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diff --git a/src/Kernels/include/gudhi/kernel.h b/src/Kernels/include/gudhi/kernel.h new file mode 100644 index 00000000..3293cc62 --- /dev/null +++ b/src/Kernels/include/gudhi/kernel.h @@ -0,0 +1,365 @@ +/* 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) 2018 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 KERNEL_H_ +#define KERNEL_H_ + +#include <cstdlib> +#include <vector> +#include <algorithm> +#include <cmath> +#include <random> +#include <limits> //for numeric_limits<> +#include <utility> //for pair<> + +#include <boost/math/constants/constants.hpp> + + +namespace Gudhi { +namespace kernel { + +using PD = std::vector<std::pair<double,double> >; +double pi = boost::math::constants::pi<double>(); + + + + +// ******************************************************************** +// Utils. +// ******************************************************************** + +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;} + +double pss_weight(std::pair<double,double> P){ + if(P.second > P.first) return 1; + else return -1; +} + +double arctan_weight(std::pair<double,double> P){ + return atan(P.second - P.first); +} + +// Compute the angle formed by two points of a PD +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(vect.first*vect.first + vect.second*vect.second); + return asin(vect.second/norm); +} + +// Compute the integral of |cos()| between alpha and beta, valid only if alpha is in [-pi,pi] and beta-alpha is in [0,pi] +double compute_int_cos(const double & alpha, const double & beta){ + double res = 0; + if (alpha >= 0 && alpha <= pi){ + if (cos(alpha) >= 0){ + if(pi/2 <= beta){res = 2-sin(alpha)-sin(beta);} + else{res = sin(beta)-sin(alpha);} + } + else{ + if(1.5*pi <= beta){res = 2+sin(alpha)+sin(beta);} + else{res = sin(alpha)-sin(beta);} + } + } + if (alpha >= -pi && alpha <= 0){ + if (cos(alpha) <= 0){ + if(-pi/2 <= beta){res = 2+sin(alpha)+sin(beta);} + else{res = sin(alpha)-sin(beta);} + } + else{ + if(pi/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, const PD & PD1, const PD & PD2){ + double norm = std::sqrt( (PD1[p].first-PD2[q].first)*(PD1[p].first-PD2[q].first) + (PD1[p].second-PD2[q].second)*(PD1[p].second-PD2[q].second) ); + double angle1; + if (PD1[p].first > PD2[q].first) + angle1 = theta1 - asin( (PD1[p].second-PD2[q].second)/norm ); + else + angle1 = theta1 - asin( (PD2[q].second-PD1[p].second)/norm ); + double angle2 = angle1 + theta2 - theta1; + double integral = compute_int_cos(angle1,angle2); + return norm*integral; +} + +template<class Weight = std::function<double (std::pair<double,double>) > > +std::vector<std::pair<double,double> > Fourier_feat(PD D, std::vector<std::pair<double,double> > Z, Weight weight = arctan_weight){ + 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 += weight(D[j])*cos(x*zx + y*zy); + d2 += weight(D[j])*sin(x*zx + y*zy); + } + B.emplace_back(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++){ + std::mt19937 e1(rd()); std::mt19937 e2(rd()); + double zx = distrib(e1); double zy = distrib(e2); + Z.emplace_back(zx/sigma,zy/sigma); + } + return Z; +} + + + + + + + + + + +// ******************************************************************** +// Kernel computation. +// ******************************************************************** + + + + + +/** \brief Computes the Linear Persistence Weighted Gaussian Kernel between two persistence diagrams with random Fourier features. + * \ingroup kernel + * + * @param[in] PD1 first persistence diagram. + * @param[in] PD2 second persistence diagram. + * @param[in] sigma bandwidth parameter of the Gaussian Kernel used for the Kernel Mean Embedding of the diagrams. + * @param[in] weight weight function for the points in the diagrams. + * @param[in] M number of Fourier features (set -1 for exact computation). + * + */ +template<class Weight = std::function<double (std::pair<double,double>) > > +double linear_persistence_weighted_gaussian_kernel(const PD & PD1, const PD & PD2, double sigma, Weight weight = arctan_weight, int M = 1000){ + + if(M == -1){ + 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 += weight(PD1[i])*weight(PD2[j])*exp(-((PD1[i].first-PD2[j].first)*(PD1[i].first-PD2[j].first) + (PD1[i].second-PD2[j].second)*(PD1[i].second-PD2[j].second))/(2*sigma*sigma)); + return k; + } + else{ + std::vector<std::pair<double,double> > Z = random_Fourier(sigma, M); + std::vector<std::pair<double,double> > B1 = Fourier_feat(PD1,Z,weight); + std::vector<std::pair<double,double> > B2 = Fourier_feat(PD2,Z,weight); + double d = 0; for(int i = 0; i < M; i++) d += B1[i].first*B2[i].first + B1[i].second*B2[i].second; + return d/M; + } +} + +/** \brief Computes the Persistence Scale Space Kernel between two persistence diagrams with random Fourier features. + * \ingroup kernel + * + * @param[in] PD1 first persistence diagram. + * @param[in] PD2 second persistence diagram. + * @param[in] sigma bandwidth parameter of the Gaussian Kernel used for the Kernel Mean Embedding of the diagrams. + * @param[in] M number of Fourier features (set -1 for exact computation). + * + */ +double persistence_scale_space_kernel(const PD & PD1, const PD & PD2, double sigma, int M = 1000){ + PD pd1 = PD1; int numpts = PD1.size(); for(int i = 0; i < numpts; i++) pd1.emplace_back(PD1[i].second,PD1[i].first); + PD pd2 = PD2; numpts = PD2.size(); for(int i = 0; i < numpts; i++) pd2.emplace_back(PD2[i].second,PD2[i].first); + return linear_persistence_weighted_gaussian_kernel(pd1, pd2, 2*sqrt(sigma), pss_weight, M) / (2*8*pi*sigma); +} + + +/** \brief Computes the Gaussian Persistence Weighted Gaussian Kernel between two persistence diagrams with random Fourier features. + * \ingroup kernel + * + * @param[in] PD1 first persistence diagram. + * @param[in] PD2 second persistence diagram. + * @param[in] sigma bandwidth parameter of the Gaussian Kernel used for the Kernel Mean Embedding of the diagrams. + * @param[in] tau bandwidth parameter of the Gaussian Kernel used between the embeddings. + * @param[in] weight weight function for the points in the diagrams. + * @param[in] M number of Fourier features (set -1 for exact computation). + * + */ +template<class Weight = std::function<double (std::pair<double,double>) > > +double gaussian_persistence_weighted_gaussian_kernel(const PD & PD1, const PD & PD2, double sigma, double tau, Weight weight = arctan_weight, int M = 1000){ + double k1 = linear_persistence_weighted_gaussian_kernel(PD1,PD1,sigma,weight,M); + double k2 = linear_persistence_weighted_gaussian_kernel(PD2,PD2,sigma,weight,M); + double k3 = linear_persistence_weighted_gaussian_kernel(PD1,PD2,sigma,weight,M); + return exp( - (k1+k2-2*k3) / (2*tau*tau) ); +} + + +/** \brief Computes the Sliced Wasserstein Kernel between two persistence diagrams with sampled directions. + * \ingroup kernel + * + * @param[in] PD1 first persistence diagram. + * @param[in] PD2 second persistence diagram. + * @param[in] sigma bandwidth parameter. + * @param[in] N number of points sampled on the circle (set -1 for exact computation). + * + */ +double sliced_wasserstein_kernel(PD PD1, PD PD2, double sigma, int N = 100){ + + if(N == -1){ + + // Add projections onto diagonal. + int n1, n2; n1 = PD1.size(); n2 = PD2.size(); double max_ordinate = std::numeric_limits<double>::lowest(); + for (int i = 0; i < n2; i++){ + max_ordinate = std::max(max_ordinate, PD2[i].second); + PD1.emplace_back( (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.emplace_back( (PD1[i].first+PD1[i].second)/2, (PD1[i].first+PD1[i].second)/2 ); + } + int num_pts_dgm = PD1.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 < num_pts_dgm; 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 < num_pts_dgm; i++){ + for (int j = i+1; j < num_pts_dgm; j++){ + double theta1 = compute_angle(PD1,i,j); double theta2 = compute_angle(PD2,i,j); + angles1.emplace_back(theta1, std::pair<int,int>(i,j)); + angles2.emplace_back(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 PDs (given by ordinates when theta = -pi/2). + std::vector<int> orderp1, orderp2; + for (int i = 0; i < num_pts_dgm; i++){ orderp1.push_back(i); orderp2.push_back(i); } + std::sort( orderp1.begin(), orderp1.end(), [=](int i, int j){ if(PD1[i].second != PD1[j].second) return (PD1[i].second < PD1[j].second); else return (PD1[i].first > PD1[j].first); } ); + std::sort( orderp2.begin(), orderp2.end(), [=](int i, int j){ if(PD2[i].second != PD2[j].second) return (PD2[i].second < PD2[j].second); else return (PD2[i].first > PD2[j].first); } ); + + // Find the inverses of the orders. + std::vector<int> order1(num_pts_dgm); std::vector<int> order2(num_pts_dgm); + for(int i = 0; i < num_pts_dgm; i++) for (int j = 0; j < num_pts_dgm; j++) if(orderp1[j] == i){ order1[i] = j; break; } + for(int i = 0; i < num_pts_dgm; i++) for (int j = 0; j < num_pts_dgm; j++) if(orderp2[j] == i){ order2[i] = j; break; } + + // 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(num_pts_dgm); + std::vector<std::vector<std::pair<int,double> > > anglePerm2(num_pts_dgm); + + 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]].emplace_back(p,theta); + anglePerm1[order1[q]].emplace_back(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]].emplace_back(p,theta); + anglePerm2[order2[q]].emplace_back(q,theta); + int a = order2[p]; int b = order2[q]; order2[p] = b; order2[q] = a; + } + + for (int i = 0; i < num_pts_dgm; i++){ + anglePerm1[order1[i]].emplace_back(i,pi/2); + anglePerm2[order2[i]].emplace_back(i,pi/2); + } + + // Compute the SW distance with the list of inversions. + double sw = 0; + for (int i = 0; i < num_pts_dgm; i++){ + std::vector<std::pair<int,double> > U,V; U = anglePerm1[i]; V = anglePerm2[i]; + double theta1, theta2; theta1 = -pi/2; + unsigned int ku, kv; ku = 0; kv = 0; theta2 = std::min(U[ku].second,V[kv].second); + while(theta1 != pi/2){ + if(PD1[U[ku].first].first != PD2[V[kv].first].first || PD1[U[ku].first].second != PD2[V[kv].first].second) + if(theta1 != theta2) + sw += compute_int(theta1, theta2, U[ku].first, V[kv].first, PD1, PD2); + 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 exp( -(sw/pi)/(2*sigma*sigma) ); + + } + + + else{ + double step = pi/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.emplace_back( (PD2[i].first + PD2[i].second)/2, (PD2[i].first + PD2[i].second)/2 ); + for (int i = 0; i < n1; i++) + PD2.emplace_back( (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.emplace_back( j, PD1[j].first*cos(-pi/2+i*step) + PD1[j].second*sin(-pi/2+i*step) ); + L2.emplace_back( j, PD2[j].first*cos(-pi/2+i*step) + PD2[j].second*sin(-pi/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 exp( -(sw/pi)/(2*sigma*sigma) ); + } +} + + +} // namespace kernel + +} // namespace Gudhi + +#endif //KERNEL_H_ |