# -*- coding: utf-8 -*- # @File : demo2.py # @Author : CJH # @Date : 2019/4/9 # @Software: PyCharm # @Desc : 天氣PM2.5預測import csv import numpy as np from numpy import * import matplotlib.pyplot as plt from sklearn import linear_modeltrainingData = 'trainingData/'x_data = []y_data = []with open(trainingData+'xData_pm25.csv', encoding='utf-8') as f:x_data = np.loadtxt(f, delimiter=",")with open(trainingData+'yData_pm25.csv') as f:y_data = np.loadtxt(f, delimiter=",")regs = linear_model.RidgeCV(np.linspace(1,1000)) regs.fit(x_data, y_data) # alpha = regs.alpha_ print('alpha',alpha)reg = linear_model.Ridge(alpha) reg.fit(x_data, y_data)# 14,16,13,14,21,19,23,18,17 # 26,39,36,35,31,28,25,20,19 # 33,39,39,25,18,18,17,9,4 # 50,70,70,73,61,55,47,38,26test_x_data = []test_y_data = []testingData = 'testingData/'with open(testingData+'xData_pm25.csv', encoding='utf-8') as f:test_x_data = np.loadtxt(f, delimiter=",")with open(testingData+'yData_pm25.csv') as f:test_y_data = np.loadtxt(f, delimiter=",")pre_y_data = reg.predict(test_x_data)err = abs(pre_y_data - test_y_data) print('err',len(err)) plt.plot(np.linspace(1, len(err), len(err)), err) plt.xlabel(r'$trainingIndex$', fontsize=16) plt.ylabel(r'$error$', fontsize=16) plt.title('abs trainingError') plt.show()print(err.sum(axis=0)/len(err)*1.0)

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