Minor k_means_.py cleanup.

Fixed two PEP8 warnings about breaking line before binary operator by simply
putting the binary operator on the line before.

Fixed one PEP8 warning about line being too long (extending 79 symbols limit).

Removed `best_inertia = np.infty` line, because `best_inertia` is initialized
again few lines after and never used in between.

Author: kirillbobyrev

sklearn/cluster/k_means_.py

@@ -279,7 +279,6 @@ def k_means(X, n_clusters, init='k-means++', precompute_distances='auto',
         raise ValueError('Number of iterations should be a positive number,'
                          ' got %d instead' % max_iter)
 
-    best_inertia = np.infty
     X = as_float_array(X, copy=copy_x)
     tol = _tolerance(X, tol)
 
@@ -883,7 +882,8 @@ def fit(self, X, y=None):
         self.cluster_centers_, self.labels_, self.inertia_, self.n_iter_ = \
             k_means(
                 X, n_clusters=self.n_clusters, init=self.init,
-                n_init=self.n_init, max_iter=self.max_iter, verbose=self.verbose,
+                n_init=self.n_init, max_iter=self.max_iter,
+                verbose=self.verbose,
                 precompute_distances=self.precompute_distances,
                 tol=self.tol, random_state=random_state, copy_x=self.copy_x,
                 n_jobs=self.n_jobs, algorithm=self.algorithm,
@@ -1165,8 +1165,8 @@ def _mini_batch_convergence(model, iteration_idx, n_iter, tol,
     else:
         no_improvement += 1
 
-    if (model.max_no_improvement is not None
-            and no_improvement >= model.max_no_improvement):
+    if (model.max_no_improvement is not None and
+            no_improvement >= model.max_no_improvement):
         if verbose:
             print('Converged (lack of improvement in inertia)'
                   ' at iteration %d/%d'
@@ -1487,8 +1487,8 @@ def partial_fit(self, X, y=None):
         x_squared_norms = row_norms(X, squared=True)
         self.random_state_ = getattr(self, "random_state_",
                                      check_random_state(self.random_state))
-        if (not hasattr(self, 'counts_')
-                or not hasattr(self, 'cluster_centers_')):
+        if (not hasattr(self, 'counts_') or
+                not hasattr(self, 'cluster_centers_')):
             # this is the first call partial_fit on this object:
             # initialize the cluster centers
             self.cluster_centers_ = _init_centroids(