def test_histogram_density(self, xp, dtype):
x = testing.shaped_arange((10, ), xp, dtype)
y, bin_edges = xp.histogram(x, density=True)
# check normalization
area = xp.sum(y * xp.diff(bin_edges))
testing.assert_allclose(area, 1)
return y, bin_edges
def test_count_nonzero_int_axis(self, dtype):
for ax in range(3):
def func(xp):
m = testing.shaped_random((2, 3, 4), xp, xp.bool_)
a = testing.shaped_random((2, 3, 4), xp, dtype) * m
return xp.count_nonzero(a, axis=ax)
testing.assert_allclose(func(numpy), func(cupy))
def check_returned(self, a, axis, weights):
average_cpu, sum_weights_cpu = numpy.average(
a, axis, weights, returned=True)
result = cupy.average(
cupy.asarray(a), axis, weights, returned=True)
self.assertTrue(isinstance(result, tuple))
self.assertEqual(len(result), 2)
average_gpu, sum_weights_gpu = result
testing.assert_allclose(average_cpu, average_gpu)
testing.assert_allclose(sum_weights_cpu, sum_weights_gpu)
def test_count_nonzero_tuple_axis(self, dtype):
for ax in range(3):
for ay in range(3):
if ax == ay:
continue
def func(xp):
m = testing.shaped_random((2, 3, 4), xp, xp.bool_)
a = testing.shaped_random((2, 3, 4), xp, dtype) * m
return xp.count_nonzero(a, axis=(ax, ay))
testing.assert_allclose(func(numpy), func(cupy))
def test_histogram_range_with_weights_and_density(self, xp, dtype):
a = xp.arange(10, dtype=dtype) + .5
w = xp.arange(10, dtype=dtype) + .5
h, b = xp.histogram(a, range=[1, 9], weights=w, density=True)
testing.assert_allclose(float((h * xp.diff(b)).sum()), 1)
return h
def test_histogram_range_with_density(self, xp, dtype):
a = xp.arange(10, dtype=dtype) + .5
h, b = xp.histogram(a, range=[1, 9], density=True)
# check normalization
testing.assert_allclose(float((h * xp.diff(b)).sum()), 1)
return h