NPDerivativeChecker: Checking at given x.

NSolver/NPDerivativeChecker.hh

@@ -61,36 +61,40 @@ public:
   };
   
   template<class ProblemInterface>
-  bool check_all(ProblemInterface* _np, double _dx, double _eps)
+  bool check_all(ProblemInterface* _np, const double* _x, double _dx, double _eps)
   {
     conf_.dx = _dx;
     conf_.eps = _eps;
-    return check_all(_np);
+    return check_all(_np, _x);
   }
 
   template<class ProblemInterface>
-  bool check_all(ProblemInterface* _np)
+  bool check_all(ProblemInterface* _np, const double* _x)
   {
-    bool d1_ok = check_d1(_np);
-    bool d2_ok = check_d2(_np);
+    bool d1_ok = check_d1(_np, _x);
+    bool d2_ok = check_d2(_np, _x);
 
     return ( d1_ok && d2_ok);
   }
 
   template<class ProblemInterface>
-  bool check_d1(ProblemInterface* _np)
+  bool check_d1(ProblemInterface* _np, const double* _x)
   {
     int n_ok     = 0;
     int n_errors = 0;
 
 
     int n = _np->n_unknowns();
-    std::vector<double> x(n), g(n);
+    std::vector<double> x(n); // Copy x because we don't want to change the input
+    for (int i = 0; i < n; ++i)
+        x[i] = _x[i];
 
-    for(int i=0; i<conf_.n_iters; ++i)
-    {
+    std::vector<double> g(n);
+
+//    for(int i=0; i<conf_.n_iters; ++i)
+//    {
       // get random x
-      get_random_x(x, conf_.x_min, conf_.x_max);
+//      get_random_x(x, conf_.x_min, conf_.x_max);
       // gradient
       _np->eval_gradient(P(x), P(g));
 
@@ -111,7 +115,7 @@ public:
                     << " should be " << fd << " (" << std::abs(fd-g[j]) << ")" << std::endl;
         }
         else ++ n_ok;
-      }
+//      }
     }
 
     std::cerr << "############## Gradient Checker Summary #############\n";
@@ -125,19 +129,22 @@ public:
   inline double getCoeff(const MatrixType &m, int r, int c);
 
   template<class ProblemInterface>
-  bool check_d2(ProblemInterface* _np)
+  bool check_d2(ProblemInterface* _np, const double* _x)
   {
     int n_ok     = 0;
     int n_errors = 0;
 
     int n = _np->n_unknowns();
-    std::vector<double> x(n);
+    std::vector<double> x(n); // Copy x because we don't want to change the input
+    for (int i = 0; i < n; ++i)
+        x[i] = _x[i];
+
     typename ProblemInterface::SMatrixNP H(n,n);
 
-    for(int i=0; i<conf_.n_iters; ++i)
-    {
+//    for(int i=0; i<conf_.n_iters; ++i)
+//    {
       // get random x
-      get_random_x(x, conf_.x_min, conf_.x_max);
+//      get_random_x(x, conf_.x_min, conf_.x_max);
       // gradient
       _np->eval_hessian(P(x), H);
 
@@ -167,7 +174,7 @@ public:
                       << ", relative delta:" << (std::abs(fd-getCoeff(H, j,k))/std::max(std::abs(getCoeff(H, j,k)), 1.0)) << ")" << std::endl;
           }
           else ++ n_ok;
-        }
+//        }
     }
 
     std::cerr << "############## Hessian Checker Summary #############\n";