def main():
np.random.seed(42)
X_train = np.array([
[-3],
[-1],
[3],
[1],
[2],
])
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)
hyps = {
'signal': 0.5,
'lengthscales': 0.5,
'noise': 0.02,
}
mu, sigma = gp.predict_optimized(X_train,
Y_train,
X_test,
str_cov=STR_COV,
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():
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_grid(bounds, 50)
mu, sigma = gp.predict_optimized(X_init, fun_target(X_init), X_test, is_fixed_noise=is_fixed_noise, debug=True)
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 = 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
model_bo = bo.BO(INFO_TARGET.get('bounds'), debug=True)
X_init = model_bo.get_initial('uniform',
fun_objective=fun_target,
int_samples=num_points)
X_test = bo.get_grid(INFO_TARGET.get('bounds'), 50)
mu, sigma = gp.predict_optimized(X_init,
np.expand_dims(fun_target(X_init),
axis=1),
X_test,
is_fixed_noise=is_fixed_noise,
debug=True)
def main():
X_train = np.array([
[-3.0],
[-2.0],
[-1.0],
[2.0],
[1.2],
[1.1],
])
Y_train = np.cos(X_train) * 0.01
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) * 0.01
mu, sigma = gp.predict_optimized(X_train, Y_train, X_test, is_fixed_noise=True)
utils_plotting.plot_gp(X_train, Y_train, X_test, mu, sigma, Y_test_truth, PATH_SAVE, 'test_optimized_large_y')
def main():
X_train = np.array([
[-3.0],
[-2.0],
[-1.0],
[2.0],
[1.2],
[1.1],
])
Y_train = np.cos(X_train) + 10.0
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) + 10.0
mu, sigma = gp.predict_optimized(X_train, Y_train, X_test)
utils_plotting.plot_gp(X_train, Y_train, X_test, mu, sigma, Y_test_truth,
PATH_SAVE, 'optimized')
def main():
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
prior_mu = cosine
mu, sigma = gp.predict_optimized(X_train,
Y_train,
X_test,
prior_mu=prior_mu)
utils_plotting.plot_gp(X_train, Y_train, X_test, mu, sigma, Y_test_truth,
PATH_SAVE, 'optimized_prior_cosine')
X_train = np.array([
[2.],
[3.],
[5.],
[7.],
[9.],
]) / 10.0
Y_train = np.array([
[-0.2],
[-0.25],
[0.05],
[-0.1],
[0.1],
]) * 1.0
mu, sigma = gp.predict_optimized(X_train, Y_train, X_test, str_cov='matern52')
mu = np.squeeze(mu)
sigma = np.squeeze(sigma)
fig = plt.figure(figsize=(4, 3))
ax = fig.gca()
ax.plot(X_test.flatten(), mu, color=(255./255., 179./255., 0./255.), linewidth=8)
'''
range_shade = 4.0
ax.fill_between(
X_test.flatten(),
mu - range_shade * sigma,
mu + range_shade * sigma,
color=(102./255., 102./255., 92./255.),
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 = gp.predict_optimized(X, Y, X_test)
print(mu_Xs)
print(sigma_Xs)
