Fixes issue with singular matrices

ThatGeoGuy · ThatGeoGuy · commit 2226a65c75c0 · 2015-04-06T08:23:18.000-06:00
diff --git a/sklearn/covariance/robust_covariance.py b/sklearn/covariance/robust_covariance.py
@@ -118,7 +118,7 @@ def _c_step(X, n_support, random_state, remaining_iterations=30,
     # Iterative procedure for Minimum Covariance Determinant computation
     det = fast_logdet(covariance)
     previous_det = np.inf
-    while (det < previous_det) and (remaining_iterations > 0) and (det != -np.inf):
+    while (det < previous_det) and (remaining_iterations > 0) and not (np.isinf(det)):
         # save old estimates values
         previous_location = location
         previous_covariance = covariance
@@ -140,15 +140,9 @@ def _c_step(X, n_support, random_state, remaining_iterations=30,
 
     previous_dist = dist
     dist = (np.dot(X - location, precision) * (X - location)).sum(axis=1)
-    # Catch computation errors
+    # Check if best fit already found (det => 0, logdet => -inf)
     if np.isinf(det):
         results = location, covariance, det, support, dist
-        # raise ValueError(
-        #     "Singular covariance matrix. "
-        #     "Please check that the covariance matrix corresponding "
-        #     "to the dataset is full rank and that MinCovDet is used with "
-        #     "Gaussian-distributed data (or at least data drawn from a "
-        #     "unimodal, symmetric distribution.")
     # Check convergence
     if np.allclose(det, previous_det):
         # c_step procedure converged
