tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File ExchangeUser-contributed code library2015-07-31T05:23:16-04:00245281100tag:www.mathworks.com,2005:FileInfo/523502015-07-31T08:35:08Z2015-07-31T08:35:08ZFast Nero fuzzy Classificationfast Classification data or image fuzzy with new method, use method for find best member function<p>fast Classification data or image fuzzy with new method, use method for find best member function.</p>Ebrahim Parchamhttp://www.mathworks.com/matlabcentral/profile/authors/2911919-ebrahim-parchamMATLAB 8.5 (R2015a)falsetag:www.mathworks.com,2005:FileInfo/523492015-07-31T07:13:02Z2015-07-31T07:15:21ZBatchRunnerA universal tool which runs a batch of scripts or functions<p>A universal tool which runs a batch of scripts or functions and notifies the user per E-Mail when the job is finished. Any errors and exceptions are caught, saved and the user is notified in the report E-Mail. This is especially useful, as the execution of all iterations is independent and is not stopped if an error occurs in one iteration.
<br />Usage
<br />---------
<br />1. Set up the mail server configuration in lines 60-63.</p>
<p>2. Initialize the job:
<br /> a) jobName: Any name for the current job.
<br /> b) email: The E-Mail address to send the report to.
<br /> c) n: Which numbers to iterate through.
<br /> d) fcn: A function handle which will be executed.
<br /> e) args: All arguments to the function handle. The function call will be fcn(args). Any parameter with the content $$RUNVAR$$ will be replaced with an integer containing the current iteration number.</p>
<p>3. Run the script.</p>
<p>Feature Suggestions and Bug Reports:
<br />--------------------------------------------------
<br />This tool is hosted on Github (<a href="https://github.com/HBadertscher/Matlab_BatchRunner">https://github.com/HBadertscher/Matlab_BatchRunner</a>). Feel free to add feature suggestions, as well as bug reports as issue on Github.</p>
<p>License
<br />-----------
<br />"THE BEER-WARE LICENSE" (Revision 42): Hannes Badertscher (<a href="mailto:hbaderts@hsr.ch">hbaderts@hsr.ch</a>) wrote this software. As long as you retain this notice you can do whatever you want with this stuff. If we meet some day, and you think this stuff is worth it, you can buy me a beer in return.
<br />- Hannes Badertscher</p>HBadertscherhttp://www.mathworks.com/matlabcentral/profile/authors/5480108-hbadertscherMATLAB 8.3 (R2014a)falsetag:www.mathworks.com,2005:FileInfo/523392015-07-30T12:21:51Z2015-07-31T05:49:12ZSimple backpropA simple implementation of MLP Neural Network with back-propagation algorithm in matlab.<p>A simple implementation of multilayer perceptron (MLP) Neural Network with back-propagation algorithm in matlab.</p>Hamid Ehttp://www.mathworks.com/matlabcentral/profile/authors/2778949-hamid-eMATLAB 8.4 (R2014b)falsetag:www.mathworks.com,2005:FileInfo/508172015-05-11T17:27:16Z2015-07-31T05:24:06Zparentpath - returns the path shared by multiple subpathsReturns the filesystem path shared by multiple subpaths<p>Simple utility to return the parent path shared by multiple input subpaths.
<br />USAGE: parpath = parentpath(subpaths)</p>gooeyhttp://www.mathworks.com/matlabcentral/profile/authors/3893829-gooeyMATLAB 8.5 (R2015a)falsetag:www.mathworks.com,2005:FileInfo/523482015-07-31T04:49:53Z2015-07-31T05:11:32ZUlcer Index and Performance IndexUlcer Index and Ulcer Performance Index (Martin Ratio)<p>This is the well known ulcer index and associated ulcer performance index, used as performance metrics or sometimes as technical indicators.</p>Matthaeushttp://www.mathworks.com/matlabcentral/profile/authors/4584767-matthaeusMATLAB 8.3 (R2014a)falsetag:www.mathworks.com,2005:FileInfo/487072014-12-10T15:29:25Z2015-07-31T05:06:16ZbraidlabA Matlab package for analyzing data using braids<p>braidlab is a Matlab package for analyzing data using braids. It was designed to be fast, so it can be used on relatively large problems. It uses the object-oriented features of Matlab to provide a class for braids on punctured disks and a class for equivalence classes of simple closed loops. The growth of loops under iterated action by braids is used to compute the topological entropy of braids, as well as for determining the equality of braids.
<br />braidlab was written by Jean-Luc Thiffeault and Marko Budisic.
<br />See <a href="https://github.com/jeanluct/braidlab">https://github.com/jeanluct/braidlab</a> for more information and the braidlab guide.</p>Jean-Luchttp://www.mathworks.com/matlabcentral/profile/authors/3084257-jean-lucMATLAB 8.4 (R2014b)falsetag:www.mathworks.com,2005:FileInfo/475312014-08-13T09:53:09Z2015-07-31T05:06:01Zjosecamachop/MEDA-ToolboxMultivariate Exploratory Data Analysis Toolbox for Matlab<p>The Multivariate Exploratory Data Analysis (MEDA) Toolbox in Matlab is a set of multivariate analysis tools for the exploration of data sets. In the MEDA Toolbox, traditional exploratory plots based on Principal Component Analysis (PCA) or Partial Least Squares (PLS), such as score, loading and residual plots, are combined with new methods like MEDA, oMEDA and SVI plots. The latter are aimed at solving some of the limitations found in the former to
<br />adequately extract conclusions from a data set. Also, other useful tools such as cross-validation algorithms, Multivariate Statistical Process Control (MSPC) charts and data simulation/approximation algorithms (ADICOV) are included in the toolbox. Finally, most of the exploratory tools are extended for their use with very large data sets (Big Data), with unlimited number of observations.</p>Joséhttp://www.mathworks.com/matlabcentral/profile/authors/3921882-joseMATLAB 8.3 (R2014a)MATLABfalsetag:www.mathworks.com,2005:FileInfo/463922014-04-28T15:28:37Z2015-07-31T05:04:58ZPattern Recognition ToolboxFree pattern recognition toolbox for MATLAB<p>The Pattern Recognition Toolbox (PRT) for MATLAB (tm) is a framework of pattern recognition and machine learning tools that are powerful, expressive, and easy to use.
<br />Create a data set from your data (X ~ N x F) and labels (Y ~ N x 1):
<br />ds = prtDataSetClass(X,Y);</p>
<p>and run Z-score normalization + an SVM:</p>
<p>algo = prtPreProcZmuv + prtClassLibSvm;
<br />dsOut = algo.kfolds(ds);</p>
<p>And score the results:</p>
<p>prtScoreRoc(dsOut);</p>
<p>It's that easy. It's free. Have fun.</p>
<p>Installation instructions:
<br /><a href="http://newfolder.github.io/prtdoc/prtDocInstallation.html">http://newfolder.github.io/prtdoc/prtDocInstallation.html</a></p>
<p>Additional documentation & (rarely updated) blog available here:
<br /><a href="http://newfolder.github.io/">http://newfolder.github.io/</a>
</p>Peterhttp://www.mathworks.com/matlabcentral/profile/authors/1936940-peterMATLAB 8.0 (R2012b)MATLABfalsetag:www.mathworks.com,2005:FileInfo/470232014-06-21T11:40:25Z2015-07-31T05:01:59ZChebfunChebfun is an open-source package for numerical computation with functions to 15-digit accuracy<p>Chebfun is an open-source software system for numerical computing with functions. The mathematical basis is piecewise polynomial interpolation implemented with what we call “Chebyshev technology”. The foundations are described, with Chebfun examples, in the book Approximation Theory and Approximation Practice (L. N. Trefethen, SIAM 2013). Chebfun has extensive capabilities for dealing with linear and nonlinear differential and integral operators, and also includes continuous analogues of linear algebra notions like QR and singular value decomposition. The Chebfun2 extension works with functions of two variables defined on a rectangle in the x-y plane.</p>
<p>Most Chebfun commands are overloads of familiar MATLAB commands — for example sum(f) computes an integral, roots(f) finds zeros, and u = L\f solves a differential equation.</p>
<p>To get a sense of the breadth and power of Chebfun, a good place to start is by looking at our Examples (<a href="http://www.chebfun.org/examples/">http://www.chebfun.org/examples/</a>) or the introductory Guide (<a href="http://www.chebfun.org/docs/guide/">http://www.chebfun.org/docs/guide/</a>).</p>
<p>Please contact us with any questions/comments at <a href="mailto:help@chebfun.org">help@chebfun.org</a>.</p>Chebfun Teamhttp://www.mathworks.com/matlabcentral/profile/authors/1823057-chebfun-teamMATLAB 8.2 (R2013b)MATLAB23972falsetag:www.mathworks.com,2005:FileInfo/466372014-05-16T11:40:09Z2015-07-31T05:01:27ZA Simple Finite Volume Solver for MatlabA simple yet general purpose FVM solver for transient convection diffusion PDE<p>A simple Finite volume tool
<br />This code is the result of the efforts of a chemical/petroleum engineer to develop a simple tool to solve the general form of convection-diffusion equation:
<br />α∂ϕ/∂t+∇.(uϕ)+∇.(−D∇ϕ)+βϕ=γ
<br />on simple uniform mesh over 1D, 1D axisymmetric (radial), 2D, 2D axisymmetric (cylindrical), and 3D domains.
<br />The code accepts Dirichlet, Neumann, and Robin boundary conditions (which can be achieved by changing a, b, and c in the following equation) on a whole or part of a boundary:
<br />a∇ϕ.n+bϕ=c.
<br />It also accepts periodic boundary conditions.
<br />The main purpose of this code is to serve as a handy tool for those who try to play with mathematical models, solve the model numerically in 1D, compare it to analytical solutions, and extend their numerical code to 2D and 3D with the minimum number of modifications in the 1D code.
<br />The discretizaion schemes include
<br /> * central difference
<br /> * upwind scheme for convective terms
<br /> * TVD schemes for convective terms</p>
<p>A short html document is available to get you started. Simply type
<br />showdemo FVTdemo
<br />after downloading and extracting the code.</p>
<p>A few calculus functions (divergence, gradient, etc) and averaging techniques (arithmetic average, harmonic average, etc) are available, which can be helpful specially for solving nonlinear or coupled equations or implementing explicit schemes.</p>
<p>I have used the code to solve coupled nonlinear systems of PDE. You can find some of them in the Examples/advanced folder.</p>
<p>There are a few functions in the 'PhysicalProperties' folder for the calculation of the physical properties of fluids. Some of them are not mine, which is specified inside the file.</p>
<p>I'll try to update the documents regularly, in the github repository. Please give me your feedback/questions by writing a comment in my weblog: <<a href="http://fvt.simulkade.com/">http://fvt.simulkade.com/</a>>
<br />Special thanks: I vastly benefited from the ideas behind Fipy <<a href="http://www.ctcms.nist.gov/fipy/">http://www.ctcms.nist.gov/fipy/</a>>, a python-based finite volume solver.</p>
<p>To start the solver, download and extract the zip archive, open and run 'FVToolStartUp' function.
<br />To see the code in action, copy and paste the following in your Matlab command window:</p>
<p>clc; clear;
<br />L = 50; % domain length
<br />Nx = 20; % number of cells
<br />m = createMesh3D(Nx,Nx,Nx, L,L,L);
<br />BC = createBC(m); % all Neumann boundary condition structure
<br />BC.left.a(:) = 0; BC.left.b(:)=1; BC.left.c(:)=1; % Dirichlet for the left boundary
<br />BC.right.a(:) = 0; BC.right.b(:)=1; BC.right.c(:)=0; % Dirichlet for the right boundary
<br />D_val = 1; % value of the diffusion coefficient
<br />D = createCellVariable(m, D_val); % assign the diffusion coefficient to the cells
<br />D_face = harmonicMean(m, D); % calculate harmonic average of the diffusion coef on the cell faces
<br />Mdiff = diffusionTerm(m, D_face); % matrix of coefficients for the diffusion term
<br />[Mbc, RHSbc] = boundaryCondition(m, BC); % matix of coefficients and RHS vector for the BC
<br />M = Mdiff + Mbc; % matrix of cefficients for the PDE
<br />c = solvePDE(m,M, RHSbc); % send M and RHS to the solver
<br />visualizeCells(m, c); % visualize the results</p>
<p>You can find some animated results of this code in my youtube channel:
<br /><a href="https://www.youtube.com/user/processsimulation/videos">https://www.youtube.com/user/processsimulation/videos</a></p>Ehsanhttp://www.mathworks.com/matlabcentral/profile/authors/2336153-ehsanMATLAB 8.3 (R2014a)MATLAB35710false