model = "GP" # model = "KF" # model = "AR" # model = "AC" if model.lower() == 'kf': p = 100 kf = KalmanFilter(data_dict, p) 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":
# model = "GP" model = "AR" # model = "AC" # model = "KF" if model.lower() == 'kf': p = 25 kf = KalmanFilter(data_dict, p) 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":
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)