MLDataPattern.jl
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JuliaML
Utility package for subsetting, resampling, iteration, and partitioning of various types of data sets in Machine Learning
My current problem is that I want to predict some transformation result of an ill-posed problem (deconvolution). Thus I have a lot of input function plots (x,y-arrays) and their convolutions...
According to the docs: ```julia using MLDataUtils X, Y = MLDataUtils.load_iris() # iterate over the first 2 batches of 15 observation each for (x,y) in batchview((X,Y), size=15, count=2) @assert typeof(x)
Based on a suggestion from @oxinabox I agree it would be a good idea to introduce a function called `separateobs` (or something of that sorts). It should have a similar...
Would be nice to have a new data iterator that samples the given data in such a way, that each iteration a batch is returned that contains an equal amount...
I think the current behaviour is a design flaw that is an artefact of the early days. Right now when you iterate over a `RandomObs` or a `BalancedObs` iterator it...
Would be nice to have a new data iterator that samples the given data in such a way, that each iteration a batch is returned that has approximately the same...