Max: a sort of illustration of using scaling to 'weigh' distance in kdtree searches

Now, this is not documented yet and quite ugly, but since we spoke about it a lot, and I tried it with mixed results in actual searches, I’ve done a quick patch to check the impact of normalisation/standardisation and scaling of the latter when data is sparse-ish. Feel free to try it and explore and see what you think…


----------begin_max5_patcher----------
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-----------end_max5_patcher-----------
2 Likes

I’ll do one in 3D (colours) later to illustrate, in SC probably for fairness :slight_smile:

Not 100% as to what I’m supposed to be looking at, and get a few dict.unpack errors, and an occasional fluid.kdtree~ error (’DatSet is smaller than k), but I really like the use of nodes for visualization.

Had a cool chat with @tedmoore last week about ways to improve or think about my predictive analysis stuff and at the center of that was visualizing the data to see what may be possible in terms of differentiating these sounds. So definitely itching for a low impact way of visualizing datasets without having to build a ton of plumbing each time.

ok here is an updated version with the following changes:

  • since the normalization post standardization is the same thing as a normalization post normalization, or a normalization alone, I have moved the reference to older processes, i.e. everything is done from the raw input
  • there was a bug with the ordering of fitting too
  • I made 1D equally distributed, and 1D gaussian. It is much easier to see the impact that way.

proposed activity:

  • randomize the dataset and find a point that has 3 different values for nearest neighbours (raw, norm’d, stand’d)
  • then play with the scale of Y norm and observe the impact on NN both graphically and through the query.

I hope you enjoy!

pa

===


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-----------end_max5_patcher-----------

I have the colour SC 3d version going on if anyone is interested. I’m not sure it has the limpidity of the Max 2D one yet.

I thought it was a loadbang thing, but even when saving and reopening the file I get a parse error when banging #1, and then a stream of errors when manipulating #2.

fluid.dataset~: Parse error
fluid.dataset~: Point not found
fluid.dataset~: Point not found
fluid.dataset~: Point not found

I sent the version with the updated dict support… let me amend that to the version you have (dict.deserialize needs inserting)

here we go!


----------begin_max5_patcher----------
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-----------end_max5_patcher-----------
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