def test_density(self):
# Check that the integral of the density equals 1.
n = 100
v = dpnp.random.rand(n)
a, b = dpnp.histogram(v, density=True)
area = dpnp.sum(a * dpnp.diff(b)[0])[0]
numpy.testing.assert_almost_equal(area, 1)
# Check with non-constant bin widths
v = dpnp.arange(10)
bins = [0, 1, 3, 6, 10]
a, b = dpnp.histogram(v, bins, density=True)
numpy.testing.assert_array_equal(a, .1)
numpy.testing.assert_equal(dpnp.sum(a * dpnp.diff(b))[0], 1)
# Test that passing False works too
a, b = dpnp.histogram(v, bins, density=False)
numpy.testing.assert_array_equal(a, [1, 2, 3, 4])
# Variable bin widths are especially useful to deal with
# infinities.
v = dpnp.arange(10)
bins = [0, 1, 3, 6, numpy.inf]
a, b = dpnp.histogram(v, bins, density=True)
numpy.testing.assert_array_equal(a, [.1, .1, .1, 0.])
# Taken from a bug report from N. Becker on the numpy-discussion
# mailing list Aug. 6, 2010.
counts, dmy = dpnp.histogram([1, 2, 3, 4], [0.5, 1.5, numpy.inf],
density=True)
numpy.testing.assert_equal(counts, [.25, 0])
def test_sum_axis():
a = numpy.array([[[-2., 3.], [9.1, 0.2]], [[-2., 5.0], [-2, -1.2]], [[1.0, -2.], [5.0, -1.1]]])
ia = dpnp.array(a)
result = dpnp.sum(ia, axis=1)
expected = numpy.sum(a, axis=1)
numpy.testing.assert_array_equal(expected, result)
def test_sum_int():
a = numpy.array([1, 0, 2, -3, -1, 2, 21, -9])
ia = dpnp.array(a)
result = dpnp.sum(ia)
expected = numpy.sum(a)
numpy.testing.assert_array_equal(expected, result)
def test_sum_float64():
a = numpy.array([[[-2., 3.], [9.1, 0.2]], [[-2., 5.0], [-2, -1.2]], [[1.0, -2.], [5.0, -1.1]]])
ia = dpnp.array(a)
for axis in range(len(a)):
result = dpnp.sum(ia, axis=axis)
expected = numpy.sum(a, axis=axis)
numpy.testing.assert_array_equal(expected, result)
def test_simple(self):
n = 100
v = dpnp.random.rand(n)
(a, b) = dpnp.histogram(v)
# check if the sum of the bins equals the number of samples
numpy.testing.assert_equal(dpnp.sum(a, axis=0), n)
# check that the bin counts are evenly spaced when the data is from
# a linear function
(a, b) = dpnp.histogram(numpy.linspace(0, 10, 100))
numpy.testing.assert_array_equal(a, 10)
def sum(self,
axis=None,
dtype=None,
out=None,
keepdims=False,
initial=0,
where=True):
"""
Returns the sum along a given axis.
.. seealso::
:obj:`dpnp.sum` for full documentation,
:meth:`dpnp.dparray.sum`
"""
return dpnp.sum(self, axis, dtype, out, keepdims, initial, where)