fixed the ordering of constraints weights in RankSVM, ref #57, ref #106

CMA-ES · Jan 30, 2015 · 60d329f · 60d329f
1 parent b0eca41
commit 60d329f
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Showing 3 changed files with 20 additions and 18 deletions.
diff --git a/src/surrogates/rankingsvm.hpp b/src/surrogates/rankingsvm.hpp
@@ -150,8 +150,8 @@ class RankingSVM
     int nalphas = x.cols()-1;
     _C = dMat::Constant(nalphas,1,_Cval);
     for (int i=0;i<nalphas;i++)
-      _C(i) = _Cval*pow(nalphas-i,3);
-    _dKij = dMat(nalphas,nalphas);
+      _C(nalphas-1-i) = _Cval*pow(nalphas-i,2);
+    _dKij = dMat::Zero(nalphas,nalphas);
     _alpha = dVec::Zero(nalphas);
 
     if (_encode)
@@ -183,7 +183,7 @@ class RankingSVM
   {
     if (_alpha.size() == 0)
       return; // model is not yet trained.
-    fit = dVec(x_test.cols());
+    fit = dVec::Zero(x_test.cols());
     if (_encode)
       {
 	encode(x_train,covinv,xmean);
@@ -254,8 +254,8 @@ class RankingSVM
 		const int &niter)
   {
     // initialization of temporary variables
-    dVec sum_alphas(_dKij.cols());
-    dMat div_dKij(_dKij.rows(),_dKij.cols());
+    dVec sum_alphas = dVec::Zero(_dKij.cols());
+    dMat div_dKij = dMat::Zero(_dKij.rows(),_dKij.cols());
 #pragma omp parallel
     {
 #pragma omp for
@@ -277,7 +277,6 @@ class RankingSVM
 	      div_dKij(i,j) = _dKij(i,j) / _dKij(j,j);
 	    }
 	  sum_alphas(i) = (_epsilon - sum_alpha) / _dKij(i,i);
-
 	}
     }
 
@@ -292,7 +291,7 @@ class RankingSVM
 	new_alpha = old_alpha + sum_alphas(i1);
 	new_alpha = std::max(std::min(new_alpha,_C(i1)),0.0);
 	delta_alpha = new_alpha - old_alpha;
-	double dL = delta_alpha * _dKij(i1,i1) * (sum_alphas(i1) - 0.5*delta_alpha);
+	double dL = delta_alpha * _dKij(i1,i1) * (sum_alphas(i1) - 0.5*delta_alpha + _epsilon);
 	if (dL > 0)
 	  {
 	    sum_alphas -= delta_alpha * div_dKij.row(i1);
@@ -347,7 +346,7 @@ class RankingSVM
   dVec _alpha; /**< vector of Ranking SVM parameters over ranking constraints. */
   dMat _dKij;
   dMat _C; /**< constraint violation weights. */
-  double _Cval = 10e6; /**< constraing violation base weight value. */
+  double _Cval = 1e6; /**< constraing violation base weight value. */
   double _epsilon = 1.0;
 
   TKernel _kernel; /**< kernel class. */

diff --git a/src/surrogates/rsvm_surr_strategy.hpp b/src/surrogates/rsvm_surr_strategy.hpp
@@ -32,10 +32,12 @@ namespace libcmaes
 
   // Interfacing candidates with an Eigen matrix of double x and a vector of objective function value.
   void to_mat_vec(std::vector<Candidate> &cp,
-		  dMat &x, dVec &fvalues)
+		  dMat &x, dVec &fvalues,
+		  const bool &train)
   {
-    std::sort(cp.begin(),cp.end(),
-	      [](Candidate const &c1, Candidate const &c2){return c1.get_fvalue() > c2.get_fvalue();}); // descending sort
+    if (train)
+      std::sort(cp.begin(),cp.end(),
+		[](Candidate const &c1, Candidate const &c2){return c1.get_fvalue() > c2.get_fvalue();}); // descending sort
     x = dMat(cp.at(0).get_x_size(),cp.size());
     fvalues = dVec(cp.size());
     for (int i=0;i<(int)cp.size();i++)
@@ -62,7 +64,7 @@ namespace libcmaes
 	dMat x;
 	dVec fvalues;
 	std::vector<Candidate> cp = c;
-	to_mat_vec(cp,x,fvalues);
+	to_mat_vec(cp,x,fvalues,true);
 	dVec xmean = eostrat<TGenoPheno>::get_solutions().xmean();
 	_rsvm = RankingSVM<RBFKernel>();
 	_rsvm._encode = true;
@@ -78,7 +80,7 @@ namespace libcmaes
 	dMat x_train;
 	dVec fvalues;
 	std::vector<Candidate> tset = this->_tset;
-	to_mat_vec(tset,x_train,fvalues);
+	to_mat_vec(tset,x_train,fvalues,true);
 
 	dVec fit;
 	dVec xmean = eostrat<TGenoPheno>::get_solutions().xmean();
@@ -90,10 +92,10 @@ namespace libcmaes
       };
     }
 
-      ~RSVMSurrogateStrategy() {};
+      ~RSVMSurrogateStrategy() {}
 
       RankingSVM<RBFKernel> _rsvm;
-      int _rsvm_iter = 5e6; /**< number of iterations for optimizing the ranking SVM */
+      int _rsvm_iter = 1e6; /**< number of iterations for optimizing the ranking SVM */
   };
 
 }

diff --git a/tests/surr-tests.cc b/tests/surr-tests.cc
@@ -138,11 +138,12 @@ template<template <class U, class V> class TStrategy, class TCovarianceUpdate=Co
     optim.set_exploit(!FLAGS_no_exploit);
   optim.set_prelambda(FLAGS_prelambda);
   optim.set_lambdaprime(lambdaprime);
-  optim._rsvm_iter = FLAGS_rsvm_iter;
+  //optim._rsvm_iter = FLAGS_rsvm_iter;
+  optim._rsvm_iter = 50000*std::sqrt(dim);
   if (FLAGS_fname == "elli"
       || FLAGS_fname == "rosenbrock")
-    optim.set_l(std::floor(40.0*std::sqrt(dim)));
-  else if (FLAGS_fname == "rastrigin")
+    optim.set_l(std::floor(70.0*std::sqrt(dim)));
+    else if (FLAGS_fname == "rastrigin")
     {
       optim.set_theta_sel0(0.6);
       optim.set_theta_sel1(0.6);