def mplot(x, mat):
m, n = mat.shape
# linestyles = ['_', '-', '--', ':', '-.']
linestyles = ['-', '--', '-.']
# styles = ['+', '*', 'x']
styles = ['']
colors = ('b', 'g', 'r', 'c', 'm', 'y', 'k')
for i in range(n):
color = colors[i % len(colors)]
style = styles[i % len(styles)]
ls = linestyles[i % len(linestyles)]
plt.plot(x, mat[:, i], linestyle=ls, marker=style,
color=color, markersize=4)
for i in xrange(m):
D[i, j] = I_rec[i][j, seq_map[args.entro]]
x = HeuristicRefinePL(D, args.lamb, 50, 0.5, 0.01)
if x is None:
print('No feasible selection')
# import sys
sys.exit(0)
print('solution: ', x)
select_D = D[:, np.nonzero(x)[0]]
org_entro = np.nanmin(D, axis=1)
select_entro = np.nanmin(select_D, axis=1)
plt.subplot(411)
plt.plot(D)
plt.title('divergence between traffic and all candidate PLs')
plt.setp(plt.gca().get_xticklabels(), visible=False)
plt.ylabel('divergence')
plt.subplot(412)
# plt.plot(select_D)
mplot(range(m), select_D)
plt.title('divergence between traffic and selected PLs')
plt.setp(plt.gca().get_xticklabels(), visible=False)
plt.ylabel('divergence')
##### plot selected model id with PL Identification #######
plt.subplot(413)
selected_model = []
