diff options
Diffstat (limited to 'src/Nerve_GIC/example/mapper_parameter.cpp')
| -rw-r--r-- | src/Nerve_GIC/example/mapper_parameter.cpp | 165 |
1 files changed, 165 insertions, 0 deletions
diff --git a/src/Nerve_GIC/example/mapper_parameter.cpp b/src/Nerve_GIC/example/mapper_parameter.cpp new file mode 100644 index 00000000..2302d164 --- /dev/null +++ b/src/Nerve_GIC/example/mapper_parameter.cpp @@ -0,0 +1,165 @@ +#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; +} |