# Speed comparison with dimensionality reduction and fluid.kdtree~

**URL:** https://discourse.flucoma.org/t/speed-comparison-with-dimensionality-reduction-and-fluid-kdtree/583
**Category:** Pre-Release Toolbox2 Code Sharing
**Created:** [July 17, 2020, 10:10pm UTC](https://discourse.flucoma.org/t/speed-comparison-with-dimensionality-reduction-and-fluid-kdtree/583 "2020-07-17T22:10:45Z")
**Posts on this page:** 1
**Showing post:** 5

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### Author: ![rodrigo.constanzo](https://discourse.flucoma.org/user_avatar/discourse.flucoma.org/rodrigo.constanzo/32/12_2.png) [@rodrigo.constanzo](https://discourse.flucoma.org/u/rodrigo.constanzo)
#### Post date: [July 18, 2020, 12:27am UTC](https://discourse.flucoma.org/t/speed-comparison-with-dimensionality-reduction-and-fluid-kdtree/583/5 "2020-07-18T00:27:13Z")

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Ran a quick test and running it with 2d (vs 20) is a tiiny amount faster. On my (faster) laptop, I get 0.37ms for the real-time transform on 20d space and 0.35ms for the same but going down to a 2d space.

I’m curious to see how far down I can push it. I’m aiming for keeping as much accuracy as possible since it will make a bit part of how I actually query the corpus space [(pseudo-predictive matching](https://discourse.flucoma.org/t/regression-classification-regressification/547/37)). If that can be 2d, even better!

I may very well do something where I make a whole bunch of 2d reductions (e.g. 2d “loudness” space, 2d "spectral space, etc…), and then concatenate them into a manually curated dimensionality reduced space, for the final matching step. But mucho experiments to try before I get it there.

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