def kfold_gauss_func(x, t, m, k):
n = x.shape[0]
mse_train = np.zeros(k)
mse_test = np.zeros(k)
for i in range(0, k):
x_train = x[np.fmod(range(n), k) != i] #(A)
t_train = t[np.fmod(range(n), k) != i] #(A)
x_test = x[np.fmod(range(n), k) == i] #(A)
t_test = t[np.fmod(range(n), k) == i] #(A)
wm = fit_gauss_func(x_train, t_train, m)
mse_train[i] = mse_gauss_func(x_train, t_train, wm)
mse_test[i] = mse_gauss_func(x_test, t_test, wm)
return mse_train, mse_test
#-*- coding:UTF-8 -*-
'''
Created on 2019/01/06
@author: T.Wakasugi
'''
import numpy as np
import matplotlib.pyplot as plt
from new_machinelearning.gauss_func import fit_gauss_func, mse_gauss_func
outfile = np.load('ch5_data.npz')
X = outfile['X']
X_min = outfile['X_min']
X_max = outfile['X_max']
X_n = outfile['X_n']
T = outfile['T']
"リスト 5-2-(9)"
plt.figure(figsize=(5, 4))
M = range(2, 10)
mse2 = np.zeros(len(M))
for i in range(len(M)):
W = fit_gauss_func(X, T, M[i])
mse2[i] = np.sqrt(mse_gauss_func(X, T, W))
plt.plot(M, mse2, marker='o', color='cornflowerblue', markeredgecolor='black')
plt.grid(True)
plt.show()
#-*- coding:UTF-8 -*-
'''
Created on 2019/01/06
@author: T.Wakasugi
'''
import numpy as np
import matplotlib.pyplot as plt
from new_machinelearning.gauss_func import fit_gauss_func, show_gauss_func, mse_gauss_func
outfile = np.load('ch5_data.npz')
X = outfile['X']
X_min = outfile['X_min']
X_max = outfile['X_max']
X_n = outfile['X_n']
T = outfile['T']
#リスト 5-2-(16)
M = 3
plt.figure(figsize=(4,4))
W = fit_gauss_func(X,T,M)
show_gauss_func(W,X_min,X_max)
plt.plot(X,T,marker='o',linestyle='None',color='cornflowerblue',markeredgecolor='black')
plt.xlim([X_min,X_max])
plt.grid(True)
mse = mse_gauss_func(X,T,W)
print("SD={0:.2f} cm".format(np.sqrt(mse)))
plt.show()
T_train = T[int(X_n / 4):]
#メイン
plt.figure(figsize=(10, 2.5))
M = [2, 4, 7, 9]
for i in range(len(M)):
plt.subplot(1, len(M), i + 1)
W = fit_gauss_func(X_train, T_train, M[i])
show_gauss_func(W, X_min, X_max)
plt.plot(X_train,
T_train,
marker='o',
linestyle='None',
color='white',
markeredgecolor='black',
label='training')
plt.plot(X_test,
T_test,
marker='o',
linestyle='None',
color='cornflowerblue',
markeredgecolor='black',
label='test')
plt.legend(loc='lower right', fontsize=10, numpoints=1)
plt.xlim(X_min, X_max)
plt.ylim(120, 180)
plt.grid(True)
mse = mse_gauss_func(X_test, T_test, W)
plt.title("M={0:d},SD={1:.1f}".format(M[i], np.sqrt(mse)))
plt.show()
#リスト5-2-(10)
#訓練データとテストデータ
X_test = X[:int(X_n / 4)]
T_test = T[:int(X_n / 4)]
X_train = X[int(X_n / 4):]
T_train = T[int(X_n / 4):]
#リスト 5-2-(11)
plt.figure(figsize=(5, 4))
M = range(2, 10)
mse_train = np.zeros(len(M))
mse_test = np.zeros(len(M))
for i in range(len(M)):
W = fit_gauss_func(X_train, T_train, M[i])
mse_train[i] = np.sqrt(mse_gauss_func(X_train, T_train, W))
mse_test[i] = np.sqrt(mse_gauss_func(X_test, T_test, W))
plt.plot(M,
mse_train,
marker='o',
linestyle='-',
markerfacecolor='white',
markeredgecolor='black',
color='black',
label='training')
plt.plot(M,
mse_test,
marker='o',
linestyle='-',
markerfacecolor='blue',