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)
lt = {}
for i in range(8):
lt[i] = i
mix = OrderedDPMixture(
[clst1, clst2, clst3, clst4, clst5, clst6, clst7, clst8],
lt,
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 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)
lt = {}
for i in range(8):
lt[i] = i
mix = OrderedDPMixture(
[clst1, clst2, clst3, clst4, clst5, clst6, clst7, clst8],
lt,
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.clust1 = DPCluster(.5 / 3, self.mu1, self.sig)
self.clust2 = DPCluster(.5 / 3, self.mu2, self.sig)
self.clusters = [
self.clust1, self.clust2, self.clust1, self.clust2, self.clust1,
self.clust2
]
self.lookup = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 5}
self.mix = OrderedDPMixture(self.clusters,
self.lookup,
niter=3,
identified=True)
