void testRNG()
{
const int n = 500000;
std::cerr << "Testing random number generator (n = " << n << " * bins)..." << std::endl;
const int min = 1;
const int minMax = 2;
const int maxMax = 10;
QVector<QVector<int>> numbers(maxMax - minMax + 1);
QVector<double> chisq(maxMax - minMax + 1);
for (int max = minMax; max <= maxMax; ++max) {
numbers[max - minMax] = rng->makeNumbersVector(n * (max - min + 1), min, max);
chisq[max - minMax] = rng->testRandom(numbers[max - minMax]);
}
for (int i = 0; i <= maxMax - min; ++i) {
std::cerr << (min + i);
for (int j = 0; j < numbers.size(); ++j) {
if (i < numbers[j].size())
std::cerr << "\t" << numbers[j][i];
else
std::cerr << "\t";
}
std::cerr << std::endl;
}
std::cerr << std::endl << "Chi^2 =";
for (int j = 0; j < chisq.size(); ++j)
std::cerr << "\t" << QString::number(chisq[j], 'f', 3).toStdString();
std::cerr << std::endl << "k =";
for (int j = 0; j < chisq.size(); ++j)
std::cerr << "\t" << (j - min + minMax);
std::cerr << std::endl << std::endl;
}
void compute_metrics(const Ty* X1, const Ty* X2, Ty* M, int d, int n1, int n2, int opcode, const mxArray *prhs[])
{
switch (opcode)
{
case 1:
cityblock(X1, X2, M, d, n1, n2);
break;
case 2:
mindiff(X1, X2, M, d, n1, n2);
break;
case 3:
maxdiff(X1, X2, M, d, n1, n2);
break;
case 4:
minkowski(X1, X2, M, d, n1, n2, *((const Ty*)mxGetData(prhs[3])));
break;
case 5:
intersect(X1, X2, M, d, n1, n2);
break;
case 6:
chisq(X1, X2, M, d, n1, n2);
break;
case 7:
kldiv(X1, X2, M, d, n1, n2);
break;
case 8:
jeffrey(X1, X2, M, d, n1, n2);
break;
}
}
virtual double execute( const GenoVec& geno,
const PhenoVec& pheno,
const unsigned cardGenotype,
const unsigned cardPhenotype ) const {
return chisq(geno, pheno, cardGenotype, cardPhenotype);
}
double Calibration::MeanFeed::chisq (const MEAL::Complex2* model) const
{
const Feed* feed = dynamic_cast<const Feed*>(model);
if (!feed)
throw Error (InvalidParam, "Calibration::MeanFeed::chisq",
"Complex2 model is not a Feed");
return chisq (feed);
}
/*c.d.f of Chi-square*/
double Chisq(int df, double x)
{
switch(df){
case 1: return 2*Phi(sqrt(x))-1;
case 2: return 1-exp(-x/2);
default: break;
}
return ( Chisq(df-2,x)-2*chisq(df,x) );
}
void Dtest(n)
/* requires static 'size', static int array P[size] */
/* generates n Dran()'s, tests output */
{ double x=0,y,s=0,*E;
int kb=0,ke=1000,i,j=0,*M;
E=malloc(size*sizeof(double));
M=malloc(size*sizeof(int));
for(i=0;i<size;i++) {E[i]=(n+0.)*P[i]/1073741824.;M[i]=0;}
s=0; for(i=0;i<size;i++) {s+=E[i]; if(s>10){kb=i;E[kb]=s;break;} }
s=0; for(i=size-1;i>0;i--) {s+=E[i]; if(s>10){ke=i;E[ke]=s;break;} }
s=0; x=0; for(i=0;i<n;i++)
{j=Dran(); if(j<kb+offset) j=kb+offset; if(j>ke+offset) j=ke+offset;
M[j-offset]++;}
printf("\n D Observed Expected (O-E)^2/E sum\n");
fprintf(fp,"\n D Observed Expected (O-E)^2/E sum\n");
for(i=kb;i<=ke;i++){ y=M[i]-E[i]; y=y*y/E[i]; s+=y;
printf("%4d %10d %12.2f %7.2f %7.2f\n",i+offset,M[i],E[i],y,s);
fprintf(fp,"%4d %10d %12.2f %7.2f %7.2f\n",i+offset,M[i],E[i],y,s);
}
printf(" chisquare for %d d.f.=%7.2f, p=%7.5f\n",ke-kb,s,chisq(s,ke-kb));
fprintf(fp," chisquare for %d d.f.=%7.2f, p=%7.5f\n",ke-kb,s,chisq(s,ke-kb));
}
int main(int iargc, char *argv[]){
//d=8 -> delta_chisq=15.5
//d=5 -> delta_chisq=11
int seed=99;
int dim,ncenters;
int nsamples=10000;
seed=atoi(argv[1]);
dim=atoi(argv[2]);
ncenters=atoi(argv[3]);
if(iargc>4){
nsamples=atoi(argv[4]);
}
char timingname[letters],outname[letters];
//what is the name of the file where APS will store its timing information
sprintf(timingname,"timingFiles/ellipse_d%d_c%d_timing.sav",dim,ncenters);
//what is the name of the file where APS will output the points it sampled
sprintf(outname,"outputFiles/ellipse_d%d_c%d_output.sav",dim,ncenters);
if(seed<0){
seed=int(time(NULL));
}
printf("seed %d\n",seed);
//declare the covariogram for APS's Gaussian process
matern_covariance cv;
//declare the chisquared function APS will be searching
//ellipses_integrable chisq(dim,ncenters);
ellipses chisq(dim,ncenters);
//declare APS
//the '20' below is the number of nearest neighbors to use when seeding the
//Gaussian process
//
//the '11.0' is the \Delta\chi^2 corresponding to a 95% confidence limit
//on a 5-dimensional parameter space
aps aps_test(dim,20,11.0,seed);
//pass chisq to the aps object
aps_test.assign_chisquared(&chisq);
//pass the covariogram to the aps object
aps_test.assign_covariogram(&cv);
//how often will APS stop and write its output
aps_test.set_write_every(3000);
//set the G parameter from equation (4) in the paper
aps_test.set_grat(1.0);
//set the maximum and minimum values in parameter space
array_1d<double> max,min;
max.set_name("driver_max");
min.set_name("driver_min");
max.set_dim(dim);
min.set_dim(dim);
int i,j;
for(i=0;i<dim;i++){
min.set(i,-100.0);
max.set(i,100.0);
}
aps_test.set_timingname(timingname);
aps_test.set_outname(outname);
//initialize aps with 1000 random samples
printf("time to initialize\n");
aps_test.initialize(100,min,max);
double chival,chivaltest,err;
i=-1;
//search parameter space until the
//chisquared function has been called
//10000 times
while(aps_test.get_called()<nsamples){
aps_test.search();
}
aps_test.write_pts();
array_1d<double> minpt;
//what is the point in parameter space corresponding to the
//minimum chisquared
aps_test.get_minpt(minpt);
printf("chimin %e\n",aps_test.get_chimin());
printf("ct_aps %d ct_simplex %d total %d\n",
aps_test.get_ct_aps(),aps_test.get_ct_simplex(),
aps_test.get_called());
}
