int xybinning_quantiles(deque<type> &c, deque<type> &d, int number_of_bins, deque<double> & xs, deque<double> & ys, deque<double> & var, deque<int> & nums, deque<deque<double> > & Mq, double qa, double qb) {
// so, this function takes two datasets (c and d) and gathers the data in bin, takes xs and ys as the average in each bin, var is the variance of the y average
// the difference with the same stuff called not_norm_histogram is that the other one averages x with y weights.
xs.clear();
ys.clear();
var.clear();
nums.clear();
Mq.clear();
double min=double(c[0]);
double max=double(c[0]);
for (int i=0; i<int(c.size()); i++) {
if (min>double(c[i]))
min=double(c[i]);
if (max<double(c[i]))
max=double(c[i]);
}
min-=1e-6;
max+=1e-6;
if (max==min)
max+=1e-3;
deque<deque<double> > hist_x; // x values in the bin
deque<deque<double> > hist_y; // y values in the bin
double step=min;
double bin=(max-min)/number_of_bins; // bin width
deque<double> f;
while (step<=max+2*bin) {
hist_x.push_back(f);
hist_y.push_back(f);
step+=bin;
}
for (int i=0; i<int(c.size()); i++) {
double data=double(c[i]);
if (data>=min && data<=max) {
int index=int((data-min)/bin);
//cout<<data<<" "<<exp(data)<<" "<<index<<endl;
hist_x[index].push_back(double(c[i]));
hist_y[index].push_back(double(d[i]));
}
}
for (int i=0; i<hist_x.size()-1; i++) {
double x=average_func(hist_x[i]);
double y=average_func(hist_y[i]);
//cout<<x<<" "<<exp(x)<<" "<<y<<endl;
if (hist_y[i].size()>0) {
xs.push_back(x);
ys.push_back(y);
var.push_back(variance_func(hist_y[i])/double(hist_y[i].size()));
nums.push_back(hist_y[i].size());
sort(hist_y[i].begin(), hist_y[i].end());
deque<double> qs;
compute_quantiles(qa, hist_y[i], qs);
compute_quantiles(qb, hist_y[i], qs);
Mq.push_back(qs);
//cout<<x<<" "<<exp(x)<<" "<<y<<" "<<(hist_y[i].size())<<" "<<variance_func(hist_y[i])<<endl;
}
}
for(int i=0; i<var.size(); i++)
if(var[i]<1e-8)
var[i]=1e-8;
return 0;
}
int print_network(deque<set<int> > & E, const deque<deque<int> > & member_list, const deque<deque<int> > & member_matrix, deque<int> & num_seq) {
int edges=0;
int num_nodes=member_list.size();
deque<double> double_mixing;
for (int i=0; i<E.size(); i++) {
double one_minus_mu = double(internal_kin(E, member_list, i))/E[i].size();
double_mixing.push_back(1.- one_minus_mu);
edges+=E[i].size();
}
//cout<<"\n----------------------------------------------------------"<<endl;
//cout<<endl;
double density=0;
double sparsity=0;
for (int i=0; i<member_matrix.size(); i++) {
double media_int=0;
double media_est=0;
for (int j=0; j<member_matrix[i].size(); j++) {
double kinj = double(internal_kin_only_one(E[member_matrix[i][j]], member_matrix[i]));
media_int+= kinj;
media_est+=E[member_matrix[i][j]].size() - double(internal_kin_only_one(E[member_matrix[i][j]], member_matrix[i]));
}
double pair_num=(member_matrix[i].size()*(member_matrix[i].size()-1));
double pair_num_e=((num_nodes-member_matrix[i].size())*(member_matrix[i].size()));
if(pair_num!=0)
density+=media_int/pair_num;
if(pair_num_e!=0)
sparsity+=media_est/pair_num_e;
}
density=density/member_matrix.size();
sparsity=sparsity/member_matrix.size();
ofstream out1("network.dat");
for (int u=0; u<E.size(); u++) {
set<int>::iterator itb=E[u].begin();
while (itb!=E[u].end())
out1<<u+1<<"\t"<<*(itb++)+1<<endl;
}
out1.close();
ofstream out2("community.dat");
for (int i=0; i<member_list.size(); i++) {
out2<<i+1<<"\t";
for (int j=0; j<member_list[i].size(); j++)
out2<<member_list[i][j]+1<<" ";
out2<<endl;
}
out2.close();
cout<<"\n\n---------------------------------------------------------------------------"<<endl;
cout<<"network of "<<num_nodes<<" vertices and "<<edges/2<<" edges"<<";\t average degree = "<<double(edges)/num_nodes<<endl;
cout<<"\naverage mixing parameter: "<<average_func(double_mixing)<<" +/- "<<sqrt(variance_func(double_mixing))<<endl;
cout<<"p_in: "<<density<<"\tp_out: "<<sparsity<<endl;
ofstream statout("statistics.dat");
deque<int> degree_seq;
for (int i=0; i<E.size(); i++)
degree_seq.push_back(E[i].size());
statout<<"degree distribution (probability density function of the degree in logarithmic bins) "<<endl;
log_histogram(degree_seq, statout, 10);
statout<<"\ndegree distribution (degree-occurrences) "<<endl;
int_histogram(degree_seq, statout);
statout<<endl<<"--------------------------------------"<<endl;
statout<<"community distribution (size-occurrences)"<<endl;
int_histogram(num_seq, statout);
statout<<endl<<"--------------------------------------"<<endl;
statout<<"mixing parameter"<<endl;
not_norm_histogram(double_mixing, statout, 20, 0, 0);
statout<<endl<<"--------------------------------------"<<endl;
statout.close();
cout<<endl<<endl;
return 0;
}
int print_network(deque<set<int> > & E, const deque<deque<int> > & member_list, const deque<deque<int> > & member_matrix,
deque<int> & num_seq, deque<map <int, double > > & neigh_weigh, double beta, double mu, double mu0) {
int edges=0;
int num_nodes=member_list.size();
deque<double> double_mixing;
for (int i=0; i<E.size(); i++) {
double one_minus_mu = double(internal_kin(E, member_list, i))/E[i].size();
double_mixing.push_back(1.- one_minus_mu);
edges+=E[i].size();
}
//cout<<"\n----------------------------------------------------------"<<endl;
//cout<<endl;
double density=0;
double sparsity=0;
for (int i=0; i<member_matrix.size(); i++) {
double media_int=0;
double media_est=0;
for (int j=0; j<member_matrix[i].size(); j++) {
double kinj = double(internal_kin_only_one(E[member_matrix[i][j]], member_matrix[i]));
media_int+= kinj;
media_est+=E[member_matrix[i][j]].size() - double(internal_kin_only_one(E[member_matrix[i][j]], member_matrix[i]));
}
double pair_num=(member_matrix[i].size()*(member_matrix[i].size()-1));
double pair_num_e=((num_nodes-member_matrix[i].size())*(member_matrix[i].size()));
if(pair_num!=0)
density+=media_int/pair_num;
if(pair_num_e!=0)
sparsity+=media_est/pair_num_e;
}
density=density/member_matrix.size();
sparsity=sparsity/member_matrix.size();
ofstream out1("network.dat");
for (int u=0; u<E.size(); u++) {
set<int>::iterator itb=E[u].begin();
while (itb!=E[u].end())
out1<<u+1<<"\t"<<*(itb++)+1<<"\t"<<neigh_weigh[u][*(itb)]<<endl;
}
ofstream out2("community.dat");
for (int i=0; i<member_list.size(); i++) {
out2<<i+1<<"\t";
for (int j=0; j<member_list[i].size(); j++)
out2<<member_list[i][j]+1<<" ";
out2<<endl;
}
cout<<"\n\n---------------------------------------------------------------------------"<<endl;
cout<<"network of "<<num_nodes<<" vertices and "<<edges/2<<" edges"<<";\t average degree = "<<double(edges)/num_nodes<<endl;
cout<<"\naverage mixing parameter (topology): "<< average_func(double_mixing)<<" +/- "<<sqrt(variance_func(double_mixing))<<endl;
cout<<"p_in: "<<density<<"\tp_out: "<<sparsity<<endl;
ofstream statout("statistics.dat");
deque<int> degree_seq;
for (int i=0; i<E.size(); i++)
degree_seq.push_back(E[i].size());
statout<<"degree distribution (probability density function of the degree in logarithmic bins) "<<endl;
log_histogram(degree_seq, statout, 10);
statout<<"\ndegree distribution (degree-occurrences) "<<endl;
int_histogram(degree_seq, statout);
statout<<endl<<"--------------------------------------"<<endl;
statout<<"community distribution (size-occurrences)"<<endl;
int_histogram(num_seq, statout);
statout<<endl<<"--------------------------------------"<<endl;
statout<<"mixing parameter (topology)"<<endl;
not_norm_histogram(double_mixing, statout, 20, 0, 0);
statout<<endl<<"--------------------------------------"<<endl;
//*
deque<double> inwij;
deque<double> outwij;
//deque<double> inkij;
//deque<double> outkij;
double csi=(1. - mu) / (1. - mu0);
double csi2=mu /mu0;
double tstrength=0;
deque<double> one_minus_mu2;
for(int i=0; i<neigh_weigh.size(); i++) {
double internal_strength_i=0;
double strength_i=0;
for(map<int, double>::iterator itm = neigh_weigh[i].begin(); itm!=neigh_weigh[i].end(); itm++) {
if(they_are_mate(i, itm->first, member_list)) {
inwij.push_back(itm->second);
//inkij.push_back(csi * pow(E[i].size(), beta-1));
internal_strength_i+=itm->second;
}
else {
outwij.push_back(itm->second);
//outkij.push_back(csi2 * pow(E[i].size(), beta-1));
}
tstrength+=itm->second;
strength_i+=itm->second;
}
one_minus_mu2.push_back(1 - internal_strength_i/strength_i);
}
//cout<<"average strength "<<tstrength / E.size()<<"\taverage internal strenght: "<<average_internal_strenght<<endl;
cout<<"\naverage mixing parameter (weights): "<<average_func(one_minus_mu2)<<" +/- "<<sqrt(variance_func(one_minus_mu2))<<endl;
statout<<"mixing parameter (weights)"<<endl;
not_norm_histogram(one_minus_mu2, statout, 20, 0, 0);
statout<<endl<<"--------------------------------------"<<endl;
//cout<<" expected internal "<<tstrength * (1 - mu) / E.size()<<endl;
//cout<<"internal links: "<<inwij.size()<<" external: "<<outwij.size()<<endl;
/*
ofstream hout1("inwij.dat");
not_norm_histogram(inwij, hout1, 20, 0, 0);
ofstream hout2("outwij.dat");
not_norm_histogram(outwij, hout2, 20, 0, 0);
ofstream hout3("corrin.dat");
not_norm_histogram_correlated(inkij, inwij, hout3, 20, 0, 0);
ofstream hout4("corrout.dat");
not_norm_histogram_correlated(outkij, outwij, hout4, 20, 0, 0);
//*/
//*/
cout<<"average weight of an internal link "<<average_func(inwij)<<" +/- "<<sqrt(variance_func(inwij))<<endl;
cout<<"average weight of an external link "<<average_func(outwij)<<" +/- "<<sqrt(variance_func(outwij))<<endl;
//cout<<"average weight of an internal link expected "<<tstrength / edges * (1. - mu) / (1. - mu0)<<endl;
//cout<<"average weight of an external link expected "<<tstrength / edges * (mu) / (mu0)<<endl;
statout<<"internal weights (weight-occurrences)"<<endl;
not_norm_histogram(inwij, statout, 20, 0, 0);
statout<<endl<<"--------------------------------------"<<endl;
statout<<"external weights (weight-occurrences)"<<endl;
not_norm_histogram(outwij, statout, 20, 0, 0);
cout<<endl<<endl;
return 0;
}
