import numpy as np
from pyugm.factor import DiscreteFactor

# Specify the potential table
factor_data = np.array([[1, 2], [2, 1]])
# The variable names ("1" and "2") and cardinalities (2 and 2).
variables_names_and_cardinalities = [(1, 2), (2, 2)]
# Construct the factor
factor = DiscreteFactor(variables_names_and_cardinalities, data=factor_data)
print factor

factor.data  # The potential table

# Marginalise out all the variables that is not named "1". (i.e. marginalise out variable "2")
marg = factor.marginalize([1])
print marg
print marg.data

from pyugm.factor import DiscreteBelief
# Create a belief that is based on a factor
belief = DiscreteBelief(factor)
# Reduce the original factor by observing variable "1" taking on the value 0. [TODO: implement efficient factor reduction]
# Evidence is set by a dictionary where the key is a variable name and the value its observed value.
belief.set_evidence({1: 0})
print belief
print belief.data 

from pyugm.model import Model

factor1 = DiscreteFactor([(1, 2), (2, 2)], data=np.array([[1, 2], [2, 1]]))
factor2 = DiscreteFactor([(2, 2), ('variable3', 3)],  # Variable names can also be strings
                         data=np.array([[0, 0.2, 0.3], [0.1, 0.5, 0.3]]))  # Cardinalities of 2 and 3 means the factor table must be 2x3
# [TODO: cardinalities can be inferred from data shape when provided]
factor3 = DiscreteFactor([('variable3', 3), (4, 2)], data=np.array([[0, 1], [1, 2], [0.5, 0]]))
model = Model([factor1, factor2, factor3])

model.edges  # returns a set of tuples

from pyugm.model import Model
from pyugm.infer_message import LoopyBeliefUpdateInference

factor1 = DiscreteFactor([(1, 2), (2, 2)], data=np.array([[1, 2], [2, 1]]))
factor2 = DiscreteFactor([(2, 2), ('variable3', 3)], data=np.array([[0, 0.2, 0.3], [0.1, 0.5, 0.3]]))  
factor3 = DiscreteFactor([('variable3', 3), (4, 2)], data=np.array([[0, 1], [1, 2], [0.5, 0.1]]))

model = Model([factor1, factor2, factor3])
inferrer = LoopyBeliefUpdateInference(model)

inferrer.calibrate()

# Calibrated marginals
print inferrer.get_marginals(1)[0], inferrer.get_marginals(1)[0].data
print inferrer.get_marginals(2)[0], inferrer.get_marginals(2)[0].data

# Natural logarithm of the normalizing factor
print inferrer.partition_approximation()

inferrer.calibrate(evidence={'variable3': 1})

# Calibrated marginals
print inferrer.get_marginals(1)[0], inferrer.get_marginals(1)[0].data
print inferrer.get_marginals(2)[0], inferrer.get_marginals(2)[0].data

# Natural logarithm of the normalizing factor
print inferrer.partition_approximation()