# gammas, ys = [], []
for filepath in filepathc:
with open(filepath, 'r') as ins:
print('reading %s' % filepath)
contents = ins.read()
match = re.match(r'(.*)_gamma(.*)', filepath, re.M | re.I)
gamma = match.group(2)
# gammas.append(gamma)
epoches, times, losses = parseData(contents)
x = np.array(epoches)
y = np.log(np.array(losses))
# ys.append(y)
# ys = np.array(ys)
# yssum = np.sum(ys, axis=1)
# opt = np.argmin(yssum)
ax.plot(x, y, ls=linestyle, color=color, label='%s: nthreads=%d, step=%s' % (scheme, P, gamma), lw=2)
utils.set_axis(ax, xlabel='iterations', ylabel='log(loss)', xticks=None, yticks=None, xlim=None, fontsize=30)
plt.tight_layout()
savefile = 'n%d_d%d_T%d.svg' % (n, d, nit)
save_dir = os.path.join(filedir, savefile)
fig.savefig(save_dir)
savefile = 'n%d_d%d_T%d.pdf' % (n, d, nit)
save_dir = os.path.join(filedir, savefile)
fig.savefig(save_dir)
savefile = 'n%d_d%d_T%d.jpg' % (n, d, nit)
save_dir = os.path.join(filedir, savefile)
fig.savefig(save_dir)
plt.cla()
print('succeed saving %s' % save_dir)
for max_deg in max_degrees:
G, th = graph.gen_corr_graph(np.abs(ncc), max_deg=max_deg)
ths.append(th)
max_degrees_actual.append(max(list(G.degree().values())))
num_edges.append(G.number_of_edges())
per_edges_used.append(num_edges[-1] / float(n * (n - 1) / 2))
print(max_degrees_actual)
print(ths)
print(num_edges)
matplotlib.rcParams.update({'font.size': 30})
fig = plt.figure(num=1, figsize=(20, 12))
ax = fig.add_subplot(1, 1, 1)
ax.plot(max_degrees_actual, ths, label='threshold', lw=2)
ax.plot(max_degrees_actual,
per_edges_used,
label='percent of edges used',
lw=2)
utils.set_axis(ax,
xlabel='max degree',
ylabel=None,
title='Tune max degree',
xticks=None,
yticks=None,
xlim=None,
fontsize=30)
plt.tight_layout()
plt.show()
save_dir = os.path.join('..', 'results', 'simulations', 'Gaussian',
'n%d_d%d_th.pdf' % (n, d))
fig.savefig(save_dir)
end_it.append(sum(times[:i + 1]))
print('P:%d, scheme:%s, end epoch:%d, time:%f' %
(P, scheme, epoches[i], end_it[-1]))
break
# ys.append(y)
# ys = np.array(ys)
# yssum = np.sum(ys, axis=1)
# opt = np.argmin(yssum)
for i in xrange(len(end_it)):
speed_up.append(float(end_it[0]) / end_it[i] * Ps[i])
ax.plot(Ps, speed_up, label=scheme, lw=2)
utils.set_axis(ax,
xlabel='iterations',
ylabel='speed up',
xticks=None,
yticks=None,
xlim=None,
fontsize=30)
plt.tight_layout()
savefile = 'speedup_n%d_T%d.svg' % (n, nit)
save_dir = os.path.join(filedir, savefile)
fig.savefig(save_dir)
savefile = 'speedup_n%d_T%d.pdf' % (n, nit)
save_dir = os.path.join(filedir, savefile)
fig.savefig(save_dir)
savefile = 'speedup_n%d_T%d.jpg' % (n, nit)
save_dir = os.path.join(filedir, savefile)
fig.savefig(save_dir)
plt.cla()
print('succeed saving %s' % save_dir)