Python KalmanFilter示例

示例#1

文件： co2_data.py 项目： lberrada/AIMS

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
    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()

示例#2

文件： mg_data.py 项目： lberrada/AIMS

sys.path.append("../")

from process_data import data_from_file
from Regression import AutoRegressive, AutoCorrelation, GaussianProcess, KalmanFilter

file_name = "mg.mat"
data_dict = data_from_file(file_name)

model = "GP"
# model = "AR"
model = "AC"
# model = "KF"

if model.lower() == 'kf':
    p = 10
    kf = KalmanFilter(data_dict, p)
    kf.fit()
    kf.display(out="./mg_kf.png")

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()

示例#3

文件： sunspots_data.py 项目： lberrada/AIMS

from process_data import data_from_file


file_name = "sunspots.mat"

data_dict = data_from_file(file_name)

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()

示例#4

文件： financial_data.py 项目： lberrada/AIMS

from process_data import data_from_file


file_name = "finPredProb.mat"

data_dict = data_from_file(file_name)

model = "GP"
model = "AR"
model = "AC"
# model = "KF"

if model.lower() == 'kf':
    p = 10
    kf = KalmanFilter(data_dict, p)
    kf.fit()
    kf.display(out="./fin_kf.png")

if model.lower() == "ar":
    p = 50
    my_ar = AutoRegressive(data_dict, p)
    my_ar.fit()
    my_ar.predict()
    my_ar.display(out="./fin_ar.png")

if model.lower() == "ac":
    p = 50
    my_ac = AutoCorrelation(data_dict, p)
    my_ac.fit()
    my_ac.predict()