def test_mean(self):
from test_utils import elementwiseMean
arys, shape, size = _generateTestArrays(2, 'uint8')
imageData = ImagesLoader(self.sc).fromArrays(arys)
meanVal = imageData.mean()
expected = elementwiseMean(arys).astype('float16')
assert_true(allclose(expected, meanVal))
assert_equals('float64', str(meanVal.dtype))
def test_mean(self):
from test_utils import elementwiseMean
arys, shape, size = _generateTestArrays(2, 'uint8')
imageData = ImagesLoader(self.sc).fromArrays(arys)
meanVal = imageData.mean()
expected = elementwiseMean(arys).astype('float16')
assert_true(allclose(expected, meanVal))
assert_equals('float64', str(meanVal.dtype))
def test_mean(self):
from numpy import mean
arys, shape, size = _generate_test_arrays(2, 'uint8')
imagedata = ImagesLoader(self.sc).fromArrays(arys)
meanval = imagedata.mean()
def elementwise_mean(arys):
# surprising that numpy doesn't have this built in?
combined = vstack([ary.ravel() for ary in arys])
meanary = mean(combined, axis=0)
return meanary.reshape(arys[0].shape)
expected = elementwise_mean(arys).astype('float16')
assert_true(allclose(expected, meanval))
assert_equals('float16', str(meanval.dtype))
