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, 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 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_equality(self):
st = SingleTransform()
for i in range(5):
print "------next-------"
p = numpy.random.rand(6, 1)
new = st.inflate(p, True)
old = st.inflate_old(p)
print new
print old
print "diff: "
print old - new
self.assertAlmostEqual(numpy.linalg.norm(old - new), 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)
