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The library supports some very useful algorithms. Both supervised and unsupervised ones. But I can't use it for our commercial non GPL projects.
If LGPL works for you, you may want to look into http://www.mlpack.org/ I'm not really sure how they compare since I haven't really used either library, but there does seem to be some overlap.
The library supports some very useful algorithms. Both supervised and unsupervised ones. But I can't use it for our commercial non GPL projects.
Just a thought...
Earlier quoted context omitted.
Speaking as a PhD student in machine learning- Implement the algorithm yourself, first, in Python+Numpy. The only reason I feel comfortable with Gaussian Processes and SVMs is due to writing code to solve them manually. Once you're happy with the basics, and can test your ideas with code you intimately understand, optimise for speed by using a library like this.
Implementing the SVM from scratch was time consuming - no?
edit: For an example of using cvxopt, check out http://www.mblondel.org/journal/2010/09/19/support-vector-ma...
The library supports some very useful algorithms. Both supervised and unsupervised ones. But I can't use it for our commercial non GPL projects.
Could you have the ML part done as a standalone process and not actually linked into your other project code? That is, process some data "out of band" so to speak, and write it out to a file, which is then used by "your stuff"? Or communicate with the ML part over a socket? Either approach would let you use this code without requiring the other parts of your project to be licensed under the GPL. Just a thought...
Earlier quoted context omitted.
Implementing the SVM from scratch was time consuming - no?
The only tricky part would be writing a quadratic solver. Alternatives: either solve a linear SVM using gradient descent (simpler to write), or offload the core of the algorithm to an existing solver like cvxopt. edit: For an example of using cvxopt, check out http://www.mblondel.org/journal/2010/09/19/support-vector-ma...
http://en.wikipedia.org/wiki/Sequential_minimal_optimization
The library supports some very useful algorithms. Both supervised and unsupervised ones. But I can't use it for our commercial non GPL projects.
I actually really don't understand why anyone uses GPL for a library. I've been doing open source for a long long time, and love the GPL. I have code in the Linux kernel, and believe free software AND open source software are great solutions to very real problems in software engineering. Having open code just gives people more options, and I firmly believe it will win over time as far as quality is concerned.
I just think only providing libraries to other GPL code is stupid. It just limits the usefulness of the software. LGPL is great here, you get the core changes contributed back to your library from a greater group of people and everyone wins. Limiting a library to GPL means a large population can not use your code, those writing applications that can't be licensed under the GPL. Limiting choice is BAD. The whole reason you should be creating and using free software and OSS is to not weld the hood shut. GPL should be for applications, LGPL just limits choices for libraries. Down with the GPL for libraries!!!
Flame Suit Off / Rant Mode Off
This looks awesome. I've been itching to try out some ideas I have after having gone through Bishop's book, but I've been hesitant to write the algorithms from scratch. Now I'll have to decide between learning matlab or a library such as this.
This looks awesome. I've been itching to try out some ideas I have after having gone through Bishop's book, but I've been hesitant to write the algorithms from scratch. Now I'll have to decide between learning matlab or a library such as this.
You should definitely use a scriptable ML library. The process is very iterative and not suited to a compiled language like C++. I use skilearn alot, but also the matlab toolboxes or R are great. At its heart ML is alot of stats, so use something built for maths, not C++. It doesn't really make sense to break out C++ until you know exactly what algorithm and settings you need and your application is real time.
Earlier quoted context omitted.
You should definitely use a scriptable ML library. The process is very iterative and not suited to a compiled language like C++. I use skilearn alot, but also the matlab toolboxes or R are great. At its heart ML is alot of stats, so use something built for maths, not C++. It doesn't really make sense to break out C++ until you know exactly what algorithm and settings you need and your application is real time.
If the OP was thinking of writing his own algorithms, and this is a linkable library with that heavy math already implemented, couldn't he write bindings for Python/Lua/Tcl/Ruby and have everything he needs for script-ability, or am I missing something?
The library supports some very useful algorithms. Both supervised and unsupervised ones. But I can't use it for our commercial non GPL projects.
Flame Suit On / Rant Mode On I actually really don't understand why anyone uses GPL for a library. I've been doing open source for a long long time, and love the GPL. I have code in the Linux kernel, and believe free software AND open source software are great solutions to very real problems in software engineering. Having open code just gives people more options, and I firmly believe it will win over time as far as…