def test_variance06():
labels = cp.asarray([2, 2, 3, 3, 4])
olderr = np.seterr(all="ignore")
try:
for type in types:
input = cp.asarray([1, 3, 8, 10, 8], type)
output = ndimage.variance(input, labels, cp.asarray([2, 3, 4]))
assert_array_almost_equal(output, [1.0, 1.0, 0.0])
finally:
np.seterr(**olderr)
def test_variance01():
olderr = np.seterr(all="ignore")
try:
for type in types:
input = cp.asarray([], type)
with suppress_warnings() as sup:
sup.filter(RuntimeWarning, "Mean of empty slice")
output = ndimage.variance(input)
assert_(cp.isnan(output))
finally:
np.seterr(**olderr)
def test_stat_funcs_2d():
a = cp.asarray([[5, 6, 0, 0, 0], [8, 9, 0, 0, 0], [0, 0, 0, 3, 5]])
lbl = cp.asarray([[1, 1, 0, 0, 0], [1, 1, 0, 0, 0], [0, 0, 0, 2, 2]])
index = cp.asarray([1, 2])
mean = ndimage.mean(a, labels=lbl, index=index)
assert_array_equal(mean, [7.0, 4.0])
var = ndimage.variance(a, labels=lbl, index=index)
assert_array_equal(var, [2.5, 1.0])
std = ndimage.standard_deviation(a, labels=lbl, index=index)
assert_array_almost_equal(std, cp.sqrt(cp.asarray([2.5, 1.0])))
med = ndimage.median(a, labels=lbl, index=index)
assert_array_equal(med, [7.0, 4.0])
min = ndimage.minimum(a, labels=lbl, index=index)
assert_array_equal(min, [5, 3])
max = ndimage.maximum(a, labels=lbl, index=index)
assert_array_equal(max, [9, 5])
def test_variance05():
labels = cp.asarray([2, 2, 3])
for type in types:
input = cp.asarray([1, 3, 8], type)
output = ndimage.variance(input, labels, 2)
assert_almost_equal(output, 1.0)
def test_variance04():
input = cp.asarray([1, 0], bool)
output = ndimage.variance(input)
assert_almost_equal(output, 0.25)
def test_variance03():
for type in types:
input = cp.asarray([1, 3], type)
output = ndimage.variance(input)
assert_almost_equal(output, 1.0)