production-phase.md

@@ -263,9 +263,7 @@ Below I sequentially list the steps of the pipeline. This is a linear and qualit
  * Here again, t-SNE has no out-of-sample method, so we need to learn a mapping between signatures type 1 and the 2D projections.
  * I suggest using AdaNet in this case, too.
  * Save the models for persistency.
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     Once reference calculations are done, we can move to the full dataset.
-
 15. Predict signatures type 1.
  * If the molecule is in reference (or is a near-duplicate of it), take signature.
  * Else, use the persistent model to predict.
@@ -289,9 +287,7 @@ Below I sequentially list the steps of the pipeline. This is a linear and qualit
  * Do 1-to-many or predict, as necessary.
  * Keep the `proj1.h5` file under `./full`.
  * Do the validation plots.
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     Points 1-19 are applicable to any dataset. Comparison of CC datasets is, **for now**, only among *exemplary* ones. From here on, we only perform the calculations on these 25 exemplary datasets.
-
 20. Link exemplary to full datasets
  * In the `./exemplary`, keep the corresponding signature files available from `./full`.
  * It is not necessary that signature files are copied, they can just be *linked* with a pointer.
@@ -310,7 +306,7 @@ Below I sequentially list the steps of the pipeline. This is a linear and qualit
 24. Prepare for inference.
  * Produce correlation matrices and placeholders necessary for inference.
  * Save them under `./exemplary`.
-25. Infer similarities
+25. Infer similarities.
  * Add datasets to `sims.h5 (*_prd)`under `./molecules`.
 26. Calculate CC scores.
  * Popularity