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-rw-r--r--src/Nerve_GIC/example/bottleneckBootstrap.cpp274
1 files changed, 0 insertions, 274 deletions
diff --git a/src/Nerve_GIC/example/bottleneckBootstrap.cpp b/src/Nerve_GIC/example/bottleneckBootstrap.cpp
deleted file mode 100644
index eac94e5e..00000000
--- a/src/Nerve_GIC/example/bottleneckBootstrap.cpp
+++ /dev/null
@@ -1,274 +0,0 @@
- #include "mapper.h"
-#define ETA 0.001
-#define SHIFT 1
-#define DELTA 0
-#define CONSTANT 10
-
-double GetUniform(){
- static default_random_engine re;
- static uniform_real_distribution<double> Dist(0,1);
- return Dist(re);
-}
-
-void SampleWithReplacement(int populationSize, int sampleSize, vector<int> & samples){
- int m = 0; double u;
- while (m < sampleSize){
- u = GetUniform();
- samples[m] = floor(u*populationSize);
- m++;
- }
-}
-
-void SampleWithoutReplacement(int populationSize, int sampleSize, vector<int> & samples){
- int& n = sampleSize; int& N = populationSize;
- int t = 0; int m = 0; double u;
- while (m < n){
- u = GetUniform();
- if ( (N - t)*u >= n - m )
- t++;
- else{samples[m] = t; t++; m++;}
- }
-}
-
-void compute_estimator(Cloud & C, char* const & cloud, const bool & VNE, const int & Nsubs, \
- const double & gain){
-
- int n = C.size();
- double** dist = new double*[n];
- for(int i = 0; i < n; i++) dist[i] = new double[n];
- double d;
- Cover I; AdjacencyMatrix G; MapperElements M;
- double maxf = C[0].func; double minf = C[0].func;
- for(int i = 1; i < n; i++){ maxf = max(maxf,C[i].func); minf = min(minf,C[i].func);}
-
- char name1[100];
- if(VNE) sprintf(name1, "%s_VNdist", cloud);
- else sprintf(name1, "%s_dist", cloud);
- char* const name2 = name1;
- ifstream input(name2, ios::out | ios::binary);
-
- if(input.good()){
- cout << " Reading distances for parameter selection..." << endl;
- for(int i = 0; i < n; i++){
- for (int j = 0; j < n; j++){
- input.read((char*) &d,8); dist[i][j] = d;
- }
- }
- input.close();
- }
- else{
- cout << " Computing distances for parameter selection..." << endl; vector<double> V;
- input.close(); ofstream output(name2, ios::out | ios::binary);
- if(VNE){
- int dim = C[0].coord.size();
- for(int i = 0; i < dim; i++){
- double meani = 0;
- for (int j = 0; j < n; j++) meani += C[j].coord[i]/n;
- double vari = 0;
- for (int j = 0; j < n; j++) vari += pow(C[j].coord[i]-meani,2)/n;
- V.push_back(vari);
- }
- }
-
- for(int i = 0; i < n; i++){
- if( (int) floor( 100*((double) i)/((double) n)+1 ) %10 == 0 ){
- cout << "\r" << floor( 100*((double) i)/((double) n) +1) << "%" << flush;}
- for (int j = 0; j < n; j++){
- if(!VNE){d = C[i].EuclideanDistance(C[j]); dist[i][j] = d;}
- else{d = C[i].VarianceNormalizedEuclideanDistance(C[j],V); dist[i][j] = d;}
- output.write((char*) &d,8);
- }
- }
- output.close();
- }
-
- int m = floor(n/pow(log(n)/log(CONSTANT),1+ETA));
- double lip = 0; double delta = 0; vector<int> samples(m);
- #pragma omp parallel for
- for (int i = 0; i < Nsubs; i++){
- SampleWithoutReplacement(n,m,samples);
- double hausdorff_dist = 0;
- for (int j = 0; j < n; j++){
- Point p = C[j]; double mj = dist[j][samples[0]]; double Mi = abs(p.func-C[0].func)/dist[j][0];
- for (int k = 1; k < m; k++) mj = min(mj, dist[j][samples[k]]);
- if(j < n-1)
- for (int k = j+1; k < n; k++) Mi = max(Mi, abs(p.func-C[k].func)/dist[j][k]);
- hausdorff_dist = max(hausdorff_dist, mj); lip = max(lip,Mi);
- }
- delta += hausdorff_dist;
- }
- delta /= Nsubs; double resolution; if(DELTA) resolution = lip*delta; else resolution = lip*delta/gain;
- //cout << delta << " " << lip << " " << resolution << endl;
- cout << " Done." << endl;
-
- G = build_neighborhood_graph_from_scratch_with_parameter(C,delta,name2,VNE);
-
- sort(C.begin(),C.end());
- I = Cover(C.begin()->func, (C.end()-1)->func, resolution, gain, SHIFT, 1);
- I.proper_value();
-
- map<int,double> colors; colors.clear(); map<int,int> num; num.clear();
- map<Point,vector<int> > clusters; vector<int> dum; dum.clear();
- for(int i = 0; i < C.size(); i++) clusters.insert(pair<Point,vector<int> >(C[i],dum)); vector<double> col(G.size());
-
- M = MapperElts(G,I,clusters,col);
- SparseGraph MG;
- if (DELTA) MG = build_mapperDelta_graph(M,G,clusters,colors,num); else MG = build_mapper_graph(M,colors,num);
-
- char mappg[100]; sprintf(mappg,"%s_mapper.txt",cloud);
- ofstream graphicg(mappg);
-
- double maxv, minv; maxv = numeric_limits<double>::min(); minv = numeric_limits<double>::max();
- for (map<int,double>::iterator iit = colors.begin(); iit != colors.end(); iit++){
- if(iit->second > maxv) maxv = iit->second;
- if(minv > iit->second) minv = iit->second;
- }
-
- int k = 0; int ke = 0;
-
- for (SparseGraph::iterator it = MG.begin(); it != MG.end(); it++){
- k++; for (int j = 0; j < it->second.size(); j++) ke++;
- }
-
- graphicg << k << " " << ke << endl; int kk = 0;
- for (SparseGraph::iterator it = MG.begin(); it != MG.end(); it++){graphicg << kk << " " << colors[it->first] << endl; kk++;}
- for (SparseGraph::iterator it = MG.begin(); it != MG.end(); it++)
- for (int j = 0; j < it->second.size(); j++)
- graphicg << it->first << " " << it->second[j] << endl;
-}
-
-void compute_bootstrapped_estimator(const vector<int> & samp, char* const & cloud, const int & Nboot, double** dist, Cloud & Cboot, \
- const int & Nsubs, const double & gain){
-
- int n = Cboot.size();
- Cover I; MapperElements M;
- double maxf = Cboot[0].func; double minf = Cboot[0].func;
- for(int i = 1; i < n; i++){ maxf = max(maxf,Cboot[i].func); minf = min(minf,Cboot[i].func);}
- int m = floor(n/pow(log(n)/log(CONSTANT),1+ETA));
- double lip = 0; double delta = 0; vector<int> samples(m);
- #pragma omp parallel for
- for (int i = 0; i < Nsubs; i++){
- SampleWithoutReplacement(n,m,samples);
- double hausdorff_dist = 0;
- for (int j = 0; j < n; j++){
- Point p = Cboot[j]; double mj = dist[samp[j]][samp[samples[0]]]; double Mi = abs(p.func-Cboot[0].func)/dist[samp[j]][samp[0]];
- for (int k = 1; k < m; k++) mj = min(mj, dist[samp[j]][samp[samples[k]]]);
- if(j < n-1)
- for (int k = j+1; k < n; k++) Mi = max(Mi, abs(p.func-Cboot[k].func)/dist[samp[j]][samp[k]]);
- hausdorff_dist = max(hausdorff_dist, mj); lip = max(lip,Mi);
- }
- delta += hausdorff_dist;
- }
- delta /= Nsubs; double resolution;
- if(DELTA) resolution = lip*delta; else resolution = lip*delta/gain;
- //cout << delta << " " << lip << " " << resolution << endl;
-
- AdjacencyMatrix G; G.clear(); vector<Point> adj;
-
- for(int i = 0; i < n; i++){
- adj.clear();
- for(int j = 0; j < n; j++)
- if(dist[samp[i]][samp[j]] <= delta)
- adj.push_back(Cboot[j]);
- G.insert(pair<Point, vector<Point> >(Cboot[i],adj));
- }
-
- sort(Cboot.begin(),Cboot.end());
- I = Cover(Cboot.begin()->func, (Cboot.end()-1)->func, resolution, gain, SHIFT, 1);
- I.proper_value();
-
- map<int,double> colors; colors.clear(); map<int,int> num; num.clear();
- map<Point,vector<int> > clusters; vector<int> dum; dum.clear();
- for(int i = 0; i < Cboot.size(); i++) clusters.insert(pair<Point,vector<int> >(Cboot[i],dum)); vector<double> col(n);
-
- M = MapperElts(G,I,clusters,col);
- SparseGraph MG;
- if(DELTA) MG = build_mapperDelta_graph(M,G,clusters,colors,num); else MG = build_mapper_graph(M,colors,num);
-
- char mappg[100]; sprintf(mappg,"%s_mapper_%d.txt",cloud,Nboot);
- ofstream graphicg(mappg);
-
- double maxv, minv; maxv = numeric_limits<double>::min(); minv = numeric_limits<double>::max();
- for (map<int,double>::iterator iit = colors.begin(); iit != colors.end(); iit++){
- if(iit->second > maxv) maxv = iit->second;
- if(minv > iit->second) minv = iit->second;
- }
-
- int k = 0; int ke = 0;
-
- for (SparseGraph::iterator it = MG.begin(); it != MG.end(); it++){
- k++; for (int j = 0; j < it->second.size(); j++) ke++;
- }
-
- graphicg << k << " " << ke << endl; int kk = 0;
- for (SparseGraph::iterator it = MG.begin(); it != MG.end(); it++){graphicg << kk << " " << colors[it->first] << endl; kk++;}
- for (SparseGraph::iterator it = MG.begin(); it != MG.end(); it++)
- for (int j = 0; j < it->second.size(); j++)
- graphicg << it->first << " " << it->second[j] << endl;
- graphicg.close();
-
-}
-
-
-int main(int argc, char** argv){
-
- if(argc <= 7 || argc >= 9){
- cout << "./bottleBoot <cloud_file> <VNE> <function:/coordinate:/eccentricity:> <func_file/number/distance matrix> <gain> <Nsubs> <Nboot>" << endl;
- return 0;}
-
- char* const cloud = argv[1]; bool normalized = atoi(argv[2]); char* const funct = argv[3]; double gain = atof(argv[5]);
- int Nsubs = atoi(argv[6]); int Nboot = atoi(argv[7]);
-
- Cloud C, D, Cboot; C = read_cloud(cloud); int n = C.size();
-
- if (strcmp(funct, "function:") == 0){
- char* const func = argv[4]; read_function_from_file(func,C);
- }
- if (strcmp(funct, "coordinate:") == 0){
- int number = atoi(argv[4]); read_coordinate(number,C);
- }
- if (strcmp(funct, "eccentricity") == 0){
- char* const matrix = argv[4]; compute_eccentricity(C,matrix);
- }
-
- D = C;
-
- cout << "Computing estimator..." << endl;
- compute_estimator(C,cloud,normalized,Nsubs,gain);
- cout << "Estimator computed." << endl;
-
- char nam[100]; sprintf(nam,"%s_mapper.txt",cloud); char dg[100]; sprintf(dg,"%s_ExDg",cloud);
- char command[100];
- sprintf(command,"cat %s | ../persistence/filtgraph 1 | ../persistence/pers 1 >> %s",nam,dg); system(command);
- char list[100]; sprintf(list,"%s_quantile",cloud);
-
- char name[100];
- if(normalized) sprintf(name, "%s_VNdist", cloud);
- else sprintf(name, "%s_dist", cloud);
- ifstream input(name, ios::out | ios::binary); double d;
- double** dist = new double*[n]; for(int i = 0; i < n; i++) dist[i] = new double[n];
- for(int i = 0; i < n; i++) for (int j = 0; j < n; j++){input.read((char*) &d,8); dist[i][j] = d;}
- input.close();
-
- for(int i = 0; i < Nboot; i++){
-
- Cboot.clear();
- vector<int> samples(n); SampleWithReplacement(n,n,samples); sort(samples.begin(), samples.end());
- for (int i = 0; i < n; i++) Cboot.push_back(D[samples[i]]);
-
- cout << "Computing " << i << "th bootstrapped estimator..." << endl;
- compute_bootstrapped_estimator(samples,cloud,i,dist,Cboot,Nsubs,gain);
- cout << "Done." << endl;
-
- char namei[100]; sprintf(namei,"%s_mapper_%d.txt",cloud,i); char dgi[100]; sprintf(dgi,"%s_ExDg_%d",cloud,i);
- char command[100];
- sprintf(command,"cat %s | ../persistence/filtgraph 1 | ../persistence/pers 1 >> %s",namei,dgi); system(command);
- sprintf(command,"../persistence/bottleneck_dist %s %s >> %s",dg,dgi,list); system(command);
- sprintf(command, "rm %s %s",namei, dgi); system(command);
-
- }
-
- delete [] dist;
-
- return 0;
-}
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