def test_incorporate_state():
state = State(
T[:,:2], cctypes=CCTYPES[:2], distargs=DISTARGS[:2], rng=gu.gen_rng(0))
state.transition(N=5)
target = state.views.keys()[0]
# Incorporate a new dim into view[0].
state.incorporate_dim(
T[:,2], outputs=[2], cctype=CCTYPES[2], distargs=DISTARGS[2], v=target)
assert state.Zv(2) == target
state.transition(N=1)
# Incorporate a new dim into view[0] with a non-contiguous output.
state.incorporate_dim(
T[:,2], outputs=[10], cctype=CCTYPES[2], distargs=DISTARGS[2], v=target)
assert state.Zv(10) == target
state.transition(N=1)
# Some crash testing queries.
state.logpdf(-1, {10:1}, constraints={0:2, 1:1})
state.simulate(-1, [10], constraints={0:2})
# Incorporating with a duplicated output should raise.
with pytest.raises(ValueError):
state.incorporate_dim(
T[:,2], outputs=[10], cctype=CCTYPES[2], distargs=DISTARGS[2],
v=target)
# Multivariate incorporate should raise.
with pytest.raises(ValueError):
state.incorporate_dim(
T[:,2], outputs=[10, 2], cctype=CCTYPES[2],
distargs=DISTARGS[2], v=target)
# Missing output should raise.
with pytest.raises(ValueError):
state.incorporate_dim(
T[:,2], outputs=[], cctype=CCTYPES[2],
distargs=DISTARGS[2], v=target)
# Wrong number of rows should raise.
with pytest.raises(ValueError):
state.incorporate_dim(
T[:,2][:-1], outputs=[11], cctype=CCTYPES[2],
distargs=DISTARGS[2], v=target)
# Inputs should raise.
with pytest.raises(ValueError):
state.incorporate_dim(
T[:,2], outputs=[11], inputs=[2], cctype=CCTYPES[2],
distargs=DISTARGS[2], v=target)
# Incorporate dim into a newly created singleton view.
target = max(state.views)+1
state.incorporate_dim(
T[:,3], outputs=[3], cctype=CCTYPES[3],
distargs=DISTARGS[3], v=target)
assert state.Zv(3) == target
state.transition(N=1)
# Incorporate dim without specifying a view.
state.incorporate_dim(T[:,4], outputs=[4],
cctype=CCTYPES[4], distargs=DISTARGS[4])
state.transition(N=1)
# Unincorporate first dim.
previous = state.n_cols()
state.unincorporate_dim(0)
assert state.n_cols() == previous-1
state.transition(N=1)
# Reincorporate dim without specifying a view.
state.incorporate_dim(
T[:,0], outputs=[0], cctype=CCTYPES[0], distargs=DISTARGS[0])
state.transition(N=1)
# Incorporate dim into singleton view, remove it, assert destroyed.
target = max(state.views)+1
state.incorporate_dim(
T[:,5], outputs=[5], cctype=CCTYPES[5], distargs=DISTARGS[5],
v=target)
previous = len(state.views)
state.unincorporate_dim(5)
assert len(state.views) == previous-1
state.transition(N=1)
# Reincorporate dim into a singleton view.
target = max(state.views)+1
state.incorporate_dim(T[:,5], outputs=[5], cctype=CCTYPES[5],
distargs=DISTARGS[5], v=target)
state.transition(N=1)
# Incorporate the rest of the dims in the default way.
for i in xrange(6, len(CCTYPES)):
state.incorporate_dim(
T[:,i], outputs=[max(state.outputs)+1],
cctype=CCTYPES[i], distargs=DISTARGS[i])
state.transition(N=1)
# Unincorporating non-existent dim should raise.
with pytest.raises(ValueError):
state.unincorporate_dim(9999)
# Unincorporate all the dims, except the last one.
for o in state.outputs[:-1]:
state.unincorporate_dim(o)
assert state.n_cols() == 1
state.transition(N=1)
# Unincorporating last dim should raise.
with pytest.raises(ValueError):
state.unincorporate_dim(state.outputs[0])
# Plot!
state.plot()
# Run some solid checks on a complex state.
test_crash_simulate_joint(state)
test_crash_logpdf_joint(state)
test_crash_simulate_conditional(state)
test_crash_logpdf_conditional(state)
test_crash_simulate_joint_observed(state)
test_crash_logpdf_joint_observed(state)
test_crash_simulate_conditional_observed(state)
test_crash_logpdf_conditional_observed(state)
# Joint equals chain rule for state 1.
joint = state.logpdf(-1, {0: 1, 1: 2})
chain = state.logpdf(-1, {0: 1}, {1: 2}) + state.logpdf(-1, {1: 2})
assert np.allclose(joint, chain)
if False:
state2 = State(T.T, cctypes=cctypes, distargs=distargs, rng=gu.gen_rng(12))
state2.transition(N=10, progress=1)
# Joint equals chain rule for state 2.
state2.logpdf(-1, {0: 1, 1: 2})
state2.logpdf(-1, {0: 1}, {1: 2}) + state2.logpdf(-1, {1: 2})
# Take the Monte Carlo average of the conditional.
mc_conditional = np.log(.5) + gu.logsumexp(
[state.logpdf(-1, {0: 1}, {1: 2}),
state2.logpdf(-1, {0: 1}, {1: 2})])
