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In #8, the possibility of using a custom NN matrix is discussed and noted to be 'easy' to implement.
DavidMChan: " It would be easy to add the ability to pass in a sparse nearest neighbors matrix, however it becomes more complicated if you want to extract the nearest neighbors from a pre-computed distance matrix."
It would be a significant improvement that would open up a lot of use cases if this were implemented.
Specifically: allowing a user to input a custom distance matrix (ie a sparse knn_graph) would be amazing.
It would be sufficient for users already familiar with and using this feature in sklearn's TSNE to directly port their workflow to tsne-cuda.
It's not that hard to do, since the rest of the TSNE algorithm only requires a float distance array of size (N x # neighbors) and a similarly shaped array of the nearest neighbor indices.
FEATURE REQUEST:
In #8, the possibility of using a custom NN matrix is discussed and noted to be 'easy' to implement.
DavidMChan: " It would be easy to add the ability to pass in a sparse nearest neighbors matrix, however it becomes more complicated if you want to extract the nearest neighbors from a pre-computed distance matrix."
It would be a significant improvement that would open up a lot of use cases if this were implemented.
Specifically: allowing a user to input a custom distance matrix (ie a sparse knn_graph) would be amazing.
It would be sufficient for users already familiar with and using this feature in sklearn's TSNE to directly port their workflow to tsne-cuda.
https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
metricstr or callable, default=’euclidean’: ...If metric is “precomputed”, X is assumed to be a distance matrix. ...
Thanks!
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