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
