fg.add_edge(fc, x4)
print(fg.nodes)
print(fg.neighbors(x4))
pos = {
x1: (-7.5, 7.5),
fa: (-4, 7.5),
x2: (0, 7.5),
fb: (4, 7.5),
x3: (7.5, 7.5),
fc: (0, 3),
x4: (0, 0)
}
fg.save_graph_fig(num=1, pos=pos, fig_name='tree-1')
fg.sum_product(x3)
# fg.max_product(x3)
# fg.max_sum(x3)
# fg.loopy_sum_product(iterations=5, plot_errors=True)
# fg.loopy_max_product(iterations=5)
# fg.loopy_max_sum(iterations=5)
beliefs = fg.get_beliefs()
for k in beliefs:
print(f"marginal prob. of node {k}:{beliefs[k]}")
fg.max_sum(x3)
map_beliefs = fg.get_beliefs()
for k in map_beliefs:
print(f"conf. of node {k} for max joint prob.:{np.argmax(map_beliefs[k])}")
from factor_graph import FactorGraph from node import VarNode, FactorNode import numpy as np fg = FactorGraph() a = VarNode(name='a', graph=fg) b = VarNode(name='b', graph=fg) c = VarNode(name='c', init=[1., 1., 1.], graph=fg) d = VarNode(name='d', graph=fg) f1_cpd = np.array([[2, 3], [6, 4]]) f2_cpd = np.array([[[7, 2, 3], [1, 5, 2]], [[8, 3, 9], [6, 4, 2]]]) f3_cpd = np.array([5, 1, 9]) f1 = FactorNode(cpd=f1_cpd, graph=fg, name='f1', ordered_variables=(a, b)) f2 = FactorNode(cpd=f2_cpd, graph=fg, name='f2', ordered_variables=(b, d, c)) f3 = FactorNode(cpd=f3_cpd, graph=fg, name='f3', ordered_variables=(c, )) fg.add_var_nodes([a, b, c, d]) fg.add_factor_nodes([f1, f2, f3]) fg.add_edge(a, f1) fg.add_edge(f1, b) fg.add_edge(b, f2) fg.add_edge(f2, c) fg.add_edge(f2, d) fg.add_edge(c, f3) fg.sum_product(node=b)