| 1 |
+ |
/* |
| 2 |
+ |
* Copyright (c) 2005 The University of Notre Dame. All Rights Reserved. |
| 3 |
+ |
* |
| 4 |
+ |
* The University of Notre Dame grants you ("Licensee") a |
| 5 |
+ |
* non-exclusive, royalty free, license to use, modify and |
| 6 |
+ |
* redistribute this software in source and binary code form, provided |
| 7 |
+ |
* that the following conditions are met: |
| 8 |
+ |
* |
| 9 |
+ |
* 1. Acknowledgement of the program authors must be made in any |
| 10 |
+ |
* publication of scientific results based in part on use of the |
| 11 |
+ |
* program. An acceptable form of acknowledgement is citation of |
| 12 |
+ |
* the article in which the program was described (Matthew |
| 13 |
+ |
* A. Meineke, Charles F. Vardeman II, Teng Lin, Christopher |
| 14 |
+ |
* J. Fennell and J. Daniel Gezelter, "OOPSE: An Object-Oriented |
| 15 |
+ |
* Parallel Simulation Engine for Molecular Dynamics," |
| 16 |
+ |
* J. Comput. Chem. 26, pp. 252-271 (2005)) |
| 17 |
+ |
* |
| 18 |
+ |
* 2. Redistributions of source code must retain the above copyright |
| 19 |
+ |
* notice, this list of conditions and the following disclaimer. |
| 20 |
+ |
* |
| 21 |
+ |
* 3. Redistributions in binary form must reproduce the above copyright |
| 22 |
+ |
* notice, this list of conditions and the following disclaimer in the |
| 23 |
+ |
* documentation and/or other materials provided with the |
| 24 |
+ |
* distribution. |
| 25 |
+ |
* |
| 26 |
+ |
* This software is provided "AS IS," without a warranty of any |
| 27 |
+ |
* kind. All express or implied conditions, representations and |
| 28 |
+ |
* warranties, including any implied warranty of merchantability, |
| 29 |
+ |
* fitness for a particular purpose or non-infringement, are hereby |
| 30 |
+ |
* excluded. The University of Notre Dame and its licensors shall not |
| 31 |
+ |
* be liable for any damages suffered by licensee as a result of |
| 32 |
+ |
* using, modifying or distributing the software or its |
| 33 |
+ |
* derivatives. In no event will the University of Notre Dame or its |
| 34 |
+ |
* licensors be liable for any lost revenue, profit or data, or for |
| 35 |
+ |
* direct, indirect, special, consequential, incidental or punitive |
| 36 |
+ |
* damages, however caused and regardless of the theory of liability, |
| 37 |
+ |
* arising out of the use of or inability to use software, even if the |
| 38 |
+ |
* University of Notre Dame has been advised of the possibility of |
| 39 |
+ |
* such damages. |
| 40 |
+ |
*/ |
| 41 |
+ |
|
| 42 |
|
#include <stdio.h> |
| 43 |
|
#include <math.h> |
| 44 |
|
#include <stdlib.h> |
| 45 |
< |
#include "MatVec3.h" |
| 45 |
> |
#include "math/MatVec3.h" |
| 46 |
|
|
| 47 |
|
/* |
| 48 |
|
* Contains various utilities for dealing with 3x3 matrices and |
| 49 |
|
* length 3 vectors |
| 50 |
|
*/ |
| 51 |
|
|
| 52 |
< |
void identityMat3(double A[3][3]) { |
| 52 |
> |
void identityMat3(RealType A[3][3]) { |
| 53 |
|
int i; |
| 54 |
|
for (i = 0; i < 3; i++) { |
| 55 |
|
A[i][0] = A[i][1] = A[i][2] = 0.0; |
| 57 |
|
} |
| 58 |
|
} |
| 59 |
|
|
| 60 |
< |
void swapVectors3(double v1[3], double v2[3]) { |
| 60 |
> |
void swapVectors3(RealType v1[3], RealType v2[3]) { |
| 61 |
|
int i; |
| 62 |
|
for (i = 0; i < 3; i++) { |
| 63 |
< |
double tmp = v1[i]; |
| 63 |
> |
RealType tmp = v1[i]; |
| 64 |
|
v1[i] = v2[i]; |
| 65 |
|
v2[i] = tmp; |
| 66 |
|
} |
| 67 |
|
} |
| 68 |
|
|
| 69 |
< |
double normalize3(double x[3]) { |
| 70 |
< |
double den; |
| 69 |
> |
RealType normalize3(RealType x[3]) { |
| 70 |
> |
RealType den; |
| 71 |
|
int i; |
| 72 |
|
if ( (den = norm3(x)) != 0.0 ) { |
| 73 |
|
for (i=0; i < 3; i++) |
| 78 |
|
return den; |
| 79 |
|
} |
| 80 |
|
|
| 81 |
< |
void matMul3(double a[3][3], double b[3][3], double c[3][3]) { |
| 82 |
< |
double r00, r01, r02, r10, r11, r12, r20, r21, r22; |
| 81 |
> |
void matMul3(RealType a[3][3], RealType b[3][3], RealType c[3][3]) { |
| 82 |
> |
RealType r00, r01, r02, r10, r11, r12, r20, r21, r22; |
| 83 |
|
|
| 84 |
|
r00 = a[0][0]*b[0][0] + a[0][1]*b[1][0] + a[0][2]*b[2][0]; |
| 85 |
|
r01 = a[0][0]*b[0][1] + a[0][1]*b[1][1] + a[0][2]*b[2][1]; |
| 98 |
|
c[2][0] = r20; c[2][1] = r21; c[2][2] = r22; |
| 99 |
|
} |
| 100 |
|
|
| 101 |
< |
void matVecMul3(double m[3][3], double inVec[3], double outVec[3]) { |
| 102 |
< |
double a0, a1, a2; |
| 101 |
> |
void matVecMul3(RealType m[3][3], RealType inVec[3], RealType outVec[3]) { |
| 102 |
> |
RealType a0, a1, a2; |
| 103 |
|
|
| 104 |
|
a0 = inVec[0]; a1 = inVec[1]; a2 = inVec[2]; |
| 105 |
|
|
| 108 |
|
outVec[2] = m[2][0]*a0 + m[2][1]*a1 + m[2][2]*a2; |
| 109 |
|
} |
| 110 |
|
|
| 111 |
< |
double matDet3(double a[3][3]) { |
| 111 |
> |
RealType matDet3(RealType a[3][3]) { |
| 112 |
|
int i, j, k; |
| 113 |
< |
double determinant; |
| 113 |
> |
RealType determinant; |
| 114 |
|
|
| 115 |
|
determinant = 0.0; |
| 116 |
|
|
| 124 |
|
return determinant; |
| 125 |
|
} |
| 126 |
|
|
| 127 |
< |
void invertMat3(double a[3][3], double b[3][3]) { |
| 127 |
> |
void invertMat3(RealType a[3][3], RealType b[3][3]) { |
| 128 |
|
|
| 129 |
|
int i, j, k, l, m, n; |
| 130 |
< |
double determinant; |
| 130 |
> |
RealType determinant; |
| 131 |
|
|
| 132 |
|
determinant = matDet3( a ); |
| 133 |
|
|
| 150 |
|
} |
| 151 |
|
} |
| 152 |
|
|
| 153 |
< |
void transposeMat3(double in[3][3], double out[3][3]) { |
| 154 |
< |
double temp[3][3]; |
| 153 |
> |
void transposeMat3(RealType in[3][3], RealType out[3][3]) { |
| 154 |
> |
RealType temp[3][3]; |
| 155 |
|
int i, j; |
| 156 |
|
|
| 157 |
|
for (i = 0; i < 3; i++) { |
| 166 |
|
} |
| 167 |
|
} |
| 168 |
|
|
| 169 |
< |
void printMat3(double A[3][3] ){ |
| 169 |
> |
void printMat3(RealType A[3][3] ){ |
| 170 |
|
|
| 171 |
|
fprintf(stderr, "[ %g, %g, %g ]\n[ %g, %g, %g ]\n[ %g, %g, %g ]\n", |
| 172 |
|
A[0][0] , A[0][1] , A[0][2], |
| 174 |
|
A[2][0] , A[2][1] , A[2][2]) ; |
| 175 |
|
} |
| 176 |
|
|
| 177 |
< |
void printMat9(double A[9] ){ |
| 177 |
> |
void printMat9(RealType A[9] ){ |
| 178 |
|
|
| 179 |
|
fprintf(stderr, "[ %g, %g, %g ]\n[ %g, %g, %g ]\n[ %g, %g, %g ]\n", |
| 180 |
|
A[0], A[1], A[2], |
| 182 |
|
A[6], A[7], A[8]); |
| 183 |
|
} |
| 184 |
|
|
| 185 |
< |
double matTrace3(double m[3][3]){ |
| 186 |
< |
double trace; |
| 185 |
> |
RealType matTrace3(RealType m[3][3]){ |
| 186 |
> |
RealType trace; |
| 187 |
|
trace = m[0][0] + m[1][1] + m[2][2]; |
| 188 |
|
|
| 189 |
|
return trace; |
| 190 |
|
} |
| 191 |
|
|
| 192 |
< |
void crossProduct3(double a[3],double b[3], double out[3]){ |
| 192 |
> |
void crossProduct3(RealType a[3],RealType b[3], RealType out[3]){ |
| 193 |
|
|
| 194 |
|
out[0] = a[1] * b[2] - a[2] * b[1]; |
| 195 |
|
out[1] = a[2] * b[0] - a[0] * b[2] ; |
| 197 |
|
|
| 198 |
|
} |
| 199 |
|
|
| 200 |
< |
double dotProduct3(double a[3], double b[3]){ |
| 200 |
> |
RealType dotProduct3(RealType a[3], RealType b[3]){ |
| 201 |
|
return a[0]*b[0] + a[1]*b[1]+ a[2]*b[2]; |
| 202 |
|
} |
| 203 |
|
|
| 204 |
< |
//---------------------------------------------------------------------------- |
| 205 |
< |
// Extract the eigenvalues and eigenvectors from a 3x3 matrix. |
| 206 |
< |
// The eigenvectors (the columns of V) will be normalized. |
| 207 |
< |
// The eigenvectors are aligned optimally with the x, y, and z |
| 208 |
< |
// axes respectively. |
| 204 |
> |
/*----------------------------------------------------------------------------*/ |
| 205 |
> |
/* Extract the eigenvalues and eigenvectors from a 3x3 matrix.*/ |
| 206 |
> |
/* The eigenvectors (the columns of V) will be normalized. */ |
| 207 |
> |
/* The eigenvectors are aligned optimally with the x, y, and z*/ |
| 208 |
> |
/* axes respectively.*/ |
| 209 |
|
|
| 210 |
< |
void diagonalize3x3(const double A[3][3], double w[3], double V[3][3]) { |
| 210 |
> |
void diagonalize3x3(const RealType A[3][3], RealType w[3], RealType V[3][3]) { |
| 211 |
|
int i,j,k,maxI; |
| 212 |
< |
double tmp, maxVal; |
| 212 |
> |
RealType tmp, maxVal; |
| 213 |
|
|
| 214 |
< |
// do the matrix[3][3] to **matrix conversion for Jacobi |
| 215 |
< |
double C[3][3]; |
| 216 |
< |
double *ATemp[3],*VTemp[3]; |
| 214 |
> |
/* do the matrix[3][3] to **matrix conversion for Jacobi*/ |
| 215 |
> |
RealType C[3][3]; |
| 216 |
> |
RealType *ATemp[3],*VTemp[3]; |
| 217 |
|
for (i = 0; i < 3; i++) |
| 218 |
|
{ |
| 219 |
|
C[i][0] = A[i][0]; |
| 223 |
|
VTemp[i] = V[i]; |
| 224 |
|
} |
| 225 |
|
|
| 226 |
< |
// diagonalize using Jacobi |
| 226 |
> |
/* diagonalize using Jacobi*/ |
| 227 |
|
JacobiN(ATemp,3,w,VTemp); |
| 228 |
|
|
| 229 |
< |
// if all the eigenvalues are the same, return identity matrix |
| 229 |
> |
/* if all the eigenvalues are the same, return identity matrix*/ |
| 230 |
|
if (w[0] == w[1] && w[0] == w[2]) |
| 231 |
|
{ |
| 232 |
|
identityMat3(V); |
| 233 |
|
return; |
| 234 |
|
} |
| 235 |
|
|
| 236 |
< |
// transpose temporarily, it makes it easier to sort the eigenvectors |
| 236 |
> |
/* transpose temporarily, it makes it easier to sort the eigenvectors*/ |
| 237 |
|
transposeMat3(V,V); |
| 238 |
|
|
| 239 |
< |
// if two eigenvalues are the same, re-orthogonalize to optimally line |
| 240 |
< |
// up the eigenvectors with the x, y, and z axes |
| 239 |
> |
/* if two eigenvalues are the same, re-orthogonalize to optimally line*/ |
| 240 |
> |
/* up the eigenvectors with the x, y, and z axes*/ |
| 241 |
|
for (i = 0; i < 3; i++) |
| 242 |
|
{ |
| 243 |
< |
if (w[(i+1)%3] == w[(i+2)%3]) // two eigenvalues are the same |
| 243 |
> |
if (w[(i+1)%3] == w[(i+2)%3]) /* two eigenvalues are the same*/ |
| 244 |
|
{ |
| 245 |
< |
// find maximum element of the independant eigenvector |
| 245 |
> |
/* find maximum element of the independant eigenvector*/ |
| 246 |
|
maxVal = fabs(V[i][0]); |
| 247 |
|
maxI = 0; |
| 248 |
|
for (j = 1; j < 3; j++) |
| 253 |
|
maxI = j; |
| 254 |
|
} |
| 255 |
|
} |
| 256 |
< |
// swap the eigenvector into its proper position |
| 256 |
> |
/* swap the eigenvector into its proper position*/ |
| 257 |
|
if (maxI != i) |
| 258 |
|
{ |
| 259 |
|
tmp = w[maxI]; |
| 261 |
|
w[i] = tmp; |
| 262 |
|
swapVectors3(V[i],V[maxI]); |
| 263 |
|
} |
| 264 |
< |
// maximum element of eigenvector should be positive |
| 264 |
> |
/* maximum element of eigenvector should be positive*/ |
| 265 |
|
if (V[maxI][maxI] < 0) |
| 266 |
|
{ |
| 267 |
|
V[maxI][0] = -V[maxI][0]; |
| 269 |
|
V[maxI][2] = -V[maxI][2]; |
| 270 |
|
} |
| 271 |
|
|
| 272 |
< |
// re-orthogonalize the other two eigenvectors |
| 272 |
> |
/* re-orthogonalize the other two eigenvectors*/ |
| 273 |
|
j = (maxI+1)%3; |
| 274 |
|
k = (maxI+2)%3; |
| 275 |
|
|
| 281 |
|
normalize3(V[k]); |
| 282 |
|
crossProduct3(V[k],V[maxI],V[j]); |
| 283 |
|
|
| 284 |
< |
// transpose vectors back to columns |
| 284 |
> |
/* transpose vectors back to columns*/ |
| 285 |
|
transposeMat3(V,V); |
| 286 |
|
return; |
| 287 |
|
} |
| 288 |
|
} |
| 289 |
|
|
| 290 |
< |
// the three eigenvalues are different, just sort the eigenvectors |
| 291 |
< |
// to align them with the x, y, and z axes |
| 290 |
> |
/* the three eigenvalues are different, just sort the eigenvectors*/ |
| 291 |
> |
/* to align them with the x, y, and z axes*/ |
| 292 |
|
|
| 293 |
< |
// find the vector with the largest x element, make that vector |
| 294 |
< |
// the first vector |
| 293 |
> |
/* find the vector with the largest x element, make that vector*/ |
| 294 |
> |
/* the first vector*/ |
| 295 |
|
maxVal = fabs(V[0][0]); |
| 296 |
|
maxI = 0; |
| 297 |
|
for (i = 1; i < 3; i++) |
| 302 |
|
maxI = i; |
| 303 |
|
} |
| 304 |
|
} |
| 305 |
< |
// swap eigenvalue and eigenvector |
| 305 |
> |
/* swap eigenvalue and eigenvector*/ |
| 306 |
|
if (maxI != 0) |
| 307 |
|
{ |
| 308 |
|
tmp = w[maxI]; |
| 310 |
|
w[0] = tmp; |
| 311 |
|
swapVectors3(V[maxI],V[0]); |
| 312 |
|
} |
| 313 |
< |
// do the same for the y element |
| 313 |
> |
/* do the same for the y element*/ |
| 314 |
|
if (fabs(V[1][1]) < fabs(V[2][1])) |
| 315 |
|
{ |
| 316 |
|
tmp = w[2]; |
| 319 |
|
swapVectors3(V[2],V[1]); |
| 320 |
|
} |
| 321 |
|
|
| 322 |
< |
// ensure that the sign of the eigenvectors is correct |
| 322 |
> |
/* ensure that the sign of the eigenvectors is correct*/ |
| 323 |
|
for (i = 0; i < 2; i++) |
| 324 |
|
{ |
| 325 |
|
if (V[i][i] < 0) |
| 329 |
|
V[i][2] = -V[i][2]; |
| 330 |
|
} |
| 331 |
|
} |
| 332 |
< |
// set sign of final eigenvector to ensure that determinant is positive |
| 332 |
> |
/* set sign of final eigenvector to ensure that determinant is positive*/ |
| 333 |
|
if (matDet3(V) < 0) |
| 334 |
|
{ |
| 335 |
|
V[2][0] = -V[2][0]; |
| 337 |
|
V[2][2] = -V[2][2]; |
| 338 |
|
} |
| 339 |
|
|
| 340 |
< |
// transpose the eigenvectors back again |
| 340 |
> |
/* transpose the eigenvectors back again*/ |
| 341 |
|
transposeMat3(V,V); |
| 342 |
|
} |
| 343 |
|
|
| 346 |
|
|
| 347 |
|
#define MAX_ROTATIONS 20 |
| 348 |
|
|
| 349 |
< |
// Jacobi iteration for the solution of eigenvectors/eigenvalues of a nxn |
| 350 |
< |
// real symmetric matrix. Square nxn matrix a; size of matrix in n; |
| 351 |
< |
// output eigenvalues in w; and output eigenvectors in v. Resulting |
| 352 |
< |
// eigenvalues/vectors are sorted in decreasing order; eigenvectors are |
| 353 |
< |
// normalized. |
| 354 |
< |
int JacobiN(double **a, int n, double *w, double **v) { |
| 349 |
> |
/* Jacobi iteration for the solution of eigenvectors/eigenvalues of a nxn*/ |
| 350 |
> |
/* real symmetric matrix. Square nxn matrix a; size of matrix in n;*/ |
| 351 |
> |
/* output eigenvalues in w; and output eigenvectors in v. Resulting*/ |
| 352 |
> |
/* eigenvalues/vectors are sorted in decreasing order; eigenvectors are*/ |
| 353 |
> |
/* normalized.*/ |
| 354 |
> |
int JacobiN(RealType **a, int n, RealType *w, RealType **v) { |
| 355 |
|
|
| 356 |
|
int i, j, k, iq, ip, numPos; |
| 357 |
|
int ceil_half_n; |
| 358 |
< |
double tresh, theta, tau, t, sm, s, h, g, c, tmp; |
| 359 |
< |
double bspace[4], zspace[4]; |
| 360 |
< |
double *b = bspace; |
| 361 |
< |
double *z = zspace; |
| 358 |
> |
RealType tresh, theta, tau, t, sm, s, h, g, c, tmp; |
| 359 |
> |
RealType bspace[4], zspace[4]; |
| 360 |
> |
RealType *b = bspace; |
| 361 |
> |
RealType *z = zspace; |
| 362 |
|
|
| 363 |
|
|
| 364 |
< |
// only allocate memory if the matrix is large |
| 364 |
> |
/* only allocate memory if the matrix is large*/ |
| 365 |
|
if (n > 4) |
| 366 |
|
{ |
| 367 |
< |
b = (double *) calloc(n, sizeof(double)); |
| 368 |
< |
z = (double *) calloc(n, sizeof(double)); |
| 367 |
> |
b = (RealType *) calloc(n, sizeof(RealType)); |
| 368 |
> |
z = (RealType *) calloc(n, sizeof(RealType)); |
| 369 |
|
} |
| 370 |
|
|
| 371 |
< |
// initialize |
| 371 |
> |
/* initialize*/ |
| 372 |
|
for (ip=0; ip<n; ip++) |
| 373 |
|
{ |
| 374 |
|
for (iq=0; iq<n; iq++) |
| 383 |
|
z[ip] = 0.0; |
| 384 |
|
} |
| 385 |
|
|
| 386 |
< |
// begin rotation sequence |
| 386 |
> |
/* begin rotation sequence*/ |
| 387 |
|
for (i=0; i<MAX_ROTATIONS; i++) |
| 388 |
|
{ |
| 389 |
|
sm = 0.0; |
| 399 |
|
break; |
| 400 |
|
} |
| 401 |
|
|
| 402 |
< |
if (i < 3) // first 3 sweeps |
| 402 |
> |
if (i < 3) /* first 3 sweeps*/ |
| 403 |
|
{ |
| 404 |
|
tresh = 0.2*sm/(n*n); |
| 405 |
|
} |
| 414 |
|
{ |
| 415 |
|
g = 100.0*fabs(a[ip][iq]); |
| 416 |
|
|
| 417 |
< |
// after 4 sweeps |
| 417 |
> |
/* after 4 sweeps*/ |
| 418 |
|
if (i > 3 && (fabs(w[ip])+g) == fabs(w[ip]) |
| 419 |
|
&& (fabs(w[iq])+g) == fabs(w[iq])) |
| 420 |
|
{ |
| 446 |
|
w[iq] += h; |
| 447 |
|
a[ip][iq]=0.0; |
| 448 |
|
|
| 449 |
< |
// ip already shifted left by 1 unit |
| 449 |
> |
/* ip already shifted left by 1 unit*/ |
| 450 |
|
for (j = 0;j <= ip-1;j++) |
| 451 |
|
{ |
| 452 |
|
MAT_ROTATE(a,j,ip,j,iq) |
| 453 |
|
} |
| 454 |
< |
// ip and iq already shifted left by 1 unit |
| 454 |
> |
/* ip and iq already shifted left by 1 unit*/ |
| 455 |
|
for (j = ip+1;j <= iq-1;j++) |
| 456 |
|
{ |
| 457 |
|
MAT_ROTATE(a,ip,j,j,iq) |
| 458 |
|
} |
| 459 |
< |
// iq already shifted left by 1 unit |
| 459 |
> |
/* iq already shifted left by 1 unit*/ |
| 460 |
|
for (j=iq+1; j<n; j++) |
| 461 |
|
{ |
| 462 |
|
MAT_ROTATE(a,ip,j,iq,j) |
| 477 |
|
} |
| 478 |
|
} |
| 479 |
|
|
| 480 |
< |
//// this is NEVER called |
| 480 |
> |
/*// this is NEVER called*/ |
| 481 |
|
if ( i >= MAX_ROTATIONS ) |
| 482 |
|
{ |
| 483 |
|
sprintf( painCave.errMsg, |
| 487 |
|
return 0; |
| 488 |
|
} |
| 489 |
|
|
| 490 |
< |
// sort eigenfunctions these changes do not affect accuracy |
| 491 |
< |
for (j=0; j<n-1; j++) // boundary incorrect |
| 490 |
> |
/* sort eigenfunctions these changes do not affect accuracy */ |
| 491 |
> |
for (j=0; j<n-1; j++) /* boundary incorrect*/ |
| 492 |
|
{ |
| 493 |
|
k = j; |
| 494 |
|
tmp = w[k]; |
| 495 |
< |
for (i=j+1; i<n; i++) // boundary incorrect, shifted already |
| 495 |
> |
for (i=j+1; i<n; i++) /* boundary incorrect, shifted already*/ |
| 496 |
|
{ |
| 497 |
< |
if (w[i] >= tmp) // why exchage if same? |
| 497 |
> |
if (w[i] >= tmp) /* why exchage if same?*/ |
| 498 |
|
{ |
| 499 |
|
k = i; |
| 500 |
|
tmp = w[k]; |
| 512 |
|
} |
| 513 |
|
} |
| 514 |
|
} |
| 515 |
< |
// insure eigenvector consistency (i.e., Jacobi can compute vectors that |
| 516 |
< |
// are negative of one another (.707,.707,0) and (-.707,-.707,0). This can |
| 517 |
< |
// reek havoc in hyperstreamline/other stuff. We will select the most |
| 518 |
< |
// positive eigenvector. |
| 515 |
> |
/* insure eigenvector consistency (i.e., Jacobi can compute vectors that*/ |
| 516 |
> |
/* are negative of one another (.707,.707,0) and (-.707,-.707,0). This can*/ |
| 517 |
> |
/* reek havoc in hyperstreamline/other stuff. We will select the most*/ |
| 518 |
> |
/* positive eigenvector.*/ |
| 519 |
|
ceil_half_n = (n >> 1) + (n & 1); |
| 520 |
|
for (j=0; j<n; j++) |
| 521 |
|
{ |
| 526 |
|
numPos++; |
| 527 |
|
} |
| 528 |
|
} |
| 529 |
< |
// if ( numPos < ceil(double(n)/double(2.0)) ) |
| 529 |
> |
/* if ( numPos < ceil(RealType(n)/RealType(2.0)) )*/ |
| 530 |
|
if ( numPos < ceil_half_n) |
| 531 |
|
{ |
| 532 |
|
for(i=0; i<n; i++) |
