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Update README.rst #68

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3 changes: 1 addition & 2 deletions README.rst
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Expand Up @@ -189,7 +189,6 @@ Computing the latent features for simulated or experimental particles can be don

import cryo_sbi.utils.estimator_utils as est_utils
latent_vecs = est_utils.compute_latent_repr(
compute_latent_repr(
estimator=posterior,
images=images,
batch_size=100,
Expand All @@ -201,7 +200,7 @@ After we computed the latent representation for the images, one possible way to
.. code:: python

import umap
reducer = umap.UMAP(metric="euclidian", n_components=2, n_neighbors=50)
reducer = umap.UMAP(metric="euclidean", n_components=2, n_neighbors=50)
embedding = reducer.fit_transform(latent_vecs.numpy())

We can quickly visualize the 2d latent space with matplotlib.
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