seckcoder
diff --git a/‎sklearn/covariance/robust_covariance.py
Lines changed: 26 additions & 16 deletions b/‎sklearn/covariance/robust_covariance.py
Lines changed: 26 additions & 16 deletions
diff --git a/‎sklearn/covariance/tests/test_robust_covariance.py
Lines changed: 9 additions & 0 deletions b/‎sklearn/covariance/tests/test_robust_covariance.py
Lines changed: 9 additions & 0 deletions
@@ -331,22 +331,32 @@ def fast_mcd(X, support_fraction=None,
     # (Rousseeuw, P. J. and Leroy, A. M. (2005) References, in Robust
     #  Regression and Outlier Detection, John Wiley & Sons, chapter 4)
     if n_features == 1:
-        # find the sample shortest halves
-        X_sorted = np.sort(np.ravel(X))
-        diff = X_sorted[n_support:] - X_sorted[:(n_samples - n_support)]
-        halves_start = np.where(diff == np.min(diff))[0]
-        # take the middle points' mean to get the robust location estimate
-        location = 0.5 * (X_sorted[n_support + halves_start]
-                          + X_sorted[halves_start]).mean()
-        support = np.zeros(n_samples).astype(bool)
-        X_centered = X - location
-        support[np.argsort(np.abs(X - location), axis=0)[:n_support]] = True
-        covariance = np.asarray([[np.var(X[support])]])
-        location = np.array([location])
-        # get precision matrix in an optimized way
-        precision = pinvh(covariance)
-        dist = (np.dot(X_centered, precision) \
-                    * (X_centered)).sum(axis=1)
+        if n_support < n_samples:
+            # find the sample shortest halves
+            X_sorted = np.sort(np.ravel(X))
+            diff = X_sorted[n_support:] - X_sorted[:(n_samples - n_support)]
+            halves_start = np.where(diff == np.min(diff))[0]
+            # take the middle points' mean to get the robust location estimate
+            location = 0.5 * (X_sorted[n_support + halves_start]
+                              + X_sorted[halves_start]).mean()
+            support = np.zeros(n_samples, dtype=bool)
+            X_centered = X - location
+            support[np.argsort(np.abs(X - location), 0)[:n_support]] = True
+            covariance = np.asarray([[np.var(X[support])]])
+            location = np.array([location])
+            # get precision matrix in an optimized way
+            precision = pinvh(covariance)
+            dist = (np.dot(X_centered, precision) \
+                        * (X_centered)).sum(axis=1)
+        else:
+            support = np.ones(n_samples, dtype=bool)
+            covariance = np.asarray([[np.var(X)]])
+            location = np.asarray([np.mean(X)])
+            X_centered = X - location
+            # get precision matrix in an optimized way
+            precision = pinvh(covariance)
+            dist = (np.dot(X_centered, precision) \
+                        * (X_centered)).sum(axis=1)
 
     ### Starting FastMCD algorithm for p-dimensional case
     if (n_samples > 500) and (n_features > 1):
 
@@ -68,6 +68,15 @@ def launch_mcd_on_dataset(
     assert_array_almost_equal(mcd_fit.mahalanobis(data), mcd_fit.dist_)
 
 
+def test_mcd_issue1127():
+    # Check that the code does not break with X.shape = (3, 1)
+    # (i.e. n_support = n_samples)
+    rnd = np.random.RandomState(0)
+    X = rnd.normal(size=(3, 1))
+    mcd = MinCovDet()
+    mcd.fit(X)
+
+
 def test_outlier_detection():
     rnd = np.random.RandomState(0)
     X = rnd.randn(100, 10)