def test_get_dfs_result():
model = PairWiseFiniteModel(4, 2)
j = np.ones((2, 2))
model.add_interaction(2, 3, j)
model.add_interaction(2, 1, j)
model.get_dfs_result() # To test cache invalidation.
model.add_interaction(1, 0, j)
dfs_edges = model.get_dfs_result().dfs_edges
assert np.allclose(dfs_edges, np.array([[0, 1], [1, 2], [2, 3]]))
def max_likelihood_tree_dp(model: PairWiseFiniteModel):
"""Max Likelihood for the pairwise model.
Performs dynamic programming on tree.
Applicable only if the interaction graph is a tree or a forest. Otherwise
throws exception.
:param model: Model for which to find most likely state.
:return: Most likely state. np.array of ints.
"""
assert not model.get_dfs_result().had_cycles, "Graph has cycles."
field = model.field.astype(np.float64, copy=False)
dfs_edges = model.get_dfs_result().dfs_edges
ints = model.get_interactions_for_edges(dfs_edges)
return _max_likelihood_internal(field, dfs_edges, ints)
def infer_tree_dp(model: PairWiseFiniteModel,
subtree_mp=False) -> InferenceResult:
"""Inference using DP on tree.
Performs dynamic programming on tree.
Applicable only if the interaction graph is a tree or a forest. Otherwise
throws exception.
:param model: Model for which to perform inference.
:param subtree_mp: If true, will return marginal probabilities for
subtrees, i.e. for each node will return probability of it having
different values if we leave only it and its subtree.
:return: InferenceResult object.
"""
assert not model.get_dfs_result().had_cycles, "Graph has cycles."
dfs_edges = model.get_dfs_result().dfs_edges
dfs_j = model.get_interactions_for_edges(dfs_edges)
lz = model.field.astype(dtype=np.float64, copy=True) # log(z)
lzc = np.zeros_like(lz) # log(zc)
# Log(z_r). z_r is partition function for all tree except subtree of given
# vertex, when value of given vertex is fixed.
lzr = np.zeros((model.gr_size, model.al_size))
_dfs1(lz, lzc, dfs_edges, dfs_j)
log_pf = logsumexp(lz[0, :])
if subtree_mp:
return InferenceResult(log_pf, lz)
_dfs2(lz, lzc, lzr, dfs_edges, dfs_j)
marg_proba = np.exp(lz + lzr - log_pf)
return InferenceResult(log_pf, marg_proba)