BUG: Fix randint when range is 2**32

bashtage · charris · commit d9d620c7eeed · 2019-09-15T07:57:03.000-06:00
Fix randint to use 32-bit path when range is exactly 2**32 closes #14189
diff --git a/doc/release/upcoming_changes/14501.improvement.rst b/doc/release/upcoming_changes/14501.improvement.rst
@@ -0,0 +1,6 @@
+`numpy.random.randint` produced incorrect value when the range was ``2**32``
+----------------------------------------------------------------------------
+The implementation introduced in 1.17.0 had an incorrect check when
+determining whether to use the 32-bit path or the full 64-bit
+path that incorrectly redirected random integer generation with a high - low
+range of ``2**32`` to the 64-bit generator.
diff --git a/numpy/random/src/distributions/distributions.c b/numpy/random/src/distributions/distributions.c
@@ -1478,7 +1478,7 @@ uint64_t random_bounded_uint64(bitgen_t *bitgen_state, uint64_t off,
                                uint64_t rng, uint64_t mask, bool use_masked) {
   if (rng == 0) {
     return off;
-  } else if (rng < 0xFFFFFFFFUL) {
+  } else if (rng <= 0xFFFFFFFFUL) {
     /* Call 32-bit generator if range in 32-bit. */
     if (use_masked) {
       return off + buffered_bounded_masked_uint32(bitgen_state, rng, mask, NULL,
@@ -1592,7 +1592,7 @@ void random_bounded_uint64_fill(bitgen_t *bitgen_state, uint64_t off,
     for (i = 0; i < cnt; i++) {
       out[i] = off;
     }
-  } else if (rng < 0xFFFFFFFFUL) {
+  } else if (rng <= 0xFFFFFFFFUL) {
     uint32_t buf = 0;
     int bcnt = 0;
 
diff --git a/numpy/random/tests/test_randomstate_regression.py b/numpy/random/tests/test_randomstate_regression.py
@@ -181,3 +181,14 @@ def test_choice_retun_dtype(self):
         assert c.dtype == np.dtype(int)
         c = np.random.choice(10, replace=False, size=2)
         assert c.dtype == np.dtype(int)
+
+    @pytest.mark.skipif(np.iinfo('l').max < 2**32,
+                        reason='Cannot test with 32-bit C long')
+    def test_randint_117(self):
+        # GH 14189
+        random.seed(0)
+        expected = np.array([2357136044, 2546248239, 3071714933, 3626093760,
+                             2588848963, 3684848379, 2340255427, 3638918503,
+                             1819583497, 2678185683], dtype='int64')
+        actual = random.randint(2**32, size=10)
+        assert_array_equal(actual, expected)
