]
return G
def vis(s1, s2, markovLik1, markovLik2, cyklik1, cyklik2):
figsize = (30, 8)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(2, 1, 1)
ax.plot(s1, "--o")
ax.set_title("Markov = %.4f \n CYK = %.4f" % (markovLik1, cyklik1))
plt.grid()
bx = fig.add_subplot(2, 1, 2)
bx.plot(s2, "--o")
bx.set_title("Markov = %.4f \n CYK = %.4f" % (markovLik2, cyklik2))
plt.grid()
plt.show()
if __name__ == "__main__":
_, s1 = sample()
_, s2 = sample2()
g = grammar()
m = markov()
markovLik1, _ = m.decode(s1)
markovLik2, _ = m.decode(s2)
cyklik1 = cyk(s1, g)
cyklik2 = cyk(s2, g)
vis(s1, s2, markovLik1, markovLik2, cyklik1, cyklik2)
# markovLearning(s1)
# markovLearning(s2)
def build(bp, root):
a, b, c = root
if c:
(i,j, g, nta) = a
(i, s1, bb, ntb) = b
(s2, j, cc, ntc) = c
print "%s[%s:%s] -> %s[%s:%s] %s[%s:%s]"%(nta, i, j, ntb, i, s1, ntc, s2, j)
build(bp, bp[i, s1, bb])
build(bp, bp[s2, j, cc])
else:
(i,j, g, nta) = a
(i, s1, bb, ntb) = b
print "%s[%s] -> %s[%s]"%(nta, i, ntb, i)
if __name__ == '__main__':
_, S = sample()
A = Dist(np.array([0.0]), np.array([0.1]))
B = Dist(np.array([5.0]), np.array([0.1]))
C = Dist(np.array([-5.0]), np.array([0.1]))
G = [ ('NT_1', A, None, 1.0), ('NT_2', B, None, 1.0), ("NT_3", C, None, 1.0),
("NT_4", "NT_1", "NT_1", 0.2), ("NT_4", "NT_4", "NT_1", 0.8),
("NT_5", "NT_2", "NT_2", 0.2), ("NT_5", "NT_5", "NT_2", 0.8),
("NT_6", "NT_3", "NT_3", 0.2), ("NT_6", "NT_6", "NT_3", 0.8),
("NT_7", "NT_5", "NT_4", 1.0), ("NT_S", "NT_7", "NT_6", 1.0) ]
print cyk(S, G)
