Thanks Jake, I was actually just reading this:
http://www.cs.mcgill.ca/~dprecup/courses/ML/Lectures/ml-lecture16.pdf
and starting to put all the pieces together when you sent this. In the
pdf, the K-means example you gave is basically Hard EM for GMM while the
latter is Soft EM that I am seeing
David,
Have you looked at the K Means algorithm? It uses a similar approach of a
two-phase iteration to determine clustering. In K means you're looking for
K cluster centers, such that when each point is assigned to the nearest
cluster, the total of the distances from points to their clusters is
m
ok, this is what I can gather from the code:
Expectation Step
--
Calculate the loglikelihood and responsibilities for each sample.
a. for each sample the loglikelihood is calculated for each gaussian
and then sum across models (logprob
2013/9/3 David Reed :
> Thanks Jake, I will look into this more. Is this the proper forum to ask
> questions about its implementation if I am confused?
Yes it is.
--
Olivier
http://twitter.com/ogrisel - http://github.com/ogrisel
Thanks Jake, I will look into this more. Is this the proper forum to ask
questions about its implementation if I am confused?
On Tue, Sep 3, 2013 at 10:41 AM, Jacob Vanderplas wrote:
> Dave,
> Have you looked at the source of the GMM estimator in scikit-learn? It's
> a pretty concisely imple
Dave,
Have you looked at the source of the GMM estimator in scikit-learn? It's a
pretty concisely implemented EM algorithm:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/mixture/gmm.py#L451
Jake
On Tue, Sep 3, 2013 at 7:36 AM, David Reed wrote:
> Hi there,
>
> Does anyone
Hi there,
Does anyone have a resource for a good example of an EM algorithm in code,
not math? I just want something workable that cuts through the theory.
Thanks,
Dave
--
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