I want to try using pycaffe interface to train caffe models.
Particularly I want to use a python layer for input data.
Will this training be slow or will pycaffe just act as an interface to actual caffe?What are the effects?
Any attempts to enlighten the novice are thanked.
pycaffe is a Python interface to caffe, so running from pycaffe just calls caffe methods under the hood.
On the other hand, having a Python layer in your model does not compel you to use pycaffe. You can run this model like any other from command line.
This can be asked as a general question, not only to caffe. What pycaffe does is simply calling the C++ methods that are implemented. Of coruse you will have an overhead to to the python interface and the calling of the function. This blog evaluates the The cost of a Python function call.
Furthermore, if you use a Python IDE like e.g Spyder you give some resources to the IDE. If you run your script in a python-console it may take less resources.
All in all it can be said that if you have a average machine pycaffe will work just as good as the C++ implementation and you will not directly feel any different.
Pycaffe will be easier to handle than the C++ impelementation thanks to abstraction.
Related
I'm looking for a function in Julia to estimate coefficients for an ARMA process.
For example using the Prediction Error Model as pem and armax in Matlab (part of system identification toolbox) do. pem documentation and armax documentation.
I've looked at the following packages, but can't see that they do what I'm looking for:
TimeSeries.jl
TimeModels.jl
One solution is of course to use Matlab.jl and use the Matlab functions, but I was hoping to do it all in Julia.
If there isn't anything right now, does anyone know of if there are any good Julia functions for multidimensional numerical minimisation (like Newton-Raphson), that can be used for implementing a PEM function?
UPDATE: I've just pushed a module to github called RARIMA.jl. This module can be used to estimate, forecast, and simulate ARIMA models (of which ARMA is a special case). Some of the functions are implemented in Julia, others (particularly estimation) call equivalent R functions using the RCall package which you will need to install and verify it works prior to using RARIMA. The package isn't officially registered (yet), so Pkg.add("RARIMA") won't work for now. If you want to use RARIMA, instead try Pkg.clone("https://github.com/colintbowers/RARIMA.jl"). If this fails, you can file an issue on the repository github page, but be sure to check RCall is installed and working before doing this. Cheers, I'll come back and update here if/when the package is officially registered.
ORIGINAL ANSWER: I just had a glance at the source, and TimeModels does not appear to have any functionality for estimating ARIMA models, although does have one function for simulating them. Given time though, I suspect this will be the package that deals with ARIMA modelling. The TimeSeries package is more about building the object type TimeSeries rather than implementing time series models, so I would be surprised if ARIMA modelling is ever merged into that package.
As near as I can tell, at this point if you want a fully functioning ARIMA package you'll need to use Matlab or R. The R one is very good (see the forecast package written by Rob Hyndman - it is very nice) and is probably easier to interface with from Julia than the Matlab option. Of course, the other option is to start it yourself and merge the code with the TimeModels package :-)
In terms of optimization procedures, Julia has a fair few that are written in Julia, and can be found under the JuliaOpt umbrella. The Optim package in particular is quite popular and well developed. However, most of the people I know who are really into this stuff use NLOpt which is a free open source library callable from many languages (including Julia). I have heard nothing but good things about this library from people who tend to work with this stuff 24/7.
I would like to write some pieces of code in Scala . It is important for me that this code can be called from matlab. AFAIK java code can easily be integrated with matlab. http://www.mathworks.co.uk/help/matlab/ref/javamethod.html
Is this valid also for scala code?
It is also valid for Scala code. Just pretend that you're doing Java interop (e.g. a method called + would actually be $plus) and make sure scala-library.jar is in Matlab's classpath (e.g. using javaaddpath).
Incidentally, I've done Java/Matlab interop before, and it's not as "easily integrated" as one might hope. Passing data is kind of awkward and you tend to trip on classloader and/or classpath issues every now and then.
I'd be wary of planning a big project with lots of tightly-connected interop. In my experience it usually works better using the Unix mindset: make independent tools that do their own thing well, and chain them together to get what you want. For instance, I routinely have Scala write Matlab code and call Matlab to run it, or have Matlab use system to fire up a Scala program to process a subset of the data.
So--calling Scala from Matlab is completely possible, and for simple interfaces looks just like Java. Just try to keep the interface simple.
You could encapsulate your Scala code in another language that's easily called by Matlab, such as Java of C. Since you seem to know about Java, you could use these examples to learn to encapsulate your Scala methods inside Jave methods that you can call from Matlab. A number of other methods are available to you with different languages (MEX-files, loadlibrary, etc...)
I would like to know which programming languages can be called by matlab.
for example I am quiet sure that matlab can use C function and maybe java.
I need this stuff for an industrial project so I need something that works well.
For example I have found some tutorial to call python function in matlab but they look to me not very good and stable solution.
I am not an expert of the field and my knowledge of matlab is very limited. So please be patience with the answer.
This project is related to machine learning and the software will probably run on a cluster.
EDIT according to this post Embedding Python in MATLAB seems that there are problem when importing numpy using python.
The only reason to use python in this environment is the numpy library.
Without that it is almost useless to me.
Do you think that I will encounter similar problem using java or c calling some mathematics libraries?
Most interfaces are listed here:
http://www.mathworks.de/de/help/matlab/external-interfaces.html
For Python I see different solutions:
COM on Windows platforms. This requires to register an application, check if this is possible and allowed on the cluster.
XMLRPC or SOAP. You may need to use Java-Classes in Matlab, but as you already realised this is very simple. Verify that the cluster has a Java VM available, many run matlab without java.
You can embed python code into c, which allows you to write mex functions which run c code: http://docs.python.org/2/extending/embedding.html
Use the command line interface for python.
Besides the documented limitations, I don't see any problem with these solution. If you are familiar with C or Matlab, I would choose the second or third option. This allows you to write a wrapper to access python with a very fundamental knowledge of matlab.
There are two main methods to achieve this:
Write MEX functions, which is basically C/C++ or Fortran methods, which use Matlab-specific API. You can then call these methods like you would any function written in M-language file. This is described here.
Call external libraries written in Java, .Net, C/C++, and COM servers. This is described here.
Both methods require good understanding of what you are doing, although I would argue that writing MEX function is much harder then referencing an existing library.
I'm not sure about programming language but You can do it by EXE file by running run('C:\someCompiledProgram.exe')
And if you need result you can use:
[status, result] = system('command')
You can read about it here:
http://nf.nci.org.au/facilities/software/Matlab/techdoc/ref/system.html
A friend of mine needs to implement some statistical calculations in hardware.
She wants it to be accomplished using VHDL.
(cross my heart, I haven't written a line of code in VHDL and know nothing about its subtleties)
In particular, she needs a direct analogue of MATLAB's betainc function.
Is there a good package around for doing this?
Any hints on the implementation are also highly appreciated.
If it's not a good idea at all, please tell me about it as well.
Thanks a lot!
There isn't a core available that performs an incomplete beta function in the Xilinx toolset. I can't speak for the other toolsets available, although I would doubt that there is such a thing.
What Xilinx does offer is a set of signal processing blocks, like multipliers, adders and RAM Blocks (amongst other things, filters, FFTs), that can be used together to implement various custom signal transforms.
In order for this to be done, there needs to be a complete understanding of the inner workings of the transform to be applied.
A good first step is to implement the function "manually" in matlab as a proof of concept:
Instead of using the built-in function in matlab, your friend can try to implement the function just using fundamental operators like multipliers and adders.
The results can be compared with those produced by the built-in function for verification.
The concept can then be moved to VHDL using the building blocks that are provided.
Doing this for the incomplete beta function isn't something for the faint-hearted, but it can be done.
As far as I know there is no tool which allow interface of VHDL and matlab.
But interface of VHDL and C is fairly easy, so if you can implement your code(MATLAB's betainc function) in C then it can be done easily with FLI(foreign language interface).
If you are using modelsim below link can be helpful.
link
First of all a word of warning, if you haven't done any VHDL/FPGA work before, this is probably not the best place to start. With VHDL (and other HDL languages) you are basically describing hardware, rather than a sequential line of commands to execute on a processor (as you are with C/C++, etc.). You thus need a completely different skill- and mind-set when doing FPGA-development. Just because something can be written in VHDL, it doesn't mean that it actually can work in an FPGA chip (that it is synthesizable).
With that said, Xilinx (one of the major manufacturers of FPGA chips and development tools) does provide the System Generator package, which interfaces with Matlab and can automatically generate code for FPGA chips from this. I haven't used it myself, so I'm not at all sure if it's usable in your friend's case - but it's probably a good place to start.
The System Generator User guide (link is on the previously linked page) also provides a short introduction to FPGA chips in general, and in the context of using it with Matlab.
You COULD write it yourself. However, the incomplete beta function is an integral. For many values of the parameters (as long as both are greater than 1) it is fairly well behaved. However, when either parameter is less than 1, a singularity arises at an endpoint, making the problem a bit nasty. The point is, don't write it yourself unless you have a solid background in numerical analysis.
Anyway, there are surely many versions in C available. Netlib must have something, or look in Numerical Recipes. Or compile it from MATLAB. Then link it in as nav_jan suggests.
As an alternative to VHDL, you could use MyHDL to write and test your beta function - that can produce synthesisable (ie. can go into an FPGA chip) VHDL (or Verilog as you wish) out of the back end.
MyHDL is an extra set of modules on top of Python which allow hardware to be modelled, verified and generated. Python will be a much more familiar environment to write validation code in than VHDL (which is missing many of the abstract data types you might take for granted in a programming language).
The code under test will still have to be written with a "hardware mindset", but that is usually a smaller piece of code than the test environment, so in some ways less hassle than figuring out how to work around the verification limitations of VHDL.
I'm doing a research project on random forest algorithm. I have found numerous implementations of the algorithm but the main part of the code is often written in Fortran while I'm completely naive in it.
I have to edit the code, change the main parameters (like tree depth, num of feature variables, ...) and trace the algorithm's performance during each run.
Currently I'm using "Windows-Precompiled-RF_MexStandalone-v0.02-". The train and predict functions are matlab mex files and can not be opened or edited. Can anyone give me a piece of advice on what to do or is there a valid and completely matlab-based version of random forests.
I've read the randomforest-matlab carefully. The main training part unfortunately is a dll file. Through reading more, most of my wonders is now resolved. My question mainly was how to run several trees simultaneously.
Have you taken a look at these libraries?
Stochastic Bosque
randomforest-matlab
If you're doing a research project on it, the best thing is probably to implement the individual tree training yourself in C and then write Mex wrappers. I'd start with an ID3 tree (before attempting C4.5 for instance.) Then write the random forest code itself, which, once you write the tree code, isn't all that hard.
You'll:
learn a lot
be able to modify them as much as you like
eventually move on to exploring new areas with them
I've implemented them myself from scratch so I can help once you post some of your own code. But I don't think anybody on this site will write the code for you.
Will it take effort? Yes. Will you come out of it with more knowledge and ability than you had going in? Undoubtably.
There is a nice library in R called randomForest. It is based on the original implementation of Breiman in Fortran but it is now mainly recoded in C.
http://cran.r-project.org/web/packages/randomForest/index.html
The main parameters you talk about (tree depth, number of features to be tested, ...) are directly available.
Another library I would recommend is Weka. It is java based and lucid.Performance is slightly off though compared to R. The source code can be downloaded from http://www.cs.waikato.ac.nz/ml/weka/