second try to tweak NoiseEnergy

nifty4/library/noise_energy.py

@@ -38,29 +38,21 @@ class NoiseEnergy(Energy):
             grad = -.5 * self.N.inverse_times(s.conjugate()*s)
             if self._gradient is None:
                 self._value = lh
-                self._gradient = grad.copy()
+                self._gradient = grad
             else:
                 self._value += lh
                 self._gradient += grad
 
         expmpos = exp(-position)
         self._value *= 1./len(res_sample_list)
-#        possum = position.sum()
-#        s1 = (alpha-1.)*possum if np.isscalar(alpha) \
-#            else (alpha-1.).vdot(position)
-#        s2 = q*expmpos.sum() if np.isscalar(q) else q.vdot(expmpos)
-#        self._value += .5*possum + s1 + s2
+        possum = position.sum()
+        s1 = (alpha-1.)*possum if np.isscalar(alpha) \
+            else (alpha-1.).vdot(position)
+        s2 = q*expmpos.sum() if np.isscalar(q) else q.vdot(expmpos)
+        self._value += .5*possum + s1 + s2
 
-#        self._gradient *= 1./len(res_sample_list)
-#        self._gradient += (alpha-0.5) - q*expmpos
-        alpha_field = Field(position.domain, val=alpha)
-        q_field = Field(position.domain, val=q)
-        self._value += .5 * self.position.sum()
-        self._value += (alpha_field-1.).vdot(self.position) + \
-            q_field.vdot(expmpos)
-
-        self._gradient *= 1./len(self.res_sample_list)
-        self._gradient += (alpha_field-0.5) - q_field*expmpos
+        self._gradient *= 1./len(res_sample_list)
+        self._gradient += (alpha-0.5) - q*expmpos
         self._gradient.lock()
 
     def at(self, position):