def learn_tran():
#examples = loadFile2()
#examples2 = loadFile()
examples3 = loadFile3("data/UPO/Experiments Folder/2014-11-17 11.08.31 AM/")
examples4 = loadFile3("data/UPO/Experiments Folder/2014-11-28 01.22.03 PM/")
tot_examples = examples3[:12] + examples4[:12]
#tot_examples = examples + examples2
disc = DiscModel()
unorm_transition_f = np.zeros([disc.tot_actions,disc.tot_states,disc.tot_states])
for example in tot_examples:
for i in range(len(example.states)-1):
state = disc.quantityToState(example.states[i])
action = disc.actionToIndex(example.actions[i+1])
next = disc.quantityToState(example.states[i+1])
unorm_transition_f[action,state,next]+=1
return unorm_transition_f
def learn_correction(num_learners, tree_depth):
def plot_fit():
f, axarr = plt.subplots(2, sharex=True)
x = range(diff_test.shape[0])
axarr[0].plot(x, diff_test[:, 0])
axarr[0].plot(x, fit[0, :], color='red', alpha=0.6)
axarr[0].set_xlabel("Data Sample")
axarr[0].set_ylabel("sin(angle difference)")
axarr[1].plot(x, diff_test[:, 1])
axarr[1].plot(x, fit[1, :], color='red', alpha=0.6)
axarr[1].set_xlabel("Data Sample")
axarr[1].set_ylabel("Distance Difference")
#learns a transition function from data using adaboost.
#------------------------------------------------------
#Load all data
examples = loadFile3("data/UPO/Experiments Folder/2014-11-17 11.08.31 AM/")
examples2 = loadFile3(
"data/UPO/Experiments Folder/2014-11-28 01.22.03 PM/")
tot_examples = examples[0:12] + examples2[0:12]
#Folds get generated here
train_examples = tot_examples
test_examples = tot_examples[0:5]
X_train, diff_train = get_dataset(train_examples)
X_test, diff_test = get_dataset(test_examples)
#plt.show()
#test_examples = tot_examples[-2::]
dimensions = examples[0].states.shape[1]
print 'Dimentions', dimensions
#Build X which is the same for all regressors
#y =np.concatenate([example.states[1:,:] for example in train_examples],axis = 0)
estimators = [
adaboost_reg(X_train, diff_train[:, i], num_learners, tree_depth)
for i in range(dimensions)
]
fit = []
#for example in train_examples:
# for n, step in enumerate(example.states[:-1]):
# fit.append(predict_next(step,exax]mple.actions[n+1],estimators))
fit = np.array([estimator.predict(X_test) for estimator in estimators])
print fit.shape
#fit[:,0] =np.arcsin(fit[:,0])*2
#plot_fit()
return estimators
def learn_correction(num_learners,tree_depth):
def plot_fit():
f,axarr = plt.subplots(2,sharex=True)
x = range(diff_test.shape[0])
axarr[0].plot(x,diff_test[:,0])
axarr[0].plot(x,fit[0,:],color='red',alpha = 0.6)
axarr[0].set_xlabel("Data Sample")
axarr[0].set_ylabel("sin(angle difference)")
axarr[1].plot(x,diff_test[:,1])
axarr[1].plot(x,fit[1,:],color='red',alpha = 0.6)
axarr[1].set_xlabel("Data Sample")
axarr[1].set_ylabel("Distance Difference")
#learns a transition function from data using adaboost.
#------------------------------------------------------
#Load all data
examples = loadFile3("data/UPO/Experiments Folder/2014-11-17 11.08.31 AM/")
examples2 = loadFile3("data/UPO/Experiments Folder/2014-11-28 01.22.03 PM/")
tot_examples = examples[0:12] + examples2[0:12]
#Folds get generated here
train_examples = tot_examples
test_examples = tot_examples[0:5]
X_train,diff_train = get_dataset(train_examples)
X_test,diff_test = get_dataset(test_examples)
#plt.show()
#test_examples = tot_examples[-2::]
dimensions = examples[0].states.shape[1]
print 'Dimentions',dimensions
#Build X which is the same for all regressors
#y =np.concatenate([example.states[1:,:] for example in train_examples],axis = 0)
estimators = [adaboost_reg(X_train,diff_train[:,i],num_learners,tree_depth) for i in range(dimensions)]
fit = []
#for example in train_examples:
# for n, step in enumerate(example.states[:-1]):
# fit.append(predict_next(step,exax]mple.actions[n+1],estimators))
fit = np.array([estimator.predict(X_test) for estimator in estimators])
print fit.shape
#fit[:,0] =np.arcsin(fit[:,0])*2
#plot_fit()
return estimators
def learn_tran():
#examples = loadFile2()
#examples2 = loadFile()
examples3 = loadFile3(
"data/UPO/Experiments Folder/2014-11-17 11.08.31 AM/")
examples4 = loadFile3(
"data/UPO/Experiments Folder/2014-11-28 01.22.03 PM/")
tot_examples = examples3[:12] + examples4[:12]
#tot_examples = examples + examples2
disc = DiscModel()
unorm_transition_f = np.zeros(
[disc.tot_actions, disc.tot_states, disc.tot_states])
for example in tot_examples:
for i in range(len(example.states) - 1):
state = disc.quantityToState(example.states[i])
action = disc.actionToIndex(example.actions[i + 1])
next = disc.quantityToState(example.states[i + 1])
unorm_transition_f[action, state, next] += 1
return unorm_transition_f