def callback(iters, current, env):
print 'Callback: ', iters
# get all traces
env.updategui = False
traces = env.evaluate_policy(current)
env.updategui = True
pickle.dump(traces, open('traces{0}.pck'.format(iters), 'w'),
pickle.HIGHEST_PROTOCOL)
# measure task performance
avg_reward = 0
for t in traces:
avg_reward += sum([i[2] for i in t])
avg_reward = avg_reward / float(len(traces))
print 'Avg reward: ', avg_reward
# find current embedding
ematrix = np.zeros((512, 512))
for (i, t) in enumerate(traces):
for (j, s) in enumerate(traces):
ematrix[i,
j] = edit_distance_vc([e[1] for e in t], [l[1] for l in s],
(1.0, 1.0, 1.2))
pickle.dump(ematrix, open('ematrix{0}.pck'.format(iters), 'w'),
pickle.HIGHEST_PROTOCOL)
y, s, adj = isomap(ematrix)
if len(y) < 512:
# fallback to mds if more than 1 connected component
print "More than 1 CC - Falling back to MDS"
y, s = mds(ematrix)
adj = None
# plot stuff later because of pylab / pygame incompat on mac
# save embedding image - multiple formats?
#scatter(y[:,0],y[:,1], filename='scatter{0}.pdf'.format(iters))
# save scree plot
#plot(s[:10], filename='scree{0}.pdf'.format(iters))
# procrustes error
gt = env.coords_array()
err = procrustes(gt, y)
print "Procrustes ", err
pickle.dump((iters, err, gt, avg_reward, current, y, s, adj),
open('misc{0}.pck'.format(iters), 'w'),
pickle.HIGHEST_PROTOCOL)
env.save('iter{0}.png'.format(iters))
# ogw.set_heatmap(obs)
# pdb.set_trace()
# ogw.phi(0,0,sparse=False)
pdb.set_trace()
traces = ogw.evaluate_func_policy(ogw.perfect_policy)
pickle.dump(traces, open("perf_pca_traces_center.pck", "w"),
pickle.HIGHEST_PROTOCOL)
traces = pickle.load(open("perf_pca_traces_center.pck"))
from dtw import edit_distance_vc
ematrix = np.zeros((512, 512))
for (i, t) in enumerate(traces):
for (j, s) in enumerate(traces):
ematrix[i,
j] = edit_distance_vc([e[1] for e in t], [l[1] for l in s],
(1.0, 1.0, 2.0))
# print ematrix
pickle.dump(ematrix, open("ematrix4.pck", "w"), pickle.HIGHEST_PROTOCOL)
ematrix = pickle.load(open('ematrix4.pck'))
# from mds import mds
# pdb.set_trace()
# y,s = mds(ematrix)
# #from utils import scatter
# #scatter(y[:,0],y[:,1])
# import pylab
# pylab.plot(y[:,0],y[:,1])
# pylab.savefig('test3.png')
# ogw.background()
# ogw.mainloop()
# s = np.zeros(ogw.nstates)
# s[:ogw.nstates/2] = 10.0
# ogw.set_heatmap(obs)
# pdb.set_trace()
# ogw.phi(0,0,sparse=False)
pdb.set_trace()
traces = ogw.evaluate_func_policy(ogw.perfect_policy)
pickle.dump(traces, open("perf_pca_traces_center.pck","w"),pickle.HIGHEST_PROTOCOL)
traces = pickle.load(open("perf_pca_traces_center.pck"))
from dtw import edit_distance_vc
ematrix = np.zeros((512,512))
for (i,t) in enumerate(traces):
for (j,s) in enumerate(traces):
ematrix[i,j] = edit_distance_vc([e[1] for e in t], [l[1] for l in s], (1.0, 1.0, 2.0))
# print ematrix
pickle.dump(ematrix, open("ematrix4.pck","w"), pickle.HIGHEST_PROTOCOL)
ematrix = pickle.load(open('ematrix4.pck'))
# from mds import mds
# pdb.set_trace()
# y,s = mds(ematrix)
# #from utils import scatter
# #scatter(y[:,0],y[:,1])
# import pylab
# pylab.plot(y[:,0],y[:,1])
# pylab.savefig('test3.png')
# ogw.background()
if r:
sucesses.append(i)
else:
failures.append(i)
# print sucesses
# print failures
# s = np.zeros(ogw.nstates)
# for (l,i) in result.items():
# s[l] = i
# ogw.set_heatmap(s)
ogw.mainloop()
if False:
traces = pickle.load(open("perf_pca_traces_center.pck"))
ematrix = np.zeros((512, 512))
for (i, t) in enumerate(traces):
for (j, s) in enumerate(traces):
ematrix[i, j] = edit_distance_vc([e[1] for e in t],
[l[1] for l in s],
(1.0, 1.0, 1.2))
pickle.dump(ematrix, open("ematrix.pck", "w"), pickle.HIGHEST_PROTOCOL)
ematrix = pickle.load(open('ematrix.pck'))
inscatter(isomap(ematrix))
