least-squares

来源： https://stackoverflow.com/questions/42388139/how-to-compute-standard-deviation-errors-with-scipy-optimize-least-squares

来源： https://stackoverflow.com/questions/42388139/how-to-compute-standard-deviation-errors-with-scipy-optimize-least-squares

来源： https://stackoverflow.com/questions/42388139/how-to-compute-standard-deviation-errors-with-scipy-optimize-least-squares

问题 I am new to python, and trying to use the lmfit package to check my own calculations, however I am unsure (1) as to how to include the errors for data (sig) for the following test (and 2) of an error I get with conf_interval2d shown below): import numpy as np from lmfit import Parameters, Minimizer, conf_interval, conf_interval2d, minimize, printfuncs x=np.array([ 0.18, 0.26, 1.14, 0.63, 0.3 , 0.22, 1.16, 0.62, 0.84,0.44, 1.24, 0.89, 1.2 , 0.62, 0.86, 0.45, 1.17, 0.59, 0.85, 0.44]) data=np

问题 I was wondering if there's a function in Python that would do the same job as scipy.linalg.lstsq but uses “least absolute deviations” regression instead of “least squares” regression (OLS). I want to use the L1 norm, instead of the L2 norm. In fact, I have 3d points, which I want the best-fit plane of them. The common approach is by the least square method like this Github link. But It's known that this doesn't give the best fit always, especially when we have interlopers in our set of data.

问题 I was wondering if there's a function in Python that would do the same job as scipy.linalg.lstsq but uses “least absolute deviations” regression instead of “least squares” regression (OLS). I want to use the L1 norm, instead of the L2 norm. In fact, I have 3d points, which I want the best-fit plane of them. The common approach is by the least square method like this Github link. But It's known that this doesn't give the best fit always, especially when we have interlopers in our set of data.

问题 I was wondering if there's a function in Python that would do the same job as scipy.linalg.lstsq but uses “least absolute deviations” regression instead of “least squares” regression (OLS). I want to use the L1 norm, instead of the L2 norm. In fact, I have 3d points, which I want the best-fit plane of them. The common approach is by the least square method like this Github link. But It's known that this doesn't give the best fit always, especially when we have interlopers in our set of data.

问题 I was wondering if there's a function in Python that would do the same job as scipy.linalg.lstsq but uses “least absolute deviations” regression instead of “least squares” regression (OLS). I want to use the L1 norm, instead of the L2 norm. In fact, I have 3d points, which I want the best-fit plane of them. The common approach is by the least square method like this Github link. But It's known that this doesn't give the best fit always, especially when we have interlopers in our set of data.

问题 I want to solve xA=b with constraint 0<=x for x . I found functions like lsqnonneg and lsqlin which solves for Ax=b . However, couldn't find a good way to solve for xA=b . How can I solve xA=b with non-negative x constraint? 回答1: As David commented, it is straightforward to show that so you can use standard methods to solve the problem with A' and b' and then transpose the answer. 来源： https://stackoverflow.com/questions/26600699/constrained-linear-least-squares-for-xa-b-in-matlab

问题 I'm trying to perform a constrained least-squares estimation using Scipy such that all of the coefficients are in the range (0,1) and sum to 1 (this functionality is implemented in Matlab's LSQLIN function). Does anybody have tips for setting up this calculation using Python/Scipy. I believe I should be using scipy.optimize.fmin_slsqp() , but am not entirely sure what parameters I should be passing to it.[1] Many thanks for the help, Nick [1] The one example in the documentation for fmin