FIX: Cast floats to int before slicing in robust_covariance

Fixes scikit-learn#1608.

Author: erg

sklearn/covariance/robust_covariance.py

@@ -365,7 +365,8 @@ def fast_mcd(X, support_fraction=None,
         n_subsets = n_samples // 300
         n_samples_subsets = n_samples // n_subsets
         samples_shuffle = random_state.permutation(n_samples)
-        h_subset = np.ceil(n_samples_subsets * (n_support / float(n_samples)))
+        h_subset = int(np.ceil(n_samples_subsets *
+                       (n_support / float(n_samples))))
         # b. perform a total of 500 trials
         n_trials_tot = 500
         # c. select 10 best (location, covariance) for each subset
@@ -398,7 +399,8 @@ def fast_mcd(X, support_fraction=None,
         ## 2. Pool the candidate supports into a merged set
         ##    (possibly the full dataset)
         n_samples_merged = min(1500, n_samples)
-        h_merged = np.ceil(n_samples_merged * (n_support / float(n_samples)))
+        h_merged = int(np.ceil(n_samples_merged *
+                       (n_support / float(n_samples))))
         if n_samples > 1500:
             n_best_merged = 10
         else: