On 7/20/10 11:56 PM, D2Hitman wrote:
Robert Kern-2 wrote:
Don't try to fit a Gaussian to a histogram using least-squares. It's an
awful
way to estimate the parameters. Just use np.mean() and np.cov() to
estimate the
mean and covariance matrix directly.
Ok, what about distributions other th
other than gaussian? Would you use leastsq in
that case? If yes, i will post that to the scipy mailing list.
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On 7/20/10 10:13 PM, D2Hitman wrote:
I want to fit an n-dimensional distribution with an n-dimensional gaussian.
So far i have managed to do this in 2d (see below). I am not sure how to
convert this to work in n-dimensions. Using "ravel" on the arrays is not
ideal, but optimize does not appear t
gauss2d(plsq, _x,_y) / plsq[0])
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