## offline ##
try:
perf = np.load('results/performance.npy')
except IOError:
try:
S = np.load('results/spikes.npz')['S']
except IOError:
S = [
csr_matrix(
cfn.runpop_js(net.W, step, 1, rate, 1000, 20, 2, ref, run))
for run in range(10)
]
np.savez_compressed('results/spikes.npz', S=S)
Tls = range(501)
rew = np.array(
[[net.R4Pi2(net.get_policy2(step, i, T), net.pstart_state) for i in S]
for T in Tls])
perf = (rew - R0) / (Rmax - R0)
perf[0] = 0
np.save('results/performance', perf)
pl.figure()
errorfill(range(len(perf)),
np.mean(perf, axis=1),
yerr=np.std(perf, axis=1) / np.sqrt(len(perf[0])))
pl.xticks([0, 200, 400], [0, 200, 400])
pl.yticks([0, .5, 1.0], [0, .5, 1.0])
pl.xlim([0, 400])
pl.ylim([0, 1])
pl.xlabel('Time [ms]')
pl.ylabel('Performance')
simpleaxis(pl.gca())
try:
perf = np.load('results/performance.npy')
except IOError:
try:
S = np.load('results/spikes.npz')['S']
except IOError:
S = [
csr_matrix(cfn.runpop_js(W, step, 1, rate, 1000, 20, 2, ref, run))
for run in range(10)
]
np.savez_compressed('results/spikes.npz', S=S)
Tls = range(501)
rew = np.array([[
net.R4Pi2(net.get_policy2(step, i, T),
0,
gamma,
epsilon=.0001,
max_iter=10**3) for i in S
] for T in Tls])
perf = (rew - R0) / (Rmax - R0)
perf[0] = 0
np.save('results/performance', perf)
pl.figure()
errorfill(range(len(perf)),
np.mean(perf, axis=1),
yerr=np.std(perf, axis=1) / np.sqrt(len(perf[0])))
pl.xticks([0, 200, 400], [0, 200, 400])
pl.yticks([0, .5, 1.0], [0, .5, 1.0])
pl.xlim([0, 400])
pl.ylim([0, 1])
pl.xlabel('Time [ms]')
