thanks very much alexander.
so if n**2 is roughly 100G, this would need 800+G of space.
cheers,
satra
On Fri, Nov 28, 2014 at 10:46 AM, Alexander Fabisch <
afabi...@informatik.uni-bremen.de> wrote:
> Hi Satra,
>
> for a training set of shape (n_samples, n_features) the required memory
> should be O(n_samples * max(n_samples, n_features, n_components)).
>
> The following additional memory is allocated:
>
> * matrix of pairwise distances 'distances' (n_samples, n_samples)
> * conditional probabilities 'conditional_P' (n_samples, n_samples)
> * joint probabilities 'P' (n_samples, n_samples)
> * matrix of pairwise distances in the embedded space 'n' (n_samples *
> (n_samples - 1))
> * Student's t-distribution 'Q' (n_samples * (n_samples - 1))
> * Kullback-Leibler divergence 'kl_divergence' (n_samples * (n_samples - 1))
> * 'PQd' (n_samples, n_samples)
> * 'grad' (n_samples * n_components)
> * embedded data 'self.embedding_' (n_samples, n_components)
>
> Best regards,
>
> Alexander
>
> > hi folks,
> >
> > does anyone know what the max memory usage is of TSNE in scikit learn?
> > for
> a n x n dense matrix, how much memory is required?
> >
> > cheers,
> >
> > satra
>
>
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