public static void inv(FMatrixRMaj mat, FMatrixRMaj inv)
{
float max = Math.Abs(mat.data[0]);
int N = mat.getNumElements();
for (int i = 1; i < N; i++)
{
float a = Math.Abs(mat.data[i]);
if (a > max)
{
max = a;
}
}
switch (mat.numRows)
{
case 2:
inv2(mat, inv, 1.0f / max);
break;
case 3:
inv3(mat, inv, 1.0f / max);
break;
case 4:
inv4(mat, inv, 1.0f / max);
break;
case 5:
inv5(mat, inv, 1.0f / max);
break;
default: throw new ArgumentException("Not supported");
}
}
public static void convert(FMatrixRMaj src, DMatrixRMaj dst)
{
int N = src.getNumElements();
for (int i = 0; i < N; i++)
{
dst.data[i] = src.data[i];
}
}
/**
* <p>
* Adds random values to each element in the matrix from an uniform distribution.<br>
* <br>
* a<sub>ij</sub> = a<sub>ij</sub> + U(min,max)<br>
* </p>
*
* @param A The matrix who is to be randomized. Modified
* @param min The minimum value each element can be.
* @param max The maximum value each element can be..
* @param rand Random number generator used to fill the matrix.
*/
public static void addUniform(FMatrixRMaj A, float min, float max, IMersenneTwister rand)
{
float[] d = A.getData();
int size = A.getNumElements();
float r = max - min;
for (int i = 0; i < size; i++)
{
d[i] += r * rand.NextFloat() + min;
}
}
/**
* <p>
* Creates a reflector from the provided vector and gamma.<br>
* <br>
* Q = I - γ u u<sup>T</sup><br>
* </p>
*
* <p>
* In practice {@link VectorVectorMult_FDRM#householder(float, FMatrixD1, FMatrixD1, FMatrixD1)} multHouseholder}
* should be used for performance reasons since there is no need to calculate Q explicitly.
* </p>
*
* @param u A vector. Not modified.
* @param gamma To produce a reflector gamma needs to be equal to 2/||u||.
* @return An orthogonal reflector.
*/
public static FMatrixRMaj createReflector(FMatrixRMaj u, float gamma)
{
if (!MatrixFeatures_FDRM.isVector(u))
{
throw new ArgumentException("u must be a vector");
}
FMatrixRMaj Q = CommonOps_FDRM.identity(u.getNumElements());
CommonOps_FDRM.multAddTransB(-gamma, u, u, Q);
return(Q);
}
/**
* Normalizes the matrix such that the Frobenius norm is equal to one.
*
* @param A The matrix that is to be normalized.
*/
public static void normalizeF(FMatrixRMaj A)
{
float val = normF(A);
if (val == 0)
{
return;
}
int size = A.getNumElements();
for (int i = 0; i < size; i++)
{
A.div(i, val);
}
}
/**
* <p>
* Performs a rank one update on matrix A using vectors u and w. The results are stored in A.<br>
* <br>
* A = A + γ u w<sup>T</sup><br>
* </p>
* <p>
* This is called a rank1 update because the matrix u w<sup>T</sup> has a rank of 1.
* </p>
*
* @param gamma A scalar.
* @param A A m by m matrix. Modified.
* @param u A vector with m elements. Not modified.
*/
public static void rank1Update(float gamma,
FMatrixRMaj A,
FMatrixRMaj u,
FMatrixRMaj w)
{
int n = u.getNumElements();
int matrixIndex = 0;
for (int i = 0; i < n; i++)
{
float elementU = u.data[i];
for (int j = 0; j < n; j++)
{
A.data[matrixIndex++] += gamma * elementU * w.data[j];
}
}
}
