python实现单纯形法（大M法）

import os
import re
import numpy as np
import time
"""
输入线性规划模型到一个文档中，程序读取线性规划模型，化其为标准型。
找到初始可行基，建立单纯形表，迭代求解，判断解的类型，输出结果。
"""
#读取线性规划模型，并返回字符串列表
def inPut():

    #  读取线性规划模型
    model_str = []
    with open("2.10.txt",'r') as f:       #建立一个文本文档  储存线性规划模型
        lines = f.readlines()
    for element in lines:
        element = element[:-1]
        model_str.append(element)
    return(model_str)

#得到价值向量c
def find_C(model_str):
    x_temple =  re.findall(r"-?\d+\.?\d*",model_str[-1])
    x_temple = [int(i) for i in x_temple]
    x_max = max(x_temple)
    c_temple =  re.findall(r"-?\d+\.?\d*",model_str[0])
    c_temple = [float(i) for i in c_temple]
    c_list_1 = c_temple[1::2]
    c_list_2 = c_temple[::2]
    c=[]
    for i in range(x_max+1):
        c.append(0)
    for i in range(len(c_list_1)):
        c[int(c_list_1[i])] = c_list_2[i]
    c = np.array(c)
    return c

#求系数矩阵+资源向量
def find_A(model_str):
    b = []
    x_temple =  re.findall(r"-?\d+\.?\d*",model_str[-1])
    x_temple = [int(i) for i in x_temple]
    x_max = max(x_temple)
    A = [[0 for i in range(x_max+1)] for i in range(len(model_str)-2)]
    k = 0
    for element in model_str[1:-1]:
        x_temple =  re.findall(r"-?\d+\.?\d*",element)
        x_temple = [float(i) for i in x_temple]
        x_list_1 = x_temple[1::2]
        x_list_2 = x_temple[:-1:2]
        x=[]
        b.append(x_temple[-1])
        for i in range(x_max+1):
            x.append(0)
        for i in range(len(x_list_1)):
            x[int(x_list_1[i])] = x_list_2[i]
        for num in range(len(x)):
            A[k][num] = x[num]
        k += 1
    for num in range(len(b)):
        A[num][0] = b[num]
    A = np.array(A,dtype = np.float_)
    return A

#由以上三个函数可得到 1.系数+资源向量矩阵 2.价值向量    A c b  三个变量

#为进行资源向量非负化，约束条件等式化，记录每个约束的形式(  =记为0，>记为1,<记为-1)