import math import acquisition_function as acq D = 10 # number of features n_training = 10 n_test = 5 max_iter = 100 # maximum number of iterations sigma_0 = 0.001 # Sample training input and ouput ytrain = np.matrix(np.random.uniform(-5, 5, (n_training, D))) fytrain = acq.sample_training_output(ytrain) # Step 3 - 6 for t in range(0, max_iter): # Set of points to be tested ytest = np.matrix(np.random.uniform(-5, 5, (n_test, D))) # Get mu and sigma mu, sigma = acq.gp_posterior(ytrain, ytest, fytrain, sigma_0, n_test) # Find ybest ybest = acq.gp_optimize(ytest, t, D, mu, sigma, n_test) # Augment the data ytrain, fytrain = acq.augment_data(ytrain, fytrain, ybest) print ybest
#define initial mu and sigma mu = 0 sigma = np.matrix(1) # Step 3 - 6 #ytrain : D dimensional dataset #ytest: Y subset #fytrain: sample from dataset (ytrain) for t in range(0, max_iter): number_of_samples = t + 1 #Step 3 : sample out of y , and create Y Y = acq.select_sample_set(number_of_samples, y) # Select points from bounded box to be tested #ytest =Y #ytest = acq.select_test_set(n_test, Y) # Get mu and sigma mu, sigma, ybest = acq.gp_posterior(ytrain, sigma, ytest, fytrain, A, t, d, number_of_samples) # Find ybest # ybest = acq.gp_optimize(ytest, t, D, mu, sigma, n_test) # Augment the data # ytrain, fytrain = acq.augment_data(ytrain, fytrain, ybest, A) print ybest
import numpy as np import math import acquisition_function as acq D = 10 # number of features n_training = 10 n_test = 5 max_iter = 100 # maximum number of iterations sigma_0 = 0.001 # Sample training input and ouput ytrain = np.matrix(np.random.uniform(-5, 5, (n_training, D))) fytrain = acq.sample_training_output(ytrain) # Step 3 - 6 for t in range(0, max_iter): # Set of points to be tested ytest = np.matrix(np.random.uniform(-5, 5, (n_test, D))) # Get mu and sigma mu, sigma = acq.gp_posterior(ytrain, ytest, fytrain, sigma_0, n_test) # Find ybest ybest = acq.gp_optimize(ytest, t, D, mu, sigma, n_test) # Augment the data ytrain, fytrain = acq.augment_data(ytrain, fytrain, ybest) print ybest
# Step 3 - 6 #ytrain : D dimensional dataset #ytest: Y subset #fytrain: sample from dataset (ytrain) for t in range(0, max_iter): number_of_samples = t+1 #Step 3 : sample out of y , and create Y Y = acq.select_sample_set(number_of_samples,y) # Select points from bounded box to be tested #ytest =Y #ytest = acq.select_test_set(n_test, Y) # Get mu and sigma mu, sigma, ybest = acq.gp_posterior(ytrain, sigma, ytest, fytrain, A, t, d, number_of_samples) # Find ybest # ybest = acq.gp_optimize(ytest, t, D, mu, sigma, n_test) # Augment the data # ytrain, fytrain = acq.augment_data(ytrain, fytrain, ybest, A) print ybest