Python中的MatLab回归

我有一些使用蒙特卡罗模拟和OLS回归来估计一些系数的Matlab代码。 如何在Python中实现这一点

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有一些方法可以在python中使用MATLAB代码！！。。检查下面的链接

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嘿，你的问题不清楚你在问什么，你在python中缺少哪些功能，你在matlab中有哪些功能？你想做最小二乘回归吗？如果是这样，试试看，sklearn有很多预测工具！我想知道如何在Python中进行类似于Matlab命令x1（1:n）的回归。您能帮我一下吗，并告诉我\n在Matlab中做了什么？我已经有一段时间没用了。它和使用的是同一个吗？它意味着回归！所以它用x1作为预测器回归y。欢迎来到堆栈溢出！请拿起，阅读，和，并提供一个。“为我实现此功能”是本网站的主题。你必须做一个诚实的尝试，然后问一个关于你的算法或技术的特定问题。答案不应该仅仅是关于堆栈溢出的其他帖子的链接。如果您认为该问题适用，则应将其标记为重复问题。请阅读并采取行动。欢迎来到SO！

Define the true DGP parameters and distributions
% Set the parameters in the model
b1 = 5;
b2 = -2;
% The variance of the error term
sigma2 = 2;
% The sample length. We will play with three different sample sizes and see how this affects the results 
N = [100 500 1000];
% The number of simulations
S = 1000;
Generate x data
% Generate the x values as draws from the multivariate normal distributions
% This is the correlation structure between the x's
Sigma   = [0.7 0.4;
            0.4 0.3];
% Simple way of drawing random numbers from the multivariate normal distribution        
x       = chol(Sigma)'*randn(2,max(N));    
% Make the x1 and x2 variables
x1      = x(1,:)';
x2      = x(2,:)';
Monte Carlo simulation 1
y = b1*x1 + e is the true model 
We will now simulate data from this model and then use OLS to estimate two versions of the model: 
y = b1mc*x1 + e and 
y = b1mc_2*x1 + b2*x2 + e 
% Always good practive to allocate empty output before loops
b1mc    = nan(S, numel(N));
b1mc_2  = nan(S, numel(N));
% Simple counter to use when allocating results into b1mc below
cnt  = 1;
for n = N % Loop over the different sample sizes N
    for s = 1 : S
        % generate random errors
        u = randn(n,1)*sqrt(sigma2);    
        % simulate the process
        y = b1*x1(1:n) + u;           
        % Estimate coefficients by OLS (easy in Matlab) and save
        b1mc(s,cnt)     = x1(1:n)\y; 
        tmp             = [x1(1:n) x2(1:n)]\y;                                
        b1mc_2(s,cnt)   = tmp(1); % Only save the first parameter
    end
    cnt = cnt + 1;
end