plt.title('Polynomial Regression Learning Curve (lambda = {})'.format(lmd))
plt.legend(['Train', 'Cross Validation'])
plt.xlabel('Number of Training Examples')
plt.ylabel('Error')
plt.axis([0, 13, 0, 100])
#plt.xticks(list(range(0,13,2)))
#plt.yticks(list(range(0,120,10)))
print('Polynomial Regression (lambda = {})'.format(lmd))
print('# Training Examples\tTrain Error\t\tCross Validation Error')
for i in range(m):
print(' \t{}\t\t{}\t{}'.format(i, error_train[i], error_val[i]))
input('Program paused. Press ENTER to continue')
# ===================== Part 8 : Validation for Selecting Lambda =====================
# You will now implement validationCurve to test various values of
# lambda on a validation set. You will then use this to select the
# 'best' lambda value.
lambda_vec, error_train, error_val = vc.validation_curve(
X_poly, y, X_poly_val, yval)
plt.figure()
plt.plot(lambda_vec, error_train, lambda_vec, error_val)
plt.legend(['Train', 'Cross Validation'])
plt.xlabel('lambda')
plt.ylabel('Error')
input('ex5 Finished. Press ENTER to exit')
plt.figure(3)
plt.scatter(X,Y,c='r',marker='x')
plt.plot(x_fit,y_fit)
plt.xlabel('Change in water level (x)')
plt.ylabel('Water folowing out of the dam (y)')
plt.ylim([0, 60])
plt.title('Polynomial Regression Fit (lambda = {})'.format(lmd))
# plt.show()
# 计算代价误差
error_train, error_val = learning_curve(X_poly, Y, X_poly_val, Yval, lmd)
plt.figure(4)
plt.plot(np.arange(m), error_train, np.arange(m), error_val)
plt.title('Polynomial Regression Learning Curve (lambda = {})'.format(lmd))
plt.legend(['Train', 'Cross Validation'])
plt.xlabel('Number of Training Examples')
plt.ylabel('Error')
plt.axis([0, 13, 0, 150])
# plt.show()
print('Polynomial Regression (lambda = {})'.format(lmd))
print('# Training Examples\tTrain Error\t\tCross Validation Error')
for i in range(m):
print(' \t{}\t\t{}\t{}'.format(i, error_train[i], error_val[i]))
# ======================= 7.通过交叉验证集选择正则项系数lambda
lambda_vec,error_train,error_val = validation_curve(X_poly,Y,X_poly_test,Ytest)
plt.figure(5)
plt.plot(lambda_vec, error_train, lambda_vec, error_val)
plt.legend(['Train', 'Test Validation'])
plt.xlabel('lambda')
plt.ylabel('Error')
plt.show()