product_num = data_list[i][4]强制转化为int型
时间: 2023-12-01 15:03:39 浏览: 35
可以使用强制类型转换将product_num转换为int类型,语法格式为:`int(product_num)`。可以将原来的代码修改为:
```
product_num = int(data_list[i][4])
```
这样就可以将data_list中第i个元素的第5个值(下标为4)转换为int类型并赋值给product_num变量。需要注意的是,如果data_list[i][4]值不是合法的整数类型,会抛出ValueError异常。
相关问题
import itertools import warnings import pandas as pd import numpy as np import statsmodels.api as sm from datetime import datetime from statsmodels.tsa.arima.model import ARIMA from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from statsmodels.stats.diagnostic import acorr_ljungbox from sklearn.model_selection import train_test_split data = pd.read_csv('data.csv', parse_dates=['x'], index_col='x') train_data1, test_data = train_test_split(data1, test_size=0.3, shuffle=False) data['lag1'] = data['y'].shift(1) data['lag2'] = data['y'].shift(2) data['lag3'] = data['y'].shift(3) data['lag4'] = data['y'].shift(4) data['lag5'] = data['y'].shift(5) data['lag6'] = data['y'].shift(6) data['lag7'] = data['y'].shift(7) data.dropna(inplace=True) train_data, test_data1 = train_test_split(data, test_size=0.3, shuffle=False) g=int(input("输入P的峰值: ")) h=int(input("输入D的峰值: ")) i=int(input("输入Q的峰值: ")) p = range(0, g) d = range(0, h) q = range(0, i) pdq = list(itertools.product(p, d, q)) best_pdq = None best_aic = np.inf for param in pdq: model = sm.tsa.ARIMA(data['y'], exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']], order=param) results = model.fit() aic = results.aic if aic < best_aic: best_pdq = param best_aic = aic a=best_pdq[0] b=best_pdq[1] c=best_pdq[2] model = ARIMA(data['y'], exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']], order=(a,b,c)) results = model.fit() max_lag = model.k_ar model_fit = model.fit() resid = model_fit.resid lb_test = acorr_ljungbox(resid) p_value=round(lb_test['lb_pvalue'][max_lag],4) if p_value>0.05: forecast = results.forecast(steps=1, exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']].iloc[-1:]) forecast.index[0].strftime('%Y-%m') print("下个月的预测结果是",round(forecast[0])) def comput_acc(real,predict,level): num_error=0 for i in range(len(real)): if abs(real[i]-predict[i])/real[i]>level: num_error+=1 return 1-num_error/len(real) print("置信水平:{},预测准确率:{}".format(0.2,comput_acc(test_x,y_pred,0.2))) else: print('输入的数据不适合使用arima模型进行预测分析,请尝试其他模型')如何修改代码使其正常运行
在你的代码中,有一些变量名没有定义,例如`data1`和`test_x`,所以我假设这些是之前定义过的变量并且进行了更改。此外,需要加入一些库的引用,如`itertools`和`sklearn`。以下是修改后的代码:
```
import itertools
import warnings
import pandas as pd
import numpy as np
import statsmodels.api as sm
from datetime import datetime
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.stats.diagnostic import acorr_ljungbox
from sklearn.model_selection import train_test_split
data = pd.read_csv('data.csv', parse_dates=['x'], index_col='x')
train_data1, test_data = train_test_split(data, test_size=0.3, shuffle=False)
data['lag1'] = data['y'].shift(1)
data['lag2'] = data['y'].shift(2)
data['lag3'] = data['y'].shift(3)
data['lag4'] = data['y'].shift(4)
data['lag5'] = data['y'].shift(5)
data['lag6'] = data['y'].shift(6)
data['lag7'] = data['y'].shift(7)
data.dropna(inplace=True)
train_data, test_data1 = train_test_split(data, test_size=0.3, shuffle=False)
g=int(input("输入P的峰值: "))
h=int(input("输入D的峰值: "))
i=int(input("输入Q的峰值: "))
p = range(0, g)
d = range(0, h)
q = range(0, i)
pdq = list(itertools.product(p, d, q))
best_pdq = None
best_aic = np.inf
for param in pdq:
model = sm.tsa.ARIMA(data['y'], exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']], order=param)
results = model.fit()
aic = results.aic
if aic < best_aic:
best_pdq = param
best_aic = aic
a=best_pdq[0]
b=best_pdq[1]
c=best_pdq[2]
model = ARIMA(data['y'], exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']], order=(a,b,c))
results = model.fit()
max_lag = model.k_ar
model_fit = model.fit()
resid = model_fit.resid
lb_test = acorr_ljungbox(resid)
p_value=round(lb_test[1][max_lag],4)
if p_value>0.05:
forecast = results.forecast(steps=1, exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']].iloc[-1:])
forecast.index[0].strftime('%Y-%m')
print("下个月的预测结果是",round(forecast[0]))
def comput_acc(real,predict,level):
num_error=0
for i in range(len(real)):
if abs(real[i]-predict[i])/real[i]>level:
num_error+=1
return 1-num_error/len(real)
y_pred = results.predict(start=test_data1.index[0], end=test_data1.index[-1], exog=test_data1[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']])
print("置信水平:{},预测准确率:{}".format(0.2,comput_acc(test_data1['y'],y_pred,0.2)))
else:
print('输入的数据不适合使用arima模型进行预测分析,请尝试其他模型')
```
需要注意的是,由于ARIMA模型需要对历史数据进行训练,因此在使用`predict`方法来进行预测时,需要指定预测的时间范围,这里已经加入了相应的代码。同时,在计算预测准确率时,需要使用测试集的真实值和预测值,因此需要将测试集的真实值作为参数传入`comput_acc`函数中。
-- 创建顾客表: CREATE TABLE Customer ( Customer_id VARCHAR(50) PRIMARY KEY, Customer_name VARCHAR(50), Customer_gender VARCHAR(10), Customer_phone VARCHAR(20), Customer_address VARCHAR(100) ); -- 创建商品表: CREATE TABLE Product ( Product_id INT PRIMARY KEY, Product_name VARCHAR(50), Product_type VARCHAR(20), Product_price DECIMAL(10, 2), Supplier_id VARCHAR(50), stock_num INT ); -- 创建员工表: CREATE TABLE Employee ( Employee_id INT PRIMARY KEY, Employee_name VARCHAR(50), Employee_gender VARCHAR(10), Employee_phone VARCHAR(20), Employee_position VARCHAR(50) ); -- 创建供货商表: CREATE TABLE Supplier ( Supplier_id VARCHAR(50) PRIMARY KEY, Product_list VARCHAR(50), Supplier_name VARCHAR(50), Supplier_contact VARCHAR(50), Supplier_phone VARCHAR(20), Supplier_address VARCHAR(100) ); -- 创建销售表: CREATE TABLE Sale ( Sale_id INT AUTO_INCREMENT PRIMARY KEY, Product_id INT, Order_id INT, Order_date DATE, Employee_id INT, Customer_id VARCHAR(50), Sale_num INT, Sale_amount DECIMAL(10, 2), FOREIGN KEY (Product_id) REFERENCES Product (Product_id), FOREIGN KEY (Employee_id) REFERENCES Employee (Employee_id), FOREIGN KEY (Customer_id) REFERENCES Customer (Customer_id) ); -- 创建库存表: CREATE TABLE Stock ( Product_id INT PRIMARY KEY, Stock_num INT, indata DATE, outdata DATE, FOREIGN KEY (Product_id) REFERENCES Product (Product_id) ); -- 创建订单表: CREATE TABLE Order_dd ( Order_id INT AUTO_INCREMENT PRIMARY KEY, Product_id INT, Product_num INT, Customer_id VARCHAR(50), Order_date DATE, Order_price DECIMAL(10, 2), Order_status ENUM('待发货', '已发货', '待确认','已完成') );向以上代码中商品表插入50个数据,同时再库存表中插入50个数据与商品对应
给商品表插入50个数据的SQL语句如下:
```
INSERT INTO Product (Product_id, Product_name, Product_type, Product_price, Supplier_id, stock_num)
VALUES (1, '商品1', '类型1', 10.00, 'S1', 100),
(2, '商品2', '类型1', 20.00, 'S2', 200),
(3, '商品3', '类型1', 30.00, 'S3', 300),
...
(48, '商品48', '类型5', 480.00, 'S3', 4800),
(49, '商品49', '类型5', 490.00, 'S4', 4900),
(50, '商品50', '类型5', 500.00, 'S5', 5000);
```
给库存表插入50个数据的SQL语句如下:
```
INSERT INTO Stock (Product_id, Stock_num, indata, outdata)
VALUES (1, 100, '2021-01-01', NULL),
(2, 200, '2021-01-02', NULL),
(3, 300, '2021-01-03', NULL),
...
(48, 4800, '2021-02-16', NULL),
(49, 4900, '2021-02-17', NULL),
(50, 5000, '2021-02-18', NULL);
```
以上SQL语句中,`Product_id`和`Stock_num`分别对应商品表和库存表中的商品编号和库存数量,`indata`和`outdata`分别表示入库时间和出库时间,这里将所有数据都设置为入库时间。同时,为了确保商品表和库存表中数据的对应性,需要保证两张表中的商品编号一致。
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