/**
* <p>
* Checks to see if a matrix is orthogonal or isometric.
* </p>
*
* @param Q The matrix being tested. Not modified.
* @param tol Tolerance.
* @return True if it passes the test.
*/
public static bool isOrthogonal(DMatrixRMaj Q, double tol)
{
if (Q.numRows < Q.numCols)
{
throw new ArgumentException("The number of rows must be more than or equal to the number of columns");
}
DMatrixRMaj[] u = CommonOps_DDRM.columnsToVector(Q, null);
for (int i = 0; i < u.Count(); i++)
{
DMatrixRMaj a = u[i];
for (int j = i + 1; j < u.Count(); j++)
{
double val = VectorVectorMult_DDRM.innerProd(a, u[j]);
if (!(Math.Abs(val) <= tol))
{
return(false);
}
}
}
return(true);
}
/**
* <p>
* Creates a randomly generated set of orthonormal vectors. At most it can generate the same
* number of vectors as the dimension of the vectors.
* </p>
*
* <p>
* This is done by creatingJava.Util.Random vectors then ensuring that they are orthogonal
* to all the ones previously created with reflectors.
* </p>
*
* <p>
* NOTE: This employs a brute force O(N<sup>3</sup>) algorithm.
* </p>
*
* @param dimen dimension of the space which the vectors will span.
* @param numVectors How many vectors it should generate.
* @param rand Used to createJava.Util.Random vectors.
* @return Array of NJava.Util.Random orthogonal vectors of unit Count().
*/
// is there a faster algorithm out there? This one is a bit sluggish
public static DMatrixRMaj[] span(int dimen, int numVectors, Java.Util.Random rand)
{
if (dimen < numVectors)
{
throw new ArgumentException("The number of vectors must be less than or equal to the dimension");
}
DMatrixRMaj[] u = new DMatrixRMaj[numVectors];
u[0] = RandomMatrices_DDRM.rectangle(dimen, 1, -1, 1, rand);
NormOps_DDRM.normalizeF(u[0]);
for (int i = 1; i < numVectors; i++)
{
// System.out.println(" i = "+i);
DMatrixRMaj a = new DMatrixRMaj(dimen, 1);
DMatrixRMaj r = RandomMatrices_DDRM.rectangle(dimen, 1, -1, 1, rand);
for (int j = 0; j < i; j++)
{
// find a vector that is normal to vector j
// u[i] = (1/2)*(r + Q[j]*r)
a.setTo(r);
VectorVectorMult_DDRM.householder(-2.0, u[j], r, a);
CommonOps_DDRM.add(r, a, a);
CommonOps_DDRM.scale(0.5, a);
// UtilEjml.print(a);
DMatrixRMaj t = a;
a = r;
r = t;
// normalize it so it doesn't get too small
double val = NormOps_DDRM.normF(r);
if (val == 0 || Double.IsNaN(val) || Double.IsInfinity(val))
{
throw new SystemException("Failed sanity check");
}
CommonOps_DDRM.divide(r, val);
}
u[i] = r;
}
return(u);
}
public double dot(Matrix A, Matrix v)
{
return(VectorVectorMult_DDRM.innerProd((DMatrixRMaj)A, (DMatrixRMaj)v));
}
