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)
