def plotDrift(idx, startpoint, estimators):
m = DiscModel()
examples = extract_info(m, "Full", "good")
for example in examples[0:idx]:
states_kin = np.array(example.states[startpoint])
states_kin2 = np.array(example.states[startpoint])
for n, i in enumerate(example.actions[startpoint:-1]):
if n == 0:
nex = predict_next(states_kin, i, estimators)
nex[0] = np.arcsin(nex[0]) * 2
states_kin = np.vstack(
[states_kin,
staticGroupSimple2(states_kin, i) - nex])
states_kin2 = np.vstack(
[states_kin2,
staticGroupSimple2(states_kin2, i)])
elif np.absolute(example.states[startpoint + n][0] -
example.states[startpoint + n - 1][0]) > 0.1:
states_kin = np.vstack(
[states_kin, example.states[startpoint + n]])
states_kin2 = np.vstack(
[states_kin2, example.states[startpoint + n]])
else:
nex = predict_next(states_kin[n, :], example.actions[n + 1],
estimators)
nex[0] = np.arcsin(nex[0]) * 2
states_kin = np.vstack([
states_kin,
staticGroupSimple2(states_kin[n, :], example.actions[n]) -
nex
])
states_kin2 = np.vstack([
states_kin2,
staticGroupSimple2(states_kin2[n, :],
example.actions[n - 1])
])
ex = np.array(example.states[startpoint::])
x = range(len(ex))
f, axarr = plt.subplots(2, sharex=True)
axarr[0].scatter(x, ex[:, 1], color="blue", label="data")
axarr[0].scatter(x,
states_kin[:, 1],
color="green",
alpha=0.4,
label="kinematic")
axarr[0].scatter(x,
states_kin2[:, 1],
color="red",
alpha=0.2,
label="kinematic2")
axarr[0].legend(bbox_to_anchor=(1., 1, 0., -0.06), loc=1)
axarr[1].scatter(x, ex[:, 0], color="blue")
axarr[1].scatter(x, states_kin[:, 0], color="green", alpha=0.4)
axarr[1].scatter(x, states_kin2[:, 0], color="red", alpha=0.2)
axarr[1].set_ylabel("Angle/rad")
axarr[0].set_ylabel("Distance")
axarr[1].set_xlabel("Example")
def plot_reg_drift(idx,startpoint):
m = DiscModel()
examples = extract_info(m,"Full","good")
example = examples[idx]
estimators = learn_tran_regression(300,4)
states_kin = np.array(example.states[startpoint])
for n,i in enumerate(example.actions[startpoint:-1]):
if n == 0:
states_kin = np.vstack([states_kin,staticGroupSimple2(states_kin,i)])
elif np.absolute(example.states[startpoint+n][0] -example.states[startpoint + n-1][0])>0.1:
states_kin = np.vstack([states_kin,example.states[startpoint+n]])
else:
print example.actions[n]
states_kin = np.vstack([states_kin,predict_next(states_kin[n,:],example.actions[n],estimators)])
ex = np.array(example.states[startpoint::])
x = range(len(ex))
f, axarr = plt.subplots(2,sharex = True)
axarr[0].scatter(x,ex[:,1],color = "blue",label = "data")
axarr[0].scatter(x,states_kin[:,1],color = "green",alpha = 0.4, label = "kinematic")
axarr[0].legend(bbox_to_anchor=(1., 1,0.,-0.06),loc=1)
axarr[1].scatter(x,ex[:,0],color = "blue")
axarr[1].scatter(x,states_kin[:,0],color = "green", alpha = 0.4)
axarr[1].set_ylabel("Angle/rad")
axarr[0].set_ylabel("Distance")
axarr[1].set_xlabel("Example")
plt.show()
def buildTransitionFunction(self, bin_samples, learn=True):
def transition_smart():
self.transition = Transition()
tot_states = self.transition.tot_states = transition_f.shape[1]
tot_actions = self.transition.tot_actions = transition_f.shape[0]
self.transition.backward = [{} for j in xrange(tot_states *
tot_actions)]
self.transition.forward = [{} for j in xrange(tot_states *
tot_actions)]
idx = np.transpose(np.array(np.nonzero(transition_f)))
for i in idx:
self.transition.backward[i[0] + i[1] * tot_actions][str(
i[2])] = transition_f[i[0], i[1], i[2]]
self.transition.forward[i[0] + i[2] * tot_actions][str(
i[1])] = transition_f[i[0], i[1], i[2]]
print self.transition.backward
#print "Got Here estemated"
transition_f = np.zeros([
self.disc.tot_actions, self.disc.tot_states, self.disc.tot_states
])
transtion_test = np.zeros([
self.disc.tot_actions, self.disc.tot_states, self.disc.tot_states
])
estimators = learn_correction(10, 10)
for i in xrange(self.disc.tot_states):
for j in xrange(self.disc.tot_actions):
bins = self.disc.stateToBins(i)
for k in xrange(bin_samples):
assert bin_samples != 0
if bin_samples == 1:
samp = False
else:
samp = True
quantity = self.disc.binsToQuantity(bins, sample=samp)
action = self.disc.indexToAction(j)
correction = predict_next(quantity, action, estimators)
correction[0] = np.arcsin(correction[0]) * 2
next_quantity = staticGroupSimple2(quantity,
action) - correction
#next_quantity2 = staticGroupSimple2(quantity,action)
#if np.sum(next_quantity[:1]-next_quantity2[:1])>1:
# print "Quantity",quantity
# print "ACTION",action
# print next_quantity[:2],next_quantity2[:2]
next_state = self.disc.quantityToState(next_quantity)
#next2 = self.disc.quantityToState(next_quantity2)
#print "state",ic
#print "actions",j
transition_f[j, i, next_state] += 1
if learn == True:
trans = learn_tran()
transition_f = np.add(transition_f, 2 * trans)
row_sums = np.sum(transition_f, axis=2)
transition_f = transition_f / row_sums[:, :, np.newaxis]
self.transition_f = transition_f
transition_smart()
def plotDrift(idx,startpoint,estimators):
m = DiscModel()
examples = extract_info(m,"Full","good")
for example in examples[0:idx]:
states_kin = np.array(example.states[startpoint])
states_kin2 =np.array(example.states[startpoint])
for n,i in enumerate(example.actions[startpoint:-1]):
if n == 0:
nex = predict_next(states_kin,i,estimators)
nex[0] = np.arcsin(nex[0])*2
states_kin = np.vstack([states_kin,staticGroupSimple2(states_kin,i)-nex])
states_kin2 = np.vstack([states_kin2,staticGroupSimple2(states_kin2,i)])
elif np.absolute(example.states[startpoint+n][0] -example.states[startpoint + n-1][0])>0.1:
states_kin = np.vstack([states_kin,example.states[startpoint+n]])
states_kin2 = np.vstack([states_kin2,example.states[startpoint+n]])
else:
nex = predict_next(states_kin[n,:],example.actions[n+1],estimators)
nex[0] = np.arcsin(nex[0])*2
states_kin = np.vstack([states_kin,staticGroupSimple2(states_kin[n,:],example.actions[n])-nex])
states_kin2 = np.vstack([states_kin2,staticGroupSimple2(states_kin2[n,:],example.actions[n-1])])
ex = np.array(example.states[startpoint::])
x = range(len(ex))
f, axarr = plt.subplots(2,sharex = True)
axarr[0].scatter(x,ex[:,1],color = "blue",label = "data")
axarr[0].scatter(x,states_kin[:,1],color = "green",alpha = 0.4, label = "kinematic")
axarr[0].scatter(x,states_kin2[:,1],color = "red",alpha = 0.2, label = "kinematic2")
axarr[0].legend(bbox_to_anchor=(1., 1,0.,-0.06),loc=1)
axarr[1].scatter(x,ex[:,0],color = "blue")
axarr[1].scatter(x,states_kin[:,0],color = "green", alpha = 0.4)
axarr[1].scatter(x,states_kin2[:,0],color = "red", alpha = 0.2)
axarr[1].set_ylabel("Angle/rad")
axarr[0].set_ylabel("Distance")
axarr[1].set_xlabel("Example")
def buildTransitionFunction(self,bin_samples,learn =True):
def transition_smart():
self.transition = Transition()
tot_states=self.transition.tot_states = transition_f.shape[1]
tot_actions=self.transition.tot_actions =transition_f.shape[0]
self.transition.backward = [{} for j in xrange(tot_states*tot_actions)]
self.transition.forward = [{} for j in xrange(tot_states*tot_actions)]
idx = np.transpose(np.array(np.nonzero(transition_f)))
for i in idx:
self.transition.backward[i[0] + i[1]*tot_actions][str(i[2])] = transition_f[i[0],i[1],i[2]]
self.transition.forward[i[0] + i[2]*tot_actions][str(i[1])] = transition_f[i[0],i[1],i[2]]
print self.transition.backward
#print "Got Here estemated"
transition_f = np.zeros([self.disc.tot_actions,self.disc.tot_states,self.disc.tot_states])
transtion_test = np.zeros([self.disc.tot_actions,self.disc.tot_states,self.disc.tot_states])
estimators = learn_correction(10,10)
for i in xrange(self.disc.tot_states):
for j in xrange(self.disc.tot_actions):
bins = self.disc.stateToBins(i)
for k in xrange(bin_samples):
assert bin_samples != 0
if bin_samples==1:
samp = False
else:
samp = True
quantity = self.disc.binsToQuantity(bins,sample = samp)
action = self.disc.indexToAction(j)
correction = predict_next(quantity,action,estimators)
correction[0] = np.arcsin(correction[0])*2
next_quantity = staticGroupSimple2(quantity,action) - correction
#next_quantity2 = staticGroupSimple2(quantity,action)
#if np.sum(next_quantity[:1]-next_quantity2[:1])>1:
# print "Quantity",quantity
# print "ACTION",action
# print next_quantity[:2],next_quantity2[:2]
next_state = self.disc.quantityToState(next_quantity)
#next2 = self.disc.quantityToState(next_quantity2)
#print "state",ic
#print "actions",j
transition_f[j,i,next_state] += 1
if learn ==True:
trans = learn_tran()
transition_f =np.add( transition_f ,2* trans)
row_sums = np.sum(transition_f,axis=2)
transition_f = transition_f/row_sums[:,:,np.newaxis]
self.transition_f = transition_f
transition_smart()
def plot_reg_drift(idx, startpoint):
m = DiscModel()
examples = extract_info(m, "Full", "good")
example = examples[idx]
estimators = learn_tran_regression(300, 4)
states_kin = np.array(example.states[startpoint])
for n, i in enumerate(example.actions[startpoint:-1]):
if n == 0:
states_kin = np.vstack(
[states_kin, staticGroupSimple2(states_kin, i)])
elif np.absolute(example.states[startpoint + n][0] -
example.states[startpoint + n - 1][0]) > 0.1:
states_kin = np.vstack(
[states_kin, example.states[startpoint + n]])
else:
print example.actions[n]
states_kin = np.vstack([
states_kin,
predict_next(states_kin[n, :], example.actions[n], estimators)
])
ex = np.array(example.states[startpoint::])
x = range(len(ex))
f, axarr = plt.subplots(2, sharex=True)
axarr[0].scatter(x, ex[:, 1], color="blue", label="data")
axarr[0].scatter(x,
states_kin[:, 1],
color="green",
alpha=0.4,
label="kinematic")
axarr[0].legend(bbox_to_anchor=(1., 1, 0., -0.06), loc=1)
axarr[1].scatter(x, ex[:, 0], color="blue")
axarr[1].scatter(x, states_kin[:, 0], color="green", alpha=0.4)
axarr[1].set_ylabel("Angle/rad")
axarr[0].set_ylabel("Distance")
axarr[1].set_xlabel("Example")
plt.show()
