndex = j * numRows + j;
float before = QR.data[diagIndex];
QR.data[diagIndex] = 1;
QrHelperFunctions_FDRM.rank1UpdateMultR(Q, QR.data, j * numRows, gammas[j], j, j, numRows, v);
QR.data[diagIndex] = before;
}
return(Q);
}
/**
* Computes the Q matrix from the imformation stored in the QR matrix. This
* operation requires about 4(m<sup>2</sup>n-mn<sup>2</sup>+n<sup>3</sup>/3) flops.
*
* @param Q The orthogonal Q matrix.
*/
//@Override
public FMatrixRMaj getQ(FMatrixRMaj Q, bool compact)
{
if (compact)
{
if (Q == null)
{
Q = CommonOps_FDRM.identity(numRows, minLength);
}
else
{
if (Q.numRows != numRows || Q.numCols != minLength)
{
throw new ArgumentException("Unexpected matrix dimension.");
}
else
{
CommonOps_FDRM.setIdentity(Q);
}
}
}
else
{
if (Q == null)
{
Q = CommonOps_FDRM.identity(numRows);
}
else
{
if (Q.numRows != numRows || Q.numCols != numRows)
{
throw new ArgumentException("Unexpected matrix dimension.");
}
else
{
CommonOps_FDRM.setIdentity(Q);
}
}
}
for (int j = minLength - 1; j >= 0; j--)
{
u[j] = 1;
for (int i = j + 1; i < numRows; i++)
{
u[i] = QR.get(i, j);
}
QrHelperFunctions_FDRM.rank1UpdateMultR(Q, u, gammas[j], j, j, numRows, v);
}
return(Q);
}
/**
* Computes the Q matrix from the information stored in the QR matrix. This
* operation requires about 4(m<sup>2</sup>n-mn<sup>2</sup>+n<sup>3</sup>/3) flops.
*
* @param Q The orthogonal Q matrix.
*/
public override FMatrixRMaj getQ(FMatrixRMaj Q, bool compact)
{
if (compact)
{
if (Q == null)
{
Q = CommonOps_FDRM.identity(numRows, minLength);
}
else
{
if (Q.numRows != numRows || Q.numCols != minLength)
{
throw new ArgumentException("Unexpected matrix dimension.");
}
else
{
CommonOps_FDRM.setIdentity(Q);
}
}
}
else
{
if (Q == null)
{
Q = CommonOps_FDRM.identity(numRows);
}
else
{
if (Q.numRows != numRows || Q.numCols != numRows)
{
throw new ArgumentException("Unexpected matrix dimension.");
}
else
{
CommonOps_FDRM.setIdentity(Q);
}
}
}
for (int j = rank - 1; j >= 0; j--)
{
float[] u = dataQR[j];
float vv = u[j];
u[j] = 1;
QrHelperFunctions_FDRM.rank1UpdateMultR(Q, u, gammas[j], j, j, numRows, v);
u[j] = vv;
}
return(Q);
}
/**
* A = Q*A
*
* @param A Matrix that is being multiplied by Q. Is modified.
*/
public void applyQ(FMatrixRMaj A)
{
if (A.numRows != numRows)
{
throw new ArgumentException("A must have at least " + numRows + " rows.");
}
for (int j = minLength - 1; j >= 0; j--)
{
int diagIndex = j * numRows + j;
float before = QR.data[diagIndex];
QR.data[diagIndex] = 1;
QrHelperFunctions_FDRM.rank1UpdateMultR(A, QR.data, j * numRows, gammas[j], 0, j, numRows, v);
QR.data[diagIndex] = before;
}
}
/**
* Computes the Q matrix from the imformation stored in the QR matrix. This
* operation requires about 4(m<sup>2</sup>n-mn<sup>2</sup>+n<sup>3</sup>/3) flops.
*
* @param Q The orthogonal Q matrix.
*/
public virtual FMatrixRMaj getQ(FMatrixRMaj Q, bool compact)
{
if (compact)
{
Q = UtilDecompositons_FDRM.checkIdentity(Q, numRows, minLength);
}
else
{
Q = UtilDecompositons_FDRM.checkIdentity(Q, numRows, numRows);
}
for (int j = minLength - 1; j >= 0; j--)
{
float[] u = dataQR[j];
float vv = u[j];
u[j] = 1;
QrHelperFunctions_FDRM.rank1UpdateMultR(Q, u, gammas[j], j, j, numRows, v);
u[j] = vv;
}
return(Q);
}
/**
* <p>
* Computes the householder vector "u" for the first column of submatrix j. The already computed
* norm is used and checks to see if the matrix is singular at this point.
* </p>
* <p>
* Q = I - γuu<sup>T</sup>
* </p>
* <p>
* This function finds the values of 'u' and 'γ'.
* </p>
*
* @param j Which submatrix to work off of.
* @return false if it is degenerate
*/
protected bool householderPivot(int j)
{
float[] u = dataQR[j];
// find the largest value in this column
// this is used to normalize the column and mitigate overflow/underflow
float max = QrHelperFunctions_FDRM.findMax(u, j, numRows - j);
if (max <= 0)
{
return(false);
}
else
{
// computes tau and normalizes u by max
tau = QrHelperFunctions_FDRM.computeTauAndDivide(j, numRows, u, max);
// divide u by u_0
float u_0 = u[j] + tau;
QrHelperFunctions_FDRM.divideElements(j + 1, numRows, u, u_0);
gamma = u_0 / tau;
tau *= max;
u[j] = -tau;
if (Math.Abs(tau) <= singularThreshold)
{
return(false);
}
}
gammas[j] = gamma;
return(true);
}
