def test_lo_hi_equal(self):
with self.assertRaises(ValueError):
random.randint(3, 3, size=3)
with self.assertRaises(ValueError):
# int(-0.2) is not less than int(0.3)
random.randint(-0.2, 0.3)
def test_goodness_of_fit(self):
mx = 5
trial = 100
vals = [random.randint(0, mx).get() for _ in range(trial)]
counts = numpy.histogram(vals, bins=numpy.arange(mx + 1))[0]
expected = numpy.array([float(trial) / mx] * mx)
self.assertTrue(hypothesis.chi_square_test(counts, expected))
def test_dtype(self, dtype):
size = (1000,)
low = numpy.iinfo(dtype).min
high = numpy.iinfo(dtype).max + 1
x = random.randint(low, high, size, dtype)
self.assertLessEqual(low, min(x))
self.assertLessEqual(max(x), high)
def test_goodness_of_fit_2(self):
mx = 5
# vals = random.randint(mx, size=(5, 20)).get()
# counts = numpy.histogram(vals, bins=numpy.arange(mx + 1))[0]
vals = random.randint(mx, size=(5, 20)).reshape(5 * 20)
counts = numpy.histogram(numpy.asarray(vals), bins=numpy.arange(mx + 1))[0]
expected = numpy.array([float(vals.size) / mx] * mx)
self.assertTrue(hypothesis.chi_square_test(counts, expected))
def test_bound_float1(self):
# generate floats s.t. int(low) < int(high)
low, high = sorted(numpy.random.uniform(-5, 5, size=2))
low -= 1
high += 1
vals = [random.randint(low, high, (2, 3)) for _ in range(10)]
for val in vals:
self.assertEqual(val.shape, (2, 3))
self.assertEqual(min(_.min() for _ in vals), int(low))
self.assertEqual(max(_.max() for _ in vals), int(high) - 1)
def test_dtype2(self, dtype):
dtype = numpy.dtype(dtype)
# randint does not support 64 bit integers
# if dtype in (numpy.int64, numpy.uint64):
# return
iinfo = numpy.iinfo(dtype)
size = (10000,)
x = random.randint(iinfo.min, iinfo.max + 1, size, dtype)
self.assertEqual(x.dtype, dtype)
self.assertLessEqual(iinfo.min, min(x))
self.assertLessEqual(max(x), iinfo.max)
# Lower bound check
with self.assertRaises(ValueError):
random.randint(iinfo.min - 1, iinfo.min + 10, size, dtype)
# Upper bound check
with self.assertRaises(ValueError):
random.randint(iinfo.max - 10, iinfo.max + 2, size, dtype)
def test_bound_float2(self):
vals = [random.randint(-1.0, 1.0, (2, 3)) for _ in range(10)]
for val in vals:
self.assertEqual(val.shape, (2, 3))
self.assertEqual(min(_.min() for _ in vals), -1)
self.assertEqual(max(_.max() for _ in vals), 0)
def test_bound_overflow(self):
# 100 - (-100) exceeds the range of int8
val = random.randint(numpy.int8(-100), numpy.int8(100), size=20)
self.assertEqual(val.shape, (20,))
self.assertGreaterEqual(val.min(), -100)
self.assertLess(val.max(), 100)
def test_bound_2(self):
vals = [random.randint(0, 2) for _ in range(20)]
for val in vals:
self.assertEqual(val.shape, ())
self.assertEqual(min(_.min() for _ in vals), 0)
self.assertEqual(max(_.max() for _ in vals), 1)
def test_zero_sizes(self):
a = random.randint(10, size=(0,))
numpy.testing.assert_array_equal(a, cupy.array(()))
a = random.randint(10, size=0)
numpy.testing.assert_array_equal(a, cupy.array(()))
def test_lo_hi_nonrandom(self):
a = random.randint(-0.9, 1.1, size=3)
numpy.testing.assert_array_equal(a, cupy.full((3,), 0))
a = random.randint(-1.1, -0.9, size=(2, 2))
numpy.testing.assert_array_equal(a, cupy.full((2, 2), -1))
def test_lo_hi_reversed(self):
with self.assertRaises(ValueError):
random.randint(100, 1)
def test_bound_1(self):
vals = [random.randint(0, 10, (2, 3)).get() for _ in range(10)]
for val in vals:
self.assertEqual(val.shape, (2, 3))
self.assertEqual(min(_.min() for _ in vals), 0)
self.assertEqual(max(_.max() for _ in vals), 9)
