def test(n, iters):
A, f, a = random_matrix(n)
labels = [str(i) for i in range(1, n + 1)]
G = spec.DirectedGraph(A, (a, f), labels=labels)
G.coloring()
print(G.colors)
stats.community_dist_hist(G.colors,
title=G.n,
logscale=False,
show=True,
save=False)
for k in range(iters):
base = random.sample(G.indexer.keys(), int(np.floor(G.n * .9)))
print('\nSpecializing on ', base)
G.specialize(base, recolor=True)
print(G.colors)
stats.community_dist_hist(G.colors,
title='$S_{}$'.format(k + 1) + str(base) +
'n=' + str(G.n),
logscale=False,
show=True,
save=False)
[0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 1],
[0, 0, 0, 1, 0, 1, 0]])
labels = ["a", "b", "c", 'd', 'e', 'f', 'g']
# G = s.DirectedGraph(A, labels)
# G.specialize_graph(["a",'e'], verbose=True)
B = np.array([[0, 0, 1], [1, 0, 0], [1, 1, 0]])
sig = lambda x: np.tanh(x)
f = np.array(
[[lambda x: 0 * x, lambda x: 0 * x, lambda x: -(1 / 2) * sig(x)],
[lambda x: 0.5 * sig(x), lambda x: 0 * x, lambda x: 0 * x],
[lambda x: 0.5 * sig(x), lambda x: 0.5 * sig(x), lambda x: 0 * x]])
a = np.array([3 / 10, 3 / 10, 3 / 10])
c = np.array([1 / 4, 0.025, 0])
labels = ['x1', 'x2', 'x3']
G = s.DirectedGraph(B, (a, f, c), labels=labels)
G.iterate(80, np.random.random(G.n), graph=True)
G.specialize_graph(['x2', 'x3'], verbose=False)
G.iterate(80, np.random.random(G.n), graph=True)
G.specialize_graph(['x1.1', 'x1.2', 'x2'], verbose=False)
G.iterate(80, np.random.random(G.n), graph=True)
# N = nx.DiGraph(G.A.T)
# nx.draw_networkx_labels(N, pos=nx.spring_layout(N), labels=G.labeler)
# nx.draw(N, pos=nx.spring_layout(N))
# plt.draw()
# plt.show()
return anp.sin(x)
def f2(x):
return 0.9 * x + 7 / 5
def f3(x):
return 0.9 * x + 0.5
f = np.array([[sig, sig, sig, sig, sig, sig, sig, sig],
[sig, sig, sig, sig, sig, sig, sig, sig],
[sig, sig, sig, sig, sig, sig, sig, sig],
[sig, sig, sig, sig, sig, sig, sig, sig],
[sig, sig, sig, sig, sig, sig, sig, sig],
[sig, sig, sig, sig, sig, sig, sig, sig],
[sig, sig, sig, sig, sig, sig, sig, sig],
[sig, sig, sig, sig, sig, sig, sig, zero]])
a = np.array([zero, f1, f1, f1, f1, f1, f1, f1])
labels = ['1', '2', '3', '4', '5', '6', '7', '8']
G = s.DirectedGraph(A, (a, f), labels=labels)
# print(G.coloring())
# G.network_vis()#use_eqp=True)
# G.iterate(20,np.random.random(8),graph=True)
base = ['1', '8'] #,'5','6','7']
G.specialize(base)
# with open('half_spec.txt', 'w') as out_file:
# out_file.write(str(G.A))
G.iterate(20, np.random.random(38), graph=True)
# G.network_vis(use_eqp=True)
# print(G.n)