def anneal_w_graphics(n=11, depth=10):
""" Make an animation of the BDST chain walking on an nxn grid and play it
"""
ni = 5
nj = 100
nk = 5
beta = mc.Uninformative('beta', value=1.)
G = nx.grid_graph([n, n])
G.orig_pos = dict([[v, v] for v in G.nodes_iter()])
G.pos = dict([[v, v] for v in G.nodes_iter()])
root = (5,5)
bdst = BDST(G, root, depth, beta)
mod_mc = mc.MCMC([beta, bdst])
mod_mc.use_step_method(STMetropolis, bdst)
mod_mc.use_step_method(mc.NoStepper, beta)
for i in range(ni):
beta.value = i*5
for j in range(nj):
mod_mc.sample(1)
T = bdst.value
for k in range(nk):
if random.random() < .95:
delta_pos = nx.spring_layout(T, pos=G.pos, fixed=[root], iterations=1)
else:
delta_pos = G.orig_pos
eps=.01
my_avg = lambda x, y: (x[0]*(1.-eps) + y[0]*eps, x[1]*(1.-eps)+y[1]*eps)
for v in G.pos:
G.pos[v] = my_avg(G.pos[v], delta_pos[v])
views.plot_graph_and_tree(G, T, time=1.*k/nk)
str = ''
str += ' beta: %.1f\n' % beta.value
str += ' cur depth: %d (target: %d)\n' % (T.depth, depth)
sm = mod_mc.step_method_dict[bdst][0]
str += ' accepted: %d of %d\n' % (sm.accepted, sm.accepted + sm.rejected)
plt.figtext(0, 0, str)
plt.figtext(1, 0, 'healthyalgorithms.wordpress.com \n', ha='right')
plt.axis([-1, n, -1, n])
plt.axis('off')
plt.subplots_adjust(0, 0, 1, 1)
plt.savefig('bdst%06d.png' % (i*nj*nk + j*nk + k))
print 'accepted:', mod_mc.step_method_dict[bdst][0].accepted
import subprocess
subprocess.call('mencoder mf://bdst*.png -mf w=800:h=600 -ovc x264 -of avi -o bdst_G_%d_d_%d.avi' % (n, depth), shell=True)
subprocess.call('mplayer -loop 0 bdst_G_%d_d_%d.avi' % (n, depth), shell=True)
subprocess.call('rm bdst*.png')
return bdst.value
import matplotlib.pylot as plt
labels = 'Python', 'C++', 'Ruby', 'Java', 'PHP', 'Perl'
sizes = [33, 52, 12, 17, 62, 48]
separated = (.1, .3, 0, 0, .6, 0)
plt.pie(sizes, labels=labels, autopct='%1.1f%%', explode=separated)
plt.axis('equal')
plt.show()
def anneal_w_graphics(n=11, depth=10):
""" Make an animation of the BDST chain walking on an nxn grid and play it
"""
ni = 5
nj = 100
nk = 5
beta = mc.Uninformative('beta', value=1.)
G = nx.grid_graph([n, n])
G.orig_pos = dict([[v, v] for v in G.nodes_iter()])
G.pos = dict([[v, v] for v in G.nodes_iter()])
root = (5, 5)
bdst = BDST(G, root, depth, beta)
mod_mc = mc.MCMC([beta, bdst])
mod_mc.use_step_method(STMetropolis, bdst)
mod_mc.use_step_method(mc.NoStepper, beta)
for i in range(ni):
beta.value = i * 5
for j in range(nj):
mod_mc.sample(1)
T = bdst.value
for k in range(nk):
if random.random() < .95:
delta_pos = nx.spring_layout(T,
pos=G.pos,
fixed=[root],
iterations=1)
else:
delta_pos = G.orig_pos
eps = .01
my_avg = lambda x, y: (x[0] * (1. - eps) + y[0] * eps, x[1] *
(1. - eps) + y[1] * eps)
for v in G.pos:
G.pos[v] = my_avg(G.pos[v], delta_pos[v])
views.plot_graph_and_tree(G, T, time=1. * k / nk)
str = ''
str += ' beta: %.1f\n' % beta.value
str += ' cur depth: %d (target: %d)\n' % (T.depth, depth)
sm = mod_mc.step_method_dict[bdst][0]
str += ' accepted: %d of %d\n' % (sm.accepted,
sm.accepted + sm.rejected)
plt.figtext(0, 0, str)
plt.figtext(1,
0,
'healthyalgorithms.wordpress.com \n',
ha='right')
plt.axis([-1, n, -1, n])
plt.axis('off')
plt.subplots_adjust(0, 0, 1, 1)
plt.savefig('bdst%06d.png' % (i * nj * nk + j * nk + k))
print 'accepted:', mod_mc.step_method_dict[bdst][0].accepted
import subprocess
subprocess.call(
'mencoder mf://bdst*.png -mf w=800:h=600 -ovc x264 -of avi -o bdst_G_%d_d_%d.avi'
% (n, depth),
shell=True)
subprocess.call('mplayer -loop 0 bdst_G_%d_d_%d.avi' % (n, depth),
shell=True)
subprocess.call('rm bdst*.png')
return bdst.value