Add docu to KL

nifty5/minimization/metric_gaussian_kl.py

@@ -58,6 +58,9 @@ class MetricGaussianKL(Energy):
         as they are equally legitimate samples. If true, the number of used
         samples doubles. Mirroring samples stabilizes the KL estimate as
         extreme sample variation is counterbalanced. Default is False.
+    napprox : int
+        Number of samples for computing preconditioner for sampling. No
+        preconditioning is done by default.
     _samples : None
         Only a parameter for internal uses. Typically not to be set by users.
 
@@ -74,7 +77,7 @@ class MetricGaussianKL(Energy):
 
     def __init__(self, mean, hamiltonian, n_samples, constants=[],
                  point_estimates=[], mirror_samples=False,
-                 _samples=None, napprox=0):
+                 napprox=0, _samples=None):
         super(MetricGaussianKL, self).__init__(mean)
 
         if not isinstance(hamiltonian, StandardHamiltonian):
@@ -94,9 +97,7 @@ class MetricGaussianKL(Energy):
             met = hamiltonian(Linearization.make_partial_var(
                 mean, point_estimates, True)).metric
             if napprox > 1:
-                print('Calculate preconditioner for sampling')
                 met._approximation = makeOp(approximation2endo(met, napprox))
-                print('Done')
             _samples = tuple(met.draw_sample(from_inverse=True)
                              for _ in range(n_samples))
             if mirror_samples:
@@ -121,7 +122,7 @@ class MetricGaussianKL(Energy):
     def at(self, position):
         return MetricGaussianKL(position, self._hamiltonian, 0,
                                 self._constants, self._point_estimates,
-                                _samples=self._samples, napprox=self._napprox)
+                                napprox=self._napprox, _samples=self._samples)
 
     @property
     def value(self):