mlpack VS SHOGUN

Compare mlpack vs SHOGUN and see what are their differences.

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mlpack SHOGUN
4 1
4,742 2,997
1.6% 0.4%
9.9 4.8
3 days ago 3 months ago
C++ C++
GNU General Public License v3.0 or later BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

mlpack

Posts with mentions or reviews of mlpack. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-23.

SHOGUN

Posts with mentions or reviews of SHOGUN. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-04-20.
  • 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:

    https://github.com/shogun-toolbox/shogun/blob/9b8d85/src/sho...

    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.

What are some alternatives?

When comparing mlpack and SHOGUN you can also consider the following projects:

tensorflow - An Open Source Machine Learning Framework for Everyone

Dlib - A toolkit for making real world machine learning and data analysis applications in C++

xgboost - 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

Caffe - Caffe: a fast open framework for deep learning.

mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

examples - TensorFlow examples

NN++ - A small and easy to use neural net implementation for C++. Just download and #include!

RNNLIB - RNNLIB is a recurrent neural network library for sequence learning problems. Forked from Alex Graves work http://sourceforge.net/projects/rnnl/