I have a large set of 3D data points to which I want to fit to an ellipsoid. My maths is pretty poor, so I\'m having trouble implementing the least squares method without any math libraries.
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I want to compute ordinary least square (OLS) estimates in R without using \"lm\", and this for several reasons. First, \"lm\" also computes lots of stuff I don\'t need (such as the fitted values) con
Is there a Ruby library that a开发者_Go百科llows me to do either linear or non-linear least squares approximation of a set of data.
I am looking for a numpy-based implementation of ordinary least squares that would allow the fit to be updated with more observations. Something along the lines of Applied Statistics alg开发者_运维问答
When using MATLAB\'s lsqnonlin function, I am trying to give a user-defined Jacobian matrix, as described in the documentation.
I am working with data from neuroimaging and because of the large amount of data, I would like to use sparse matrices for my code (scipy.sparse.lil_matrix or csr_matrix).
I am using fsolve to minimise an energy function in MATLAB. The algorithm I am using fits a grid to noisy lattice data, with costs for the distances of the grid from each data point.
Could someone explain how to get Chi^2/doF usin开发者_运维技巧g numpy.polyfit?Assume you have some data points
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