神经网络气温预测

#引用所需要的库
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.optim as optim#优化器
#过滤警告
import warnings
warnings.filterwarnings(“ignore”)
%matplotlib inline

features=pd.read_csv(‘temps.csv’)
features.head()

year 	month 	day 	week 	temp_2 	temp_1 	average 	actual 	friend

0 2016 1 1 Fri 45 45 45.6 45 29
1 2016 1 2 Sat 44 45 45.7 44 61
2 2016 1 3 Sun 45 44 45.8 41 56
3 2016 1 4 Mon 44 41 45.9 40 53
4 2016 1 5 Tues 41 40 46.0 44 41

#把列转为时间处理数据
import datetime
years=features[‘year’]
months=features[‘month’]
days=features[‘day’]
#datetime格式
dates=[str(int (year))+‘-’+str(int(month))+‘-’+str(int(day)) for year,month,day in zip(years,months,days)]
dates=[datetime.datetime.strptime(date,‘%Y-%m-%d’)for date in dates]

features.shape

(348, 9)

dates[:5]

[datetime.datetime(2016, 1, 1, 0, 0),
datetime.datetime(2016, 1, 2, 0, 0),
datetime.datetime(2016, 1, 3, 0, 0),
datetime.datetime(2016, 1, 4, 0, 0),
datetime.datetime(2016, 1, 5, 0, 0)]

#小展示，看看数据集长什么样

#独热编码
features=pd.get_dummies(features)
features.head(5)

year 	month 	day 	temp_2 	temp_1 	average 	actual 	friend 	week_Fri 	week_Mon 	week