岭回归预测PM2.5
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岭回归预测PM2.5
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# -*- coding: utf-8 -*-
# @File : demo2.py
# @Author : CJH
# @Date : 2019/4/9
# @Software: PyCharm
# @Desc : 天氣PM2.5預(yù)測(cè)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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