def main():
num_train = 200
num_test = 1000
X_train = np.random.randn(num_train, 1) * 5.0
Y_train = np.cos(X_train) + 10.0
X_test = np.linspace(-10, 10, num_test)
X_test = X_test.reshape((num_test, 1))
Y_test_truth = np.cos(X_test) + 10.0
mu, sigma, Sigma = gp.predict_optimized(X_train,
Y_train,
X_test,
debug=True)
utils_plotting.plot_gp(X_train, Y_train, X_test, mu, sigma, Y_test_truth,
'test_optimized_many_points')
def main():
num_points = 100
is_fixed_noise = False
bounds = obj_fun.get_bounds()
model_bo = bo.BO(bounds, debug=True)
X_init = model_bo.get_initial('uniform',
fun_objective=fun_target,
int_samples=num_points)
X_test = bo.get_grids(bounds, 50)
mu, sigma, Sigma = gp.predict_optimized(X_init,
fun_target(X_init),
X_test,
is_fixed_noise=is_fixed_noise,
debug=True)
def main(scale, str_postfix):
X_train = np.array([
[-3.0],
[-2.0],
[-1.0],
[2.0],
[1.2],
[1.1],
])
Y_train = np.cos(X_train) * scale
num_test = 200
X_test = np.linspace(-3, 3, num_test)
X_test = X_test.reshape((num_test, 1))
Y_test_truth = np.cos(X_test) * scale
mu, sigma, Sigma = gp.predict_optimized(X_train, Y_train, X_test, is_fixed_noise=False)
utils_plotting.plot_gp(X_train, Y_train, X_test, mu, sigma, Y_test_truth, PATH_SAVE, 'test_optimized_{}_y'.format(str_postfix))
def main(str_cov):
np.random.seed(42)
X_train = np.array([
[-3.0],
[-1.0],
[3.0],
[1.0],
[2.0],
])
Y_train = np.cos(X_train) + np.random.randn(X_train.shape[0], 1) * 0.2
num_test = 200
X_test = np.linspace(-3, 3, num_test)
X_test = X_test.reshape((num_test, 1))
Y_test_truth = np.cos(X_test)
mu, sigma, Sigma = gp.predict_optimized(X_train, Y_train, X_test, str_cov=str_cov, is_fixed_noise=False, debug=True)
utils_plotting.plot_gp(X_train, Y_train, X_test, mu, sigma, Y_test_truth, path_save=PATH_SAVE, str_postfix='cos_' + str_cov)
def main(fun_prior, str_prior):
X_train = np.array([
[-3.0],
[-2.0],
[-1.0],
])
Y_train = np.cos(X_train) + 2.0
num_test = 200
X_test = np.linspace(-3, 6, num_test)
X_test = X_test.reshape((num_test, 1))
Y_test_truth = np.cos(X_test) + 2.0
mu, sigma, Sigma = gp.predict_optimized(X_train,
Y_train,
X_test,
prior_mu=fun_prior)
utils_plotting.plot_gp(X_train, Y_train, X_test, mu, sigma, Y_test_truth,
PATH_SAVE, 'optimized_prior_{}'.format(str_prior))
def test_predict_optimized():
np.random.seed(42)
dim_X = 2
num_X = 5
num_X_test = 20
X = np.random.randn(num_X, dim_X)
Y = np.random.randn(num_X, 1)
X_test = np.random.randn(num_X_test, dim_X)
prior_mu = None
with pytest.raises(AssertionError) as error:
gp.predict_optimized(X, Y, X_test, str_cov='se', prior_mu='abc')
with pytest.raises(AssertionError) as error:
gp.predict_optimized(X, Y, X_test, str_cov=1, prior_mu=prior_mu)
with pytest.raises(AssertionError) as error:
gp.predict_optimized(X, Y, 1, str_cov='se', prior_mu=prior_mu)
with pytest.raises(AssertionError) as error:
gp.predict_optimized(X, 1, X_test, str_cov='se', prior_mu=prior_mu)
with pytest.raises(AssertionError) as error:
gp.predict_optimized(1, Y, X_test, str_cov='se', prior_mu=prior_mu)
with pytest.raises(AssertionError) as error:
gp.predict_optimized(np.random.randn(num_X, 1), Y, X_test, str_cov='se', prior_mu=prior_mu)
with pytest.raises(AssertionError) as error:
gp.predict_optimized(np.random.randn(10, dim_X), Y, X_test, str_cov='se', prior_mu=prior_mu)
with pytest.raises(AssertionError) as error:
gp.predict_optimized(X, np.random.randn(10, 1), X_test, str_cov='se', prior_mu=prior_mu)
with pytest.raises(AssertionError) as error:
gp.predict_optimized(X, Y, X_test, is_fixed_noise=1)
with pytest.raises(AssertionError) as error:
gp.predict_optimized(X, Y, X_test, debug=1)
mu_Xs, sigma_Xs, Sigma_Xs = gp.predict_optimized(X, Y, X_test)
print(mu_Xs)
print(sigma_Xs)
print(Sigma_Xs)