def get_dataset(examples):
next_kin = []
next_data = []
for example in examples:
for n, step in enumerate(example.states[:-1]):
next_kin.append(staticGroupSimple2(step, example.actions[n],
0.129))
next_data.append(example.states[n + 1])
#if np.sin((np.absolute(np.array(next_kin)[-1,0]-np.array(next_data)[-1,0]))/2) >0.1:
# print np.array(next_kin)[-1,:]
# print np.array(next_data)[-1,:]
# print step,example.actions[n+1]
# print example.states[n+2]
# print '-------------------------------------------'
next_kin = np.array(next_kin)
next_data = np.array(next_data)
diff = next_kin - next_data
diff[:, 0] = np.sin(diff[:, 0] / 2)
#simple filter
for i in range(len(diff[:, 0])):
if np.absolute(diff[i, 0]) > 0.2:
diff[i, 0] = 0
X = np.concatenate([
np.hstack((example.states[:-1, :], example.actions[:-1, :]))
for example in examples
],
axis=0)
return X, diff
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 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 get_dataset(examples): next_kin = [] next_data = [] for example in examples: for n, step in enumerate(example.states[:-1]): next_kin.append(staticGroupSimple2(step,example.actions[n],0.129)) next_data.append(example.states[n+1]) #if np.sin((np.absolute(np.array(next_kin)[-1,0]-np.array(next_data)[-1,0]))/2) >0.1: # print np.array(next_kin)[-1,:] # print np.array(next_data)[-1,:] # print step,example.actions[n+1] # print example.states[n+2] # print '-------------------------------------------' next_kin = np.array(next_kin) next_data = np.array(next_data) diff = next_kin-next_data diff[:,0] = np.sin(diff[:,0]/2) #simple filter for i in range(len(diff[:,0])): if np.absolute(diff[i,0]) >0.2: diff[i,0] = 0 X =np.concatenate([np.hstack((example.states[:-1,:],example.actions[:-1,:])) for example in examples],axis = 0) return X,diff