[tremblap]

Mostly for 2D drawing, we need matrix-wide, not column-wise, data processing to preserve the original shape and the relevance of diagonal distances. This is done for the 3 transformers.

At the moment, there are 3 questions for @weefuzzy @jamesb93 and @tedmoore :

1. It currently works as an overide of the original code (to test the algo, and review my implementation idea of keeping the processors intact post fitting (so still processing by vectors) - I think it is a good idea, but someone might disagree.
2. at the moment, the idea is to have a flag to process colwise (as before, and would be the default) or matrixwise. this implies a forking only in the algo, and I could code it, except if I'm told we should do a 'fit-full-dataset' method instead. both are trivial to implement and I'll do it in the next push on this PR if this is the agreed way
3. if this is the agreed way, the isDSwide name is appalling. Even just dswide is appalling. but I am struggling to find something for now that stays in the vocabulary (byDim, byDS, byCol, colwise, keepshape, relative) so please let's brainstorm. obviously the default will be the backward-compatible switch

Thanks!

[tremblap]

example code in SC to see it work and test the dims are right (3x2 is perfect for debugging :) )

d = FluidDataSet(s).load(Dictionary.newFrom([\cols, 2, \data, Dictionary.newFrom([\p1, [0.1, 0.2], \p2, [0.3, 0.9], \p3, [0.2, 0.5]])]))
d.print

~fp = FluidPlotter(bounds:Rect(200,200,600,600), xmin: -0.1, xmax: 1.1, ymin: -0.1, ymax: 1.1)

d.dump{|x|defer{~fp.dict = x}}

n = FluidNormalize(s)

n.fitTransform(d,d)

r = FluidRobustScale(s)

r.fitTransform(d,d)

w = FluidStandardize(s)

w.fitTransform(d,d)

~fp.xmin_(-3).ymin_(-3).xmax_(3).ymax_(3)

[tedmoore]

My first thought for the arg name was "by column" (or byCol or whatever is appropriate), which was also one of your suggestions, so there's some resonance there.