static inline bool BadDimension(cThis *t, ccount key) { if( t->ndim > NDIM ) return true; if( IsSobol(key) ) return t->ndim < SOBOL_MINDIM || (t->seed == 0 && t->ndim > SOBOL_MAXDIM); if( IsRule(key, t->ndim) ) return t->ndim < 1; return t->ndim < KOROBOV_MINDIM || t->ndim > KOROBOV_MAXDIM; }
static inline bool BadDimension(ccount ndim, cint flags, ccount key) { #if NDIM > 0 if( ndim > NDIM ) return true; #endif if( IsSobol(key) ) return ndim < SOBOL_MINDIM || (!PSEUDORNG && ndim > SOBOL_MAXDIM); if( IsRule(key, ndim) ) return ndim < 1; return ndim < KOROBOV_MINDIM || ndim > KOROBOV_MAXDIM; }
static int Integrate(This *t, real *integral, real *error, real *prob) { TYPEDEFREGION; Totals totals[NCOMP]; real nneed, weight; count dim, comp, iter, pass = 0, err, iregion; number nwant, nmin = INT_MAX; int fail; if( VERBOSE > 1 ) { char s[512]; sprintf(s, "Divonne input parameters:\n" " ndim " COUNT "\n ncomp " COUNT "\n" " epsrel " REAL "\n epsabs " REAL "\n" " flags %d\n seed %d\n" " mineval " NUMBER "\n maxeval " NUMBER "\n" " key1 %d\n key2 %d\n key3 %d\n maxpass " COUNT "\n" " border " REAL "\n maxchisq " REAL "\n mindeviation " REAL "\n" " ngiven " NUMBER "\n nextra " NUMBER, t->ndim, t->ncomp, t->epsrel, t->epsabs, t->flags, t->seed, t->mineval, t->maxeval, t->key1, t->key2, t->key3, t->maxpass, t->border.lower, t->maxchisq, t->mindeviation, t->ngiven, t->nextra); Print(s); } if( BadComponent(t) ) return -2; if( BadDimension(t, t->key1) || BadDimension(t, t->key2) || ((t->key3 & -2) && BadDimension(t, t->key3)) ) return -1; t->neval_opt = t->neval_cut = 0; t->size = CHUNKSIZE; MemAlloc(t->voidregion, t->size*sizeof(Region)); for( dim = 0; dim < t->ndim; ++dim ) { Bounds *b = &RegionPtr(0)->bounds[dim]; b->lower = 0; b->upper = 1; } RuleIni(&t->rule7); RuleIni(&t->rule9); RuleIni(&t->rule11); RuleIni(&t->rule13); SamplesIni(&t->samples[0]); SamplesIni(&t->samples[1]); SamplesIni(&t->samples[2]); if( (fail = setjmp(t->abort)) ) goto abort; t->epsabs = Max(t->epsabs, NOTZERO); /* Step 1: partition the integration region */ if( VERBOSE ) Print("Partitioning phase:"); if( IsSobol(t->key1) || IsSobol(t->key2) || IsSobol(t->key3) ) IniRandom(t); SamplesLookup(t, &t->samples[0], t->key1, (number)47, (number)INT_MAX, (number)0); SamplesAlloc(t, &t->samples[0]); t->totals = totals; Zap(totals); t->phase = 1; Explore(t, 0, &t->samples[0], INIDEPTH, 1); for( iter = 1; ; ++iter ) { Totals *maxtot; count valid; for( comp = 0; comp < t->ncomp; ++comp ) { Totals *tot = &totals[comp]; tot->avg = tot->spreadsq = 0; tot->spread = tot->secondspread = -INFTY; } for( iregion = 0; iregion < t->nregions; ++iregion ) { Region *region = RegionPtr(iregion); for( comp = 0; comp < t->ncomp; ++comp ) { cResult *r = ®ion->result[comp]; Totals *tot = &totals[comp]; tot->avg += r->avg; tot->spreadsq += Sq(r->spread); if( r->spread > tot->spread ) { tot->secondspread = tot->spread; tot->spread = r->spread; tot->iregion = iregion; } else if( r->spread > tot->secondspread ) tot->secondspread = r->spread; } } maxtot = totals; valid = 0; for( comp = 0; comp < t->ncomp; ++comp ) { Totals *tot = &totals[comp]; integral[comp] = tot->avg; valid += tot->avg == tot->avg; if( tot->spreadsq > maxtot->spreadsq ) maxtot = tot; tot->spread = sqrt(tot->spreadsq); error[comp] = tot->spread*t->samples[0].weight; } if( VERBOSE ) { char s[128 + 64*NCOMP], *p = s; p += sprintf(p, "\n" "Iteration " COUNT " (pass " COUNT "): " COUNT " regions\n" NUMBER7 " integrand evaluations so far,\n" NUMBER7 " in optimizing regions,\n" NUMBER7 " in finding cuts", iter, pass, t->nregions, t->neval, t->neval_opt, t->neval_cut); for( comp = 0; comp < t->ncomp; ++comp ) p += sprintf(p, "\n[" COUNT "] " REAL " +- " REAL, comp + 1, integral[comp], error[comp]); Print(s); } if( valid == 0 ) goto abort; /* all NaNs */ if( t->neval > t->maxeval ) break; nneed = maxtot->spread/MaxErr(maxtot->avg); if( nneed < MAXPRIME ) { cnumber n = t->neval + t->nregions*(number)ceil(nneed); if( n < nmin ) { nmin = n; pass = 0; } else if( ++pass > t->maxpass && n >= t->mineval ) break; } Split(t, maxtot->iregion, DEPTH); } /* Step 2: do a "full" integration on each region */ /* nneed = t->samples[0].neff + 1; */ nneed = 2*t->samples[0].neff; for( comp = 0; comp < t->ncomp; ++comp ) { Totals *tot = &totals[comp]; creal maxerr = MaxErr(tot->avg); tot->nneed = tot->spread/maxerr; nneed = Max(nneed, tot->nneed); tot->maxerrsq = Sq(maxerr); tot->mindevsq = tot->maxerrsq*Sq(t->mindeviation); } nwant = (number)Min(ceil(nneed), MARKMASK/40.); err = SamplesLookup(t, &t->samples[1], t->key2, nwant, (t->maxeval - t->neval)/t->nregions + 1, t->samples[0].n + 1); /* the number of points needed to reach the desired accuracy */ fail = Unmark(err)*t->nregions; if( Marked(err) ) { if( VERBOSE ) Print("\nNot enough samples left for main integration."); for( comp = 0; comp < t->ncomp; ++comp ) prob[comp] = -999; weight = t->samples[0].weight; } else { bool can_adjust = (t->key3 == 1 && t->samples[1].sampler != SampleRule && (t->key2 < 0 || t->samples[1].neff < MAXPRIME)); count df, nlimit; SamplesAlloc(t, &t->samples[1]); if( VERBOSE ) { char s[128]; sprintf(s, "\nMain integration on " COUNT " regions with " NUMBER " samples per region.", t->nregions, t->samples[1].neff); Print(s); } ResClear(integral); ResClear(error); ResClear(prob); nlimit = t->maxeval - t->nregions*t->samples[1].n; df = 0; for( iregion = 0; iregion < t->nregions; ++iregion ) { Region *region = RegionPtr(iregion); char s[64*NDIM + 256*NCOMP], *p = s; int todo; refine: t->phase = 2; t->samples[1].sampler(t, &t->samples[1], region->bounds, region->vol); if( can_adjust ) for( comp = 0; comp < t->ncomp; ++comp ) totals[comp].spreadsq -= Sq(region->result[comp].spread); nlimit += t->samples[1].n; todo = 0; for( comp = 0; comp < t->ncomp; ++comp ) { cResult *r = ®ion->result[comp]; Totals *tot = &totals[comp]; t->samples[0].avg[comp] = r->avg; t->samples[0].err[comp] = r->err; if( t->neval < nlimit ) { creal avg2 = t->samples[1].avg[comp]; creal err2 = t->samples[1].err[comp]; creal diffsq = Sq(avg2 - r->avg); #define Var(s) Sq((s.err[comp] == 0) ? r->spread*s.weight : s.err[comp]) if( err2*tot->nneed > r->spread || diffsq > Max(t->maxchisq*(Var(t->samples[0]) + Var(t->samples[1])), EPS*Sq(avg2)) ) { if( t->key3 && diffsq > tot->mindevsq ) { if( t->key3 == 1 ) { ccount xregion = t->nregions; if( VERBOSE > 2 ) Print("\nSplit"); t->phase = 1; Explore(t, iregion, &t->samples[1], POSTDEPTH, 2); if( can_adjust ) { number nnew; count ireg, xreg; for( ireg = iregion, xreg = xregion; ireg < t->nregions; ireg = xreg++ ) { cResult *result = RegionPtr(ireg)->result; count c; for( c = 0; c < t->ncomp; ++c ) totals[c].spreadsq += Sq(result[c].spread); } nnew = (tot->spreadsq/Sq(MARKMASK) > tot->maxerrsq) ? MARKMASK : (number)ceil(sqrt(tot->spreadsq/tot->maxerrsq)); if( nnew > nwant + nwant/64 ) { ccount err = SamplesLookup(t, &t->samples[1], t->key2, nnew, (t->maxeval - t->neval)/t->nregions + 1, t->samples[1].n); fail += Unmark(err)*t->nregions; nwant = nnew; SamplesFree(&t->samples[1]); SamplesAlloc(t, &t->samples[1]); if( t->key2 > 0 && t->samples[1].neff >= MAXPRIME ) can_adjust = false; if( VERBOSE > 2 ) { char s[128]; sprintf(s, "Sampling remaining " COUNT " regions with " NUMBER " points per region.", t->nregions, t->samples[1].neff); Print(s); } } } goto refine; } todo |= 3; } todo |= 1; } } } if( can_adjust ) { for( comp = 0; comp < t->ncomp; ++comp ) totals[comp].maxerrsq -= Sq(region->result[comp].spread*t->samples[1].weight); } switch( todo ) { case 1: /* get spread right */ Explore(t, iregion, &t->samples[1], 0, 2); break; case 3: /* sample region again with more points */ if( SamplesIniQ(&t->samples[2]) ) { SamplesLookup(t, &t->samples[2], t->key3, nwant, (number)INT_MAX, (number)0); SamplesAlloc(t, &t->samples[2]); } t->phase = 3; t->samples[2].sampler(t, &t->samples[2], region->bounds, region->vol); Explore(t, iregion, &t->samples[2], 0, 2); ++region->depth; /* misused for df here */ ++df; } ++region->depth; /* misused for df here */ if( VERBOSE > 2 ) { for( dim = 0; dim < t->ndim; ++dim ) { cBounds *b = ®ion->bounds[dim]; p += sprintf(p, (dim == 0) ? "\nRegion (" REALF ") - (" REALF ")" : "\n (" REALF ") - (" REALF ")", b->lower, b->upper); } } for( comp = 0; comp < t->ncomp; ++comp ) { Result *r = ®ion->result[comp]; creal x1 = t->samples[0].avg[comp]; creal s1 = Var(t->samples[0]); creal x2 = t->samples[1].avg[comp]; creal s2 = Var(t->samples[1]); creal r2 = (s1 == 0) ? Sq(t->samples[1].neff*t->samples[0].weight) : s2/s1; real norm = 1 + r2; real avg = x2 + r2*x1; real sigsq = s2; real chisq = Sq(x2 - x1); real chiden = s1 + s2; if( todo == 3 ) { creal x3 = t->samples[2].avg[comp]; creal s3 = Var(t->samples[2]); creal r3 = (s2 == 0) ? Sq(t->samples[2].neff*t->samples[1].weight) : s3/s2; norm = 1 + r3*norm; avg = x3 + r3*avg; sigsq = s3; chisq = s1*Sq(x3 - x2) + s2*Sq(x3 - x1) + s3*chisq; chiden = s1*s2 + s3*chiden; } avg = LAST ? r->avg : (sigsq *= norm = 1/norm, avg*norm); if( chisq > EPS ) chisq /= Max(chiden, NOTZERO); #define Out(s) s.avg[comp], r->spread*s.weight, s.err[comp] if( VERBOSE > 2 ) { p += sprintf(p, "\n[" COUNT "] " REAL " +- " REAL "(" REAL ")\n " REAL " +- " REAL "(" REAL ")", comp + 1, Out(t->samples[0]), Out(t->samples[1])); if( todo == 3 ) p += sprintf(p, "\n " REAL " +- " REAL "(" REAL ")", Out(t->samples[2])); p += sprintf(p, " \tchisq " REAL, chisq); } integral[comp] += avg; error[comp] += sigsq; prob[comp] += chisq; r->avg = avg; r->spread = sqrt(sigsq); r->chisq = chisq; } if( VERBOSE > 2 ) Print(s); } for( comp = 0; comp < t->ncomp; ++comp ) error[comp] = sqrt(error[comp]); df += t->nregions; if( VERBOSE > 2 ) { char s[16 + 128*NCOMP], *p = s; p += sprintf(p, "\nTotals:"); for( comp = 0; comp < t->ncomp; ++comp ) p += sprintf(p, "\n[" COUNT "] " REAL " +- " REAL " \tchisq " REAL " (" COUNT " df)", comp + 1, integral[comp], error[comp], prob[comp], df); Print(s); } for( comp = 0; comp < t->ncomp; ++comp ) prob[comp] = ChiSquare(prob[comp], df); weight = 1; } #ifdef MLVERSION if( REGIONS ) { MLPutFunction(stdlink, "List", 2); MLPutFunction(stdlink, "List", t->nregions); for( iregion = 0; iregion < t->nregions; ++iregion ) { Region *region = RegionPtr(iregion); cBounds *b = region->bounds; real lower[NDIM], upper[NDIM]; for( dim = 0; dim < t->ndim; ++dim ) { lower[dim] = b[dim].lower; upper[dim] = b[dim].upper; } MLPutFunction(stdlink, "Cuba`Divonne`region", 4); MLPutRealList(stdlink, lower, t->ndim); MLPutRealList(stdlink, upper, t->ndim); MLPutFunction(stdlink, "List", t->ncomp); for( comp = 0; comp < t->ncomp; ++comp ) { cResult *r = ®ion->result[comp]; real res[] = {r->avg, r->spread*weight, r->chisq}; MLPutRealList(stdlink, res, Elements(res)); } MLPutInteger(stdlink, region->depth); /* misused for df */ } } #endif abort: SamplesFree(&t->samples[2]); SamplesFree(&t->samples[1]); SamplesFree(&t->samples[0]); RuleFree(&t->rule13); RuleFree(&t->rule11); RuleFree(&t->rule9); RuleFree(&t->rule7); free(t->voidregion); return fail; }
static int Integrate(creal epsrel, creal epsabs, cint flags, cnumber mineval, cnumber maxeval, int key1, int key2, int key3, ccount maxpass, creal maxchisq, creal mindeviation, real *integral, real *error, real *prob) { TYPEDEFREGION; Region anchor, *region; Totals totals[NCOMP]; real nneed, weight; count dim, comp, iter, nregions, pass = 0, err; number nwant, nmin = INT_MAX; int fail = -1; if( VERBOSE > 1 ) { char s[512]; sprintf(s, "Divonne input parameters:\n" " ndim " COUNT "\n ncomp " COUNT "\n" " epsrel " REAL "\n epsabs " REAL "\n" " flags %d\n mineval " NUMBER "\n maxeval " NUMBER "\n" " key1 %d\n key2 %d\n key3 %d\n maxpass " COUNT "\n" " border " REAL "\n maxchisq " REAL "\n mindeviation " REAL "\n" " ngiven " NUMBER "\n nextra " NUMBER "\n", ndim_, ncomp_, epsrel, epsabs, flags, mineval, maxeval, key1, key2, key3, maxpass, border_.lower, maxchisq, mindeviation, ngiven_, nextra_); Print(s); } anchor.next = NULL; for( dim = 0; dim < ndim_; ++dim ) { Bounds *b = &anchor.bounds[dim]; b->lower = 0; b->upper = 1; } RuleIni(&rule7_); RuleIni(&rule9_); RuleIni(&rule11_); RuleIni(&rule13_); SamplesIni(&samples_[0]); SamplesIni(&samples_[1]); SamplesIni(&samples_[2]); #ifdef MLVERSION if( setjmp(abort_) ) goto abort; #endif /* Step 1: partition the integration region */ if( VERBOSE ) Print("Partitioning phase:"); if( IsSobol(key1) || IsSobol(key2) || IsSobol(key3) ) IniRandom(2*maxeval, flags); SamplesLookup(&samples_[0], key1, (number)47, (number)INT_MAX, (number)0); SamplesAlloc(&samples_[0]); totals_ = totals; Zap(totals); phase_ = 1; Explore(&anchor, &samples_[0], INIDEPTH, 1); for( iter = 1; ; ++iter ) { Totals *maxtot; for( comp = 0; comp < ncomp_; ++comp ) { Totals *tot = &totals[comp]; tot->avg = tot->spreadsq = 0; tot->spread = tot->secondspread = -INFTY; } nregions = 0; for( region = anchor.next; region; region = region->next ) { ++nregions; for( comp = 0; comp < ncomp_; ++comp ) { cResult *r = ®ion->result[comp]; Totals *tot = &totals[comp]; tot->avg += r->avg; tot->spreadsq += Sq(r->spread); if( r->spread > tot->spread ) { tot->secondspread = tot->spread; tot->spread = r->spread; tot->region = region; } else if( r->spread > tot->secondspread ) tot->secondspread = r->spread; } } maxtot = totals; for( comp = 0; comp < ncomp_; ++comp ) { Totals *tot = &totals[comp]; integral[comp] = tot->avg; if( tot->spreadsq > maxtot->spreadsq ) maxtot = tot; tot->spread = sqrt(tot->spreadsq); error[comp] = tot->spread*samples_[0].weight; } if( VERBOSE ) { char s[128 + 64*NCOMP], *p = s; p += sprintf(p, "\n" "Iteration " COUNT " (pass " COUNT "): " COUNT " regions\n" NUMBER7 " integrand evaluations so far,\n" NUMBER7 " in optimizing regions,\n" NUMBER7 " in finding cuts", iter, pass, nregions, neval_, neval_opt_, neval_cut_); for( comp = 0; comp < ncomp_; ++comp ) p += sprintf(p, "\n[" COUNT "] " REAL " +- " REAL, comp + 1, integral[comp], error[comp]); Print(s); } if( neval_ > maxeval ) break; nneed = maxtot->spread/MaxErr(maxtot->avg); if( nneed < MAXPRIME ) { cnumber n = neval_ + nregions*(number)ceil(nneed); if( n < nmin ) { nmin = n; pass = 0; } else if( ++pass > maxpass && n >= mineval ) break; } Split(maxtot->region, DEPTH); } /* Step 2: do a "full" integration on each region */ /* nneed = samples_[0].neff + 1; */ nneed = 2*samples_[0].neff; for( comp = 0; comp < ncomp_; ++comp ) { Totals *tot = &totals[comp]; creal maxerr = MaxErr(tot->avg); tot->nneed = tot->spread/maxerr; nneed = Max(nneed, tot->nneed); tot->maxerrsq = Sq(maxerr); tot->mindevsq = tot->maxerrsq*Sq(mindeviation); } nwant = (number)Min(ceil(nneed), MARKMASK/40.); err = SamplesLookup(&samples_[1], key2, nwant, (maxeval - neval_)/nregions + 1, samples_[0].n + 1); /* the number of points needed to reach the desired accuracy */ fail = Unmark(err)*nregions; if( Marked(err) ) { if( VERBOSE ) Print("\nNot enough samples left for main integration."); for( comp = 0; comp < ncomp_; ++comp ) prob[comp] = -999; weight = samples_[0].weight; nregions_ = nregions; } else { bool can_adjust = (key3 == 1 && samples_[1].sampler != SampleRule && (key2 < 0 || samples_[1].neff < MAXPRIME)); count df, nlimit; SamplesAlloc(&samples_[1]); if( VERBOSE ) { char s[128]; sprintf(s, "\nMain integration on " COUNT " regions with " NUMBER " samples per region.", nregions, samples_[1].neff); Print(s); } ResClear(integral); ResClear(error); ResClear(prob); nlimit = maxeval - nregions*samples_[1].n; df = nregions_ = 0; for( region = anchor.next; region; region = region->next ) { char s[64*NDIM + 256*NCOMP], *p = s; int todo; refine: phase_ = 2; samples_[1].sampler(&samples_[1], region->bounds, region->vol); if( can_adjust ) { --nregions; for( comp = 0; comp < ncomp_; ++comp ) totals[comp].spreadsq -= Sq(region->result[comp].spread); } nlimit += samples_[1].n; todo = 0; for( comp = 0; comp < ncomp_; ++comp ) { cResult *r = ®ion->result[comp]; Totals *tot = &totals[comp]; samples_[0].avg[comp] = r->avg; samples_[0].err[comp] = r->err; if( neval_ < nlimit ) { creal avg2 = samples_[1].avg[comp]; creal err2 = samples_[1].err[comp]; creal diffsq = Sq(avg2 - r->avg); #define Var(s) Sq((s.err[comp] == 0) ? r->spread*s.weight : s.err[comp]) if( err2*tot->nneed > r->spread || diffsq > Max(maxchisq*(Var(samples_[0]) + Var(samples_[1])), EPS*Sq(avg2)) ) { if( key3 && diffsq > tot->mindevsq ) { if( key3 == 1 ) { const Region *next = region->next; if( VERBOSE > 2 ) Print("\nSplit"); phase_ = 1; Explore(region, &samples_[1], POSTDEPTH, 2); if( can_adjust ) { number nnew; Region *child; for( child = region; child != next; child = child->next ) { count c; for( c = 0; c < ncomp_; ++c ) totals[c].spreadsq += Sq(child->result[c].spread); ++nregions; } nnew = (tot->spreadsq/Sq(MARKMASK) > tot->maxerrsq) ? MARKMASK : (number)ceil(sqrt(tot->spreadsq/tot->maxerrsq)); if( nnew > nwant + nwant/64 ) { ccount err = SamplesLookup(&samples_[1], key2, nnew, (maxeval - neval_)/nregions + 1, samples_[1].n); fail += Unmark(err)*nregions; nwant = nnew; SamplesFree(&samples_[1]); SamplesAlloc(&samples_[1]); if( key2 > 0 && samples_[1].neff >= MAXPRIME ) can_adjust = false; if( VERBOSE > 2 ) { char s[128]; sprintf(s, "Sampling remaining " COUNT " regions with " NUMBER " points per region.", nregions, samples_[1].neff); Print(s); } } } goto refine; } todo |= 3; } todo |= 1; } } } if( can_adjust ) { for( comp = 0; comp < ncomp_; ++comp ) totals[comp].maxerrsq -= Sq(region->result[comp].spread*samples_[1].weight); } switch( todo ) { case 1: /* get spread right */ Explore(region, &samples_[1], 0, 2); break; case 3: /* sample region again with more points */ if( MEM(&samples_[2]) == NULL ) { SamplesLookup(&samples_[2], key3, nwant, (number)INT_MAX, (number)0); SamplesAlloc(&samples_[2]); } phase_ = 3; samples_[2].sampler(&samples_[2], region->bounds, region->vol); Explore(region, &samples_[2], 0, 2); ++region->depth; /* misused for df here */ ++df; } ++region->depth; /* misused for df here */ ++nregions_; if( VERBOSE > 2 ) { for( dim = 0; dim < ndim_; ++dim ) { cBounds *b = ®ion->bounds[dim]; p += sprintf(p, (dim == 0) ? "\nRegion (" REALF ") - (" REALF ")" : "\n (" REALF ") - (" REALF ")", b->lower, b->upper); } } for( comp = 0; comp < ncomp_; ++comp ) { Result *r = ®ion->result[comp]; creal x1 = samples_[0].avg[comp]; creal s1 = Var(samples_[0]); creal x2 = samples_[1].avg[comp]; creal s2 = Var(samples_[1]); creal r2 = (s1 == 0) ? Sq(samples_[1].neff*samples_[0].weight) : s2/s1; real norm = 1 + r2; real avg = x2 + r2*x1; real sigsq = s2; real chisq = Sq(x2 - x1); real chiden = s1 + s2; if( todo == 3 ) { creal x3 = samples_[2].avg[comp]; creal s3 = Var(samples_[2]); creal r3 = (s2 == 0) ? Sq(samples_[2].neff*samples_[1].weight) : s3/s2; norm = 1 + r3*norm; avg = x3 + r3*avg; sigsq = s3; chisq = s1*Sq(x3 - x2) + s2*Sq(x3 - x1) + s3*chisq; chiden = s1*s2 + s3*chiden; } avg = LAST ? r->avg : (sigsq *= norm = 1/norm, avg*norm); if( chisq > EPS ) chisq /= Max(chiden, NOTZERO); #define Out(s) s.avg[comp], r->spread*s.weight, s.err[comp] if( VERBOSE > 2 ) { p += sprintf(p, "\n[" COUNT "] " REAL " +- " REAL "(" REAL ")\n " REAL " +- " REAL "(" REAL ")", comp + 1, Out(samples_[0]), Out(samples_[1])); if( todo == 3 ) p += sprintf(p, "\n " REAL " +- " REAL "(" REAL ")", Out(samples_[2])); p += sprintf(p, " \tchisq " REAL, chisq); } integral[comp] += avg; error[comp] += sigsq; prob[comp] += chisq; r->avg = avg; r->spread = sqrt(sigsq); r->chisq = chisq; } if( VERBOSE > 2 ) Print(s); } for( comp = 0; comp < ncomp_; ++comp ) error[comp] = sqrt(error[comp]); df += nregions_; if( VERBOSE > 2 ) { char s[16 + 128*NCOMP], *p = s; p += sprintf(p, "\nTotals:"); for( comp = 0; comp < ncomp_; ++comp ) p += sprintf(p, "\n[" COUNT "] " REAL " +- " REAL " \tchisq " REAL " (" COUNT " df)", comp + 1, integral[comp], error[comp], prob[comp], df); Print(s); } for( comp = 0; comp < ncomp_; ++comp ) prob[comp] = ChiSquare(prob[comp], df); weight = 1; } #ifdef MLVERSION if( REGIONS ) { MLPutFunction(stdlink, "List", 2); MLPutFunction(stdlink, "List", nregions_); for( region = anchor.next; region; region = region->next ) { cBounds *b = region->bounds; real lower[NDIM], upper[NDIM]; for( dim = 0; dim < ndim_; ++dim ) { lower[dim] = b[dim].lower; upper[dim] = b[dim].upper; } MLPutFunction(stdlink, "Cuba`Divonne`region", 4); MLPutRealList(stdlink, lower, ndim_); MLPutRealList(stdlink, upper, ndim_); MLPutFunction(stdlink, "List", ncomp_); for( comp = 0; comp < ncomp_; ++comp ) { cResult *r = ®ion->result[comp]; real res[] = {r->avg, r->spread*weight, r->chisq}; MLPutRealList(stdlink, res, Elements(res)); } MLPutInteger(stdlink, region->depth); /* misused for df */ } } #endif #ifdef MLVERSION abort: #endif SamplesFree(&samples_[2]); SamplesFree(&samples_[1]); SamplesFree(&samples_[0]); RuleFree(&rule13_); RuleFree(&rule11_); RuleFree(&rule9_); RuleFree(&rule7_); for( region = anchor.next; region; ) { Region *next = region->next; free(region); region = next; } return fail; }