def learn_params_mle(self, samples):
"""
Maximum liklihood estimation (MLE) parameter learing for a MRF.
Parameters
----------
samples: List
A list of fully observed samples for the spanning the total domain
of this MRF. Where samples[i][n] is the i'th sample for node n.
"""
samples = np.array(samples)
"""For every clique"""
for clq in self.cliques:
"""Obtain the evidence that is within the domain of this clique"""
local_samples = samples[:, clq.domain]
"""If there is evidence for this clique"""
if len(local_samples.tolist()) != 0:
"""Compute the counts of the samples"""
counts = general.compute_counts(local_samples, clq.pot.sizes)
"""Reshape the counts into a potentials lookup table"""
clq.unobserved_pot.T =\
general.mk_stochastic(np.array(counts, dtype=float))
clq.pot.T = clq.unobserved_pot.T.copy()
def learn_params_mle(self, samples):
"""
Maximum liklihood estimation (MLE) parameter learing for a MRF.
Parameters
----------
samples: List
A list of fully observed samples for the spanning the total domain
of this MRF. Where samples[i][n] is the i'th sample for node n.
"""
samples = np.array(samples)
"""For every clique"""
for clq in self.cliques:
"""Obtain the evidence that is within the domain of this clique"""
local_samples = samples[:, clq.domain]
"""If there is evidence for this clique"""
if len(local_samples.tolist()) != 0:
"""Compute the counts of the samples"""
counts = general.compute_counts(local_samples, clq.pot.sizes)
"""Reshape the counts into a potentials lookup table"""
clq.unobserved_pot.T =\
general.mk_stochastic(np.array(counts, dtype=float))
clq.pot.T = clq.unobserved_pot.T.copy()
def test_compute_counts():
"""
FUNCTION: compute_counts, in general.py.
"""
"""Create data required to test function"""
data = read_samples("lawn_samples.txt")
sz = np.array([2, 2, 2, 2])
"""Execute function"""
count = general.compute_counts(data, sz)
"""Assert the function executed correctly"""
assert_array_equal(count, np.array([[[[7, 0], [0, 1]], [[1, 14], [0, 1]]], [[[4, 0], [3, 14]], [[0, 1], [0, 4]]]]))
def learn_params_mle(self, samples):
"""
Maximum liklihood estimation (MLE) parameter learing for a tabular
CPD.
Parameters
----------
samples: List
A list of fully observed samples for the spanning the total domain
of this CPD. Where samples[i][n] is the i'th sample for node n.
"""
"""Compute the counts of the samples"""
counts = general.compute_counts(samples, self.fam_size)
"""Reshape the counts into a CPT"""
self.CPT = general.mk_stochastic(np.array(counts, dtype=float))
def learn_params_mle(self, samples):
"""
Maximum liklihood estimation (MLE) parameter learing for a tabular
CPD.
Parameters
----------
samples: List
A list of fully observed samples for the spanning the total domain
of this CPD. Where samples[i][n] is the i'th sample for node n.
"""
"""Compute the counts of the samples"""
counts = general.compute_counts(samples, self.fam_size)
"""Reshape the counts into a CPT"""
self.CPT = general.mk_stochastic(np.array(counts, dtype=float))
def test_compute_counts():
"""
FUNCTION: compute_counts, in general.py.
"""
"""Create data required to test function"""
data = read_samples('lawn_samples.txt')
sz = np.array([2, 2, 2, 2])
"""Execute function"""
count = general.compute_counts(data, sz)
"""Assert the function executed correctly"""
assert_array_equal(count, np.array([[[[7, 0], \
[0, 1]],\
[[1, 14], \
[0, 1]]],
[[[4, 0], \
[3, 14]],\
[[0, 1], \
[0, 4]]]]))