Similar projects and alternatives to SHOGUN
mlpack: a fast, header-only C++ machine learning library
Access the most powerful time series database as a service. Ingest, store, & analyze all types of time series data in a fully-managed, purpose-built database. Keep data forever with low-cost storage and superior data compression.
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
A toolkit for making real world machine learning and data analysis applications in C++
Caffe: a fast open framework for deep learning.
Write Clean C++ Code. Always.. Sonar helps you commit clean C++ code every time. With over 550 unique rules to find C++ bugs, code smells & vulnerabilities, Sonar finds the issues while you focus on the work.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library
A curated list of awesome places to learn and/or practice algorithms.
Jupyter kernel for the C++ programming language
A branchless stable quicksort / mergesort hybrid.
The interdicplinary of Mathematics and Computer Science, Distinguisehed by its emphasis on mathemtical technique and rigour.
A branchless unstable quicksort / mergesort hybrid 33% faster than pdqsort.
A small and easy to use neural net implementation for C++. Just download and #include!
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
SHOGUN reviews and mentions
Changing std:sort at Google’s Scale and Beyond
7 projects | news.ycombinator.com | 20 Apr 2022
The function is trying to get the median, which is not defined for an empty set. With this particular implementation, there is an assert for that:
Unrelatedly, but from the same section:
> Fixes are trivial, access the nth element only after the call being made. Be careful.
Wouldn't the proper fix to do the nth_element for the larget element first (for those cases that don't do that already) and then adjust the end to be the begin + larger_n for the second nth_element call? Otherwise the second call will check [begin + larger_n, end) again for no reason at all.
shogun-toolbox/shogun is an open source project licensed under BSD 3-clause "New" or "Revised" License which is an OSI approved license.