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()
Date: 5 Nov 2015 """ import sys sys.path.append("../") import matplotlib.pyplot as plt from Regression import AutoRegressive, AutoCorrelation, GaussianProcess, KalmanFilter from process_data import data_from_file file_name = "co2.mat" data_dict = data_from_file(file_name) # 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
pass 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)