如何从leastsq()返回的数据中获取雅可比行列式？
我在我的博士论文中经常使用 scipy.optimize.leastsq() ，但是我不知道如何从 leastsq() 的数据中获得雅可比矩阵的估计代码> 返回。我需要知道用于最小化的雅可比矩阵的估计，以与最小值的有限差分近似进行比较。
有谁有公式如何获得吗？
当您检查如何在 leastsq() 内计算协方差矩阵时，这可能有点棘手
I am using frequently scipy.optimize.leastsq() for my Ph.D thesis however I have no idea how can I get the estimate of a jacobian from the data that leastsq() returns. I need to know the estimate of a jacobian that is used in minimization to compare with the finite difference approximation at minimum.
Does anyone has a formula how to get it?
This can be a bit tricky when you check how for e.g. a covariance matrix is calculated inside leastsq()
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我认为答案是没有人能够从optimize.leastsq 恢复雅可比行列式。
最新威胁 http://mail.scipy.org/pipermail /scipy-user/2011-August/030320.html
从leastsq返回的协方差矩阵的一些例子来看，我认为有很多情况默认设置的精度不是很高。
要检查它的效果如何，您可以将返回的协方差矩阵与雅可比行列式的外积进行比较。
I think the answer is that nobody ever managed to recover the Jacobian from optimize.leastsq.
latest threat on this http://mail.scipy.org/pipermail/scipy-user/2011-August/030320.html
From some examples of the covariance matrix returned by leastsq, I think there are many cases where the precision with default settings is not very high.
To check how good it is, you can compare the returned covariance matrix with the outer product of your Jacobian.