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()
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,
use_means=use_means,
estimator=estimator,
sequential_mode=False)
my_gp.predict()
my_gp.compute_score()
my_gp.show_prediction(out="./co2_gp.png")
