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-rw-r--r--src/Nerve_GIC/example/mapper_parameter.cpp165
1 files changed, 0 insertions, 165 deletions
diff --git a/src/Nerve_GIC/example/mapper_parameter.cpp b/src/Nerve_GIC/example/mapper_parameter.cpp
deleted file mode 100644
index 2302d164..00000000
--- a/src/Nerve_GIC/example/mapper_parameter.cpp
+++ /dev/null
@@ -1,165 +0,0 @@
-#include "mapper.h"
-#define ETA 0.001
-#define VERBOSE 1
-#define DELTA 1
-#define RESOLUTION 1
-#define CONSTANT 10
-
-double GetUniform(){
- static default_random_engine re;
- static uniform_real_distribution<double> Dist(0,1);
- return Dist(re);
-}
-
-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++;}
- }
-}
-
-int main(int argc, char** argv){
-
- if(argc <= 7 || argc >= 9){cout << "./param <cloud_file> <function:/coordinate:/eccentricity:> <func_file/number/matrix_file>" << \
- " <VNE> <gain> <subsampling:/graph:> <N/graph_name>" << endl; return 0;}
-
- char* const cloud = argv[1];
- char* const funct = argv[2];
- bool VNE = atoi(argv[4]);
- double g = atof(argv[5]);
- char* const method = argv[6];
-
- Cloud C;
-
- if(VERBOSE) cout << "Reading input cloud from file " << cloud << "..." << endl;
- C = read_cloud(cloud); int n = C.size();
- if(VERBOSE) cout << " Done." << endl;
-
- if (strcmp(funct, "function:") == 0){
- char* const func = argv[3];
- if(VERBOSE) cout << "Reading input filter from file " << func << "..." << endl;
- read_function_from_file(func,C);
- }
- if (strcmp(funct, "coordinate:") == 0){
- int number = atoi(argv[3]);
- if(VERBOSE) cout << "Using coordinate " << number << " as a filter..." << endl;
- read_coordinate(number,C);
- }
- if (strcmp(funct, "eccentricity:") == 0){
- char* const matrix = argv[3];
- cout << "Computing eccentricity with distance matrix " << matrix << "..." << endl;
- compute_eccentricity(C,matrix);
- }
- if(VERBOSE) cout << " Done." << endl;
-
- 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);}
-
- double delta = 0; double lip = 0;
-
- if (strcmp(method, "subsampling:") == 0){
-
- char name[100];
- if(VNE) sprintf(name,"%s_VNdist",cloud);
- else sprintf(name,"%s_dist",cloud);
- ifstream input(name, ios::out | ios::binary);
- vector<vector<double> > dist; dist.clear(); double d;
-
- if(input.good()){
- cout << "Reading distances..." << endl;
- for(int i = 0; i < n; i++){
- vector<double> dis; dis.clear();
- for (int j = 0; j < n; j++){input.read((char*) &d,8); dis.push_back(d);}
- dist.push_back(dis);
- }
- input.close();
- }
- else{
- cout << "Computing distances..." << endl; vector<double> V;
- input.close(); ofstream output(name, 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;}
- vector<double> dis; dis.clear();
- for (int j = 0; j < n; j++){
- if(!VNE){d = C[i].EuclideanDistance(C[j]); dis.push_back(d);}
- else{d = C[i].VarianceNormalizedEuclideanDistance(C[j],V); dis.push_back(d);}
- output.write((char*) &d,8);
- }
- dist.push_back(dis);
- }
- output.close();
- }
-
- cout << "Done." << endl;
-
- int m = floor(n/pow(log(n)/log(CONSTANT),1+ETA)); m = min(m,n-1);
-
- if(VERBOSE) cout << "dimension = " << C[0].coord.size() << endl << "n = " << n << " and s(n) = " << m << endl;
- if(VERBOSE) cout << "range = [" << minf << ", " << maxf << "]" << endl;
-
- vector<int> samples(m); int N = atoi(argv[7]);
- #pragma omp parallel for
- for (int i = 0; i < N; 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]]);}
- 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 /= N;
-
- }
-
- if (strcmp(method, "graph:") == 0){
- char* const graph_name = argv[7];
- cout << "Reading neighborhood graph from file " << graph_name << "..." << endl;
- pair<double,double> P = compute_delta_from_file(C,graph_name);
- delta = P.first; lip = P.second;
- }
-
- if(VERBOSE) cout << "lip = " << lip << endl;
-
- if(VERBOSE) cout << "delta = " << delta << endl;
- else cout << delta << endl;
-
- if(VERBOSE) cout << "g = " << g << endl;
- else cout << g << endl;
-
- if(!DELTA){
- if(VERBOSE){
- if(RESOLUTION) cout << "r = " << lip*delta/g << endl;
- else cout << "r = " << floor(g*(maxf-minf)/(lip*delta*(1-g))) << endl;}
- else{
- if(RESOLUTION) cout << lip*delta/g << endl;
- else cout << floor(g*(maxf-minf)/(lip*delta*(1-g))) << endl;}}
- else{
- if(VERBOSE){
- if(RESOLUTION) cout << "r = " << lip*delta << endl;
- else cout << "r = " << floor((maxf-minf)/(lip*delta*(1-g))) << endl;}
- else{
- if(RESOLUTION) cout << lip*delta << endl;
- else cout << floor((maxf-minf)/(lip*delta*(1-g))) << endl;}}
-
- return 0;
-}
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