zsg-dea模型代码

# 导入相关库
import numpy as np
from scipy.optimize import minimize

# 定义ZSG-DEA模型
def ZSG_DEA(x, *args):
    # 输入变量
    inputs = args[0]
    # 输出变量
    outputs = args[1]
    # 权重
    weights = args[2]
    # 约束条件
    constraints = args[3]
    # 计算效率值
    efficiency = np.zeros((inputs.shape[0],))
    for i in range(inputs.shape[0]):
        # 构建约束条件
        cons = []
        for j in range(constraints.shape[0]):
            cons.append({'type': 'ineq', 'fun': lambda x, a=inputs[i], b=outputs[i], w=weights, c=constraints[j]: np.dot(w, (b / a - c) * x) - 1})
        # 求解线性规划问题
        res = minimize(lambda x: -np.dot(weights, outputs[i] * x), x0=np.ones((inputs.shape[1],)), method='SLSQP', constraints=cons)
        efficiency[i] = 1 / res.fun
    # 返回效率值
    return efficiency

# 测试ZSG-DEA模型
if __name__ == '__main__':
    # 输入变量
    inputs = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    # 输出变量
    outputs = np.array([[10, 20], [30, 40], [50, 60]])
    # 权重
    weights = np.array([0.5, 0.5])
    # 约束条件
    constraints = np.array([[0.2, 0.3, 0.4], [0.1, 0.2, 0.3]])
    # 求解ZSG-DEA模型
    efficiency = ZSG_DEA(np.ones((inputs.shape[1],)), inputs, outputs, weights, constraints)
    print(efficiency)