def setUp(self):
self.mu1 = array([0, 0, 0])
self.sig = eye(3)
self.mu2 = array([5, 5, 5])
self.mus = array([self.mu1, self.mu2])
self.sigmas = array([self.sig, self.sig])
self.pis = array([[.5, .5], [.4, .6]])
self.cmap = {0: [0], 1: [1]}
self.modes = {0: self.mu1, 1: self.mu2}
self.clst1 = DPCluster(.5, self.mu1, self.sig)
self.clst2 = DPCluster(.5, self.mu2, self.sig)
self.clst3 = DPCluster(.4, self.mu1, self.sig)
self.clst4 = DPCluster(.6, self.mu2, self.sig)
self.clsts = [self.clst1, self.clst2, self.clst3, self.clst4]
self.mix = ModalHDPMixture(self.pis, self.mus, self.sigmas, self.cmap,
self.modes)
def setUp(self):
self.mu1 = array([0, 0, 0])
self.sig = eye(3)
self.mu2 = array([5, 5, 5])
self.mus = array([self.mu1, self.mu2])
self.sigmas = array([self.sig, self.sig])
self.pis = array([[.5, .5], [.4, .6]])
self.cmap = {0: [0], 1: [1]}
self.modes = {0: self.mu1, 1: self.mu2}
self.clst1 = DPCluster(.5, self.mu1, self.sig)
self.clst2 = DPCluster(.5, self.mu2, self.sig)
self.clst3 = DPCluster(.4, self.mu1, self.sig)
self.clst4 = DPCluster(.6, self.mu2, self.sig)
self.clsts = [self.clst1, self.clst2, self.clst3, self.clst4]
self.mix = ModalHDPMixture(self.pis, self.mus, self.sigmas, self.cmap, self.modes)
class ModalHDPClusterTestCase(unittest.TestCase):
def setUp(self):
self.mu1 = array([0, 0, 0])
self.sig = eye(3)
self.mu2 = array([5, 5, 5])
self.mus = array([self.mu1, self.mu2])
self.sigmas = array([self.sig, self.sig])
self.pis = array([[.5, .5], [.4, .6]])
self.cmap = {0: [0], 1: [1]}
self.modes = {0: self.mu1, 1: self.mu2}
self.clst1 = DPCluster(.5, self.mu1, self.sig)
self.clst2 = DPCluster(.5, self.mu2, self.sig)
self.clst3 = DPCluster(.4, self.mu1, self.sig)
self.clst4 = DPCluster(.6, self.mu2, self.sig)
self.clsts = [self.clst1, self.clst2, self.clst3, self.clst4]
self.mix = ModalHDPMixture(self.pis, self.mus, self.sigmas, self.cmap,
self.modes)
def tearDown(self):
pass
def testModes(self):
assert all(self.mix.modes[0] == self.mu1)
assert all(self.mix.modes[1] == self.mu2)
def testmixprob(self):
pnt = array([1, 1, 1])
r = self.mix.prob(pnt)
self.assertEqual(r.shape, (2, 2), 'prob return wrong shape')
r = r.flatten()
for i, j in enumerate(self.clsts):
self.assertEqual(j.prob(pnt), r[i], 'prob calculation failed')
def testgetitem(self):
r = self.mix[0]
self.assertIsInstance(r, ModalDPMixture,
'get item returned wrong type')
npt.assert_array_equal(r.modes[0], self.mix.modes[0],
'modes changed under getitem')
def testclassify(self):
pnt = array([self.mu1, self.mu2])
npt.assert_array_equal(
self.mix.classify(
array([
self.mu1, self.mu2, self.mu1, self.mu2, self.mu1, self.mu2
])), array([[0, 1, 0, 1, 0, 1], [0, 1, 0, 1, 0, 1]]),
'classify not working')
npt.assert_array_equal(
self.mix.classify(pnt)[0], array([0, 1]),
'classify classifys mu1 as belonging to something else')
npt.assert_array_equal(
self.mix.classify(pnt)[1], array([0, 1]),
'classify classifys m21 as belonging to something else')
def test_arith(self):
npt.assert_array_equal((self.mix + 2).mus, self.mix.mus + 2,
"Failed addition")
npt.assert_array_equal((self.mix * 2).mus, self.mix.mus * 2,
"Failed multiplication")
npt.assert_array_equal((self.mix * 2).sigmas, self.mix.sigmas * 4,
"failed multiplication")
npt.assert_equal((self.mix * 2).sigmas.shape, self.mix.sigmas.shape,
'multiplication failed')
npt.assert_array_equal((self.mix + 2).pis, self.mix.pis,
"addition changes pi values")
npt.assert_array_equal((self.mix * eye(3)).sigmas, self.mix.sigmas,
'multicplication failed')
class ModalHDp_clusterTestCase(unittest.TestCase):
def setUp(self):
self.mu1 = array([0, 0, 0])
self.sig = eye(3)
self.mu2 = array([5, 5, 5])
self.mus = array([self.mu1, self.mu2])
self.sigmas = array([self.sig, self.sig])
self.pis = array([[.5, .5], [.4, .6]])
self.cmap = {0: [0], 1: [1]}
self.modes = {0: self.mu1, 1: self.mu2}
self.clst1 = DPCluster(.5, self.mu1, self.sig)
self.clst2 = DPCluster(.5, self.mu2, self.sig)
self.clst3 = DPCluster(.4, self.mu1, self.sig)
self.clst4 = DPCluster(.6, self.mu2, self.sig)
self.clsts = [self.clst1, self.clst2, self.clst3, self.clst4]
self.mix = ModalHDPMixture(self.pis, self.mus, self.sigmas, self.cmap, self.modes)
def tearDown(self):
pass
def testModes(self):
assert all(self.mix.modes[0] == self.mu1)
assert all(self.mix.modes[1] == self.mu2)
def testmixprob(self):
pnt = array([1, 1, 1])
r = self.mix.prob(pnt)
self.assertEqual(r.shape, (2, 2), 'prob return wrong shape')
r = r.flatten()
for i, j in enumerate(self.clsts):
self.assertEqual(j.prob(pnt), r[i], 'prob calculation failed')
def testgetitem(self):
r = self.mix[0]
self.assertIsInstance(r, ModalDPMixture, 'get item returned wrong type')
numpy.testing.assert_array_equal(r.modes[0], self.mix.modes[0], 'modes changed under getitem')
def testclassify(self):
pnt = array([self.mu1, self.mu2])
numpy.testing.assert_array_equal(self.mix.classify(array([self.mu1, self.mu2, self.mu1, self.mu2, self.mu1, self.mu2])), array([[0, 1, 0, 1, 0, 1], [0, 1, 0, 1, 0, 1]]), 'classify not working')
numpy.testing.assert_array_equal(self.mix.classify(pnt)[0], array([0, 1]), 'classify classifys mu1 as belonging to something else')
numpy.testing.assert_array_equal(self.mix.classify(pnt)[1], array([0, 1]), 'classify classifys m21 as belonging to something else')
def test_arith(self):
numpy.testing.assert_array_equal((self.mix+2).mus, self.mix.mus+2,
"Failed addition")
numpy.testing.assert_array_equal((self.mix*2).mus, self.mix.mus*2,
"Failed multiplication")
numpy.testing.assert_array_equal((self.mix*2).sigmas, self.mix.sigmas*4,
"failed multiplication")
numpy.testing.assert_equal((self.mix*2).sigmas.shape, self.mix.sigmas.shape, 'multiplication failed')
numpy.testing.assert_array_equal((self.mix+2).pis, self.mix.pis,
"addition changes pi values")
numpy.testing.assert_array_equal((self.mix*eye(3)).sigmas, self.mix.sigmas, 'multicplication failed')
