import matplotlib.pyplot as plt
import numpy as np
from old_version_gp import GaussianProcess
from GP.covariance_functions import SquaredExponential, ExpScaledSquaredExponential
from GP.plotting import plot_performance_hyper_parameter
data_params = np.array([1.0, 0.25, 0.05])
data_covariance_obj = SquaredExponential(data_params)
gp = GaussianProcess(data_covariance_obj, lambda x: 0, 'class')
num = 30
test_density = 50
dim = 2
seed = 21
iterations_1 = 500
iterations_2 = 150
plot_iterations_1 = 490
plot_iterations_2 = 140
first_algorithm_label = 'Exp-Scaled SE (Exact Gradient)'
second_algorithm_label = 'SE (Exact Gradient)'
# third_algorithm_label = 'Half-log-scaled SE'
np.random.seed(seed)
x_tr = np.random.rand(dim, num)
if dim == 2:
x_1 = np.linspace(0, 1, test_density)
x_2 = np.linspace(0, 1, test_density)
x_1, x_2 = np.meshgrid(x_1, x_2)
x_test = np.array(list(zip(x_1.reshape(-1).tolist(), x_2.reshape(-1).tolist())))
x_test = x_test.T
else:
import matplotlib.pyplot as plt
import numpy as np
from old_version_gp import GaussianProcess
from GP.covariance_functions import SquaredExponential, ExpScaledSquaredExponential
from GP.plotting import plot_performance_hyper_parameter
data_params = np.array([1.0, 0.25, 0.05])
data_covariance_obj = SquaredExponential(data_params)
gp = GaussianProcess(data_covariance_obj, lambda x: 0, 'class')
num = 30
test_density = 50
dim = 2
seed = 21
iterations_1 = 500
iterations_2 = 150
plot_iterations_1 = 490
plot_iterations_2 = 140
first_algorithm_label = 'Exp-Scaled SE (Exact Gradient)'
second_algorithm_label = 'SE (Exact Gradient)'
# third_algorithm_label = 'Half-log-scaled SE'
np.random.seed(seed)
x_tr = np.random.rand(dim, num)
if dim == 2:
x_1 = np.linspace(0, 1, test_density)
x_2 = np.linspace(0, 1, test_density)
x_1, x_2 = np.meshgrid(x_1, x_2)
x_test = np.array(
list(zip(x_1.reshape(-1).tolist(),
x_2.reshape(-1).tolist())))
mu, sigma1 = 0, 10
x_tr, y_tr = load_svmlight_file('Data/Classification/australian.txt')
x_tr = x_tr.T
x_tr = x_tr.toarray()
# y_g = y_g.toarray()
data_name = 'Australian'
x_tr = (x_tr + 1) / 2
y_tr = y_tr.reshape((y_tr.size, 1))
x_test = x_tr[:, int(x_tr.shape[1] * 0.8):]
y_test = y_tr[int(x_tr.shape[1] * 0.8):, :]
y_tr = y_tr[:int(x_tr.shape[1] * 0.8), :]
x_tr = x_tr[:, :int(x_tr.shape[1] * 0.8)]
print("Number of data points: ", x_tr.shape[1])
print("Number of test points: ", x_test.shape[1])
print("Number of features: ", x_tr.shape[0])
# #Generating the starting point
# np.random.seed(random_seed_w0)
# w0 = np.random.rand(3)
model_params = np.array([10., 0.7, 3.])
model_covariance_obj = SquaredExponential(model_params)
new_gp = GaussianProcess(model_covariance_obj, lambda x: 0, 'class')
new_gp.find_hyper_parameters(x_tr, y_tr, max_iter=100)
print(new_gp.covariance_obj.get_params())
predicted_y_test = new_gp.predict(x_test, x_tr, y_tr)
print("Mistakes: ", np.sum(predicted_y_test != y_test))
mu, sigma1 = 0, 10
x_tr, y_tr = load_svmlight_file('Data/Classification/australian.txt')
x_tr = x_tr.T
x_tr = x_tr.toarray()
# y_g = y_g.toarray()
data_name = 'Australian'
x_tr = (x_tr + 1) / 2
y_tr = y_tr.reshape((y_tr.size, 1))
x_test = x_tr[:, int(x_tr.shape[1] * 0.8):]
y_test = y_tr[int(x_tr.shape[1] * 0.8):, :]
y_tr = y_tr[:int(x_tr.shape[1] * 0.8), :]
x_tr = x_tr[:, : int(x_tr.shape[1] * 0.8)]
print("Number of data points: ", x_tr.shape[1])
print("Number of test points: ", x_test.shape[1])
print("Number of features: ", x_tr.shape[0])
# #Generating the starting point
# np.random.seed(random_seed_w0)
# w0 = np.random.rand(3)
model_params = np.array([10., 0.7, 3.])
model_covariance_obj = SquaredExponential(model_params)
new_gp = GaussianProcess(model_covariance_obj, lambda x: 0, 'class')
new_gp.find_hyper_parameters(x_tr, y_tr, max_iter=100)
print(new_gp.covariance_obj.get_params())
predicted_y_test = new_gp.predict(x_test, x_tr, y_tr)
print("Mistakes: ", np.sum(predicted_y_test != y_test))