def test_em_loglike6():
"""
"""
dim = 1
k = 1
n = 100
offset = 3.
x = nr.randn(n,dim)
y = offset+nr.randn(n,dim)
lgmm = GMM(k,dim)
lgmm.initialize(x)
lgmm.estimate(x)
ll1 = lgmm.average_log_like(x)
ll2 = lgmm.average_log_like(y)
ent = 0.5*(1+np.log(2*np.pi))
dkl = 0.5*offset**2
print ll2, ll1,dkl
assert ll2<ll1
def test_em_loglike1():
dim = 1
k = 3
n = 1000
x = nr.randn(n, dim)
lgmm = GMM(k, dim)
lgmm.initialize(x)
lgmm.estimate(x)
ll = lgmm.average_log_like(x)
ent = 0.5 * (1 + np.log(2 * np.pi))
print ll, ent
assert_true(np.absolute(ll + ent) < 3.0 / np.sqrt(n))
def test_em_loglike4():
dim = 5
k = 1
n = 1000
scale = 3.0
offset = 4.0
x = offset + scale * nr.randn(n, dim)
lgmm = GMM(k, dim)
lgmm.initialize(x)
lgmm.estimate(x)
ll = lgmm.average_log_like(x)
ent = dim * 0.5 * (1 + np.log(2 * np.pi * scale ** 2))
print ll, ent
assert_true(np.absolute(ll + ent) < dim * 3.0 / np.sqrt(n))
def test_em_loglike2():
"""
"""
dim = 1
k = 1
n = 1000
scale = 3.
offset = 4.
x = offset + scale * nr.randn(n,dim)
lgmm = GMM(k,dim)
lgmm.initialize(x)
lgmm.estimate(x)
ll = lgmm.average_log_like(x)
ent = 0.5*(1+np.log(2*np.pi*scale**2))
print ll, ent
assert np.absolute(ll+ent)<3./np.sqrt(n)
