update docs

README.md

@@ -43,7 +43,11 @@ cv_data, test_data = splitobs((Xs, Ys); at=0.85)
 
 # Next we partition the data using a 10-fold scheme.
 for (train_data, val_data) in kfolds(cv_data; k=10)
-    for epoch = 1:100
+
+    # We apply a lazy transform for data augmentation
+    train_data = mapobs(xy -> (xy[1] .+ 0.1 .* randn.(), xy[2]),  train_data)
+
+    for epoch = 1:10
         # Iterate over the data using mini-batches of 5 observations each
         for (x, y) in eachobs(train_data, batchsize=5)
             # ... train supervised model on minibatches here

src/obstransform.jl

@@ -37,11 +37,11 @@ Indexing triggers the transformation `f`.
 The batched keyword argument controls the behavior of `mdata[idx]` and `mdata[idxs]` 
 where `idx` is an integer and `idxs` is a vector of integers:
 - `batched=:auto` (default). Let `f` handle the two cases. 
-   Call `f(getobs(data, idx))` and `f(getobs(data, idxs))`.
-- `batched=:never`. `f` is always called on a single observation. 
-   Call `f(getobs(data, idx))` and `[f(getobs(data, idx)) for idx in idxs]`.
-- `batched=:always`. `f` is always called on a batch of observations.
-    Call `getobs(f(getobs(data, [idx])), 1)` and `f(getobs(data, idxs))`.
+   Calls `f(getobs(data, idx))` and `f(getobs(data, idxs))`.
+- `batched=:never`. The function `f` is always called on a single observation. 
+   Calls `f(getobs(data, idx))` and `[f(getobs(data, idx)) for idx in idxs]`.
+- `batched=:always`. The function `f` is always called on a batch of observations.
+    Calls `getobs(f(getobs(data, [idx])), 1)` and `f(getobs(data, idxs))`.
 
 # Examples