godot/thirdparty/thekla_atlas/nvmath/Matrix.cpp
Hein-Pieter van Braam bf05309af7 Import thekla_atlas
As requested by reduz, an import of thekla_atlas into thirdparty/
2017-12-08 15:47:15 +01:00

442 lines
14 KiB
C++

// This code is in the public domain -- castanyo@yahoo.es
#include "Matrix.inl"
#include "Vector.inl"
#include "nvcore/Array.inl"
#include <float.h>
#if !NV_CC_MSVC && !NV_OS_ORBIS
#include <alloca.h>
#endif
using namespace nv;
// Given a matrix a[1..n][1..n], this routine replaces it by the LU decomposition of a rowwise
// permutation of itself. a and n are input. a is output, arranged as in equation (2.3.14) above;
// indx[1..n] is an output vector that records the row permutation effected by the partial
// pivoting; d is output as -1 depending on whether the number of row interchanges was even
// or odd, respectively. This routine is used in combination with lubksb to solve linear equations
// or invert a matrix.
static bool ludcmp(float **a, int n, int *indx, float *d)
{
const float TINY = 1.0e-20f;
float * vv = (float*)alloca(sizeof(float) * n); // vv stores the implicit scaling of each row.
*d = 1.0; // No row interchanges yet.
for (int i = 0; i < n; i++) { // Loop over rows to get the implicit scaling information.
float big = 0.0;
for (int j = 0; j < n; j++) {
big = max(big, fabsf(a[i][j]));
}
if (big == 0) {
return false; // Singular matrix
}
// No nonzero largest element.
vv[i] = 1.0f / big; // Save the scaling.
}
for (int j = 0; j < n; j++) { // This is the loop over columns of Crout's method.
for (int i = 0; i < j; i++) { // This is equation (2.3.12) except for i = j.
float sum = a[i][j];
for (int k = 0; k < i; k++) sum -= a[i][k]*a[k][j];
a[i][j] = sum;
}
int imax = -1;
float big = 0.0; // Initialize for the search for largest pivot element.
for (int i = j; i < n; i++) { // This is i = j of equation (2.3.12) and i = j+ 1 : : : N
float sum = a[i][j]; // of equation (2.3.13).
for (int k = 0; k < j; k++) {
sum -= a[i][k]*a[k][j];
}
a[i][j]=sum;
float dum = vv[i]*fabs(sum);
if (dum >= big) {
// Is the figure of merit for the pivot better than the best so far?
big = dum;
imax = i;
}
}
nvDebugCheck(imax != -1);
if (j != imax) { // Do we need to interchange rows?
for (int k = 0; k < n; k++) { // Yes, do so...
swap(a[imax][k], a[j][k]);
}
*d = -(*d); // ...and change the parity of d.
vv[imax]=vv[j]; // Also interchange the scale factor.
}
indx[j]=imax;
if (a[j][j] == 0.0) a[j][j] = TINY;
// If the pivot element is zero the matrix is singular (at least to the precision of the
// algorithm). For some applications on singular matrices, it is desirable to substitute
// TINY for zero.
if (j != n-1) { // Now, finally, divide by the pivot element.
float dum = 1.0f / a[j][j];
for (int i = j+1; i < n; i++) a[i][j] *= dum;
}
} // Go back for the next column in the reduction.
return true;
}
// Solves the set of n linear equations Ax = b. Here a[1..n][1..n] is input, not as the matrix
// A but rather as its LU decomposition, determined by the routine ludcmp. indx[1..n] is input
// as the permutation vector returned by ludcmp. b[1..n] is input as the right-hand side vector
// B, and returns with the solution vector X. a, n, and indx are not modified by this routine
// and can be left in place for successive calls with different right-hand sides b. This routine takes
// into account the possibility that b will begin with many zero elements, so it is efficient for use
// in matrix inversion.
static void lubksb(float **a, int n, int *indx, float b[])
{
int ii = 0;
for (int i=0; i<n; i++) { // When ii is set to a positive value, it will become
int ip = indx[i]; // the index of the first nonvanishing element of b. We now
float sum = b[ip]; // do the forward substitution, equation (2.3.6). The
b[ip] = b[i]; // only new wrinkle is to unscramble the permutation as we go.
if (ii != 0) {
for (int j = ii-1; j < i; j++) sum -= a[i][j]*b[j];
}
else if (sum != 0.0f) {
ii = i+1; // A nonzero element was encountered, so from now on we
}
b[i] = sum; // will have to do the sums in the loop above.
}
for (int i=n-1; i>=0; i--) { // Now we do the backsubstitution, equation (2.3.7).
float sum = b[i];
for (int j = i+1; j < n; j++) {
sum -= a[i][j]*b[j];
}
b[i] = sum/a[i][i]; // Store a component of the solution vector X.
} // All done!
}
bool nv::solveLU(const Matrix & A, const Vector4 & b, Vector4 * x)
{
nvDebugCheck(x != NULL);
float m[4][4];
float *a[4] = {m[0], m[1], m[2], m[3]};
int idx[4];
float d;
for (int y = 0; y < 4; y++) {
for (int x = 0; x < 4; x++) {
a[x][y] = A(x, y);
}
}
// Create LU decomposition.
if (!ludcmp(a, 4, idx, &d)) {
// Singular matrix.
return false;
}
// Init solution.
*x = b;
// Do back substitution.
lubksb(a, 4, idx, x->component);
return true;
}
// @@ Not tested.
Matrix nv::inverseLU(const Matrix & A)
{
Vector4 Ai[4];
solveLU(A, Vector4(1, 0, 0, 0), &Ai[0]);
solveLU(A, Vector4(0, 1, 0, 0), &Ai[1]);
solveLU(A, Vector4(0, 0, 1, 0), &Ai[2]);
solveLU(A, Vector4(0, 0, 0, 1), &Ai[3]);
return Matrix(Ai[0], Ai[1], Ai[2], Ai[3]);
}
bool nv::solveLU(const Matrix3 & A, const Vector3 & b, Vector3 * x)
{
nvDebugCheck(x != NULL);
float m[3][3];
float *a[3] = {m[0], m[1], m[2]};
int idx[3];
float d;
for (int y = 0; y < 3; y++) {
for (int x = 0; x < 3; x++) {
a[x][y] = A(x, y);
}
}
// Create LU decomposition.
if (!ludcmp(a, 3, idx, &d)) {
// Singular matrix.
return false;
}
// Init solution.
*x = b;
// Do back substitution.
lubksb(a, 3, idx, x->component);
return true;
}
bool nv::solveCramer(const Matrix & A, const Vector4 & b, Vector4 * x)
{
nvDebugCheck(x != NULL);
*x = transform(inverseCramer(A), b);
return true; // @@ Return false if determinant(A) == 0 !
}
bool nv::solveCramer(const Matrix3 & A, const Vector3 & b, Vector3 * x)
{
nvDebugCheck(x != NULL);
const float det = A.determinant();
if (equal(det, 0.0f)) { // @@ Use input epsilon.
return false;
}
Matrix3 Ai = inverseCramer(A);
*x = transform(Ai, b);
return true;
}
// Inverse using gaussian elimination. From Jon's code.
Matrix nv::inverse(const Matrix & m) {
Matrix A = m;
Matrix B(identity);
int i, j, k;
float max, t, det, pivot;
det = 1.0;
for (i=0; i<4; i++) { /* eliminate in column i, below diag */
max = -1.;
for (k=i; k<4; k++) /* find pivot for column i */
if (fabs(A(k, i)) > max) {
max = fabs(A(k, i));
j = k;
}
if (max<=0.) return B; /* if no nonzero pivot, PUNT */
if (j!=i) { /* swap rows i and j */
for (k=i; k<4; k++)
swap(A(i, k), A(j, k));
for (k=0; k<4; k++)
swap(B(i, k), B(j, k));
det = -det;
}
pivot = A(i, i);
det *= pivot;
for (k=i+1; k<4; k++) /* only do elems to right of pivot */
A(i, k) /= pivot;
for (k=0; k<4; k++)
B(i, k) /= pivot;
/* we know that A(i, i) will be set to 1, so don't bother to do it */
for (j=i+1; j<4; j++) { /* eliminate in rows below i */
t = A(j, i); /* we're gonna zero this guy */
for (k=i+1; k<4; k++) /* subtract scaled row i from row j */
A(j, k) -= A(i, k)*t; /* (ignore k<=i, we know they're 0) */
for (k=0; k<4; k++)
B(j, k) -= B(i, k)*t;
}
}
/*---------- backward elimination ----------*/
for (i=4-1; i>0; i--) { /* eliminate in column i, above diag */
for (j=0; j<i; j++) { /* eliminate in rows above i */
t = A(j, i); /* we're gonna zero this guy */
for (k=0; k<4; k++) /* subtract scaled row i from row j */
B(j, k) -= B(i, k)*t;
}
}
return B;
}
Matrix3 nv::inverse(const Matrix3 & m) {
Matrix3 A = m;
Matrix3 B(identity);
int i, j, k;
float max, t, det, pivot;
det = 1.0;
for (i=0; i<3; i++) { /* eliminate in column i, below diag */
max = -1.;
for (k=i; k<3; k++) /* find pivot for column i */
if (fabs(A(k, i)) > max) {
max = fabs(A(k, i));
j = k;
}
if (max<=0.) return B; /* if no nonzero pivot, PUNT */
if (j!=i) { /* swap rows i and j */
for (k=i; k<3; k++)
swap(A(i, k), A(j, k));
for (k=0; k<3; k++)
swap(B(i, k), B(j, k));
det = -det;
}
pivot = A(i, i);
det *= pivot;
for (k=i+1; k<3; k++) /* only do elems to right of pivot */
A(i, k) /= pivot;
for (k=0; k<3; k++)
B(i, k) /= pivot;
/* we know that A(i, i) will be set to 1, so don't bother to do it */
for (j=i+1; j<3; j++) { /* eliminate in rows below i */
t = A(j, i); /* we're gonna zero this guy */
for (k=i+1; k<3; k++) /* subtract scaled row i from row j */
A(j, k) -= A(i, k)*t; /* (ignore k<=i, we know they're 0) */
for (k=0; k<3; k++)
B(j, k) -= B(i, k)*t;
}
}
/*---------- backward elimination ----------*/
for (i=3-1; i>0; i--) { /* eliminate in column i, above diag */
for (j=0; j<i; j++) { /* eliminate in rows above i */
t = A(j, i); /* we're gonna zero this guy */
for (k=0; k<3; k++) /* subtract scaled row i from row j */
B(j, k) -= B(i, k)*t;
}
}
return B;
}
#if 0
// Copyright (C) 1999-2004 Michael Garland.
//
// Permission is hereby granted, free of charge, to any person obtaining a
// copy of this software and associated documentation files (the
// "Software"), to deal in the Software without restriction, including
// without limitation the rights to use, copy, modify, merge, publish,
// distribute, and/or sell copies of the Software, and to permit persons
// to whom the Software is furnished to do so, provided that the above
// copyright notice(s) and this permission notice appear in all copies of
// the Software and that both the above copyright notice(s) and this
// permission notice appear in supporting documentation.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT
// OF THIRD PARTY RIGHTS. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
// HOLDERS INCLUDED IN THIS NOTICE BE LIABLE FOR ANY CLAIM, OR ANY SPECIAL
// INDIRECT OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING
// FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT,
// NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION
// WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
//
// Except as contained in this notice, the name of a copyright holder
// shall not be used in advertising or otherwise to promote the sale, use
// or other dealings in this Software without prior written authorization
// of the copyright holder.
// Matrix inversion code for 4x4 matrices using Gaussian elimination
// with partial pivoting. This is a specialized version of a
// procedure originally due to Paul Heckbert <ph@cs.cmu.edu>.
//
// Returns determinant of A, and B=inverse(A)
// If matrix A is singular, returns 0 and leaves trash in B.
//
#define SWAP(a, b, t) {t = a; a = b; b = t;}
double invert(Mat4& B, const Mat4& m)
{
Mat4 A = m;
int i, j, k;
double max, t, det, pivot;
/*---------- forward elimination ----------*/
for (i=0; i<4; i++) /* put identity matrix in B */
for (j=0; j<4; j++)
B(i, j) = (double)(i==j);
det = 1.0;
for (i=0; i<4; i++) { /* eliminate in column i, below diag */
max = -1.;
for (k=i; k<4; k++) /* find pivot for column i */
if (fabs(A(k, i)) > max) {
max = fabs(A(k, i));
j = k;
}
if (max<=0.) return 0.; /* if no nonzero pivot, PUNT */
if (j!=i) { /* swap rows i and j */
for (k=i; k<4; k++)
SWAP(A(i, k), A(j, k), t);
for (k=0; k<4; k++)
SWAP(B(i, k), B(j, k), t);
det = -det;
}
pivot = A(i, i);
det *= pivot;
for (k=i+1; k<4; k++) /* only do elems to right of pivot */
A(i, k) /= pivot;
for (k=0; k<4; k++)
B(i, k) /= pivot;
/* we know that A(i, i) will be set to 1, so don't bother to do it */
for (j=i+1; j<4; j++) { /* eliminate in rows below i */
t = A(j, i); /* we're gonna zero this guy */
for (k=i+1; k<4; k++) /* subtract scaled row i from row j */
A(j, k) -= A(i, k)*t; /* (ignore k<=i, we know they're 0) */
for (k=0; k<4; k++)
B(j, k) -= B(i, k)*t;
}
}
/*---------- backward elimination ----------*/
for (i=4-1; i>0; i--) { /* eliminate in column i, above diag */
for (j=0; j<i; j++) { /* eliminate in rows above i */
t = A(j, i); /* we're gonna zero this guy */
for (k=0; k<4; k++) /* subtract scaled row i from row j */
B(j, k) -= B(i, k)*t;
}
}
return det;
}
#endif // 0