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)