def test_deflate(self):
st = SingleTransform([0, 0, 0, 0, 1, 0])
for i in range(5):
print "next test_deflate"
p = numpy.random.rand(6, 1)
st.inflate(p)
result = st.deflate()
print result
print p
self.assertAlmostEqual(numpy.linalg.norm(p - result), 0.0, 6)
def generate_transformation_dict(transformation_list, listener):
transformation_dict = {}
for i in range(len(transformation_list)):
if transformation_list[i] != '**' and transformation_list[i] != '':
try:
if transformation_list[i + 1] in ['', '**']:
continue
except IndexError:
pass
else:
transform = get_single_transform(transformation_list[i], transformation_list[i + 1], listener)
print transform
t = SingleTransform()
t.inflate_rpy(reshape(transform,(6,1)))
transform = reshape(t.deflate(),(1,6)).tolist()[0]
print "new format:", transform
transformation_dict[transformation_list[i + 1]] = transform
return transformation_dict
def test_deflate(self):
st = SingleTransform([0, 0, 0, 0, 0, 0])
p = reshape(matrix([1, 0, 0, 0, 0, 0], float), (-1, 1))
st.inflate(p)
result = st.deflate()
self.assertAlmostEqual(numpy.linalg.norm(p - result), 0.0, 6)
def test_deflate(self):
st = SingleTransform([0, 0, 0, 0, 0, 0])
p = reshape( matrix([1, 0, 0, 0, 0, 0], float), (-1,1) )
st.inflate(p)
result = st.deflate()
self.assertAlmostEqual(numpy.linalg.norm(p-result), 0.0, 6)
