action_traces = []
for i in [91, 93, 95, 97]:
action_traces.append([j[1] for j in v.single_episode(policy, start=i)])
print action_traces
def costf(a, b):
return np.abs(a - b)
n = len(action_traces)
ematrix = np.zeros((n, n))
for (i, t) in enumerate(action_traces):
for (j, s) in enumerate(action_traces):
ematrix[i, j] = non_dtw_distance(t, s, default=0, costf=costf)
y, s = mds(ematrix)
pylab.clf()
pylab.title("Sensorimotor Distances")
colors = ['red', 'orange', 'green', 'blue']
pts = []
for i in range(4):
lbl = ""
if i == 0:
lbl = "Farthest Object"
elif i == 3:
lbl = "Closest Object"
else:
lbl = "None"
print lbl
ematrix = pickle.load(open('ematrix_revised{0}'.format(k)))
y,s = mds(ematrix)
pylab.clf()
pylab.title("Iteration {0}".format(k))
pylab.scatter(y[:,0],y[:,1],c=colors)
pylab.savefig("embed_{0}".format(k))
if False:
traces = pickle.load(open('traces13.pck'))
# 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, 1.5))
ematrix[i,j] = non_dtw_distance([e[1] for e in t], [l[1] for l in s], default = 8, costf=adist)
y,s = mds(ematrix)#isomap(ematrix)
pylab.scatter(y[:,0],y[:,1])
pylab.show()
if False:
for (i, y) in enumerate(projections):
pylab.clf()
pylab.title("Iteration {0}".format(i))
pylab.scatter(y[:,0],y[:,1])
pylab.savefig("embed_{0}".format(i))
if False:
pylab.clf()
pylab.title("Embedding Quality")
def dtw_apply(i, j, t, s):
return i, j, non_dtw_distance([e[1] for e in t], [l[1] for l in s], costf=cost_func)
y, s = mds(ematrix)
pylab.clf()
pylab.title("Iteration {0}".format(k))
pylab.scatter(y[:, 0], y[:, 1], c=colors)
pylab.savefig("embed_{0}".format(k))
if False:
traces = pickle.load(open('traces13.pck'))
# 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, 1.5))
ematrix[i, j] = non_dtw_distance([e[1] for e in t],
[l[1] for l in s],
default=8,
costf=adist)
y, s = mds(ematrix) #isomap(ematrix)
pylab.scatter(y[:, 0], y[:, 1])
pylab.show()
if False:
for (i, y) in enumerate(projections):
pylab.clf()
pylab.title("Iteration {0}".format(i))
pylab.scatter(y[:, 0], y[:, 1])
pylab.savefig("embed_{0}".format(i))
if False:
pylab.clf()
pylab.title("Embedding Quality")
# test points
action_traces = []
for i in [91,93,95,97]:
action_traces.append([j[1] for j in v.single_episode(policy, start=i)])
print action_traces
def costf(a,b):
return np.abs(a-b)
n = len(action_traces)
ematrix = np.zeros((n, n))
for (i, t) in enumerate(action_traces):
for (j, s) in enumerate(action_traces):
ematrix[i, j] = non_dtw_distance(t,s,default=0, costf=costf)
y,s = mds(ematrix)
pylab.clf()
pylab.title("Sensorimotor Distances")
colors = ['red','orange','green','blue']
pts = []
for i in range(4):
lbl = ""
if i == 0:
lbl = "Farthest Object"
elif i == 3:
lbl = "Closest Object"
else:
lbl = "None"
print lbl
def dtw_apply(i, j, t, s):
return i, j, non_dtw_distance([e[1] for e in t], [l[1] for l in s],
costf=cost_func)
