Comments (6)
Hey @esrah-ap-niht. Unfortunately, full covariance matrices are currently not implemented. I'm in the process of refactoring the library though to make the interface even closer to sklearn's. I'll make sure to add it then :)
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Hi @esrah-ap-niht ,
Are you using diagonal covariance?
pycave/pycave/bayes/gmm/model.py
Line 57 in 691179b
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@groadabike
No, I had gmm = GMM(num_components=20, num_features=test2.shape[1], covariance='spherical')
specified. However, you are absolutely correct that the specification explains the shape of the matrix. I feel stupid I didn't catch that myself.
Thinking about this, the thread title should actually change to a feature request for a full covariance matrix option. Or if that's already implemented, knowing what text to use in the specification. I tried using "full" in the same style as Sklearn and received an error message.
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Thank you @borchero, I'll look forward to it. I'm using your code for research and would like to cite your work. Do you have a reference you'd like me to use? Otherwise I can use a generic reference along the lines of:
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Happy to see it being used in your research, @esrah-ap-niht ! :D unfortunately, there is no public work using the library, so the generic reference is definitely fine :) Thanks for asking!
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@esrah-ap-niht full covariances matrices are finally implemented :)
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Related Issues (20)
- Computing BIC HOT 1
- How can i disable model output prints during fit_preditct? HOT 3
- How to get covariance matrix? And confused about precisions cholesky... HOT 1
- installation failure HOT 2
- Mini-batch training on GMM HOT 3
- GMM fitting with full covariance crashes unexpectedly unlike sklearn GMM fitting HOT 7
- Batch size must be factor or total dataset size HOT 2
- Covariance matrices not symmetric HOT 2
- Diagonal/spherical covariance GMMs have no covariance variable
- Long initialization time when using GPU HOT 5
- multi-GPU error
- GMM with Mini-Batches HOT 1
- Allow pytorch 2.0 & pytorch-lightning 2.0 HOT 3
- GPU memory leak. HOT 3
- nll is nan in training HOT 3
- GMM Training with Mini-Batch
- mini-batch training GMM sample HOT 4
- When K equals num_features it causes a problem if covariance is diag HOT 1
- Runtime error in log_normal function HOT 1
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