def test_inflate(self):
st = SingleTransform([0, 0, 0, 0, 0, 0])
st.inflate(reshape(matrix([1, 0, 0, 0, 0, 0], float), (-1, 1)))
expected = numpy.matrix(
[[1, 0, 0, 1], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], float)
print ""
print st.transform
self.assertAlmostEqual(numpy.linalg.norm(st.transform - expected), 0.0,
6)
def test_inflate(self):
st = SingleTransform([0, 0, 0, 0, 0, 0])
st.inflate( reshape( matrix([1, 0, 0, 0, 0, 0], float), (-1,1) ))
expected = numpy.matrix( [[ 1, 0, 0, 1],
[ 0, 1, 0, 0],
[ 0, 0, 1, 0],
[ 0, 0, 0, 1]], float )
print ""
print st.transform
self.assertAlmostEqual(numpy.linalg.norm(st.transform-expected), 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)
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)