用python写一个logistic程序预测中国二氧化碳排放量与年份的关系，带测试数据

好的，以下是一个简单的实现，使用sklearn库的逻辑回归模型进行训练和预测：

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# 读取数据
data = pd.read_csv('co2_emissions.csv')

# 分割数据集为训练集和测试集
train_data = data[:len(data)//2]
test_data = data[len(data)//2:]

# 准备训练数据
X_train = train_data['Year'].values.reshape(-1, 1)
y_train = train_data['CO2_Emissions'].values

# 创建并训练模型
model = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial')
model.fit(X_train, y_train)

# 测试模型
X_test = test_data['Year'].values.reshape(-1, 1)
y_test = test_data['CO2_Emissions'].values
y_pred = model.predict(X_test)

# 打印模型精度
print("Accuracy:", accuracy_score(y_test, y_pred))

其中，`co2_emissions.csv`是包含年份和二氧化碳排放量的数据文件，训练数据集和测试数据集按照一定比例分割。

以下是 `co2_emissions.csv` 中的样例数据：

Year,CO2_Emissions
1960,556.831
1961,597.379
1962,685.204
1963,688.015
1964,745.792
1965,793.478
1966,861.746
1967,905.324
1968,978.685
1969,1035.215
1970,1199.733
1971,1214.319
1972,1347.356
1973,1468.274
1974,1501.328
1975,1539.885
1976,1670.901
1977,1783.106
1978,1955.263
1979,1940.038
1980,1966.66
1981,2092.913
1982,2174.674
1983,2235.644
1984,2367.202
1985,2507.02
1986,2612.438
1987,2754.703
1988,2968.96
1989,3103.542
1990,3272.761
1991,3271.037
1992,3376.436
1993,3573.393
1994,3692.371
1995,3939.763
1996,4142.206
1997,4361.041
1998,4586.113
1999,4828.51
2000,5143.053
2001,5357.696
2002,5818.874
2003,6381.493
2004,7007.509
2005,7488.447
2006,8027.668
2007,8588.026
2008,9195.34
2009,9055.394
2010,10072.849
2011,10296.199
2012,10538.977
2013,10888.768
2014,11249.365
2015,11488.124
2016,11710.103
2017,12008.692
2018,12305.907
2019,12566.911

运行程序后，会输出模型的精度。

需要注意的是，逻辑回归模型需要满足一定的前提条件，比如数据点之间的相关性、线性可分性等。在实际应用中，需要根据具体数据情况选择适合的模型。