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author | mcarrier <mcarrier@636b058d-ea47-450e-bf9e-a15bfbe3eedb> | 2017-12-08 09:22:24 +0000 |
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committer | mcarrier <mcarrier@636b058d-ea47-450e-bf9e-a15bfbe3eedb> | 2017-12-08 09:22:24 +0000 |
commit | fd79fc0f0a216e5b1dc8b2cb466d383eb32c1fd4 (patch) | |
tree | 6507a75cc1c053b5c7f5e5c933ede91392785c43 | |
parent | 3d53679384a104792d29262d7bf826b20e729f57 (diff) |
git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/kernels@3056 636b058d-ea47-450e-bf9e-a15bfbe3eedb
Former-commit-id: 6fd757a9dfd0abe7ddfbaeca1af667a4c93af34b
-rw-r--r-- | src/Kernels/include/gudhi/PSS.h | 108 | ||||
-rw-r--r-- | src/Kernels/include/gudhi/PWG.h | 204 | ||||
-rw-r--r-- | src/Kernels/include/gudhi/SW.h | 288 |
3 files changed, 600 insertions, 0 deletions
diff --git a/src/Kernels/include/gudhi/PSS.h b/src/Kernels/include/gudhi/PSS.h new file mode 100644 index 00000000..70743c47 --- /dev/null +++ b/src/Kernels/include/gudhi/PSS.h @@ -0,0 +1,108 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Mathieu Carrière + * + * Copyright (C) 2017 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef PSS_H_ +#define PSS_H_ + +#define NUMPI 3.14159265359 + +#include <stdlib.h> +#include <stdio.h> +#include <cstdlib> +#include <cstdio> +#include <iomanip> + +#include <string> +#include <iostream> +#include <sstream> +#include <fstream> + +#include <set> +#include <map> +#include <vector> +#include <algorithm> +#include <limits> +#include <assert.h> + +#include <cmath> +#include <math.h> + +#include <memory> +#include <stdexcept> +#include <omp.h> + +#include <gmp.h> +#include <gmpxx.h> +#include <random> +#include <chrono> +#include <ctime> + +#include "../../figtree-0.9.3/include/figtree.h" +#include "../../figtree-0.9.3/external/ann_1.1.1/include/ANN/ANN.h" + +using PD = std::vector<std::pair<double,double> >; + +namespace Gudhi { +namespace persistence_scale_space { + +double compute_exact_pss(PD PD1, PD PD2, double sigma = 1){ + double k = 0; + for(int i = 0; i < PD1.size(); i++){ + for(int j = 0; j < PD2.size(); j++){ + k += exp( -( pow(PD1[i].first - PD2[j].first, 2) + pow(PD1[i].second - PD2[j].second, 2) )/(8*sigma)) -\ + exp( -( pow(PD1[i].first - PD2[j].second, 2) + pow(PD1[i].second - PD2[j].first, 2) )/(8*sigma)); + } + } + return k/(8*NUMPI*sigma); +} + +double compute_approximate_pss(PD PD1, PD PD2, double sigma = 1, double error = 1e-2){ + + double k = 0; + + int d = 2; int N = PD1.size(); int M = PD2.size(); double h = std::sqrt(8*sigma); + double* x = new double[2*N]; double* y = new double[2*M]; double* q = new double[N]; + for(int i = 0; i < N; i++){ + q[i] = 1.0/(8*NUMPI*sigma); + x[2*i] = PD1[i].first; x[2*i+1] = PD1[i].second; + } + for(int i = 0; i < M; i++){ y[2*i] = PD2[i].first; y[2*i+1] = PD2[i].second; } + double* g_auto = new double[M]; + memset(g_auto, 0, sizeof(double)*M); + + figtree(d, N, M, 1, x, h, q, y, error, g_auto); + for(int i = 0; i < M; i++) k += g_auto[i]; + + for(int i = 0; i < M; i++){ y[2*i] = PD2[i].second; y[2*i+1] = PD2[i].first; } + + figtree(d, N, M, 1, x, h, q, y, error, g_auto); + for(int i = 0; i < M; i++) k -= g_auto[i]; + + delete[] x; delete[] y; delete[] q; delete[] g_auto; + return k; +} + +} // namespace persistence_scale_space + +} // namespace Gudhi + +#endif // PSS_H_ diff --git a/src/Kernels/include/gudhi/PWG.h b/src/Kernels/include/gudhi/PWG.h new file mode 100644 index 00000000..bc491ae7 --- /dev/null +++ b/src/Kernels/include/gudhi/PWG.h @@ -0,0 +1,204 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Mathieu Carrière + * + * Copyright (C) 2017 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef PWG_H_ +#define PWG_H_ + +#define NUMPI 3.14159265359 + +#include <stdlib.h> +#include <stdio.h> +#include <cstdlib> +#include <cstdio> +#include <iomanip> + +#include <string> +#include <iostream> +#include <sstream> +#include <fstream> + +#include <set> +#include <map> +#include <vector> +#include <algorithm> +#include <limits> +#include <assert.h> + +#include <cmath> +#include <math.h> + +#include <memory> +#include <stdexcept> +#include <omp.h> + +#include <gmp.h> +#include <gmpxx.h> +#include <random> +#include <chrono> +#include <ctime> + +using PD = std::vector<std::pair<double,double> >; + +namespace Gudhi { +namespace persistence_weighted_gaussian { + +double compute_exact_linear_pwg(PD PD1, PD PD2, double sigma, double C, int p){ + + int num_pts1 = PD1.size(); + int num_pts2 = PD2.size(); + + double k = 0; + for(int i = 0; i < num_pts1; i++){ + for(int j = 0; j < num_pts2; j++){ + k += atan(C*pow(PD1[i].second-PD1[i].first,p))*atan(C*pow(PD2[j].second-PD2[j].first,p))*\ + exp( -( pow(PD1[i].first-PD2[j].first,2) + pow(PD1[i].second-PD2[j].second,2) )/(2*pow(sigma,2)) ); + } + } + + return k; + +} + +double compute_exact_gaussian_pwg(PD PD1, PD PD2, double sigma, double C, int p, double tau){ + + int num_pts1 = PD1.size(); + int num_pts2 = PD2.size(); + + double k1 = 0; + for(int i = 0; i < num_pts1; i++){ + for(int j = 0; j < num_pts1; j++){ + k1 += atan(C*pow(PD1[i].second-PD1[i].first,p))*atan(C*pow(PD1[j].second-PD1[j].first,p))*\ + exp( -( pow(PD1[i].first-PD1[j].first,2) + pow(PD1[i].second-PD1[j].second,2) )/(2*pow(sigma,2)) ); + } + } + + double k2 = 0; + for(int i = 0; i < num_pts2; i++){ + for(int j = 0; j < num_pts2; j++){ + k2 += atan(C*pow(PD2[i].second-PD2[i].first,p))*atan(C*pow(PD2[j].second-PD2[j].first,p))*\ + exp( -( pow(PD2[i].first-PD2[j].first,2) + pow(PD2[i].second-PD2[j].second,2) )/(2*pow(sigma,2)) ); + } + } + + double k3 = compute_exact_linear_pwg(PD1,PD2,sigma,C,p); + return exp( - (k1+k2-2*k3) / (2*pow(tau,2)) ); + +} + +double compute_exact_gaussian_RKHSdist(PD PD1, PD PD2, double sigma, double C, int p){ + + int num_pts1 = PD1.size(); + int num_pts2 = PD2.size(); + + double k1 = 0; + for(int i = 0; i < num_pts1; i++){ + for(int j = 0; j < num_pts1; j++){ + k1 += atan(C*pow(PD1[i].second-PD1[i].first,p))*atan(C*pow(PD1[j].second-PD1[j].first,p))*\ + exp( -( pow(PD1[i].first-PD1[j].first,2) + pow(PD1[i].second-PD1[j].second,2) )/(2*pow(sigma,2)) ); + } + } + + double k2 = 0; + for(int i = 0; i < num_pts2; i++){ + for(int j = 0; j < num_pts2; j++){ + k2 += atan(C*pow(PD2[i].second-PD2[i].first,p))*atan(C*pow(PD2[j].second-PD2[j].first,p))*\ + exp( -( pow(PD2[i].first-PD2[j].first,2) + pow(PD2[i].second-PD2[j].second,2) )/(2*pow(sigma,2)) ); + } + } + + double k3 = compute_exact_linear_pwg(PD1,PD2,sigma,C,p); + return std::sqrt(k1+k2-2*k3); + +} + +double compute_approximate_linear_pwg_from_Fourier_features(const std::vector<std::pair<double,double> >& B1, \ + const std::vector<std::pair<double,double> >& B2){ + double d = 0; int M = B1.size(); + for(int i = 0; i < M; i++) d += B1[i].first*B2[i].first + B1[i].second*B2[i].second; + return (1.0/M)*d; +} + +double compute_approximate_gaussian_pwg_from_Fourier_features(const std::vector<std::pair<double,double> >& B1, \ + const std::vector<std::pair<double,double> >& B2, double tau){ + int M = B1.size(); + double d3 = compute_approximate_linear_pwg_from_Fourier_features(B1, B2); + double d1 = 0; double d2 = 0; + for(int i = 0; i < M; i++){d1 += pow(B1[i].first,2) + pow(B1[i].second,2); d2 += pow(B2[i].first,2) + pow(B2[i].second,2);} + return exp( -((1.0/M)*(d1+d2)-2*d3) / (2*pow(tau,2)) ); +} + +double compute_approximate_gaussian_RKHSdist_from_Fourier_features(const std::vector<std::pair<double,double> >& B1, \ + const std::vector<std::pair<double,double> >& B2){ + int M = B1.size(); + double d3 = compute_approximate_linear_pwg_from_Fourier_features(B1, B2); + double d1 = 0; double d2 = 0; + for(int i = 0; i < M; i++){d1 += pow(B1[i].first,2) + pow(B1[i].second,2); d2 += pow(B2[i].first,2) + pow(B2[i].second,2);} + return std::sqrt((1.0/M)*(d1+d2)-2*d3); +} + +std::vector<std::pair<double,double> > compute_Fourier_features(double C, int p, PD D, std::vector<std::pair<double,double> > Z){ + int m = D.size(); std::vector<std::pair<double,double> > B; int M = Z.size(); + for(int i = 0; i < M; i++){ + double d1 = 0; double d2 = 0; double zx = Z[i].first; double zy = Z[i].second; + for(int j = 0; j < m; j++){ + double x = D[j].first; double y = D[j].second; + d1 += atan(C*pow(y-x,p))*cos(x*zx + y*zy); + d2 += atan(C*pow(y-x,p))*sin(x*zx + y*zy); + } + B.push_back(std::pair<double,double>(d1,d2)); + } + return B; +} + +std::vector<std::pair<double,double> > random_Fourier(double sigma, int M = 1000){ + std::normal_distribution<double> distrib(0,1); std::vector<std::pair<double,double> > Z; + std::random_device rd; + for(int i = 0; i < M; i++){ + //unsigned seedx = 2*i; unsigned seedy = 2*i+1; + //std::default_random_engine generatorx(seedx); std::default_random_engine generatory(seedy); + std::mt19937 e1(rd()); std::mt19937 e2(rd()); + double zx = distrib(e1/*generatorx*/); double zy = distrib(e2/*generatory*/); + Z.push_back(std::pair<double,double>((1/sigma)*zx,(1/sigma)*zy)); + } + return Z; +} + +double compute_approximate_linear_pwg(PD PD1, PD PD2, double sigma, double C, int p, int M = 1000){ + std::vector<std::pair<double,double> > Z = random_Fourier(sigma, M); + std::vector<std::pair<double,double> > B1 = compute_Fourier_features(C,p,PD1,Z); + std::vector<std::pair<double,double> > B2 = compute_Fourier_features(C,p,PD2,Z); + return compute_approximate_linear_pwg_from_Fourier_features(B1,B2); +} + +double compute_approximate_gaussian_pwg(PD PD1, PD PD2, double sigma, double C, int p, double tau, int M = 1000){ + std::vector<std::pair<double,double> > Z = random_Fourier(sigma, M); + std::vector<std::pair<double,double> > B1 = compute_Fourier_features(C,p,PD1,Z); + std::vector<std::pair<double,double> > B2 = compute_Fourier_features(C,p,PD2,Z); + return compute_approximate_gaussian_pwg_from_Fourier_features(B1,B2,tau); +} + + +} // namespace persistence_weighted_gaussian + +} // namespace Gudhi + +#endif //PWG_H_ diff --git a/src/Kernels/include/gudhi/SW.h b/src/Kernels/include/gudhi/SW.h new file mode 100644 index 00000000..6871d990 --- /dev/null +++ b/src/Kernels/include/gudhi/SW.h @@ -0,0 +1,288 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Mathieu Carrière + * + * Copyright (C) 2017 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef SW_H_ +#define SW_H_ + +#define NUMPI 3.14159265359 + +#include <stdlib.h> +#include <cstdlib> +#include <string> +#include <iostream> +#include <sstream> +#include <fstream> +#include <vector> +#include <algorithm> +#include <set> +#include <map> +#include <limits> +#include <cmath> +#include <math.h> +#include <string.h> +#include <stdio.h> +#include <cstdio> +#include <memory> +#include <stdexcept> +#include <omp.h> +#include <assert.h> +#include <iomanip> +#include <gmp.h> +#include <gmpxx.h> +#include <random> +#include <chrono> +#include <ctime> +#include <math.h> + +using PD = std::vector<std::pair<double,double> >; + +std::vector<std::pair<double,double> > PDi, PDj; + +bool compOri(const int& p, const int& q){ + if(PDi[p].second != PDi[q].second) + return (PDi[p].second < PDi[q].second); + else + return (PDi[p].first > PDi[q].first); +} + +bool compOrj(const int& p, const int& q){ + if(PDj[p].second != PDj[q].second) + return (PDj[p].second < PDj[q].second); + else + return (PDj[p].first > PDj[q].first); +} + +bool sortAngle(const std::pair<double, std::pair<int,int> >& p1, const std::pair<double, std::pair<int,int> >& p2){ + return p1.first < p2.first; +} + +bool myComp(const std::pair<int,double> & P1, const std::pair<int,double> & P2){return P1.second < P2.second;} + +namespace Gudhi { +namespace sliced_wasserstein { + + +double compute_approximate_SW(PD PD1, PD PD2, int N = 100){ + + double step = NUMPI/N; double sw = 0; + + // Add projections onto diagonal. + // ****************************** + int n1, n2; n1 = PD1.size(); n2 = PD2.size(); + for (int i = 0; i < n2; i++) + PD1.push_back(std::pair<double,double>( (PD2[i].first+PD2[i].second)/2, (PD2[i].first+PD2[i].second)/2) ); + for (int i = 0; i < n1; i++) + PD2.push_back(std::pair<double,double>( (PD1[i].first+PD1[i].second)/2, (PD1[i].first+PD1[i].second)/2) ); + int n = PD1.size(); + + // Sort and compare all projections. + // ********************************* + //#pragma omp parallel for + for (int i = 0; i < N; i++){ + std::vector<std::pair<int,double> > L1, L2; + for (int j = 0; j < n; j++){ + L1.push_back( std::pair<int,double>(j, PD1[j].first*cos(-NUMPI/2+i*step) + PD1[j].second*sin(-NUMPI/2+i*step)) ); + L2.push_back( std::pair<int,double>(j, PD2[j].first*cos(-NUMPI/2+i*step) + PD2[j].second*sin(-NUMPI/2+i*step)) ); + } + std::sort(L1.begin(),L1.end(), myComp); std::sort(L2.begin(),L2.end(), myComp); + double f = 0; for (int j = 0; j < n; j++) f += std::abs(L1[j].second - L2[j].second); + sw += f*step; + } + return sw/NUMPI; +} + +double compute_int_cos(const double& alpha, const double& beta){ // Valid only if alpha is in [-pi,pi] and beta-alpha is in [0,pi] + double res; + assert((alpha >= 0 && alpha <= NUMPI) || (alpha >= -NUMPI && alpha <= 0)); + if (alpha >= 0 && alpha <= NUMPI){ + if (cos(alpha) >= 0){ + if(NUMPI/2 <= beta){res = 2-sin(alpha)-sin(beta);} + else{res = sin(beta)-sin(alpha);} + } + else{ + if(1.5*NUMPI <= beta){res = 2+sin(alpha)+sin(beta);} + else{res = sin(alpha)-sin(beta);} + } + } + if (alpha >= -NUMPI && alpha <= 0){ + if (cos(alpha) <= 0){ + if(-NUMPI/2 <= beta){res = 2+sin(alpha)+sin(beta);} + else{res = sin(alpha)-sin(beta);} + } + else{ + if(NUMPI/2 <= beta){res = 2-sin(alpha)-sin(beta);} + else{res = sin(beta)-sin(alpha);} + } + } + return res; +} + +double compute_int(const double& theta1, const double& theta2, const int& p, const int& q){ + double norm = std::sqrt(pow(PDi[p].first-PDj[q].first,2) + pow(PDi[p].second-PDj[q].second,2)); + double angle1; + if (PDi[p].first > PDj[q].first) + angle1 = theta1 - asin( (PDi[p].second-PDj[q].second)/norm ); + else + angle1 = theta1 - asin( (PDj[q].second-PDi[p].second)/norm ); + double angle2 = angle1+theta2-theta1; + double integral = compute_int_cos(angle1,angle2); + return norm*integral; +} + +double compute_sw(const std::vector<std::vector<std::pair<int,double> > >& V1, \ + const std::vector<std::vector<std::pair<int,double> > >& V2){ + int N = V1.size(); double sw = 0; + for (int i = 0; i < N; i++){ + std::vector<std::pair<int,double> > U,V; U = V1[i]; V = V2[i]; + double theta1, theta2; theta1 = -NUMPI/2; + int ku, kv; ku = 0; kv = 0; theta2 = std::min(U[ku].second,V[kv].second); + while(theta1 != NUMPI/2){ + if(PDi[U[ku].first].first != PDj[V[kv].first].first || PDi[U[ku].first].second != PDj[V[kv].first].second) + if(theta1 != theta2) + sw += compute_int(theta1,theta2,U[ku].first,V[kv].first); + theta1 = theta2; + if ( (theta2 == U[ku].second) && ku < U.size()-1 ){ku++;} + if ( (theta2 == V[kv].second) && kv < V.size()-1 ){kv++;} + theta2 = std::min(U[ku].second, V[kv].second); + } + } + return sw/NUMPI; +} + +double compute_angle(const PD& PersDiag, const int& i, const int& j){ + std::pair<double,double> vect; double x1,y1, x2,y2; + x1 = PersDiag[i].first; y1 = PersDiag[i].second; + x2 = PersDiag[j].first; y2 = PersDiag[j].second; + if (y1 - y2 > 0){ + vect.first = y1 - y2; + vect.second = x2 - x1;} + else{ + if(y1 - y2 < 0){ + vect.first = y2 - y1; + vect.second = x1 - x2; + } + else{ + vect.first = 0; + vect.second = abs(x1 - x2);} + } + double norm = std::sqrt(pow(vect.first,2) + pow(vect.second,2)); + return asin(vect.second/norm); +} + +double compute_exact_SW(PD PD1, PD PD2){ + + // Add projections onto diagonal. + // ****************************** + int n1, n2; n1 = PD1.size(); n2 = PD2.size(); double max_ordinate = std::numeric_limits<double>::min(); + for (int i = 0; i < n2; i++){ + max_ordinate = std::max(max_ordinate, PD2[i].second); + PD1.push_back(std::pair<double,double>( ((PD2[i].first+PD2[i].second)/2), ((PD2[i].first+PD2[i].second)/2)) ); + } + for (int i = 0; i < n1; i++){ + max_ordinate = std::max(max_ordinate, PD1[i].second); + PD2.push_back(std::pair<double,double>( ((PD1[i].first+PD1[i].second)/2), ((PD1[i].first+PD1[i].second)/2)) ); + } + int N = PD1.size(); assert(N==PD2.size()); + + // Slightly perturb the points so that the PDs are in generic positions. + // ********************************************************************* + int mag = 0; while(max_ordinate > 10){mag++; max_ordinate/=10;} + double thresh = pow(10,-5+mag); + srand(time(NULL)); + for (int i = 0; i < N; i++){ + PD1[i].first += thresh*(1.0-2.0*rand()/RAND_MAX); PD1[i].second += thresh*(1.0-2.0*rand()/RAND_MAX); + PD2[i].first += thresh*(1.0-2.0*rand()/RAND_MAX); PD2[i].second += thresh*(1.0-2.0*rand()/RAND_MAX); + } + + // Compute all angles in both PDs. + // ******************************* + std::vector<std::pair<double, std::pair<int,int> > > angles1, angles2; + for (int i = 0; i < N; i++){ + for (int j = i+1; j < N; j++){ + double theta1 = compute_angle(PD1,i,j); double theta2 = compute_angle(PD2,i,j); + angles1.push_back(std::pair<double, std::pair<int,int> >(theta1, std::pair<int,int>(i,j))); + angles2.push_back(std::pair<double, std::pair<int,int> >(theta2, std::pair<int,int>(i,j))); + } + } + + // Sort angles. + // ************ + std::sort(angles1.begin(), angles1.end(), sortAngle); std::sort(angles2.begin(), angles2.end(), sortAngle); + + // Initialize orders of the points of both PD (given by ordinates when theta = -pi/2). + // *********************************************************************************** + PDi = PD1; PDj = PD2; + std::vector<int> orderp1, orderp2; + for (int i = 0; i < N; i++){orderp1.push_back(i); orderp2.push_back(i);} + std::sort(orderp1.begin(),orderp1.end(),compOri); std::sort(orderp2.begin(),orderp2.end(),compOrj); + + // Find the inverses of the orders. + // ******************************** + std::vector<int> order1(N); std::vector<int> order2(N); + for(int i = 0; i < N; i++){ + for (int j = 0; j < N; j++) + if(orderp1[j] == i) + order1[i] = j; + } + for(int i = 0; i < N; i++){ + for (int j = 0; j < N; j++) + if(orderp2[j] == i) + order2[i] = j; + } + + // Record all inversions of points in the orders as theta varies along the positive half-disk. + // ******************************************************************************************* + std::vector<std::vector<std::pair<int,double> > > anglePerm1(N); + std::vector<std::vector<std::pair<int,double> > > anglePerm2(N); + + int M1 = angles1.size(); + for (int i = 0; i < M1; i++){ + double theta = angles1[i].first; int p = angles1[i].second.first; int q = angles1[i].second.second; + anglePerm1[order1[p]].push_back(std::pair<int, double>(p,theta)); + anglePerm1[order1[q]].push_back(std::pair<int, double>(q,theta)); + int a = order1[p]; int b = order1[q]; order1[p] = b; order1[q] = a; + } + + int M2 = angles2.size(); + for (int i = 0; i < M2; i++){ + double theta = angles2[i].first; int p = angles2[i].second.first; int q = angles2[i].second.second; + anglePerm2[order2[p]].push_back(std::pair<int, double>(p,theta)); + anglePerm2[order2[q]].push_back(std::pair<int, double>(q,theta)); + int a = order2[p]; int b = order2[q]; order2[p] = b; order2[q] = a; + } + + for (int i = 0; i < N; i++){ + anglePerm1[order1[i]].push_back(std::pair<int, double>(i,NUMPI/2)); + anglePerm2[order2[i]].push_back(std::pair<int, double>(i,NUMPI/2)); + } + + // Compute the SW distance with the list of inversions. + // **************************************************** + return compute_sw(anglePerm1,anglePerm2); + +} + +}} + +#endif + + |