def make_debug_graph(num_nodes, graph_mat):
# Create factor graph
fg = graphs.FactorGraph()
# Create variable nodes
X = {}
F = {}
dist_fa = np.zeros([30, 2, 2])
for k in range(30):
dist_fa[k] = np.random.rand(2, 2)
for i in range(num_nodes):
X['x' + str(i + 1)] = nodes.VNode("x" + str(i + 1), rv.Discrete)
fg.set_nodes(X.values())
for i in range(len(graph_mat)):
edge = graph_mat[i]
nodez = [X["x" + str(edge[m])] for m in range(len(edge))]
F['f' + str(i + 1)] = nodes.FNode("f" + str(i + 1),
rv.Discrete(dist_fa[i], *nodez))
fg.set_nodes(F.values())
for i in range(len(graph_mat)):
node_name = graph_mat[i]
fg.set_edge(X['x' + str(node_name[0])], F['f' + str(i + 1)])
fg.set_edge(F['f' + str(i + 1)], X['x' + str(node_name[1])])
draw(fg, nx.circular_layout(fg))
plt.show()
print([attr["type"].value for (n, attr) in fg.nodes(data=True)])
belief = inference.loopy_belief_propagation(
fg, 1000, [*X.values()], order=[*F.values(), *X.values()])
print(X['x1'].belief())
print(X['x2'].belief())
print(X['x3'].belief())
def make_debug_graph_new():
fg = graphs.FactorGraph()
x1 = nodes.VNode('x1', rv.Discrete)
x2 = nodes.VNode('x2', rv.Discrete)
x3 = nodes.VNode('x3', rv.Discrete)
dist_fa = [[0.1, 0.7], [0.3, 0.2]]
dist_f2 = [0.3, 0.7]
f1 = nodes.FNode('f1', rv.Discrete(dist_f2, x1))
f2 = nodes.FNode('f2', rv.Discrete(dist_fa, x1, x2))
#f3 = nodes.FNode('f3', rv.Discrete(dist_fa, x1,x2 ))
f4 = nodes.FNode('f4', rv.Discrete(dist_fa, x2, x3))
print(f1.factor)
fg.set_nodes([x1, x2, x3])
fg.set_nodes([f1, f2, f4])
# Add edges to factor graph
fg.set_edge(f1, x1)
fg.set_edge(x1, f2)
fg.set_edge(f2, x2)
#fg.set_edge(x2, f3)
#fg.set_edge(f3, x1)
fg.set_edge(x2, f4)
fg.set_edge(x3, f4)
draw(fg, nx.circular_layout(fg))
plt.show()
belief = inference.loopy_belief_propagation(fg,
10, [x1, x2, x3],
order=[f1, f2, f4, x1, x2, x3])
def make_arbit_graph(num_nodes, graph_mat):
# Create factor graph
fg = graphs.FactorGraph()
# Create variable nodes
X = {}
F = {}
dist_fa = np.zeros([len(graph_mat), 2, 2])
for k in range(len(graph_mat)):
dist_fa[k] = np.random.rand(2, 2)
for i in range(num_nodes):
X['x' + str(i + 1)] = nodes.VNode("x" + str(i + 1), rv.Discrete, P)
fg.set_nodes(X.values())
for i in range(len(graph_mat)):
edge = graph_mat[i]
nodez = [X["x" + str(edge[m])] for m in range(len(edge))]
F['f' + str(i + 1)] = nodes.FNode("f" + str(i + 1),
rv.Discrete(dist_fa[i], *nodez))
fg.set_nodes(F.values())
for i in range(len(graph_mat)):
node_name = graph_mat[i]
fg.set_edge(X['x' + str(node_name[0])], F['f' + str(i + 1)])
fg.set_edge(F['f' + str(i + 1)], X['x' + str(node_name[1])])
print([attr["type"].value for (n, attr) in fg.nodes(data=True)])
draw(fg, nx.spectral_layout(fg))
plt.show()
print([*F.values(), *X.values()])
#belief = inference.loopy_belief_propagation(fg, 1000, [*X.values()], order=[*F.values(), *X.values()])
grid = inference.nash_propagation(fg,
10,
epsilon,
1 / 10, [*X.values()],
order=[*F.values(), *X.values()])
## COmpute the mixed strategy
prob = []
prob_i = 0
print(len(grid.keys()))
length = 0
for i in grid.keys():
if (np.where(grid[str(i)] == 1) != []):
length = length + 1
prob_i = np.where(grid[str(i)] == 1)
print('new')
print(prob_i)
def make_graph():
G = graphs.FactorGraph()
x1 = nodes.VNode('x1', rv.Discrete, P)
x2 = nodes.VNode('x2', rv.Discrete, P)
x3 = nodes.VNode('x3', rv.Discrete, P)
x4 = nodes.VNode('x4', rv.Discrete, P)
x5 = nodes.VNode('x5', rv.Discrete, P)
dist_fa = np.array([[20, 10], [20, 80]])
dist_fb = np.array([[40, 30], [30, 50]])
dist_fc = np.array([[30, 50], [40, 30]])
dist_fd = np.array([[70, 10], [10, 20]])
dist_fe = np.array([[120, 50], [80, 100]])
dist_ff = np.array([[40, 100], [70, 30]])
dist_fg = np.array([[20, 10], [50, 30]])
fa = nodes.FNode('f1', rv.Discrete(dist_fa, x1, x2))
fb = nodes.FNode('f2', rv.Discrete(dist_fb, x2, x3))
fc = nodes.FNode('f3', rv.Discrete(dist_fc, x3, x4))
fd = nodes.FNode('f4', rv.Discrete(dist_fd, x4, x1))
fe = nodes.FNode('f5', rv.Discrete(dist_fe, x4, x5))
ff = nodes.FNode('f6', rv.Discrete(dist_ff, x5, x3))
fg = nodes.FNode('f7', rv.Discrete(dist_fg, x1, x3))
G.set_nodes([x1, x2, x3, x4, x5])
G.set_nodes([fa, fb, fc, fd, fe, ff, fg])
# Add edges to factor graph
G.set_edge(x1, fa)
G.set_edge(fa, x2)
G.set_edge(x2, fb)
G.set_edge(fb, x3)
G.set_edge(x3, fc)
G.set_edge(fc, x4)
G.set_edge(x4, fd)
G.set_edge(fd, x1)
G.set_edge(fe, x4)
G.set_edge(x5, fe)
G.set_edge(ff, x5)
G.set_edge(x3, ff)
G.set_edge(fg, x1)
G.set_edge(x3, fg)
draw(G, nx.spectral_layout(G))
plt.show()
inference.nash_propagation(
G,
20,
epsilon,
1 / 30, [x1, x2, x3, x4, x5],
order=[fa, fb, fc, fd, fe, ff, fg, x1, x2, x3, x4, x5])