int main() {
// limits of integration
const double a = -1;
const double b = 7;
// actual integral value
double trueVal = 1.0 / c * (std::exp(c*b) - std::exp(c*a)) - 1.0 / d * (std::cos(d*b) - std::cos(d*a));
printf("True integral value = %22.16e\n\n", trueVal);
// test composite numerical integration function
int n[] = {6, 12, 24, 36, 48, 60, 72, 84, 96, 108};
int ntests = 10;
Mat errs(ntests);
Mat hvals(ntests);
Mat approxs(ntests);
// calculate approxs, errs, convergence rates
for(unsigned int i = 0; i < ntests; i++) {
approxs(i) = composite_int(f, a, b, n[i]);
errs(i) = std::abs((trueVal - approxs(i)) / trueVal);
hvals(i) = (b - a) / n[i];
}
// output results
std::cout << "Composite Numerical Integration Approximation:" << std::endl;
for(unsigned int i = 0; i < ntests; i++) {
printf(" %6i", n[i]);
if(i == 0)
printf(" %22.16e %7.1e ----\n", approxs(i), errs(i));
else
printf(" %22.16e %7.1e %f\n", approxs(i), errs(i), (std::log(errs(i-1)) - std::log(errs(i)))/(std::log(hvals(i-1)) - std::log(hvals(i))));
}
return 0;
}
uint64_t CRedisClient::hvals(const string &key, CRedisClient::VecString &values)
{
CResult result;
hvals( key, result );
ReplyType type = result.getType();
if ( REDIS_REPLY_ERROR == type )
{
throw ReplyErr( result.getErrorString() );
}else if ( REDIS_REPLY_ARRAY != type )
{
throw ProtocolErr( "HVALS: data recved is not arry");
}
CResult::ListCResult::const_iterator it = result.getArry().begin();
CResult::ListCResult::const_iterator end = result.getArry().end();
for ( ; it != end; ++it )
{
values.push_back( static_cast<string>(*it) );
}
return values.size();
}
vector<float> hasher::get_cur_scores(){
const rmf& hvals = rand_scores;
vector<float> scores(tor_hashes.size()-2, 0);
for(size_t g = 2; g < tor_hashes.size(); g++){
vector<vector<size_t> >& hvec = tor_hashes[g];
//preceding has table gives probability that distance is more than
//current measure
vector<vector<size_t> >& hvecm2 = tor_hashes[g-2];
//gval hvec = dval[i]*2
//gval hvecm1 = dval[i]
//gval hvecm2 = dval[i]/2
//if(!in hvecm1), pr bad is for 2*gval = dval*2
//if(!in hvecm2), pr bad is for 2*gval = dval
//if(in hvec) pr good is for gval/2 = dval
//therefore need to be minus two hash funcs
for(size_t i = 0; i < (size_t)hvals.rows(); i++){
float pr_far;
size_t num_near = 0;
size_t num_far=0;
pr_far = 0;
size_t num_ave = 0;
for(size_t j = 0; j < (size_t)hvals.cols(); j++){
// increment corresponding hash table bin
// gamma/2, 2*gamma, 1/2, 1/3 sensitive
// for now we treat not being near as zero probability of begin near
// for now, hold two counts: number of vectors for which
// the hash function claims the rvec is near
// and number of times the rvec claims it is not near a vector
//
// if (n_near*((1/2)/(1/3) < n_far)) then
// say it is near a vector. Else, it is not
size_t num_near_tmp = hvec[j][get_bin(hvals(i, j), hvec[j].size())];
long num_far_tmp = (long)hvecm2[j][get_bin(hvals(i, j), hvec[j].size())];
num_near += num_near_tmp;
//if not zero, make zero. otherwise make 1
num_far_tmp = (num_far_tmp == 0);
num_far += num_far_tmp;
pr_far += (pow(0.5, num_near_tmp) + num_far_tmp);
num_ave += (1+num_far_tmp);
}
//cout << num_near*1.0/hvals.cols() << endl;
pr_far/= num_ave;
// if(pr_far < 0.2){
if(num_far < 1){
scores[g-2]++;
}
}
scores[g-2]/=rand_scores.rows();
}
size_t num_bel_min = 0;
list<rmf>::iterator beg;
for(size_t r = 0; r < (size_t)rand_vecs.rows(); r++){
float mindis=mnorm;
list<rmf>::iterator lcur;
for(lcur=in_vecs.begin(); lcur != in_vecs.end(); lcur++){
for(size_t i = 0; i < (size_t)lcur->rows(); i++){
float cdist = (rand_vecs.row(r) - lcur->row(i)).norm();
if(cdist < mindis){
mindis = cdist;
}
}
}
if(mindis < 0.25){
num_bel_min++;
}
}
// float act_bel_min = (1.0*num_bel_min)/(1.0*rand_vecs.rows());
cout << scores[0]*rand_vecs.rows() << ", " << num_bel_min << endl;
return scores;
}
