def setUp(self):
mus = np.array([[0, 0], [0, 10], [10, 0], [10, 10]])
sigmas = np.array([np.eye(2), np.eye(2), np.eye(2), np.eye(2)])
pis = np.array([1.0 / 3, 1.0 / 3, 1.0 / 3, 1.0 / 3])
clusters = [DPCluster(i, j, k) for i, j, k in zip(pis, mus, sigmas)]
self.mx = DPMixture(clusters[0:]).get_submodel([0, 1, 2, 3])
self.my = DPMixture(clusters[1:])
def testBiasSubsample(self):
neg_mu = array([0, 1, 2])
pos_mu = array([3, 4, 5])
sig = array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
neg_cluster = DPCluster(1.0, neg_mu, sig)
neg_mix = DPMixture([neg_cluster])
pos_cluster0 = DPCluster(0.5, neg_mu, sig)
pos_cluster1 = DPCluster(0.5, pos_mu, sig)
pos_mix = DPMixture([pos_cluster0, pos_cluster1])
self.fcm.subsample(1, 'bias', pos=pos_mix, neg=neg_mix)
self.assertEqual(1, self.fcm.shape[0], 'bias subsampling failed')
self.assertTrue(self.fcm[0] in self.pnts,
'bias subsample geneterated non-existant point')
def setUp(self):
self.mux = np.array([0, 0, 0])
self.sigx = 2 * np.eye(3)
self.muy = np.array([0, .5, .5])
self.sigy = np.eye(3)
self.clustx = DPCluster(1, self.mux, self.sigx)
self.clustersx = [self.clustx]
self.clusty = DPCluster(1, self.muy, self.sigy)
self.clustersy = [self.clusty]
self.x = DPMixture(self.clustersx, niter=1, identified=True)
self.y = DPMixture(self.clustersy, niter=1, identified=True)
def test_last(self):
cluster1 = DPCluster(0.5, self.mu1, self.sig)
cluster3 = DPCluster(0.5, self.mu1 + 3, self.sig)
cluster5 = DPCluster(0.5, self.mu1 + 5, self.sig)
cluster7 = DPCluster(0.5, self.mu1 + 7, self.sig)
cluster2 = DPCluster(0.5, self.mu2 + 2, self.sig)
cluster4 = DPCluster(0.5, self.mu2 + 4, self.sig)
cluster6 = DPCluster(0.5, self.mu2 + 6, self.sig)
cluster8 = DPCluster(0.5, self.mu2 + 8, self.sig)
mix = DPMixture([
cluster1, cluster2, cluster3, cluster4, cluster5, cluster6,
cluster7, cluster8
],
niter=4)
new_r = mix.last()
assert len(new_r.clusters) == 2
assert all(new_r.clusters[0].mu == cluster7.mu)
assert all(new_r.clusters[1].mu == cluster8.mu)
new_r = mix.last(2)
assert len(new_r.clusters) == 4
assert all(new_r.clusters[0].mu == cluster5.mu)
assert all(new_r.clusters[1].mu == cluster6.mu)
assert all(new_r.clusters[2].mu == cluster7.mu)
assert all(new_r.clusters[3].mu == cluster8.mu)
def testLast(self):
clst1 = DPCluster(0.5, self.mu1, self.sig)
clst3 = DPCluster(0.5, self.mu1 + 3, self.sig)
clst5 = DPCluster(0.5, self.mu1 + 5, self.sig)
clst7 = DPCluster(0.5, self.mu1 + 7, self.sig)
clst2 = DPCluster(0.5, self.mu2 + 2, self.sig)
clst4 = DPCluster(0.5, self.mu2 + 4, self.sig)
clst6 = DPCluster(0.5, self.mu2 + 6, self.sig)
clst8 = DPCluster(0.5, self.mu2 + 8, self.sig)
mix = DPMixture(
[clst1, clst2, clst3, clst4, clst5, clst6, clst7, clst8], niter=4)
new_r = mix.last()
assert len(new_r.clusters) == 2
assert all(new_r.clusters[0].mu == clst7.mu)
assert all(new_r.clusters[1].mu == clst8.mu)
new_r = mix.last(2)
assert len(new_r.clusters) == 4
assert all(new_r.clusters[0].mu == clst5.mu)
assert all(new_r.clusters[1].mu == clst6.mu)
assert all(new_r.clusters[2].mu == clst7.mu)
assert all(new_r.clusters[3].mu == clst8.mu)
try:
new_r = mix.last(10)
except ValueError:
pass
def setUp(self):
self.mu1 = array([0, 0, 0])
self.sig = eye(3)
self.mu2 = array([5, 5, 5])
self.clst1 = DPCluster(.5, self.mu1, self.sig)
self.clst2 = DPCluster(.5, self.mu2, self.sig)
self.mix = DPMixture([self.clst1, self.clst2])
def testAnomalySubsample(self):
mu = array([0, 1, 2])
sig = array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
cluster = DPCluster(1.0, mu, sig)
mix = DPMixture([cluster])
self.fcm.subsample(1, 'anomaly', mix)
self.assertEqual(1, self.fcm.shape[0], 'anomaly subsampling failed')
self.assertTrue(self.fcm[0] in self.pnts,
'anomaly subsample geneterated non-existant point')
def setUp(self):
self.mu1 = array([0, 0, 0])
self.sig = eye(3)
self.mu2 = array([5, 5, 5])
self.cluster1 = DPCluster(.5 / 3, self.mu1, self.sig)
self.cluster2 = DPCluster(.5 / 3, self.mu2, self.sig)
self.clusters = [
self.cluster1, self.cluster2, self.cluster1, self.cluster2,
self.cluster1, self.cluster2
]
self.mix = DPMixture(self.clusters, niter=3, identified=True)
def testClassify(self):
from fcm.statistics import DPCluster, DPMixture
mu1 = array([0, 0, 0])
sig = eye(3)
mu2 = array([5, 5, 5])
clst1 = DPCluster(.5, mu1, sig)
clst2 = DPCluster(.5, mu2, sig)
mix = DPMixture([clst1, clst2])
cls = self.fcms.classify(mix)
assert_array_equal(cls['test_fcm1'], array([0, 1]), 'Calssify failed')
assert_array_equal(cls['test_fcm2'], array([0, 1]), 'Calssify failed')
def setUp(self):
self.mu1 = np.array([0, 0, 0])
self.sig = 2 * np.eye(3)
self.mu2 = np.array([5, 5, 5])
self.clust1 = DPCluster(.5, self.mu1, self.sig)
self.clust2 = DPCluster(.5, self.mu2, self.sig)
self.clusters = [self.clust1, self.clust2]
self.x = DPMixture(self.clusters, niter=1, identified=True)
self.diag = DiagonalAlignData(self.x, size=100000)
self.comp = CompAlignData(self.x, size=100000)
self.full = FullAlignData(self.x, size=200000)
def testAverage(self):
clst1 = DPCluster(0.5, self.mu1, self.sig)
clst3 = DPCluster(0.5, self.mu1, self.sig)
clst5 = DPCluster(0.5, self.mu1, self.sig)
clst7 = DPCluster(0.5, self.mu1, self.sig)
clst2 = DPCluster(0.5, self.mu2, self.sig)
clst4 = DPCluster(0.5, self.mu2, self.sig)
clst6 = DPCluster(0.5, self.mu2, self.sig)
clst8 = DPCluster(0.5, self.mu2, self.sig)
mix = DPMixture(
[clst1, clst2, clst3, clst4, clst5, clst6, clst7, clst8], niter=4)
avg = mix.average()
assert len(avg.clusters) == 2
assert all(avg.mus[0] == self.mu1)
assert all(avg.mus[1] == self.mu2)
assert all(avg.sigmas[0] == self.sig)
assert all(avg.sigmas[1] == self.sig)
assert avg.pis[0] == 0.5, 'pis should be 0.5 but is %f' % avg.pis()[0]
assert avg.pis[1] == 0.5, 'pis should be 0.5 but is %f' % avg.pis()[0]
def test_average(self):
cluster1 = DPCluster(0.5, self.mu1, self.sig)
cluster3 = DPCluster(0.5, self.mu1, self.sig)
cluster5 = DPCluster(0.5, self.mu1, self.sig)
cluster7 = DPCluster(0.5, self.mu1, self.sig)
cluster2 = DPCluster(0.5, self.mu2, self.sig)
cluster4 = DPCluster(0.5, self.mu2, self.sig)
cluster6 = DPCluster(0.5, self.mu2, self.sig)
cluster8 = DPCluster(0.5, self.mu2, self.sig)
mix = DPMixture([
cluster1, cluster2, cluster3, cluster4, cluster5, cluster6,
cluster7, cluster8
],
niter=4)
avg = mix.average()
assert len(avg.clusters) == 2
assert all(avg.mus[0] == self.mu1)
assert all(avg.mus[1] == self.mu2)
assert all(avg.sigmas[0] == self.sig)
assert all(avg.sigmas[1] == self.sig)
assert avg.pis[0] == 0.5, 'pis should be 0.5 but is %f' % avg.pis()[0]
assert avg.pis[1] == 0.5, 'pis should be 0.5 but is %f' % avg.pis()[0]
