Smile VS clerk

> I don't think it's right to recommend that new users move away from the package because of licensing issues

I was going to chime in to agree but then I saw how this was done - a completely innocuous looking commit:

https://github.com/haifengl/smile/commit/6f22097b233a3436519...

And literally no mention in the release notes:

https://github.com/haifengl/smile/releases/tag/v3.0.0

I think if you are going to change license especially in a way that makes it less permissive you need to be super open and clear about both the fact you are doing it and your reasons for that. This is done so silently as to look like it is intentionally trying to mislead and trick people.

So maybe I wouldn't say to move away because of the specific license, but it's legitimate to avoid something when it's so clearly driven by a single entity and that entity acts in a way that isn't trustworthy.

Check out Smile too.

Although it may be a bit more work, you can do both machine learning and AI in Java. If you are doing deep learning, you can use DeepJavaLibrary (I do work on this one at Amazon). If you are looking for other ML algorithms, I have seen Smile, Tribuo, or some around Spark.

For deploying a trained model there are a bunch of options that use Java on top of some native runtime like TF-Java (which I co-lead), ONNX Runtime, pytorch has inference for TorchScript models. Training deep learning models is harder, though you can do it for some of them in DJL. Training more standard ML models is much simpler, either via Tribuo, or using things like LibSVM & XGBoost directly, or other libraries like SMILE or WEKA.

I use smile https://github.com/haifengl/smile with ammonite and it feels pretty easy/good to work with. Of course for pure looking at data, and exploration, you're not going to beat python.

Actually, it does. Scala has Spark for data science and some ML libs like Smile.

I guess I must have a mistake somewhere. It's not much code. it's written in Kotlin with smile. My dataset is only about 32MB. I load the dataset into memory. I then use 80% of the data for training, and the other for later testing. I get just the columns I need and store them in the variable dataset.

I've heard good things about Smile, probably beats libs like Weka by far. I'm not sure if you can load a scikit-learn model though, so you might need to retrain the model in Kotlin.

I was using Smile for some period - https://haifengl.github.io/ - it's quite small and lightweight Java lib with some very basic algorithms - I was using in particularly cauterization. Along with this it provides Scala API.

Something I really like in the Clojure data science stack that isn't mentioned is Clerk* — an interesting take on notebooks. I think it's a good gateway into Clojure for those coming from a Python or R background.

*https://clerk.vision/

Clojure's lazy sequences by default are wonderful ergonomically, but it provides many ways to use strict evaluation if you want to. They aren't really a hassle either. I've been doing Clojure for the last few years and have a few grievances, but overall it's the most coherent, well thought out language I've used and I can't recommend it enough.

There is the issue of startup time with the JVM, but you can also do AOT compilation now so that really isn't a problem. Here are some other cool projects to look at if you're interested:

Malli: https://github.com/metosin/malli

Babashka: https://github.com/babashka/babashka

Clerk: https://github.com/nextjournal/clerk

I'm really enjoying using Clojure with Clerk: https://github.com/nextjournal/clerk

It's a bit like a Jupyter notebook, but you get to use your own editor, you still have a normal Clojure REPL, it's stored in git like "normal" code, etc.

Hey all, I'm new to Clojure and would appreciate your help with a few questions I had getting started. I'm using Leiningen to setup my projects and manage my packages as recommended in Brave & True. So far I've been able to add any dependencies I've needed without much issue, Neanderthal, tech.v3.dataset, etc. I'm interested in data science, and was hoping to set up a notebook environment to be able to quickly produce data visualizations on the fly since I'm used to working with Jupyter. I came across Clerk, but I'm having some trouble adding it to my project. Here's what I tried:

Clojure also has Clerk, which is like Jupyter, but more befitting Clojure's overall philosophy: https://clerk.vision/

If I were working long enough with gnarly enough equations I'd look into using Clerk to visualize the equations with MathJax or similar, probably following Sam Ritchie's footsteps with SICMUtils. To me this is the true readability answer: lisp notation for precise implementations, compiling to a rich & familiar visual representation.