def test_fit():
p1 = Normal(mu=T.constant(0.0), sigma=T.constant(2.0))
p2 = Normal(mu=T.constant(3.0), sigma=T.constant(2.0))
p3 = Exponential(inverse_scale=T.constant(0.5))
g = theano.shared(0.5)
m = Mixture(components=[p1, p2, p3], weights=[g, g*g])
X = np.concatenate([st.norm(loc=0.0, scale=2.0).rvs(300, random_state=0),
st.norm(loc=3.0, scale=2.0).rvs(100, random_state=1),
st.expon(scale=1. / 0.5).rvs(500, random_state=2)])
X = X.reshape(-1, 1)
s0 = m.score(X)
m.fit(X)
assert np.abs(g.eval() - 1. / 3.) < 0.05
assert m.score(X) <= s0
def test_fit():
p1 = Normal(mu=T.constant(0.0), sigma=T.constant(2.0))
p2 = Normal(mu=T.constant(3.0), sigma=T.constant(2.0))
p3 = Exponential(inverse_scale=T.constant(0.5))
g = theano.shared(0.5)
m = Mixture(components=[p1, p2, p3], weights=[g, g*g])
X = np.concatenate([st.norm(loc=0.0, scale=2.0).rvs(300, random_state=0),
st.norm(loc=3.0, scale=2.0).rvs(100, random_state=1),
st.expon(scale=1. / 0.5).rvs(500, random_state=2)])
X = X.reshape(-1, 1)
s0 = m.score(X)
m.fit(X)
assert np.abs(g.eval() - 1. / 3.) < 0.05
assert m.score(X) >= s0
