algorithmic modeling for Rhino



The purpose of the Owl plug-in is to constitute a new data type named Tensor, thanks to which the Grasshopper users will be able to work with so-called "big-data". This will further open up new possibilities to use more sophisticated machine-learning tools, which require big data sets to be effective. 

The core library of the Owl plug-in is open sourced, and provides the developers with methods to read/write and use the Tensor data within the GH (and outside of it).

Additionally the Owl.Accord.GH.gha plug-in is the first extension based on the Owl core, utilizing few of the machine-learning methods sourced from the Accord framework. 

Download at:

Core libraries, open-sourced:

NuGet packages: 

Install-Package Owl.Core

Install-Package Owl.GH.Common 

Some parts of the plug-in depend on the Accord framework:

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Latest Activity: Mar 21

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Discussion Forum

Examples discussion 10 Replies

Placeholder for the examples to come.Continue

Started by Mateusz Zwierzycki. Last reply by Samuel Wilkinson Jan 26.

Hungarian Algorithm - T-SNE to grid assignment problem 2 Replies

Hi everyone & Mateusz - Owl is brilliant, and thanks for creating it.I wanted to represent the T-SNE data in grid form, so I've copy-pasted and hooked some things up implemented …Continue

Started by Dan Taeyoung. Last reply by Pablogomez Dec 22, 2017.

Python & Getting outside of GH 2 Replies

While Owl contains some small-scale methods for machine learning, you might want to use more recent deep-learning methods like the ones available in TensorFlow.The solution is:Get the data from…Continue

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Started by Mateusz Zwierzycki. Last reply by Mateusz Zwierzycki Apr 15, 2017.

Developers Developers Developers

Here you can find the Owl core libraries: can easily develop your own plugin based on the…Continue

Started by Mateusz Zwierzycki Apr 14, 2017.

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Comment by Ángel Linares on August 7, 2017 at 2:01am

Hi Ortler,

Yes, that is what I meant by using scripting. But I just wanted to know if was there any built-in component doing the work ;) looks like there is no :P

Thanks for the reply.

Comment by Ortler Mark on August 7, 2017 at 1:53am

@Ángel Linares...
you could split the tensor in its nummeric parts and then use the same method as for euclidean vectors to calculate the distance:
sqrt(a²+b²+c²+d²+e²+...) u see? simple pythagoras...

If u need more operations like "dotproduct" or "crossproduct" for tensors let me know...


Comment by Ángel Linares on August 7, 2017 at 1:41am

Hi all,

Has anyone try to calculate the distance between several tensors in nD? As far as I can see there is no component for that straight away. If scripting is the only way I will go that way, but just wanted to check first in here.

Thanks in advance.


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