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 loss_augmentation(amount): examples = loadFile() disc = DiscModel() loss_aug = np.ones([disc.tot_states])*amount for example in examples: for step in range(len(example.states)): state = disc.quantityToState(example.states[step]) action = disc.actionToIndex(example.actions[step]) loss_aug[state] = 1 return loss_aug
def loss_augmentation(amount):
examples = loadFile()
disc = DiscModel()
loss_aug = np.ones([disc.tot_states]) * amount
for example in examples:
for step in range(len(example.states)):
state = disc.quantityToState(example.states[step])
action = disc.actionToIndex(example.actions[step])
loss_aug[state] = 1
return loss_aug
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
