formatting

nifty5/minimization/metric_gaussian_kl.py

@@ -21,16 +21,19 @@ from .. import utilities
 
 
 class MetricGaussianKL(Energy):
-    """Provides the sampled Kullback-Leibler divergence between a distribution and a Metric Gaussian.
-
-        A Metric Gaussian is used to approximate some other distribution.
-        It is a Gaussian distribution that uses the Fisher Information Metric
-        of the other distribution at the location of its mean to approximate the variance.
-        In order to infer the mean, the a stochastic estimate of the Kullback-Leibler divergence
-        is minimized. This estimate is obtained by drawing samples from the Metric Gaussian at the current mean.
-        During minimization these samples are kept constant, updating only the mean. Due to the typically nonlinear
-        structure of the true distribution these samples have to be updated by re-initializing this class at some point.
-        Here standard parametrization of the true distribution is assumed.
+    """Provides the sampled Kullback-Leibler divergence between a distribution
+    and a Metric Gaussian.
+
+    A Metric Gaussian is used to approximate some other distribution.
+    It is a Gaussian distribution that uses the Fisher Information Metric
+    of the other distribution at the location of its mean to approximate the
+    variance. In order to infer the mean, the a stochastic estimate of the
+    Kullback-Leibler divergence is minimized. This estimate is obtained by
+    drawing samples from the Metric Gaussian at the current mean.
+    During minimization these samples are kept constant, updating only the
+    mean. Due to the typically nonlinear structure of the true distribution
+    these samples have to be updated by re-initializing this class at some
+    point. Here standard parametrization of the true distribution is assumed.
 
     Parameters
     ----------
@@ -53,7 +56,8 @@ class MetricGaussianKL(Energy):
 
     Notes
     -----
-    For further details see: Metric Gaussian Variational Inference (in preparation)
+    For further details see: Metric Gaussian Variational Inference
+    (in preparation)
     """
 
     def __init__(self, mean, hamiltonian, n_sampels, constants=[],
@@ -106,7 +110,8 @@ class MetricGaussianKL(Energy):
     def _get_metric(self):
         if self._metric is None:
             lin = self._lin.with_want_metric()
-            mymap = map(lambda v: self._hamiltonian(lin+v).metric, self._samples)
+            mymap = map(lambda v: self._hamiltonian(lin+v).metric,
+                        self._samples)
             self._metric = utilities.my_sum(mymap)
             self._metric = self._metric.scale(1./len(self._samples))