Define a 2D Gaussian probability with five peaks

Question

I have a 2D data and it contains five peaks. Could I fit five 2D Gaussians function to obtain the peaks? In my problem, the peaks do not refer to the clustering problem. Which I think EM would be an appropriate answer for it. In my case I measure a variable in x-y space and it shows maximum in more than one position. Is still fitting Fourier series or using Expectation-Maximization method an applicable solution to my problem?

In order to make my likelihood, do I need to just add up the five 2D Gaussians distributions with x and y and the height of each peak as variables?

you can fit a Fourier series of two variables to your data using `numpy.linalg.lstsq()`, for example, and then find the peaks analytically... — Saullo G. P. Castro, May 22 '14 at 13:50

score 1 · Answer 1 · answered May 22 '14 at 14:06

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If I understand what you're asking, check out Gaussian Mixture Models and Expectation Maximization. I don't know of any pre-implemented versions of these in Python, although I haven't looked too hard.

answered May 22 '14 at 14:06

AMacK

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A simple google would would have turned up http://scikit-learn.org/stable/modules/generated/sklearn.mixture.GMM.html – ebarr May 22 '14 at 14:14

Define a 2D Gaussian probability with five peaks

1 Answers1