Scala environment for statistical computing and graphics? [closed] - scala

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Please advise on Scala environment for statistical computing and graphics similar to R or Weka.
Thanks!

Perhaps Scalalab is what you're looking for.
The scripting language is based on the Scala programming language
enhanced with high level scientific operators and with an integrated
environment that provides a Matlab-like working style. Also, all the
huge libraries of Java scientific code can be easily accessible (and
many times with a more convenient syntax). [...] A major design
priority of ScalaLab is its user-friendly interface.
See also Is there a good math/stats library for Scala?

I'd suggest Breeze : http://www.scalanlp.org/ , https://github.com/dlwh/breeze/
It has some predefined Classifiers, Learning Algorithms (SVM , Regeression...), a Plotting Library and support for matrix Multiplication.
Here you can get a quick overview: https://github.com/dlwh/breeze/wiki/Quickstart

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Parallel Computing with Scilab or Octave [closed]

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I have a large set of data to process [40000x50] values.
I use Matlab on my laptop but it takes a very long time.
Recently I had an access to an HPC station with theoretically I can process parallel computing. So how can I do that? I think I can't use Matlab without a proper toolbox for "Cloud computing" so I tried Scilab and octave but things were very complicated to me.
My main objectives are:
- Processing the data and Optimizing a model.
so my questions are:
- Do I have to work on Linux to perform parallel computing? (I use windows)
- How to perform parallel computing using a free software like Scilab or Octave ( I am a little bit familiar with Scilab).
Best regards.
Scilab provides several ways for parallel computing. A good starting point is the parallel computing wiki..
A simple example using parallel_run:
function [r_min, r_med, r_max]=min_med_max(a, b, c)
r_min=min(a,b,c); r_med=median([a,b,c]); r_max=max(a,b,c);
endfunction
N=10;
A=rand(1:N);B=rand(1:N);C=rand(1:N);
[Min,Med,Max]=parallel_run(A,B,C,"min_med_max");
Note however that this does not multi process on Windows.

Large Scale Quadratic Programming [closed]

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I have a scalable quadratic programming problem which has around 50-1000 variables with linear constraints. I am trying to find an effective solver for this. The matlab qp solver can only solve to optimality for 100 variables for my problem. Will cplex be any better? Are there any other tools which I can use.
CPLEX will definitely be better. Everything depends on the environment you are working in. If it's not a problem for you to write a C++ program you can try COIN-OR projects, in particular Couenne http://www.coin-or.org/projects/Couenne.xml for nonlinear MIP's and IPOPT http://www.coin-or.org/projects/Ipopt.xml; for Python lovers they have Coopr, DilPy, GrumPy and other projects. You can also try GAMS https://www.gams.com/, it's great for any optimization problem. It's not a solver, more like modeling system with a large number of built in commercial and open-source solvers. It's free as long as your problem has no more than 50 variables and 50 constraints (as far as I remember).
You can try the Opti Toolbox: http://www.i2c2.aut.ac.nz/Wiki/OPTI/
It has an interface to different solvers which you can use in Matlab. (Precompiled mex files.) It can solve quadratic problems.

Modelica differential equations [closed]

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I am going through Modelica libraries. I was wondering, that there are (or i can't found them) ODE's.
For example in the dynamic pipe model of the standard library.
Generally, the Modelica Standard Library contains many dynamic models - i.e. models that can be described with DAEs or ODEs (look for models using the der() operator).
The reason why you don't see any der() operators in DynamicPipe is that it inherits much of its functionality from several base classes - including Modelica.Fluid.Interfaces.PartialDistributedVolume where you will find the differential equation for mass and energy balances.
Modelica.Blocks.Continuous.FirstOrder is an example of a very simple ODE - a first-order low-pass filter.
You might want to consult the free online Modelica book "Modelica by Example". It shows many examples involving ordinary differential equations.

How to create block diagram and analysis a simple RLC circuit in MATLAB? [closed]

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i have this! RLC simple circuit
i want to analysis this circuit with different values of Resistance
but i dont know how can i draw a block diagram and simulink it with matlab, i just searched internet and found some libraries,but i didn't find anything special for my work, please if you have experience in this analysis help me and let me know how can i analysis it with MATLAB simulink
i think its very simple circuit , and if i found a way to create this in MATLAB , Matlab can analysis that easily, and i want to analysis this for different values of resistance , and i have constant L and C , for inductor and capacitor
If you're allowed and have access to it, the easiest way would be to use Simscape, which has an electrical library. See Simulink Simscape simple circuit not working for a similar question.

Linear Algebra Library for Sparse Matrices [closed]

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I am interested in porting my Matlab implementation to C++ for speed improvement. I tried armadillo. It is perfect for porting code from Matlab because Armadillo's library functions names/syntax is very close to that of Matlab programming. However I realized that at some places Matlab out performed armadillo because my data is mainly sparse and armadillo does not give it any special treatment and simply treats it as dense matrices. Armadillo team is working on sparse matrices support but currently it is not available. So I am looking for a library which like Armadillo has syntax very close to Matlab (or is easy to use) and supports sparse matrices for speed and space optimization.
Not sure what you are trying to do but try SPAMS. it's a good and fast sparse modeling implementation.
Version 3.6.2 has more Sparse Matrices support.
As of version 3.4, Armadillo has preliminary support for sparse matrices. This is likely to get more complete in future versions.