Calculate weight of a cluster in Opencv

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Thank you for your answer! I think this one isn't the correct solution because... I currently do:

• I call trainE
• I modify things using these results like the labels...
• If I recall trainE I will get the same clustering that the previous call.... and I want the weight of the clusters with the new labels which I'm modified...

I hope someone could hit the mark!

THANKS!!

If your labels are changed, then it is impossible to get the same clustering as from the previous call. Restarting the trainE step is exactly what you need to do.

But the labels aren't a input of the clustering... I use positions (x,y) how input of the clustering I have "particles" like these:

• X | Y | .... | LABEL | .....

I use like input: (x,y) I use like OUTPUT label for asign labels at each particle.

Do you think I need restart trainE? With labels like input? because if I use the (x,y) (which have not been modified) I will get the same result....

I don't understand what is the meaning of the weight... is related with the distance between input and means of the clusters?

THANKS for your HELP!!!

You can get the labels as optional output parameters. Your changes to the labels won't be used since EM is an unsupervised algorithm!

I think we aren't understanding properly.

I have some particles with this values: position (x,y) and label(empty before execute the EM).

At this moment I execute the trainE using like INPUT only the position (x,y). And I get (output):

• Labels from EM and I assign these to the particles.
• Weights, covs and means.

At this moment we have the particles with its new labels.

Now I CHANGE some labels based in others things.... And I want get the weights of these modified clusters!

How could I achieve these new weights??

THANKS in advance and thanks for your patience!!

Somehow you really speak from something else since the weights of a GMM which you train via EM are not related to the labels at all, only throught the sum of posteriors! Thus, you must be talking of another weight parameter ^^

OK, thank you for your explanations.

But I have a doubt...

If I use how input a cloud of points (particles x, y) ... What determines the weight of the clusters??

• Dense cluster -> high weight?
• No dense cluster -> low weight?

THANKS!!!

Afaik the weights of GMM models are an indication of the influence of each gaussian model inside the mixture and how important it is.

OK Thank you everyone helped me! I finally implemented recalling the train function using the LABELS modified how input. THANKS