Adding regression tests for binomial coefficient overflow.

Follow-up to #156.
dhermes · Jan 4, 2020 · c4e473a · c4e473a
1 parent ce54b48
commit c4e473a
Showing 1 changed file with 113 additions and 0 deletions.
diff --git a/tests/unit/test__curve_helpers.py b/tests/unit/test__curve_helpers.py
@@ -242,6 +242,119 @@ def test_quadratic(self):
         expected[1, :] = 2.0 * s_vals * (2.0 * s_vals - 1.0)
         self.assertEqual(result, expected)
 
+    def test_binomial_overflow_int32(self):
+        s_vals = np.asfortranarray([0.5])
+        degree = 30
+        nodes = np.eye(degree + 1, order="F")
+
+        expected = np.asfortranarray(
+            [
+                1.0,
+                30.0,
+                435.0,
+                4060.0,
+                27405.0,
+                142506.0,
+                593775.0,
+                2035800.0,
+                5852925.0,
+                14307150.0,
+                30045015.0,
+                54627300.0,
+                86493225.0,
+                119759850.0,
+                145422675.0,
+                155117520.0,
+                145422675.0,
+                119759850.0,
+                86493225.0,
+                54627300.0,
+                30045015.0,
+                14307150.0,
+                5852925.0,
+                2035800.0,
+                593775.0,
+                142506.0,
+                27405.0,
+                4060.0,
+                435.0,
+                30.0,
+                1.0,
+            ]
+        )
+        evaluated = self._call_function_under_test(nodes, s_vals)
+        binomial_coefficients = evaluated.flatten() * 2.0 ** degree
+        self.assertEqual(expected, binomial_coefficients)
+
+    def test_binomial_roundoff(self):
+        s_vals = np.asfortranarray([0.5])
+        degree = 55
+        nodes = np.eye(degree + 1, order="F")
+
+        expected = np.asfortranarray(
+            [
+                1.0,
+                55.0,
+                1485.0,
+                26235.0,
+                341055.0,
+                3478761.0,
+                28989675.0,
+                202927725.0,
+                1217566350.0,
+                6358402050.0,
+                29248649430.0,
+                119653565850.0,
+                438729741450.0,
+                1451182990950.0,
+                4353548972850.0,
+                11899700525790.0,
+                29749251314475.0,
+                68248282427325.0,
+                144079707346575.0,
+                280576272201225.0,
+                505037289962205.0,
+                841728816603675.0,
+                1300853625660225.0,
+                1866442158555975.0,
+                2488589544741300.0,
+                3085851035479212.0,
+                3560597348629859.5,
+                3824345300380219.5,
+                3824345300380219.5,
+                3560597348629859.5,
+                3085851035479211.5,
+                2488589544741299.5,
+                1866442158555974.5,
+                1300853625660224.8,
+                841728816603674.9,
+                505037289962204.94,
+                280576272201224.94,
+                144079707346574.97,
+                68248282427324.984,
+                29749251314474.992,
+                11899700525789.996,
+                4353548972849.9985,
+                1451182990949.9995,
+                438729741449.9998,
+                119653565849.99995,
+                29248649429.99999,
+                6358402049.999997,
+                1217566349.9999995,
+                202927724.9999999,
+                28989674.999999985,
+                3478760.999999998,
+                341054.9999999998,
+                26234.999999999985,
+                1484.999999999999,
+                54.999999999999964,
+                1.0,
+            ],
+        )
+        evaluated = self._call_function_under_test(nodes, s_vals)
+        binomial_coefficients = evaluated.flatten() * 2.0 ** degree
+        self.assertEqual(expected, binomial_coefficients)
+
 
 @utils.needs_speedup
 class Test_speedup_evaluate_multi(Test__evaluate_multi):