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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 t

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.

bch 14y ago
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?
- andrewguenther 14y ago
  You aren't missing anything, you are absolutely correct, but the question isn't "can they?" the question is "will they?"
  - bch 14y ago
    I'm in the Stanford/Coursera machine learning course right now, and something like this is nearly excactly what I've been looking for. As some others have said, GPLv3 is off-putting, but there is the LGPL mlpack lib (http://www.mlpack.org/ http://www.mlpack.org/) (also C++). Personally, project-wise, the only way this could be improved is if the project were pure C, and a BSD, MIT, or similar license. Quite looking forward to checking these out, though.
    - tlarkworthy 14y ago
      honestly you guys are crazy if you think shark is gonna help you learn machine learning. Its ideal for deployment of ML on things like embedded computer, robotics, games etc. where real time learning is required. Machine learning requires alot of experimentation and C++ is a terrible medium for that. There are loads of good machine learning libraries implemented for python and matlab. Pretty much every good paper in machine learning is accompanied by an algorithm implemented in matlab or python or R. learn using those reference designs. Once you figured out what you want, then deploy on a system in C++ by all means using shark. I do robotics for a living, and I do go from scripting to C++. Unless its absolutely necessary I avoid C++. Only things like vision which is so CPU hungry that it has its own computer do I require C++, every other algorithm stays in python.
      - shut_up 14y ago
        Dear person: > Machine learning requires alot of experimentation and C++ is a terrible medium for that. Is it really? I suspect you do not know how to use C++ effectively. Why not try parameterizing your machine learning algorithms with templates? That way, you can plug in different loss functions, different regularizers, different metrics, or different initialization routines without having to modify your internal algorithm code. In addition, because you are using templates, it is bound at compile time, so you don't pay the vtable lookup penalty that you do with inheritance. Python might be better for your uses, and I can respect that, but don't tell people that C++ is a terrible way to implement machine learning algorithms when you actually are just ignorant of how to do it well.
        rdouble 14y ago
        C++ isn't a terrible medium for experimentation. Games and every "creative coding" interactive projector art type project are done in C++.
      - jhartmann 14y ago
        Actually you forget that performance when you need to train for days at a time is critical, if I use Octave/Matlab/R my current project might take months to train instead of weeks. All my ML code is high performance threaded C++. I recommend you use a good template linear algebra library like Eigen, you can do plenty of experimentation in C++. I find with a set of a few modern libraries and the required experience a C++ programmer is just as if not more efficient than a Python/R/Matlab programmer. It comes down to the skill of the programmer and the proper choice of libraries.
        tlarkworthy 14y ago
        True that matlab octave and R are all rubbish for performance. I use python + numpy which all delegates to BLAS for the hardcore linear algebra stuff. I don't normally find C++ gains me all that much. You can also do GPU acceleration pretty easy using theano (e.g. http://deeplearning.net/software/theano/tutorial/using_gpu.html http://deeplearning.net/software/theano/tutorial/using_gpu.h...) So I reckon my GPU accelerated python still beats a C++ pthreads approach, and is alot faster to develop on. Your mileage may vary, from what you said you probably know what you are doing, maybe GPU is not applicable. I was really replying to the initial comments that said they want to start learning machine learning on a C++ system. Training for days suggests you are doing something hardcore like MCMC/DBN/Guassian Processes, learners should not start there though....
pmelendez 14y ago
I am very agreed with this. I had even been thinking in making a DSL (Scheme based probably) oriented to ML instead of a library. I would found that more useful in the exploring phase.

bch 14y ago

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?

andrewguenther 14y ago
You aren't missing anything, you are absolutely correct, but the question isn't "can they?" the question is "will they?"
- bch 14y ago
  I'm in the Stanford/Coursera machine learning course right now, and something like this is nearly excactly what I've been looking for. As some others have said, GPLv3 is off-putting, but there is the LGPL mlpack lib (http://www.mlpack.org/ http://www.mlpack.org/) (also C++). Personally, project-wise, the only way this could be improved is if the project were pure C, and a BSD, MIT, or similar license. Quite looking forward to checking these out, though.
  - tlarkworthy 14y ago
    honestly you guys are crazy if you think shark is gonna help you learn machine learning. Its ideal for deployment of ML on things like embedded computer, robotics, games etc. where real time learning is required. Machine learning requires alot of experimentation and C++ is a terrible medium for that. There are loads of good machine learning libraries implemented for python and matlab. Pretty much every good paper in machine learning is accompanied by an algorithm implemented in matlab or python or R. learn using those reference designs. Once you figured out what you want, then deploy on a system in C++ by all means using shark. I do robotics for a living, and I do go from scripting to C++. Unless its absolutely necessary I avoid C++. Only things like vision which is so CPU hungry that it has its own computer do I require C++, every other algorithm stays in python.
    - shut_up 14y ago
      Dear person: > Machine learning requires alot of experimentation and C++ is a terrible medium for that. Is it really? I suspect you do not know how to use C++ effectively. Why not try parameterizing your machine learning algorithms with templates? That way, you can plug in different loss functions, different regularizers, different metrics, or different initialization routines without having to modify your internal algorithm code. In addition, because you are using templates, it is bound at compile time, so you don't pay the vtable lookup penalty that you do with inheritance. Python might be better for your uses, and I can respect that, but don't tell people that C++ is a terrible way to implement machine learning algorithms when you actually are just ignorant of how to do it well.
      - rdouble 14y ago
        C++ isn't a terrible medium for experimentation. Games and every "creative coding" interactive projector art type project are done in C++.
    - jhartmann 14y ago
      Actually you forget that performance when you need to train for days at a time is critical, if I use Octave/Matlab/R my current project might take months to train instead of weeks. All my ML code is high performance threaded C++. I recommend you use a good template linear algebra library like Eigen, you can do plenty of experimentation in C++. I find with a set of a few modern libraries and the required experience a C++ programmer is just as if not more efficient than a Python/R/Matlab programmer. It comes down to the skill of the programmer and the proper choice of libraries.
      - tlarkworthy 14y ago
        True that matlab octave and R are all rubbish for performance. I use python + numpy which all delegates to BLAS for the hardcore linear algebra stuff. I don't normally find C++ gains me all that much. You can also do GPU acceleration pretty easy using theano (e.g. http://deeplearning.net/software/theano/tutorial/using_gpu.html http://deeplearning.net/software/theano/tutorial/using_gpu.h...) So I reckon my GPU accelerated python still beats a C++ pthreads approach, and is alot faster to develop on. Your mileage may vary, from what you said you probably know what you are doing, maybe GPU is not applicable. I was really replying to the initial comments that said they want to start learning machine learning on a C++ system. Training for days suggests you are doing something hardcore like MCMC/DBN/Guassian Processes, learners should not start there though....

pmelendez 14y ago

I am very agreed with this. I had even been thinking in making a DSL (Scheme based probably) oriented to ML instead of a library. I would found that more useful in the exploring phase.