/**
* <p>
* Returns true if the matrix is symmetric within the tolerance. Only square matrices can be
* symmetric.
* </p>
* <p>
* A matrix is symmetric if:<br>
* |a<sub>ij</sub> - a<sub>ji</sub>| ≤ tol
* </p>
*
* @param m A matrix. Not modified.
* @param tol Tolerance for how similar two elements need to be.
* @return true if it is symmetric and false if it is not.
*/
public static bool isSymmetric(DMatrixRMaj m, double tol)
{
if (m.numCols != m.numRows)
{
return(false);
}
double max = CommonOps_DDRM.elementMaxAbs(m);
for (int i = 0; i < m.numRows; i++)
{
for (int j = 0; j < i; j++)
{
double a = m.get(i, j) / max;
double b = m.get(j, i) / max;
double diff = Math.Abs(a - b);
if (!(diff <= tol))
{
return(false);
}
}
}
return(true);
}
/**
* <p>
* Element wise p-norm:<br>
* <br>
* norm = {∑<sub>i=1:m</sub> ∑<sub>j=1:n</sub> { |a<sub>ij</sub>|<sup>p</sup>}}<sup>1/p</sup>
* </p>
*
* <p>
* This is not the same as the induced p-norm used on matrices, but is the same as the vector p-norm.
* </p>
*
* @param A Matrix. Not modified.
* @param p p value.
* @return The norm's value.
*/
public static double elementP(DMatrix1Row A, double p)
{
if (p == 1)
{
return(CommonOps_DDRM.elementSumAbs(A));
}
if (p == 2)
{
return(normF(A));
}
else
{
double max = CommonOps_DDRM.elementMaxAbs(A);
if (max == 0.0)
{
return(0.0);
}
double total = 0;
int size = A.getNumElements();
for (int i = 0; i < size; i++)
{
double a = A.get(i) / max;
total += Math.Pow(Math.Abs(a), p);
}
return(max * Math.Pow(total, 1.0 / p));
}
}
/**
* <p>
* To decompose the matrix 'A' it must have full rank. 'A' is a 'm' by 'n' matrix.
* It requires about 2n*m<sup>2</sup>-2m<sup>2</sup>/3 flops.
* </p>
*
* <p>
* The matrix provided here can be of different
* dimension than the one specified in the constructor. It just has to be smaller than or equal
* to it.
* </p>
*/
public override bool decompose(DMatrixRMaj A)
{
setExpectedMaxSize(A.numRows, A.numCols);
convertToColumnMajor(A);
maxAbs = CommonOps_DDRM.elementMaxAbs(A);
// initialize pivot variables
setupPivotInfo();
// go through each column and perform the decomposition
for (int j = 0; j < minLength; j++)
{
if (j > 0)
{
updateNorms(j);
}
swapColumns(j);
// if its degenerate stop processing
if (!householderPivot(j))
{
break;
}
updateA(j);
rank = j + 1;
}
return(true);
}
/**
* Computes the p=∞ norm. If A is a matrix then the induced norm is computed.
*
* @param A Matrix or vector.
* @return The norm.
*/
public static double normPInf(DMatrixRMaj A)
{
if (MatrixFeatures_DDRM.isVector(A))
{
return(CommonOps_DDRM.elementMaxAbs(A));
}
else
{
return(inducedPInf(A));
}
}
/**
* <p>
* Computes the Frobenius matrix norm:<br>
* <br>
* normF = Sqrt{ ∑<sub>i=1:m</sub> ∑<sub>j=1:n</sub> { a<sub>ij</sub><sup>2</sup>} }
* </p>
* <p>
* This is equivalent to the element wise p=2 norm. See {@link #fastNormF} for another implementation
* that is faster, but more prone to underflow/overflow errors.
* </p>
*
* @param a The matrix whose norm is computed. Not modified.
* @return The norm's value.
*/
public static double normF(DMatrixD1 a)
{
double total = 0;
double scale = CommonOps_DDRM.elementMaxAbs(a);
if (scale == 0.0)
{
return(0.0);
}
int size = a.getNumElements();
for (int i = 0; i < size; i++)
{
double val = a.get(i) / scale;
total += val * val;
}
return(scale * Math.Sqrt(total));
}
/**
* Sums up the square of each element in the matrix. This is equivalent to the
* Frobenius norm squared.
*
* @param m Matrix.
* @return Sum of elements squared.
*/
public static double elementSumSq(DMatrixD1 m)
{
// minimize round off error
double maxAbs = CommonOps_DDRM.elementMaxAbs(m);
if (maxAbs == 0)
{
return(0);
}
double total = 0;
int N = m.NumElements;
for (int i = 0; i < N; i++)
{
double d = m.data[i] / maxAbs;
total += d * d;
}
return(maxAbs * total * maxAbs);
}
public double elementMaxAbs(Matrix A)
{
return(CommonOps_DDRM.elementMaxAbs((DMatrixRMaj)A));
}
/**
* Computes the QR decomposition of the provided matrix.
*
* @param A Matrix which is to be decomposed. Not modified.
*/
public void decompose(DMatrixRMaj A)
{
this.QR = (DMatrixRMaj)A.copy();
int N = Math.Min(A.numCols, A.numRows);
gammas = new double[A.numCols];
DMatrixRMaj A_small = new DMatrixRMaj(A.numRows, A.numCols);
DMatrixRMaj A_mod = new DMatrixRMaj(A.numRows, A.numCols);
DMatrixRMaj v = new DMatrixRMaj(A.numRows, 1);
DMatrixRMaj Q_k = new DMatrixRMaj(A.numRows, A.numRows);
for (int i = 0; i < N; i++)
{
// reshape temporary variables
A_small.reshape(QR.numRows - i, QR.numCols - i, false);
A_mod.reshape(A_small.numRows, A_small.numCols, false);
v.reshape(A_small.numRows, 1, false);
Q_k.reshape(v.getNumElements(), v.getNumElements(), false);
// use extract matrix to get the column that is to be zeroed
CommonOps_DDRM.extract(QR, i, QR.numRows, i, i + 1, v, 0, 0);
double max = CommonOps_DDRM.elementMaxAbs(v);
if (max > 0 && v.getNumElements() > 1)
{
// normalize to reduce overflow issues
CommonOps_DDRM.divide(v, max);
// compute the magnitude of the vector
double tau = NormOps_DDRM.normF(v);
if (v.get(0) < 0)
{
tau *= -1.0;
}
double u_0 = v.get(0) + tau;
double gamma = u_0 / tau;
CommonOps_DDRM.divide(v, u_0);
v.set(0, 1.0);
// extract the submatrix of A which is being operated on
CommonOps_DDRM.extract(QR, i, QR.numRows, i, QR.numCols, A_small, 0, 0);
// A = (I - γ*u*u<sup>T</sup>)A
CommonOps_DDRM.setIdentity(Q_k);
CommonOps_DDRM.multAddTransB(-gamma, v, v, Q_k);
CommonOps_DDRM.mult(Q_k, A_small, A_mod);
// save the results
CommonOps_DDRM.insert(A_mod, QR, i, i);
CommonOps_DDRM.insert(v, QR, i, i);
QR.unsafe_set(i, i, -tau * max);
// save gamma for recomputing Q later on
gammas[i] = gamma;
}
}
}