def test_lo_hi_equal(self):
with self.assertRaises(ValueError):
random.randint(3, 3, size=0)
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(mx).get() for _ in six.moves.xrange(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(self):
mx = 5
trial = 100
vals = [random.randint(mx).get() for _ in six.moves.xrange(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).get()
assert low <= min(x)
assert max(x) <= high
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)).get() for _ in range(10)]
for val in vals:
assert val.shape == (2, 3)
assert min(_.min() for _ in vals) == int(low)
assert 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_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).get()
assert x.dtype == dtype
assert iinfo.min <= min(x)
assert 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_zero_sizes(self):
a = random.randint(10, size=(0,))
testing.assert_array_equal(a, cupy.array(()))
a = random.randint(10, size=0)
testing.assert_array_equal(a, cupy.array(()))
def test_high_and_size_are_none(self):
random.randint(3)
self.m.interval.assert_called_with(2, None)
def test_value_error(self):
with self.assertRaises(ValueError):
random.randint(100, 1)
def test_high_and_size_are_none(self):
random.randint(3)
self.m.interval.assert_called_with(2, None)
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)
def test_bound_float2(self):
vals = [random.randint(-1.0, 1.0, (2, 3)).get() for _ in range(10)]
for val in vals:
assert val.shape == (2, 3)
assert min(_.min() for _ in vals) == -1
assert 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).get()
assert val.shape == (20,)
assert val.min() >= -100
assert val.max() < 100
def test_no_none(self):
random.randint(3, 5, (1, 2, 3))
self.m.interval.assert_called_with(1, (1, 2, 3))
def test_upper_bound(self):
val = random.randint(0, 2).get()
self.assertEqual(1, val)
def test_within_interval(self):
val = random.randint(0, 10, (2, 3)).get()
numpy.testing.assert_array_less(
numpy.full((2, 3), -1, dtype=numpy.int64), val)
numpy.testing.assert_array_less(
val, numpy.full((2, 3), 10, dtype=numpy.int64))
def test_no_none(self):
random.randint(3, 5, (1, 2, 3))
self.m.interval.assert_called_with(1, (1, 2, 3))
def test_high_is_none(self):
random.randint(3, None, (1, 2, 3))
self.m.interval.assert_called_with(2, (1, 2, 3))
def test_size_is_none(self):
random.randint(3, 5)
self.m.interval.assert_called_with(1, None)
def test_bound_1(self):
vals = [random.randint(0, 10, (2, 3)).get() for _ in range(20)]
for val in vals:
assert val.shape == (2, 3)
assert min(_.min() for _ in vals) == 0
assert max(_.max() for _ in vals) == 9
def test_bound_2(self):
vals = [random.randint(0, 2).get() for _ in range(20)]
for val in vals:
assert val.shape == ()
assert min(vals) == 0
assert max(vals) == 1
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]
expected = numpy.array([float(vals.size) / mx] * mx)
self.assertTrue(hypothesis.chi_square_test(counts, expected))
def test_size_is_none(self):
random.randint(3, 5)
self.m.interval.assert_called_with(1, None)
def test_bound_overflow(self):
# 100 - (-100) exceeds the range of int8
val = random.randint(numpy.int8(-100), numpy.int8(100), size=20).get()
self.assertEqual(val.shape, (20,))
self.assertGreaterEqual(val.min(), -100)
self.assertLess(val.max(), 100)
def test_lo_hi_reversed(self):
with self.assertRaises(ValueError):
random.randint(100, 1)
def test_upper_bound(self):
val = random.randint(0, 2).get()
self.assertEqual(1, val)
def test_bound_2(self):
vals = [random.randint(0, 2).get() for _ in range(20)]
for val in vals:
self.assertEqual(val.shape, ())
self.assertEqual(min(vals), 0)
self.assertEqual(max(vals), 1)
def test_high_is_none(self):
random.randint(3, None, (1, 2, 3))
self.m.interval.assert_called_with(2, (1, 2, 3))
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]
expected = numpy.array([float(vals.size) / mx] * mx)
self.assertTrue(hypothesis.chi_square_test(counts, expected))
def test_lo_hi_nonrandom(self):
a = random.randint(-0.9, 1.1, size=3)
testing.assert_array_equal(a, cupy.full((3,), 0))
a = random.randint(-1.1, -0.9, size=(2, 2))
testing.assert_array_equal(a, cupy.full((2, 2), -1))
def test_within_interval(self):
val = random.randint(0, 10, (2, 3)).get()
numpy.testing.assert_array_less(
numpy.full((2, 3), -1, dtype=numpy.int64), val)
numpy.testing.assert_array_less(
val, numpy.full((2, 3), 10, dtype=numpy.int64))
def test_value_error(self):
with self.assertRaises(ValueError):
random.randint(100, 1)
