def main():
np.random.seed(42)
X_train = np.array([
[-3.0],
[-1.0],
[0.0],
[1.0],
[2.0],
[4.0],
])
Y_train = np.cos(X_train) + np.random.randn(X_train.shape[0], 1) * 0.2
num_test = 10000
X_test = np.linspace(-5, 5, num_test)
X_test = X_test.reshape((num_test, 1))
Y_test = np.cos(X_test)
num_trees = 100
depth_max = 5
size_min_leaf = 2
ratio_sampling = 0.8
replace_samples = False
num_features = 1
split_random_location = True
trees = trees_generic_trees.get_generic_trees(
X_train, Y_train, num_trees, depth_max, size_min_leaf, ratio_sampling, replace_samples, num_features, split_random_location
)
mu, sigma = trees_common.predict_by_trees(X_test, trees)
utils_plotting.plot_gp_via_distribution(X_train, Y_train, X_test, mu, sigma, Y_test, path_save=PATH_SAVE, str_postfix='cos')
def main():
time_start = time.time()
np.random.seed(42)
X_train = np.array([
[-3.0],
[-1.0],
[0.0],
[1.0],
[2.0],
[4.0],
])
Y_train = np.cos(X_train) + np.random.randn(X_train.shape[0], 1) * 0.2
num_test = 10000
X_test = np.linspace(-5, 5, num_test)
X_test = X_test.reshape((num_test, 1))
Y_test = np.cos(X_test)
num_trees = 100
depth_max = 5
size_min_leaf = 2
num_features = 1
trees = trees_random_forest.get_random_forest(
X_train, Y_train, num_trees, depth_max, size_min_leaf, num_features
)
mu, sigma = trees_common.predict_by_trees(X_test, trees)
time_end = time.time()
print('time consumed: {:.4f}'.format(time_end - time_start))
utils_plotting.plot_gp_via_distribution(X_train, Y_train, X_test, mu, sigma, Y_test, path_save=PATH_SAVE, str_postfix='cos')
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 = np.cos(X_test)
nu, mu, sigma, Sigma = tp.predict_with_optimized_hyps(X_train,
Y_train,
X_test,
str_cov=str_cov,
fix_noise=False,
debug=True)
utils_plotting.plot_gp_via_distribution(X_train,
Y_train,
X_test,
mu,
sigma,
Y_test,
path_save=PATH_SAVE,
str_postfix='cos_' + str_cov)
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 = np.cos(X_test) + 10.0
mu, sigma, Sigma = gp.predict_with_optimized_hyps(X_train,
Y_train,
X_test,
debug=True)
utils_plotting.plot_gp_via_distribution(X_train, Y_train, X_test, mu,
sigma, Y_test,
'test_optimized_many_points')
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 = np.cos(X_test) + 2.0
mu, sigma, Sigma = gp.predict_with_optimized_hyps(X_train,
Y_train,
X_test,
prior_mu=fun_prior)
utils_plotting.plot_gp_via_distribution(
X_train, Y_train, X_test, mu, sigma, Y_test, PATH_SAVE,
'optimized_prior_{}'.format(str_prior))
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 = np.cos(X_test)
hyps = {
'signal': 0.5,
'lengthscales': 0.5,
'noise': 0.02,
}
mu, sigma, Sigma = gp.predict_with_hyps(X_train, Y_train, X_test, hyps)
utils_plotting.plot_gp_via_distribution(X_train, Y_train, X_test, mu, sigma, Y_test, path_save=PATH_SAVE, str_postfix='cos')
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 = np.cos(X_test) * scale
mu, sigma, Sigma = gp.predict_with_optimized_hyps(X_train,
Y_train,
X_test,
fix_noise=False,
debug=True)
utils_plotting.plot_gp_via_distribution(
X_train, Y_train, X_test, mu, sigma, Y_test, PATH_SAVE,
'test_optimized_{}_y'.format(str_postfix))
def test_plot_gp_via_distribution():
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:
package_target.plot_gp_via_distribution(X_train, Y_train, X_test, mu,
1)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train, Y_train, X_test, mu,
np.arange(0, num_test))
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train, Y_train, X_test, 1,
sigma)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train, Y_train, X_test,
np.arange(0, num_test), sigma)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train, Y_train, 1, mu, sigma)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train, Y_train,
np.arange(0, num_test), mu,
sigma)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train, 1, X_test, mu, sigma)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train,
np.arange(0, num_train),
X_test, mu, sigma)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(1, Y_train, X_test, mu, sigma)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(np.arange(0, num_test),
Y_train, X_test, mu, sigma)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(np.zeros((num_train, 2)),
Y_train, X_test, mu, sigma)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train, Y_train,
np.zeros((num_train, 2)), mu,
sigma)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train, np.ones(
(num_train, 2)), X_test, mu, sigma)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train, np.ones((10, 1)),
X_test, mu, sigma)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train, Y_train, X_test,
np.zeros((num_test, 2)), sigma)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train, Y_train, X_test, mu,
np.zeros((num_test, 2)))
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train, Y_train, X_test, mu,
np.zeros((11, 1)))
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train, Y_train, X_test,
np.zeros((11, 1)), sigma)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train,
Y_train,
X_test,
mu,
sigma,
Y_test=np.arange(0, num_test))
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train,
Y_train,
X_test,
mu,
sigma,
Y_test=np.zeros((num_test, 2)))
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train,
Y_train,
X_test,
mu,
sigma,
Y_test=np.zeros((20, 1)))
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train,
Y_train,
X_test,
mu,
sigma,
Y_test=1)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train,
Y_train,
X_test,
mu,
sigma,
path_save=1)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train,
Y_train,
X_test,
mu,
sigma,
str_postfix=1)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train,
Y_train,
X_test,
mu,
sigma,
str_x_axis=1)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train,
Y_train,
X_test,
mu,
sigma,
str_y_axis=1)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train,
Y_train,
X_test,
mu,
sigma,
use_tex=1)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train,
Y_train,
X_test,
mu,
sigma,
draw_zero_axis=1)
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train,
Y_train,
X_test,
mu,
sigma,
pause_figure='abc')
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train,
Y_train,
X_test,
mu,
sigma,
time_pause='abc')
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train,
Y_train,
X_test,
mu,
sigma,
range_shade='abc')
with pytest.raises(AssertionError) as error:
package_target.plot_gp_via_distribution(X_train,
Y_train,
X_test,
mu,
sigma,
colors='abc')
