def run(file_name=None, variable=None, use_kernels=None, use_means=None, estimator=None, sequential_mode=None, params=None): data_dict = data_from_file(file_name, variable=variable) my_gp = GaussianProcess(data_dict=data_dict, variable=variable, use_kernels=use_kernels, use_means=use_means, estimator=estimator, sequential_mode=sequential_mode, params=None) my_gp.predict() my_gp.compute_score() my_gp.show_prediction()
my_ar.display(out="./co2_ar.png") if model.lower() == "ac": p = 50 my_ac = AutoCorrelation(data_dict, p) my_ac.fit() my_ac.predict() my_ac.display(out="./co2_ac.png") my_ac.spectrum() if model.lower() == "gp": Q = 3 use_kernels = "exponential_quadratic* cosine" for _ in range(Q - 1): use_kernels += "+ exponential_quadratic * cosine" # use_kernels = 'rational_quadratic + periodic' use_means = "constant" estimator = "MLE" my_gp = GaussianProcess(data_dict=data_dict, use_kernels=use_kernels, use_means=use_means, estimator=estimator, sequential_mode=False) my_gp.predict() my_gp.compute_score() my_gp.show_prediction(out="./co2_gp.png")
my_ac = AutoCorrelation(data_dict, p) my_ac.fit() my_ac.predict() my_ac.display(out="./sun_ac.png") my_ac.spectrum() if model.lower() == "gp": # Q = 3 # use_kernels = "exponential_quadratic* cosine" # for _ in range(Q - 1): # use_kernels += "+ exponential_quadratic * cosine" use_kernels = "matern_32 + periodic" use_means = "constant" estimator = "MLE" params = [0.34, 1., 26.5, 1e-06, 3.18, -2.9] my_gp = GaussianProcess(data_dict=data_dict, use_kernels=use_kernels, params=params, use_means=use_means, estimator=estimator, sequential_mode=True) my_gp.predict() my_gp.compute_score() my_gp.show_prediction(out="./sun_gp.png")
if model.lower() == "ac": p = 50 my_ac = AutoCorrelation(data_dict, p) my_ac.fit() my_ac.predict() my_ac.display(out="./mg_ac.png") my_ac.spectrum() my_ac.plot_attr("spectrum", show=True) if model.lower() == "gp": Q = 3 use_kernels = "exponential_quadratic* cosine" for _ in range(Q - 1): use_kernels += "+ exponential_quadratic * cosine" # use_kernels = 'rational_quadratic + periodic' use_means = "constant" estimator = "MLE" my_gp = GaussianProcess(data_dict=data_dict, use_kernels=use_kernels, use_means=use_means, estimator=estimator, sequential_mode=False) my_gp.predict() my_gp.compute_score() my_gp.show_prediction(out="./mg_gp.png")