least_error = 100
target_alpha = 1
alphas = np.linspace(-20, -18, 10)
for alp in alphas:
w = do_regression(phi, y, alp)
y_prime = (grand_order(polyx, order) * w)
error = data_reading.MeanSquareError(y_prime, polyy)
if error <= least_error:
target_alpha = alp
least_error = error
return target_alpha
if '__main__' == __name__:
poly_data, poly_keys = data_reading.readMatFile("poly_data.mat")
order = 10
X, y = grand_order(poly_data['sampx'][0], order), poly_data['sampy']
polyx, polyy = poly_data['polyx'][0], poly_data['polyy']
count_data, count_keys = data_reading.readMatFile("count_data.mat")
target_alpha = choose_hyper(X, y, polyx, polyy, order)
w = do_regression(X, y, target_alpha)
y_prime = grand_order(polyx, order) * w
fig = plt.figure("LASSO")
ax = fig.add_subplot(111)
ax.plot(poly_data['sampx'][0],
y,
color='r',
linestyle='',
#-*- coding:utf-8 -*-
import numpy as np
import data_reading
import matplotlib.pyplot as plt
from data_reading import grand_order
from LS import do_regression as LSregression
from RLS import do_regression as RLSregression
from RLS import choose_hyper as RLS_hyper
from LASSO import do_regression as LASSOregression
from LASSO import choose_hyper as LASSO_hyper
from RR import do_regression as RRregression
from BR import do_regression as BRregression
from BR import choose_hyper as BR_hyper
if __name__ == "__main__":
poly_data, poly_keys = data_reading.readMatFile("poly_data.mat")
order = 5
sampx = poly_data['sampx'][0]
sampy = poly_data['sampy']
polyx = poly_data['polyx'][0]
polyy = poly_data['polyy']
total_length = len(sampx)
percents = [0.1, 0.25, 0.5, 0.75]
result = np.zeros((4, 5))
for j in range(1000):
MSEs = []
for percent in percents:
try:
mse = []