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():
X_train = np.array([
[-3],
[-1],
[1],
[2],
])
Y_train = np.cos(X_train) + np.random.randn(X_train.shape[0], 1) * 0.1
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, Sigma = gp.predict_test(X_train, Y_train, X_test, hyps)
utils_plotting.plot_gp(X_train,
Y_train,
X_test,
mu,
sigma,
Y_test_truth,
path_save=PATH_SAVE,
str_postfix='cos')
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(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():
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(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():
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')
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_plot_gp():
dim_X = 1
dim_Y = 1
num_train = 5
num_test = 10
X_train = np.zeros((num_train, dim_X))
Y_train = np.ones((num_train, dim_Y))
X_test = np.zeros((num_test, dim_X))
mu = np.zeros((num_test, dim_Y))
sigma = np.zeros((num_test, dim_Y))
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, Y_train, X_test, mu, 1)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, Y_train, X_test, mu,
np.arange(0, num_test))
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, Y_train, X_test, 1, sigma)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, Y_train, X_test,
np.arange(0, num_test), sigma)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, Y_train, 1, mu, sigma)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, Y_train, np.arange(0, num_test), mu,
sigma)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, 1, X_test, mu, sigma)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, np.arange(0, num_train), X_test, mu,
sigma)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(1, Y_train, X_test, mu, sigma)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(np.arange(0, num_test), Y_train, X_test, mu,
sigma)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(np.zeros((num_train, 2)), Y_train, X_test, mu,
sigma)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, Y_train, np.zeros((num_train, 2)), mu,
sigma)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, np.ones((num_train, 2)), X_test, mu,
sigma)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, np.ones((10, 1)), X_test, mu, sigma)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, Y_train, X_test, np.zeros(
(num_test, 2)), sigma)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, Y_train, X_test, mu,
np.zeros((num_test, 2)))
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, Y_train, X_test, mu, np.zeros((11, 1)))
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, Y_train, X_test, np.zeros((11, 1)),
sigma)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train,
Y_train,
X_test,
mu,
sigma,
Y_test_truth=np.arange(0, num_test))
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train,
Y_train,
X_test,
mu,
sigma,
Y_test_truth=np.zeros((num_test, 2)))
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train,
Y_train,
X_test,
mu,
sigma,
Y_test_truth=np.zeros((20, 1)))
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train,
Y_train,
X_test,
mu,
sigma,
Y_test_truth=1)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train,
Y_train,
X_test,
mu,
sigma,
path_save=1)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train,
Y_train,
X_test,
mu,
sigma,
str_postfix=1)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train,
Y_train,
X_test,
mu,
sigma,
str_x_axis=1)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train,
Y_train,
X_test,
mu,
sigma,
str_y_axis=1)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train, Y_train, X_test, mu, sigma, is_tex=1)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train,
Y_train,
X_test,
mu,
sigma,
is_zero_axis=1)
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train,
Y_train,
X_test,
mu,
sigma,
time_pause='abc')
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train,
Y_train,
X_test,
mu,
sigma,
range_shade='abc')
with pytest.raises(AssertionError) as error:
utils_plotting.plot_gp(X_train,
Y_train,
X_test,
mu,
sigma,
colors='abc')