Merge branch 'fix_KL' into 'NIFTy_6'

Fix KL

See merge request !429

nifty6/minimization/metric_gaussian_kl.py

@@ -231,8 +231,12 @@ class MetricGaussianKL(Energy):
             else:
                 mymap = map(lambda v: self._hamiltonian(lin+v).metric,
                             self._samples)
-                self.unscaled_metric = utilities.my_sum(mymap)
-                self._metric = self.unscaled_metric.scale(1./self._n_eff_samples)
+                unscaled_metric = utilities.my_sum(mymap)
+                if self._mirror_samples:
+                    mymap = map(lambda v: self._hamiltonian(lin-v).metric,
+                            self._samples)
+                    unscaled_metric = unscaled_metric + utilities.my_sum(mymap)
+                self._metric = unscaled_metric.scale(1./self._n_eff_samples)
 
     def apply_metric(self, x):
         self._get_metric()
@@ -253,7 +257,7 @@ class MetricGaussianKL(Energy):
             res = res + tuple(-item for item in res)
         return res
 
-    def _unscaled_metric_sample(self, from_inverse=False, dtype=np.float64):
+    def _metric_sample(self, from_inverse=False, dtype=np.float64):
         if from_inverse:
             raise NotImplementedError()
         lin = self._lin.with_want_metric()
@@ -265,7 +269,4 @@ class MetricGaussianKL(Energy):
             if self._mirror_samples:
                 samp = samp + self._hamiltonian(lin-v).metric.draw_sample(from_inverse=False, dtype=dtype)
             random.pop_sseq()
-        return _allreduce_sum_field(self._comm, samp)
-
-    def _metric_sample(self, from_inverse=False, dtype=np.float64):
-        return self._unscaled_metric_sample(from_inverse, dtype)/self._n_eff_samples
+        return _allreduce_sum_field(self._comm, samp)/self._n_eff_samples