def createSimpleGraph(self):
# create the orphaned nodes
node_names = ['x1', 'x2', 'x3', 'x4']
dims = [2, 3, 2, 2]
# pad array just so that reference like x[1] later is easier to read
x = [None] + [Variable(node_names[i], dims[i]) for i in range(4)]
f3 = Factor('f3', np.array([0.2, 0.8]))
f4 = Factor('f4', np.array([0.5, 0.5]))
# first index is x3, second index is x4, third index is x2
# looking at it like: arr[0][0][0]
f234 = Factor('f234', np.array([
[
[0.3, 0.5, 0.2], [0.1, 0.1, 0.8]
], [
[0.9, 0.05, 0.05], [0.2, 0.7, 0.1]
]
]))
# first index is x2
f12 = Factor('f12', np.array([[0.8, 0.2], [0.2, 0.8], [0.5, 0.5]]))
# attach nodes to graph in right order (connections matching
# factor's potential's dimensions order)
g = FactorGraph(x[3], silent=False, debug=True)
g.append('x3', f234)
g.append('f234', x[4])
g.append('f234', x[2])
g.append('x2', f12)
g.append('f12', x[1])
g.append('x3', f3)
g.append('x4', f4)
print('yes')
g.compute_marginals();
g.observe('x1', 1);
g.compute_marginals();
g.nodes['x1'].marginal();
return g
temp1 = Variable(str(reviewer2id[reviewer1]), 2)
temp2 = Variable(str(reviewer2id[reviewer2]), 2)
common_prod = float(row[3])
common_burst = float(row[4])
dist = float(row[5])
dist_fact_name = 'distf' + str(reviewer2id[reviewer1]) + '_' + str(
reviewer2id[reviewer2])
dist_factor = Factor(dist_fact_name,
np.array([[1 - dist, dist], [dist, 1 - dist]]))
graph.add(dist_factor)
graph.append(dist_fact_name, temp2)
graph.append(dist_fact_name, temp1)
print 'adding pair no:', i
i += 1
num_of_reviewers = len(reviewer2id)
reviewers = list(reviewer2id.keys())
random.shuffle(reviewers)
reviewers = reviewers[:num_of_reviewers / 5]
for i in reviewers:
graph.observe(str(reviewer2id[i]), 2)
graph.compute_marginals(max_iter=500, tolerance=1e-4)
with open('output.csv', 'w') as f:
for i in xrange(num_of_reviewers):
output = str(i) + ', ' + id2reviewer[i] + ', ' + str(
graph.nodes[str(i)].marginal()[0])
f.write(output + '\n')