int main(int argc,char *args[]){
//get input
int n=0;
clock_t begin,end;
if(argc<2){
puts("Too less input");
return 1;
}else{
n = num2str(args[1]);
}
begin = clock();
int* A = randMatrix(n,n*n);
end = clock();
double time_spent = (double)(end-begin)/CLOCKS_PER_SEC;
//InsertionSort
insertionSort(A,n);
puts("SORTED");
for(int i=0;i<n;i++){
printf("index %d : %d\n",i,A[i]);
}
//RECORD
FILE* fout = fopen("cResult.txt","a");
fprintf(fout,"input size : %10d , time %10f\n",n,time_spent);
fclose(fout);
}
GMMDesc initutil::gonzalezKwedlo(commonutil::DataSet const& input, idx_type k, std::mt19937& gen, fp_type sampleFactor)
{
// std::cout << "sampleFactor = " << sampleFactor << std::endl;
// std::cout << "use2GMM = " << use2GMM << std::endl;
idx_type sampleSize = sampleFactor * input.points.cols();
idx_type d = input.points.rows();
idx_type n = input.points.cols();
initutil::check(k, d, n);
GMMDesc desc;
Vector sample;
GMMDesc gmmdesc = gmmutil::optimalGaussian(input);
Matrix covar = gmmdesc.covariances.at(0);
fp_type trace = covar.trace()/(10.*d*k);
desc.means.resize(0,0);
desc.weights.resize(0);
std::uniform_real_distribution<> urd(0,1);
std::uniform_int_distribution<> uid(0,input.points.cols()-1);
Matrix randMatrix(d,d);
Matrix randOrthonormalMatrix(d,d);
Vector eigenvalues(d);
for (idx_type i=0; i<k; ++i)
{
if (i==0)
// draw first point uniformly
sample = input.points.col(commonutil::randomIndex(input.weights, gen));
else
{
// draw next point w.r.t. current mixture
if(sampleFactor < 1)
{
commonutil::DataSet samples;
samples.points.resize(d,sampleSize);
samples.weights.resize(sampleSize);
idx_type index;
for(idx_type j=0; j<sampleSize; ++j){
index = uid(gen);
// std::cout << "index = " << index << std::endl;
samples.points.col(j) = input.points.col(index);
samples.weights(j) = input.weights(index);
}
Vector densities = gmmutil::adaptiveDensities(samples, desc, 1.);
// std::cout << "densities.size() = " << densities.size() << std::endl;
densities.maxCoeff(&index);
sample = samples.points.col(index);
}
else
{
idx_type index;
Vector densities = gmmutil::adaptiveDensities(input, desc, 1.);
// std::cout << "densities.size() = " << densities.size() << std::endl;
densities.maxCoeff(&index);
sample = input.points.col(index);
}
}
desc.means.conservativeResize(d,i+1);
desc.means.col(i) = sample;
desc.weights.conservativeResize(i+1);
desc.weights(i) = urd(gen);
// random covariance
for (idx_type i=0; i<d; ++i)
for (idx_type j=0; j<d; ++j)
randMatrix(i,j)=urd(gen);
Eigen::HouseholderQR<Matrix> qr(randMatrix);
randOrthonormalMatrix = qr.householderQ();
commonutil::fill(eigenvalues,gen, 1.,10.);
fp_type tmp = trace/eigenvalues.sum();
eigenvalues = tmp * eigenvalues;
randMatrix = randOrthonormalMatrix.transpose()*eigenvalues.asDiagonal()*randOrthonormalMatrix;
desc.covariances.push_back(randMatrix);
}
desc.weights /= desc.weights.sum();
return desc;
}
