/**
* Converts {@link DMatrix6} into {@link DMatrixRMaj}.
*
* @param input Input matrix.
* @param output Output matrix. If null a new matrix will be declared.
* @return Converted matrix.
*/
public static DMatrixRMaj convert(DMatrix6 input, DMatrixRMaj output)
{
if (output == null)
{
output = new DMatrixRMaj(6, 1);
}
if (output.getNumRows() != 1 && output.getNumCols() != 1)
{
throw new ArgumentException("One row or column must have a length of 1 for it to be a vector");
}
int length = Math.Max(output.getNumRows(), output.getNumCols());
if (length != 6)
{
throw new ArgumentException("Length of input vector is not 6. It is " + length);
}
output.data[0] = input.a1;
output.data[1] = input.a2;
output.data[2] = input.a3;
output.data[3] = input.a4;
output.data[4] = input.a5;
output.data[5] = input.a6;
return(output);
}
/**
* Converts {@link DMatrixRMaj} into {@link DMatrix6}
*
* @param input Input matrix.
* @param output Output matrix. If null a new matrix will be declared.
* @return Converted matrix.
*/
public static DMatrix6 convert(DMatrixRMaj input, DMatrix6 output)
{
if (output == null)
{
output = new DMatrix6();
}
if (input.getNumRows() != 1 && input.getNumCols() != 1)
{
throw new ArgumentException("One row or column must have a length of 1 for it to be a vector");
}
int length = Math.Max(input.getNumRows(), input.getNumCols());
if (length != 6)
{
throw new ArgumentException("Length of input vector is not 6. It is " + length);
}
output.a1 = input.data[0];
output.a2 = input.data[1];
output.a3 = input.data[2];
output.a4 = input.data[3];
output.a5 = input.data[4];
output.a6 = input.data[5];
return(output);
}
public static bool hasUncountable(DMatrix6 a)
{
if (UtilEjml.isUncountable(a.a1))
{
return(true);
}
if (UtilEjml.isUncountable(a.a2))
{
return(true);
}
if (UtilEjml.isUncountable(a.a3))
{
return(true);
}
if (UtilEjml.isUncountable(a.a4))
{
return(true);
}
if (UtilEjml.isUncountable(a.a5))
{
return(true);
}
if (UtilEjml.isUncountable(a.a6))
{
return(true);
}
return(false);
}
public static bool isIdentical(DMatrix6 a, DMatrix6 b, double tol)
{
if (!MatrixFeatures_DDRM.isIdentical(a.a1, b.a1, tol))
{
return(false);
}
if (!MatrixFeatures_DDRM.isIdentical(a.a2, b.a2, tol))
{
return(false);
}
if (!MatrixFeatures_DDRM.isIdentical(a.a3, b.a3, tol))
{
return(false);
}
if (!MatrixFeatures_DDRM.isIdentical(a.a4, b.a4, tol))
{
return(false);
}
if (!MatrixFeatures_DDRM.isIdentical(a.a5, b.a5, tol))
{
return(false);
}
if (!MatrixFeatures_DDRM.isIdentical(a.a6, b.a6, tol))
{
return(false);
}
return(true);
}
public static double normF(DMatrix6 M)
{
double scale = CommonOps_DDF6.elementMaxAbs(M);
if (scale == 0.0)
{
return(0.0);
}
double a1 = M.a1 / scale,
a2 = M.a2 / scale,
a3 = M.a3 / scale,
a4 = M.a4 / scale,
a5 = M.a5 / scale,
a6 = M.a6 / scale;
double sum = a1 * a1 + a2 * a2 + a3 * a3 + a4 * a4 + a5 * a5 + a6 * a6;
return(scale * Math.Sqrt(sum));
}
public static double fastNormF(DMatrix6 M)
{
double sum = M.a1 * M.a1 + M.a2 * M.a2 + M.a3 * M.a3 + M.a4 * M.a4 + M.a5 * M.a5 + M.a6 * M.a6;
return(Math.Sqrt(sum));
}
public static void normalizeF(DMatrix6 M)
{
double val = normF(M);
CommonOps_DDF6.divide(M, val);
}
