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Common Lisp is a great language to build new tools for data science, but currently has pretty awful library support existing data science workflows. Common Lisp is sorely lacking in high-quality statistics, plotting, and sparse arrays. There’s been a long work-in-progress library to bring flexible and high-performance linear algebra to Lisp, but it needs more contributors.
For example, numcl aims to be a clone of numpy. The published benchmarks are not impressive. My aim is not to second guess the author or belittle his project and effort.
burgled batteries
py4cl
cl-python
Yes, simulator, compiler, paper is some of it.
Yes, simulator, compiler, paper is some of it.
Hissp is an option. It's a Lisp that compiles to Python expressions. One of the data science guys here said he liked it better than Hy.
There are some interesting efforts concurrent with scicloj work by Chris Nuernberger specifically dtype-next, and the earlier tech-jna stuff. It's the same stuff underlying libpython-clj and libjulia-clj. recent talk.
Did you have any occasion to evaluate neanderthal during your research? People seem to prefer it over core.matrix because it focus on primitive speed and sticking to BLAS idioms (as well as offering a decent api for working with GPU backends via cuda and opencl). I am curious to see if you did and found anything lacking there. I have a project on the backburner to try and target neanderthal for local search stuff, expressing problems in a high-level API that can then be baked into some numerically-friendly representation for efficient execution. It's often easier (trivial) to express solution representations, neighborhood functions, and objectives/constraints in a general purpose language, of which none of the things we like (sparse data structures, dynamically allocated stuff) are amenable to the contiguous memory, primitive numeric model that the hardware wants.
Yeah, I use CL for data science, despite lack of suitable tools. I even ended up writing my own: https://github.com/sirherrbatka/clusters https://github.com/sirherrbatka/vellum https://github.com/sirherrbatka/vellum-plot https://github.com/sirherrbatka/statistical-learning
Yeah, I use CL for data science, despite lack of suitable tools. I even ended up writing my own: https://github.com/sirherrbatka/clusters https://github.com/sirherrbatka/vellum https://github.com/sirherrbatka/vellum-plot https://github.com/sirherrbatka/statistical-learning
Yeah, I use CL for data science, despite lack of suitable tools. I even ended up writing my own: https://github.com/sirherrbatka/clusters https://github.com/sirherrbatka/vellum https://github.com/sirherrbatka/vellum-plot https://github.com/sirherrbatka/statistical-learning
Yeah, I use CL for data science, despite lack of suitable tools. I even ended up writing my own: https://github.com/sirherrbatka/clusters https://github.com/sirherrbatka/vellum https://github.com/sirherrbatka/vellum-plot https://github.com/sirherrbatka/statistical-learning