sample_random: Split the Training and Test Set
In tpq/caress: Caress, a Gentler Introduction to Keras

This function splits a list of inputs and a list of outputs by random sampling. All inputs and outputs get split the same.

1	sample_random(x, y, split = 67, scale = FALSE, normalize = FALSE)

`x`	The input data or a list of input data.
`y`	The output data or a list of output data.
`split`	An integer. The training-test set split as a percentage.
`scale`	A logical. Toggles whether to scale each sample by the total sum, turning the measurements into proportions.
`normalize`	A logical. Toggles whether to normalize each feature by subtracting the training set minimum and dividing by the training set range.

A nested list with two slots "train" and "test", each of which contain another list with two more slots "x" and "y". These provide a list of the processed inputs and outputs, respectively. For example, access the training input with $train$x. You can feed these lists to build.

tpq/caress documentation built on March 11, 2021, 8:03 p.m.