https://github.com/LaranIkal/predictive-maintenance

First version of and improved maintenance predicion model usig EIF(Extended Isolation Forest)

Helping to give a push on Scala Analytics. I like to call it ScalaLytics :)

This is and improved version of my previous R project where I used standard Isolation Forest.

Someone interested? I can Install one station as a free service, for you to test.

We are currently considering switching the default implementation of Seq to Vector (currently it's List). If you have any doubts about this change let us know at https://contributors.scala-lang.org/t/slc-make-vector-the-default-implementation-of-seq-instead-of-list/

We just dropped a massive performance update! If you're parsing from String to AST nodes, you're going to see up to 10x faster speeds and ~20x less memory allocation per operation.

🔗 Release Notes: scala-yaml v0.3.3 on GitHub

We benchmarked it on some massive real-world YAML schemas, and the JMH results are wild. Here’s a quick look at the YamlParsingBench.fromStringToNode throughput and allocation rates:

Diff

--- v0.3.2 ---
cgmes-core.yml     :   72 ops/s  | ~129.1 MB/op
cgmes-dynamics.yml :   15 ops/s  | ~615.6 MB/op
TC57CIM.yml        :  4.5 ops/s  |   ~2.0 GB/op

+++ v0.3.3 +++
cgmes-core.yml     :  676 ops/s  |   ~5.7 MB/op  (🚀 9x faster, 22x less memory)
cgmes-dynamics.yml :  153 ops/s  |  ~25.8 MB/op  (🚀 10x faster, 23x less memory)
TC57CIM.yml        :   34 ops/s  |  ~92.0 MB/op  (🚀 7x faster, 21x less memory)

Give it a try and let us know what you think!

P.S. This update was sponsored by NeverBlink as a part of an OSS contribution for LinkML-Scala.