def learn_tran_regression(num_learners, tree_depth):
#learns a transition function from data using adaboost.
#------------------------------------------------------
#Load all data
examples = loadFile2()
examples2 = loadFile()
tot_examples = examples + examples2
#Folds get generated here
train_examples = tot_examples
#test_examples = tot_examples[-2::]
dimensions = examples[0].states.shape[1]
#Build X which is the same for all regressors
estimators = []
X = np.concatenate([
np.hstack((example.states[:-1, :], example.actions[:-1, :]))
for example in train_examples
],
axis=0)
y = np.concatenate([example.states[1:, :] for example in train_examples],
axis=0)
estimators = [
adaboost_reg(X, y[:, i], num_learners, tree_depth)
for i in range(dimensions)
]
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_regression(num_learners,tree_depth): #learns a transition function from data using adaboost. #------------------------------------------------------ #Load all data examples = loadFile2() examples2 = loadFile() tot_examples = examples + examples2 #Folds get generated here train_examples = tot_examples #test_examples = tot_examples[-2::] dimensions = examples[0].states.shape[1] #Build X which is the same for all regressors estimators = [] X =np.concatenate([np.hstack((example.states[:-1,:],example.actions[:-1,:])) for example in train_examples],axis = 0) y =np.concatenate([example.states[1:,:] for example in train_examples],axis = 0) estimators = [adaboost_reg(X,y[:,i],num_learners,tree_depth) for i in range(dimensions)] 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
state_freq_test, state_action_frequencies_test = forward_sa(policy_test, model.transition, start_states, steps)
reward_diff = np.sum(np.sum(np.absolute(model.reward_f - r_initial)))
policy_diff = np.sum(np.sum(np.absolute(policy_test - policy_ref)))
print "Difference in Reward --->", reward_diff
print "Difference in Policy --->", policy_diff
X = np.reshape(model.feature_f, (disc_model.tot_states * disc_model.tot_actions, 4))
Y = (state_action_frequencies_ref - state_action_frequencies_test).reshape(
(disc_model.tot_states * disc_model.tot_actions)
)
# X = X[np.nonzero(Y)[0]]
# Y = Y[np.nonzero(Y)[0]]
print np.nonzero(Y)[0].shape
print Y.shape
print "starting adaboost"
ht = adaboost_reg(X, Y, 100, 10)
print "finished adaboost"
print "reducing gradient"
model.reward_f = (
model.reward_f.reshape(states * actions)
* np.exp(-1.0 * ht.predict(model.feature_f.reshape(states * actions, features)))
).reshape(actions, states)
print "finished gradient"
def sign(x):
if x != 0:
return math.copysign(1, x)
else:
return 0
avg_counts+=example["counts"] avg_counts/=len(examples) X= np.reshape(model.feature_f,(disc_model.tot_states*disc_model.tot_actions,4)) Y = np.zeros([disc_model.tot_states*disc_model.tot_actions]) def sign(x): if x!=0: return math.copysign(1,x) else: return 0 #Y = np.array(map(int,map(sign,(avg_counts - state_action_frequencies).reshape((disc_model.tot_states*disc_model.tot_actions))))) Y = (avg_counts - state_action_frequencies).reshape((disc_model.tot_states*disc_model.tot_actions)) X = X[np.nonzero(Y)[0]] Y = Y[np.nonzero(Y)[0]] ht = adaboost_reg(X,Y,200,4) for i in range(disc_model.tot_states): for j in range(disc_model.tot_actions): print "Before--->", model.reward_f[j,i] model.reward_f[j,i] = model.reward_f[j,i]*np.exp(-0.001*ht.predict([model.feature_f[j,i]])) print "After---->",model.reward_f[j,i] print np.amax(model.reward_f)
policy_test, model.transition, start_states, steps)
reward_diff = np.sum(np.sum(np.absolute(model.reward_f - r_initial)))
policy_diff = np.sum(np.sum(np.absolute(policy_test - policy_ref)))
print "Difference in Reward --->", reward_diff
print "Difference in Policy --->", policy_diff
X = np.reshape(model.feature_f,
(disc_model.tot_states * disc_model.tot_actions, 4))
Y = (state_action_frequencies_ref - state_action_frequencies_test).reshape(
(disc_model.tot_states * disc_model.tot_actions))
#X = X[np.nonzero(Y)[0]]
#Y = Y[np.nonzero(Y)[0]]
print np.nonzero(Y)[0].shape
print Y.shape
print "starting adaboost"
ht = adaboost_reg(X, Y, 100, 10)
print "finished adaboost"
print "reducing gradient"
model.reward_f = (model.reward_f.reshape(states * actions) * np.exp(
-1. * ht.predict(model.feature_f.reshape(states * actions, features)))
).reshape(actions, states)
print "finished gradient"
def sign(x):
if x != 0:
return math.copysign(1, x)
else:
return 0
avg_counts += example["counts"]
avg_counts /= len(examples)
X = np.reshape(model.feature_f,
(disc_model.tot_states * disc_model.tot_actions, 4))
Y = np.zeros([disc_model.tot_states * disc_model.tot_actions])
def sign(x):
if x != 0:
return math.copysign(1, x)
else:
return 0
#Y = np.array(map(int,map(sign,(avg_counts - state_action_frequencies).reshape((disc_model.tot_states*disc_model.tot_actions)))))
Y = (avg_counts - state_action_frequencies).reshape(
(disc_model.tot_states * disc_model.tot_actions))
X = X[np.nonzero(Y)[0]]
Y = Y[np.nonzero(Y)[0]]
ht = adaboost_reg(X, Y, 200, 4)
for i in range(disc_model.tot_states):
for j in range(disc_model.tot_actions):
print "Before--->", model.reward_f[j, i]
model.reward_f[j, i] = model.reward_f[j, i] * np.exp(
-0.001 * ht.predict([model.feature_f[j, i]]))
print "After---->", model.reward_f[j, i]
print np.amax(model.reward_f)
