把下列这段Matlab代码转换成python代码：runn = 10; for i = 1:runn disp(['Run: ', num2str(i)]); [M, per] = M.Regression(X, T); %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Error = per.Error; %%% O1 = M.GetOutput(X); %%% O2 = M.GetOutput(X2); %%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% if i == 1 Train_Error(i,:) = Error; elseif i>1 && i<=runn if size(Error,2) > size(Train_Error,2) Train_Error = padarray(Train_Error, [0, (size(Error,2)-size(Train_Error,2))], 'post'); else Error = padarray(Error, [0, (size(Train_Error,2)-size(Error,2))], 'post'); end Train_Error(i,:) = Error; end O1_(i,:) = O1'; O2_(i,:) = O2'; end

以下是Python代码的转换：

runn = 10
Train_Error = np.zeros((runn, 1))
O1_ = np.zeros((runn, len(X)))
O2_ = np.zeros((runn, len(X2)))
for i in range(runn):
    print('Run: ' + str(i+1))
    M, per = M.Regression(X, T)
    Error = per.Error
    O1 = M.GetOutput(X)
    O2 = M.GetOutput(X2)
    if i == 0:
        Train_Error[i,:] = Error
    elif i>0 and i<runn:
        if Error.shape[1] > Train_Error.shape[1]:
            Train_Error = np.pad(Train_Error, [(0,0), (0, Error.shape[1]-Train_Error.shape[1])], 'constant')
        else:
            Error = np.pad(Error, [(0,0), (0, Train_Error.shape[1]-Error.shape[1])], 'constant')
        Train_Error[i,:] = Error
    O1_[i,:] = O1.T
    O2_[i,:] = O2.T

请注意，在Python中，你需要导入numpy库使用np.zeros和np.pad函数。此外，Python从0开始索引列表，因此在迭代器中需要添加1。