kf.fit() kf.display(out="./co2_kf.png") if model.lower() == "ar": p = 50 my_ar = AutoRegressive(data_dict, p) my_ar.fit() my_ar.predict() 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,
kf.fit() kf.display(out="./mg_kf.png") if model.lower() == "ar": p = 50 my_ar = AutoRegressive(data_dict, p) my_ar.fit() my_ar.predict() my_ar.display(out="./mg_ar.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,
kf.fit() kf.display(out="./sun_kf.png") if model.lower() == "ar": p = 50 my_ar = AutoRegressive(data_dict, p) my_ar.fit() my_ar.predict() my_ar.display(out="./sun_ar.png") if model.lower() == "ac": p = 50 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]
kf.fit() kf.display(out="./fin_kf.png") if model.lower() == "ar": p = 50 my_ar = AutoRegressive(data_dict, p) my_ar.fit() my_ar.predict() my_ar.display(out="./fin_ar.png") if model.lower() == "ac": p = 50 my_ac = AutoCorrelation(data_dict, p) my_ac.fit() my_ac.predict() my_ac.display(out="./fin_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,