static void IniTest()
{
IniFile pNull((const char *)NULL);
assert(pNull.sections.Count() == 0);
IniFile pEmpty("");
assert(pEmpty.sections.Count() == 1 && !pEmpty.sections.At(0)->name);
assert(pEmpty.FindSection(NULL)->lines.Count() == 0);
IniFile pNoNL1("key = value");
assert(str::Eq(pNoNL1.sections.At(0)->lines.At(0).value, "value"));
IniFile pNoNL2("[Section ]");
assert(pNoNL2.FindSection("Section "));
static const char *iniData = "\
; NULL section \n\
key1=value1\n\
[Section 1]\n\
key2 = value2 \n\
# comment \n\
key3: \"quoted value\" \n\n\
[ Empty Section ]\n\n\
]Section 2 \n\
key without separator\n\
";
IniLine verify[] = {
IniLine(NULL, NULL), IniLine("key1", "value1"),
IniLine("Section 1", NULL), IniLine("Key2", "value2"), IniLine("key3", "\"quoted value\""),
IniLine(" Empty Section ", NULL),
IniLine("Section 2", NULL), IniLine("key", "without separator"),
};
IniFile p(iniData);
size_t idxSec = (size_t)-1, idxLine = (size_t)-1;
for (int i = 0; i < dimof(verify); i++) {
if (!verify[i].value) {
if (idxSec != (size_t)-1) {
assert(p.sections.At(idxSec)->lines.Count() == idxLine);
}
idxSec++;
idxLine = 0;
assert(p.FindSection(verify[i].key) == p.sections.At(idxSec));
continue;
}
assert(idxSec < p.sections.Count() && idxLine < p.sections.At(idxSec)->lines.Count());
IniLine *line = p.sections.At(idxSec)->FindLine(verify[i].key);
assert(line && str::EqI(line->key, verify[i].key) && str::Eq(line->value, verify[i].value));
idxLine++;
}
assert(p.sections.Count() == idxSec + 1 && p.sections.Last()->lines.Count() == idxLine);
assert(p.FindSection("Section 1", 1) && p.FindSection("Section 2", 2));
assert(!p.FindSection("missing") && !p.FindSection("Section 1", 2));
assert(!p.FindSection("Section 1")->FindLine("missing"));
}
void ObjectTestFixture::testHighlyChunkedAppend()
{
listref a = listref(empty_list());
listref b = listref(empty_list());
void_object* null = new void_object(g_pContext);
objref pNull(null);
a->append(pNull);
a->append(pNull);
a->append(pNull);
b->append(pNull);
b->append(pNull);
b->append(pNull);
CPPUNIT_ASSERT(a->chunks()==3);
CPPUNIT_ASSERT(b->chunks()==3);
list_object* c = new list_object(g_pContext,*a);
c->append(b);
CPPUNIT_ASSERT(c->size()==6);
}
int GlmTest::summary(glm *fit)
{
double lambda;
unsigned int k;
unsigned int nRows=tm->nRows, nVars=tm->nVars, nParam=tm->nParam;
unsigned int mtype = fit->mmRef->model-1;
PoissonGlm pNull(fit->mmRef), pAlt(fit->mmRef);
BinGlm binNull(fit->mmRef), binAlt(fit->mmRef);
NBinGlm nbNull(fit->mmRef), nbAlt(fit->mmRef);
glm *PtrNull[3] = { &pNull, &nbNull, &binNull };
glm *PtrAlt[3] = { &pAlt, &nbAlt, &binAlt };
gsl_vector_view teststat, unitstat;
gsl_matrix_view L1;
// To estimate initial Beta from PtrNull->Beta
// gsl_vector *ref=gsl_vector_alloc(nParam);
// gsl_matrix *BetaO=gsl_matrix_alloc(nParam, nVars);
smryStat = gsl_matrix_alloc((nParam+1), nVars+1);
Psmry = gsl_matrix_alloc((nParam+1), nVars+1);
gsl_matrix_set_zero (Psmry);
// initialize the design matrix for all hypo tests
GrpMat *GrpXs = (GrpMat *)malloc((nParam+2)*sizeof(GrpMat));
GrpXs[0].matrix = gsl_matrix_alloc(nRows, nParam);
gsl_matrix_memcpy(GrpXs[0].matrix, fit->Xref); // the alt X
GrpXs[1].matrix = gsl_matrix_alloc(nRows, 1); // overall test
gsl_matrix_set_all (GrpXs[1].matrix, 1.0);
for (k=2; k<nParam+2; k++) { // significance tests
GrpXs[k].matrix = gsl_matrix_alloc(nRows, nParam-1);
subX2(fit->Xref, k-2, GrpXs[k].matrix);
}
// Calc test statistics
if ( tm->test == WALD ) {
// the overall test compares to mean
teststat = gsl_matrix_row(smryStat, 0);
L1=gsl_matrix_submatrix(L,1,0,nParam-1,nParam);
lambda=gsl_vector_get(tm->smry_lambda, 0);
GetR(fit->Res, tm->corr, lambda, Rlambda);
GeeWald(fit, &L1.matrix, &teststat.vector);
// the significance test
for (k=2; k<nParam+2; k++) {
teststat = gsl_matrix_row(smryStat, k-1);
L1 = gsl_matrix_submatrix(L, k-2, 0, 1, nParam);
GeeWald(fit, &L1.matrix, &teststat.vector);
}
}
else if (tm->test==SCORE) {
for (k=1; k<nParam+2; k++) {
teststat=gsl_matrix_row(smryStat, k-1);
PtrNull[mtype]->regression(fit->Yref,GrpXs[k].matrix,fit->Oref,NULL);
lambda=gsl_vector_get(tm->smry_lambda, k);
GetR(PtrNull[mtype]->Res, tm->corr, lambda, Rlambda);
GeeScore(GrpXs[0].matrix, PtrNull[mtype], &teststat.vector);
}
}
else {
for (k=1; k<nParam+2; k++) {
teststat=gsl_matrix_row(smryStat, k-1);
PtrNull[mtype]->regression(fit->Yref,GrpXs[k].matrix,fit->Oref,NULL);
GeeLR(fit, PtrNull[mtype], &teststat.vector); // works better
}
}
// sort id if the unitvaraite test is free step-down
gsl_permutation **sortid;
sortid=(gsl_permutation **)malloc((nParam+1)*sizeof(gsl_permutation *));
for ( k=0; k<(nParam+1); k++ ) {
teststat = gsl_matrix_row (smryStat, k);
unitstat = gsl_vector_subvector(&teststat.vector, 1, nVars);
sortid[k] = gsl_permutation_alloc(nVars);
gsl_sort_vector_index (sortid[k], &unitstat.vector);
gsl_permutation_reverse(sortid[k]); // rearrange in descending order
}
if (tm->resamp==MONTECARLO) {
lambda=gsl_vector_get(tm->smry_lambda,0);
GetR(fit->Res, tm->corr, lambda, Sigma);
setMonteCarlo(fit, XBeta, Sigma);
}
nSamp=0;
double *suj, *buj, *puj;
gsl_matrix *bStat = gsl_matrix_alloc((nParam+1), nVars+1);
gsl_matrix_set_zero (bStat);
gsl_matrix *bY = gsl_matrix_alloc(nRows, nVars);
gsl_matrix *bO = gsl_matrix_alloc(nRows, nVars);
gsl_matrix_memcpy (bO, fit->Eta);
double diff, timelast=0;
clock_t clk_start=clock();
for ( unsigned int i=0; i<tm->nboot; i++) {
if ( tm->resamp==CASEBOOT )
resampSmryCase(fit,bY,GrpXs,bO,i);
else resampNonCase(fit, bY, i);
if ( tm->test == WALD ) {
PtrAlt[mtype]->regression(bY,GrpXs[0].matrix,bO,NULL);
// the overall test compares to mean
teststat = gsl_matrix_row(bStat, 0);
L1=gsl_matrix_submatrix(L,1,0,nParam-1,nParam);
lambda=gsl_vector_get(tm->smry_lambda, 0);
GetR(PtrAlt[mtype]->Res, tm->corr, lambda, Rlambda);
GeeWald(PtrAlt[mtype], &L1.matrix, &teststat.vector);
// the significance test
for (k=2; k<nParam+2; k++) {
teststat = gsl_matrix_row(bStat, k-1);
L1 = gsl_matrix_submatrix(L, k-2, 0, 1, nParam);
GeeWald(PtrAlt[mtype], &L1.matrix, &teststat.vector);
}
}
else if (tm->test==SCORE) {
for (k=1; k<nParam+2; k++) {
teststat=gsl_matrix_row(bStat, k-1);
PtrNull[mtype]->regression(bY,GrpXs[k].matrix,bO,NULL);
lambda=gsl_vector_get(tm->smry_lambda,k);
GetR(PtrNull[mtype]->Res, tm->corr, lambda, Rlambda);
GeeScore(GrpXs[0].matrix, PtrNull[mtype], &teststat.vector);
}
}
else { // use single bAlt estimate works better
PtrAlt[mtype]->regression(bY,GrpXs[0].matrix,bO,NULL);
for (k=1; k<nParam+2; k++) {
teststat=gsl_matrix_row(bStat, k-1);
PtrNull[mtype]->regression(bY,GrpXs[k].matrix,bO,NULL);
GeeLR(PtrAlt[mtype], PtrNull[mtype], &teststat.vector);
}
}
for (k=0; k<(nParam+1); k++) {
buj = gsl_matrix_ptr (bStat, k, 0);
suj = gsl_matrix_ptr (smryStat, k, 0);
puj = gsl_matrix_ptr (Psmry, k, 0);
if ( *buj >= *suj ) *puj=*puj+1;
calcAdjustP(tm->punit, nVars, buj+1, suj+1, puj+1, sortid[k]);
} // end for j loop
nSamp++;
// Prompts
if ((tm->showtime==TRUE)&(i%100==0)) {
diff=(float)(clock()-clk_start)/(float)CLOCKS_PER_SEC;
timelast+=(double)diff/60;
printf("\tResampling run %d finished. Time elapsed: %.2f min ...\n", i, timelast);
clk_start=clock();
}
} // end for i loop
// ========= Get P-values ========= //
if ( tm->punit == FREESTEP ) {
for (k=0; k<(nParam+1); k++) {
puj = gsl_matrix_ptr (Psmry, k, 1);
reinforceP( puj, nVars, sortid[k] );
} }
// p = (#exceeding observed stat + 1)/(#nboot+1)
gsl_matrix_add_constant (Psmry, 1.0);
gsl_matrix_scale (Psmry, (double)1.0/(nSamp+1));
for (k=0; k<nVars; k++) aic[k]=-fit->ll[k]+2*(nParam+1);
// === release memory ==== //
PtrAlt[mtype]->releaseGlm();
if ( tm->test!=WALD )
PtrNull[mtype]->releaseGlm();
gsl_matrix_free(bStat);
gsl_matrix_free(bY);
gsl_matrix_free(bO);
for (k=0; k<nParam+1; k++)
if (sortid[k]!=NULL) gsl_permutation_free(sortid[k]);
free(sortid);
if ( GrpXs != NULL ) {
for ( unsigned int k=0; k<nParam+2; k++ )
if ( GrpXs[k].matrix != NULL )
gsl_matrix_free (GrpXs[k].matrix);
free(GrpXs);
}
return SUCCESS;
}
int GlmTest::anova(glm *fit, gsl_matrix *isXvarIn)
{
// Assume the models have been already sorted (in R)
Xin = isXvarIn;
nModels = Xin->size1;
double *rdf = new double [nModels];
unsigned int nP, i, j, k;
unsigned int ID0, ID1, nP0, nP1;
unsigned int nRows=tm->nRows, nVars=tm->nVars, nParam=tm->nParam;
unsigned int mtype = fit->mmRef->model-1;
dfDiff = new unsigned int [nModels-1];
anovaStat = gsl_matrix_alloc((nModels-1), nVars+1);
Panova = gsl_matrix_alloc((nModels-1), nVars+1);
gsl_vector *bStat = gsl_vector_alloc(nVars+1);
gsl_matrix_set_zero (anovaStat);
gsl_matrix_set_zero (Panova);
gsl_vector_set_zero (bStat);
PoissonGlm pNull(fit->mmRef), pAlt(fit->mmRef);
BinGlm binNull(fit->mmRef), binAlt(fit->mmRef);
NBinGlm nbNull(fit->mmRef), nbAlt(fit->mmRef);
PoissonGlm pNullb(fit->mmRef), pAltb(fit->mmRef);
BinGlm binNullb(fit->mmRef), binAltb(fit->mmRef);
NBinGlm nbNullb(fit->mmRef), nbAltb(fit->mmRef);
glm *PtrNull[3] = { &pNull, &nbNull, &binNull };
glm *PtrAlt[3] = { &pAlt, &nbAlt, &binAlt };
glm *bNull[3] = { &pNullb, &nbNullb, &binNullb };
glm *bAlt[3] = { &pAltb, &nbAltb, &binAltb };
double *suj, *buj, *puj;
gsl_vector_view teststat, unitstat,ref1, ref0;
gsl_matrix *X0=NULL, *X1=NULL, *L1=NULL, *tmp1=NULL, *BetaO=NULL;
gsl_matrix *bO=NULL, *bY=gsl_matrix_alloc(nRows, nVars);
bO = gsl_matrix_alloc(nRows, nVars);
gsl_permutation *sortid=NULL;
if (tm->punit==FREESTEP) sortid = gsl_permutation_alloc(nVars);
// ======= Fit the (first) Alt model =========//
for (i=0; i<nModels; i++) {
nP = 0;
for (k=0; k<nParam; k++)
if (gsl_matrix_get(Xin,i,k)!=FALSE) nP++;
rdf[i] = nRows-nP;
}
for (i=1; i<nModels; i++) {
// ======= Fit the Null model =========//
ID0 = i; ID1 = i-1;
nP0 = nRows - (unsigned int)rdf[ID0];
nP1 = nRows - (unsigned int)rdf[ID1];
// Degrees of freedom
dfDiff[i-1] = nP1 - nP0;
ref1=gsl_matrix_row(Xin, ID1);
ref0=gsl_matrix_row(Xin, ID0);
X0 = gsl_matrix_alloc(nRows, nP0);
subX(fit->Xref, &ref0.vector, X0);
X1 = gsl_matrix_alloc(nRows, nP1);
subX(fit->Xref, &ref1.vector, X1);
// ======= Get multivariate test statistics =======//
// Estimate shrinkage parametr only once under H1
// See "FW: Doubts R package "mvabund" (12/14/11)
teststat = gsl_matrix_row(anovaStat, (i-1));
PtrNull[mtype]->regression(fit->Yref, X0, fit->Oref, NULL);
if (tm->test == SCORE) {
lambda = gsl_vector_get(tm->anova_lambda, ID0);
GetR(PtrNull[mtype]->Res, tm->corr, lambda, Rlambda);
GeeScore(X1, PtrNull[mtype], &teststat.vector);
}
else if (tm->test==WALD) {
PtrAlt[mtype]->regression(fit->Yref, X1, fit->Oref, NULL);
L1 = gsl_matrix_alloc (nP1-nP0, nP1);
tmp1 = gsl_matrix_alloc (nParam, nP1);
subX(L, &ref1.vector, tmp1);
subXrow1(tmp1, &ref0.vector, &ref1.vector, L1);
lambda = gsl_vector_get(tm->anova_lambda, ID1);
GetR(PtrAlt[mtype]->Res, tm->corr, lambda, Rlambda);
GeeWald(PtrAlt[mtype], L1, &teststat.vector);
}
else {
BetaO = gsl_matrix_alloc(nP1, nVars);
addXrow2(PtrNull[mtype]->Beta, &ref1.vector, BetaO);
PtrAlt[mtype]->regression(fit->Yref, X1, fit->Oref, BetaO);
GeeLR(PtrAlt[mtype], PtrNull[mtype], &teststat.vector);
}
if (tm->resamp==MONTECARLO) {
lambda=gsl_vector_get(tm->anova_lambda,ID0);
GetR(fit->Res, tm->corr, lambda, Sigma);
setMonteCarlo (PtrNull[mtype], XBeta, Sigma);
}
// ======= Get univariate test statistics =======//
if (tm->punit == FREESTEP) {
unitstat=gsl_vector_subvector(&teststat.vector,1,nVars);
gsl_sort_vector_index (sortid, &unitstat.vector);
gsl_permutation_reverse(sortid);
}
// ======= Get resampling distribution under H0 ===== //
nSamp=0;
double dif, timelast=0;
clock_t clk_start=clock();
if (tm->showtime==TRUE)
printf("Resampling begins for test %d.\n", i);
for (j=0; j<tm->nboot; j++) {
// printf("simu %d :", j);
gsl_vector_set_zero (bStat);
if ( tm->resamp == CASEBOOT ) {
resampAnovaCase(PtrAlt[mtype],bY,X1,bO,j);
subX(X1, &ref0.vector, X0);
}
else {
resampNonCase(PtrNull[mtype], bY, j);
gsl_matrix_memcpy(bO, fit->Oref);
}
if ( tm->test == WALD ) {
bAlt[mtype]->regression(bY,X1,bO,NULL);
lambda = gsl_vector_get(tm->anova_lambda, ID1);
GetR(bAlt[mtype]->Res, tm->corr, lambda, Rlambda);
GeeWald(bAlt[mtype], L1, bStat);
}
else if ( tm->test == SCORE ) {
bNull[mtype]->regression(bY,X0,bO,NULL);
lambda = gsl_vector_get(tm->anova_lambda, ID0);
GetR(bNull[mtype]->Res, tm->corr, lambda, Rlambda);
GeeScore(X1, bNull[mtype], bStat);
}
else {
bNull[mtype]->regression(bY,X0,bO,NULL);
addXrow2(bNull[mtype]->Beta, &ref1.vector, BetaO);
bAlt[mtype]->regression(bY,X1,bO,BetaO);
GeeLR(bAlt[mtype], bNull[mtype], bStat);
}
// ----- get multivariate counts ------- //
buj = gsl_vector_ptr (bStat,0);
suj = gsl_matrix_ptr (anovaStat, i-1, 0);
puj = gsl_matrix_ptr (Panova, i-1, 0);
if ( *(buj) > (*(suj)-1e-8) ) *puj=*puj+1;
// ------ get univariate counts ---------//
calcAdjustP(tm->punit,nVars,buj+1,suj+1,puj+1,sortid);
nSamp++;
// Prompts
if ((tm->showtime==TRUE)&(j%100==0)) {
dif = (float)(clock() - clk_start)/(float)CLOCKS_PER_SEC;
timelast+=(double)dif/60;
printf("\tResampling run %d finished. Time elapsed: %.2f minutes...\n", j, timelast);
clk_start=clock();
}
} // end j for loop
// ========= get p-values ======== //
if ( tm->punit == FREESTEP) {
puj = gsl_matrix_ptr (Panova, i-1, 1);
reinforceP(puj, nVars, sortid);
}
if (BetaO!=NULL) gsl_matrix_free(BetaO);
if (X0!=NULL) gsl_matrix_free(X0);
if (X1!=NULL) gsl_matrix_free(X1);
if (tm->test == WALD) {
if (L1!=NULL) gsl_matrix_free(L1);
if (tmp1!=NULL) gsl_matrix_free(tmp1);
}
} // end i for loop and test for loop
// p = (#exceeding observed stat + 1)/(#nboot+1)
gsl_matrix_add_constant (Panova, 1.0);
gsl_matrix_scale (Panova, (double)1/(nSamp+1.0));
bAlt[mtype]->releaseGlm();
PtrAlt[mtype]->releaseGlm();
if ( tm->test!=WALD ) {
bNull[mtype]->releaseGlm();
PtrNull[mtype]->releaseGlm();
}
delete []rdf;
if (sortid != NULL )
gsl_permutation_free(sortid);
gsl_vector_free(bStat);
gsl_matrix_free(bY);
if (bO!=NULL) gsl_matrix_free(bO);
return SUCCESS;
}
int testPlane() {
int numErr = 0;
logMessage(_T("TESTING - class GM_3dPlane ...\n\n"));
// Default constructor, plane must be invalid
GM_3dPlane p;
if (p.isValid()) {
logMessage(_T("\tERROR - Default constructor creates valid plane\n"));
numErr++;
}
else {
logMessage(_T("\tOK - Default constructor creates invalid plane\n"));
}
// Constructor (coeff)
GM_3dPlane p1(getRandomDouble(), getRandomDouble(), getRandomDouble(), getRandomDouble());
GM_3dPlane pNull(0.0, 0.0, 0.0, getRandomDouble());
if (!p1.isValid() || pNull.isValid()) {
logMessage(_T("\tERROR - Coeff. constructor not working\n"));
numErr++;
}
else {
logMessage(_T("\tOK - Coeff. constructor working\n"));
}
// Copy constructor
GM_3dPlane copyPlane(p1);
if (!copyPlane.isValid() || copyPlane != p1) {
logMessage(_T("\tERROR - Copy constructor not working\n"));
numErr++;
}
else {
logMessage(_T("\tOK - Copy constructor working\n"));
}
// Constructor (coeff vector)
double pointsVect[4];
for (int i = 0 ; i < 4 ; i++) {
pointsVect[i] = getRandomDouble();
}
GM_3dPlane p2(pointsVect);
if (!p2.isValid()) {
logMessage(_T("\tERROR - Coeff. vector constructor not working\n"));
numErr++;
}
else {
logMessage(_T("\tOK - Coeff. vector constructor working\n"));
}
// Constructor (points)
GM_3dPoint pt1(getRandomDouble(), getRandomDouble(), getRandomDouble());
GM_3dPoint pt2(getRandomDouble(), getRandomDouble(), getRandomDouble());
GM_3dPoint pt3(getRandomDouble(), getRandomDouble(), getRandomDouble());
GM_3dLine ln(getRandomDouble(), getRandomDouble(), getRandomDouble(),getRandomDouble(), getRandomDouble(), getRandomDouble());
GM_3dPoint ptLn1 = ln.begin();
GM_3dPoint ptLn2 = ln.center();
GM_3dPoint ptLn3 = ln.end();
GM_3dPlane p3(pt1, pt2, pt3);
GM_3dPlane pLine(ptLn2, ptLn2, ptLn3);
double distPt1 = pt1.x()*p3[0] + pt1.y()*p3[1] + pt1.z()*p3[2] + p3[3];
double distPt2 = pt2.x()*p3[0] + pt2.y()*p3[1] + pt2.z()*p3[2] + p3[3];
double distPt3 = pt3.x()*p3[0] + pt3.y()*p3[1] + pt3.z()*p3[2] + p3[3];
if (!p3.isValid() || pLine.isValid() || fabs(distPt1) > GM_NULL_TOLERANCE || fabs(distPt2) > GM_NULL_TOLERANCE || fabs(distPt3) > GM_NULL_TOLERANCE) {
logMessage(_T("\tERROR - Points constructor not working\n"));
numErr++;
}
else {
logMessage(_T("\tOK - Points constructor working\n"));
}
// Point distance
GM_3dPlane p4(getRandomDouble(), getRandomDouble(), getRandomDouble(),getRandomDouble());
GM_3dPoint checkPoint(getRandomDouble(), getRandomDouble(), getRandomDouble());
if (fabs(p4[0]) > GM_NULL_TOLERANCE) {
checkPoint.x(-(checkPoint.y()*p4[1] + checkPoint.z()*p4[2] + p4[3]) / p4[0]);
}
else if (fabs(p4[1]) > GM_NULL_TOLERANCE) {
checkPoint.y(-(checkPoint.x()*p4[0] + checkPoint.z()*p4[2] + p4[3]) / p4[1]);
}
else if (fabs(p4[2]) > GM_NULL_TOLERANCE) {
checkPoint.z(-(checkPoint.x()*p4[0] + checkPoint.y()*p4[1] + p4[3]) / p4[2]);
}
else {
p4.invalidate();
}
double checkSignedDist = getRandomDouble();
double checkDist = fabs(checkSignedDist);
GM_3dVector p4Norm = p4.normalVector();
checkPoint = (GM_3dVector)checkPoint + (p4Norm * checkSignedDist);
GM_3dPoint checkPointOnPlane1;
GM_3dPoint checkPointOnPlane2;
double dist = p4.pointDistance(checkPoint);
double signedDist = p4.pointDistanceSgn(checkPoint);
double signedDistOnPlane = p4.pointDistanceSgn(checkPoint, checkPointOnPlane1);
double distOnPlane = p4.pointDistance(checkPoint, checkPointOnPlane2);
distPt1 = checkPointOnPlane1.x()*p4[0] + checkPointOnPlane1.y()*p4[1] + checkPointOnPlane1.z()*p4[2] + p4[3];
distPt2 = checkPointOnPlane2.x()*p4[0] + checkPointOnPlane2.y()*p4[1] + checkPointOnPlane2.z()*p4[2] + p4[3];
if (!p4.isValid() || !checkPointOnPlane1.isValid() || !checkPointOnPlane2.isValid() ||
fabs(distPt1) > GM_NULL_TOLERANCE || fabs(distPt2) > GM_NULL_TOLERANCE ||
fabs(distOnPlane - checkDist) > GM_NULL_TOLERANCE || fabs(signedDistOnPlane - checkSignedDist) > GM_NULL_TOLERANCE ||
fabs(dist - checkDist) > GM_NULL_TOLERANCE || fabs(signedDist - checkSignedDist) > GM_NULL_TOLERANCE) {
logMessage(_T("\tERROR - Point distance computation not working\n"));
numErr++;
}
else {
logMessage(_T("\tOK - Point distance computation working\n"));
}
// XY Angle
double angle = ((((double)rand()) / ((double)RAND_MAX)) * GM_PI) - (GM_PI / 2.0);
GM_3dVector normVector(angle/* + GM_HALFPI*/);
normVector.z(normVector.y());
normVector.y(0.0);
GM_3dPlane angleP(normVector, GM_3dPoint(getRandomDouble(), getRandomDouble(), getRandomDouble()));
double checkAngle = angleP.xyAngle();
if (checkAngle > GM_PI) {
checkAngle -= 2.0 * GM_PI;
}
if (!angleP.isValid() || fabs(angle - checkAngle) > GM_NULL_TOLERANCE) {
logMessage(_T("\tERROR - XY angle computation not working\n"));
numErr++;
}
else {
logMessage(_T("\tOK - XY angle computation working\n"));
}
return numErr;
}
