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, use_kernels=use_kernels, use_means=use_means,
from process_data import data_from_file from Regression import AutoRegressive, AutoCorrelation # file_name = "finPredProb.mat" # file_name = "co2.mat" # file_name = "sunspots.mat" # file_name = "mg.mat" file_name = "fXSamples.mat" ix = 1 p = 5 args = data_from_file(file_name, ix=ix) my_ar = AutoRegressive(*args, p=p) my_ar.fit() my_ar.predict() # my_ar.plot_var('ypred') my_ac = AutoCorrelation(*args, p=p) my_ac.fit() my_ac.predict() # my_ac.plot_var('ypred', show=True) my_ac.spectrum() my_ac.plot_attr('spectrum', show=True)