Freemat: Why Are We Doing This and How Do I Get Started?
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A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya 1 Introduction
A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya Sai K. Popuri and Matthias K. Gobbert* Department of Mathematics and Statistics, University of Maryland, Baltimore County *Corresponding author: [email protected], www.umbc.edu/~gobbert Technical Report HPCF{2017{3, hpcf.umbc.edu > Publications Abstract Matlab is the most popular commercial package for numerical computations in mathematics, statistics, the sciences, engineering, and other fields. Octave is a freely available software used for numerical computing. R is a popular open source freely available software often used for statistical analysis and computing. Julia is a recent open source freely available high-level programming language with a sophisticated com- piler for high-performance numerical and statistical computing. They are all available to download on the Linux, Windows, and Mac OS X operating systems. We investigate whether the three freely available software are viable alternatives to Matlab for uses in research and teaching. We compare the results on part of the equipment of the cluster maya in the UMBC High Performance Computing Facility. The equipment has 72 nodes, each with two Intel E5-2650v2 Ivy Bridge (2.6 GHz, 20 MB cache) proces- sors with 8 cores per CPU, for a total of 16 cores per node. All nodes have 64 GB of main memory and are connected by a quad-data rate InfiniBand interconnect. The tests focused on usability lead us to conclude that Octave is the most compatible with Matlab, since it uses the same syntax and has the native capability of running m-files. R was hampered by somewhat different syntax or function names and some missing functions. -
Freemat V3.6 Documentation
FreeMat v3.6 Documentation Samit Basu November 16, 2008 2 Contents 1 Introduction and Getting Started 5 1.1 INSTALL Installing FreeMat . 5 1.1.1 General Instructions . 5 1.1.2 Linux . 5 1.1.3 Windows . 6 1.1.4 Mac OS X . 6 1.1.5 Source Code . 6 2 Variables and Arrays 7 2.1 CELL Cell Array Definitions . 7 2.1.1 Usage . 7 2.1.2 Examples . 7 2.2 Function Handles . 8 2.2.1 Usage . 8 2.3 GLOBAL Global Variables . 8 2.3.1 Usage . 8 2.3.2 Example . 9 2.4 INDEXING Indexing Expressions . 9 2.4.1 Usage . 9 2.4.2 Array Indexing . 9 2.4.3 Cell Indexing . 13 2.4.4 Structure Indexing . 14 2.4.5 Complex Indexing . 16 2.5 MATRIX Matrix Definitions . 17 2.5.1 Usage . 17 2.5.2 Examples . 17 2.6 PERSISTENT Persistent Variables . 19 2.6.1 Usage . 19 2.6.2 Example . 19 2.7 STRING String Arrays . 20 2.7.1 Usage . 20 2.8 STRUCT Structure Array Constructor . 22 2.8.1 Usage . 22 2.8.2 Example . 22 3 4 CONTENTS 3 Functions and Scripts 25 3.1 ANONYMOUS Anonymous Functions . 25 3.1.1 Usage . 25 3.1.2 Examples . 25 3.2 FUNCTION Function Declarations . 26 3.2.1 Usage . 26 3.2.2 Examples . 28 3.3 KEYWORDS Function Keywords . 30 3.3.1 Usage . 30 3.3.2 Example . 31 3.4 NARGIN Number of Input Arguments . 32 3.4.1 Usage . -
Towards a Fully Automated Extraction and Interpretation of Tabular Data Using Machine Learning
UPTEC F 19050 Examensarbete 30 hp August 2019 Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Per Hedbrant Master Thesis in Engineering Physics Department of Engineering Sciences Uppsala University Sweden Abstract Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Teknisk- naturvetenskaplig fakultet UTH-enheten Motivation A challenge for researchers at CBCS is the ability to efficiently manage the Besöksadress: different data formats that frequently are changed. Significant amount of time is Ångströmlaboratoriet Lägerhyddsvägen 1 spent on manual pre-processing, converting from one format to another. There are Hus 4, Plan 0 currently no solutions that uses pattern recognition to locate and automatically recognise data structures in a spreadsheet. Postadress: Box 536 751 21 Uppsala Problem Definition The desired solution is to build a self-learning Software as-a-Service (SaaS) for Telefon: automated recognition and loading of data stored in arbitrary formats. The aim of 018 – 471 30 03 this study is three-folded: A) Investigate if unsupervised machine learning Telefax: methods can be used to label different types of cells in spreadsheets. B) 018 – 471 30 00 Investigate if a hypothesis-generating algorithm can be used to label different types of cells in spreadsheets. C) Advise on choices of architecture and Hemsida: technologies for the SaaS solution. http://www.teknat.uu.se/student Method A pre-processing framework is built that can read and pre-process any type of spreadsheet into a feature matrix. Different datasets are read and clustered. An investigation on the usefulness of reducing the dimensionality is also done. -
The Freemat Primer Page 1 of 218 Table of Contents About the Authors
The Freemat 4.0 Primer By Gary Schafer Timothy Cyders First Edition August 2011 The Freemat Primer Page 1 of 218 Table of Contents About the Authors......................................................................................................................................5 Acknowledgements...............................................................................................................................5 User Assumptions..................................................................................................................................5 How This Book Was Put Together........................................................................................................5 Licensing...............................................................................................................................................6 Using with Freemat v4.0 Documentation .............................................................................................6 Topic 1: Working with Freemat.................................................................................................................7 Topic 1.1: The Main Screen - Ver 4.0..................................................................................................7 Topic 1.1.1: The File Browser Section...........................................................................................10 Topic 1.1.2: The History Section....................................................................................................10 Topic 1.1.3: The -
Freemat V4.0 Documentation
FreeMat v4.0 Documentation Samit Basu October 4, 2009 2 Contents 1 Introduction and Getting Started 39 1.1 INSTALL Installing FreeMat . 39 1.1.1 General Instructions . 39 1.1.2 Linux . 39 1.1.3 Windows . 40 1.1.4 Mac OS X . 40 1.1.5 Source Code . 40 2 Variables and Arrays 41 2.1 CELL Cell Array Definitions . 41 2.1.1 Usage . 41 2.1.2 Examples . 41 2.2 Function Handles . 42 2.2.1 Usage . 42 2.3 GLOBAL Global Variables . 42 2.3.1 Usage . 42 2.3.2 Example . 42 2.4 INDEXING Indexing Expressions . 43 2.4.1 Usage . 43 2.4.2 Array Indexing . 43 2.4.3 Cell Indexing . 46 2.4.4 Structure Indexing . 47 2.4.5 Complex Indexing . 49 2.5 MATRIX Matrix Definitions . 49 2.5.1 Usage . 49 2.5.2 Examples . 49 2.6 PERSISTENT Persistent Variables . 51 2.6.1 Usage . 51 2.6.2 Example . 51 2.7 STRUCT Structure Array Constructor . 51 2.7.1 Usage . 51 2.7.2 Example . 52 3 4 CONTENTS 3 Functions and Scripts 55 3.1 ANONYMOUS Anonymous Functions . 55 3.1.1 Usage . 55 3.1.2 Examples . 55 3.2 FUNCTION Function Declarations . 56 3.2.1 Usage . 56 3.2.2 Examples . 57 3.3 KEYWORDS Function Keywords . 60 3.3.1 Usage . 60 3.3.2 Example . 60 3.4 NARGIN Number of Input Arguments . 61 3.4.1 Usage . 61 3.4.2 Example . 61 3.5 NARGOUT Number of Output Arguments . -
Sageko Matlabin Korvaaja?
Mikko Moilanen Sageko Matlabin korvaaja? Opinnäytetyö Tietotekniikan koulutusohjelma Elokuu 2012 KUVAILULEHTI Opinnäytetyön päivämäärä 4.9.2012 Tekijä(t) Koulutusohjelma ja suuntautuminen Mikko Moilanen Tietotekniikan koulutusohjelma Nimeke Sageko Matlabin korvaaja? Tiivistelmä Tässä insinöörityössä tutkitaan voiko Sagella korvata Matlabin korkeakouluopinnoissa matemaattisen analyysin opiskelutyövälineenä. Tutkimuksen perusteella arvioidaan, että onko Sagella tulevaisuutta ohjelmistona, varmistetaan Sagen toimivuus matemaattisessa analyysissa ja selvitetään, että miten helppoa yksittäisen käyttäjän on ottaa Sage käyttöön ja miten oppilaitos saa Sagen käyttöönsä. Matlab on kiistaton standardiohjelmisto matemaattisessa mallinnuksessa ja analyysissa. Sen peruslisenssimaksut ja Toolboxien lisenssimaksut ovat kuitenkin kohonneet ehkä juuri tästä syystä korkeiksi ja mikään ei estä lisenssimaksujen jatkuvaa kohoamista. Näin ollen halvemman vaihtoehdon löytäminen Matlabille korkeakouluopintojen työvälineenä on mielenkiintoinen ja hyödyllinen tutkimuskohde. Eräs mielenkiintoinen vaihtoehto Matlabille on Sage, koska se kokoaa lähes 100 matemaattista laskentaohjelmistoa yhden ja saman käyttöliittymän alle. Sage on myös suunniteltu toimimaan palvelin/asiakas -mallilla WWW-selaimella, mistä voi olla erityistä hyötyä laajoissa asennuksissa ylläpidon ja luokkahuoneidan varaamisen kannalta . Tutkimuksen tuloksena havaittiin Sagen olevan niin erilainen ja epäyhteensopiva Matlabin kanssa, että Matlab ei ole suoraan korvattavissa Sagella. Sen sijaan todettiin, -
They Have Very Good Docs At
Intro to the Julia programming language Brendan O’Connor CMU, Dec 2013 They have very good docs at: http://julialang.org/ I’m borrowing some slides from: http://julialang.org/blog/2013/03/julia-tutorial-MIT/ 1 Tuesday, December 17, 13 Julia • A relatively new, open-source numeric programming language that’s both convenient and fast • Version 0.2. Still in flux, especially libraries. But the basics are very usable. • Lots of development momentum 2 Tuesday, December 17, 13 Why Julia? Dynamic languages are extremely popular for numerical work: ‣ Matlab, R, NumPy/SciPy, Mathematica, etc. ‣ very simple to learn and easy to do research in However, all have a “split language” approach: ‣ high-level dynamic language for scripting low-level operations ‣ C/C++/Fortran for implementing fast low-level operations Libraries in C — no productivity boost for library writers Forces vectorization — sometimes a scalar loop is just better slide from ?? 2012 3 Bezanson, Karpinski, Shah, Edelman Tuesday, December 17, 13 “Gang of Forty” Matlab Maple Mathematica SciPy SciLab IDL R Octave S-PLUS SAS J APL Maxima Mathcad Axiom Sage Lush Ch LabView O-Matrix PV-WAVE Igor Pro OriginLab FreeMat Yorick GAUSS MuPad Genius SciRuby Ox Stata JLab Magma Euler Rlab Speakeasy GDL Nickle gretl ana Torch7 slide from March 2013 4 Bezanson, Karpinski, Shah, Edelman Tuesday, December 17, 13 Numeric programming environments Core properties Dynamic Fast? and math-y? C/C++/ Fortran/Java − + Matlab + − Num/SciPy + − R + − Older table: http://brenocon.com/blog/2009/02/comparison-of-data-analysis-packages-r-matlab-scipy-excel-sas-spss-stata/ Tuesday, December 17, 13 - Dynamic vs Fast: the usual tradeof - PL quality: more subjective. -
Pipenightdreams Osgcal-Doc Mumudvb Mpg123-Alsa Tbb
pipenightdreams osgcal-doc mumudvb mpg123-alsa tbb-examples libgammu4-dbg gcc-4.1-doc snort-rules-default davical cutmp3 libevolution5.0-cil aspell-am python-gobject-doc openoffice.org-l10n-mn libc6-xen xserver-xorg trophy-data t38modem pioneers-console libnb-platform10-java libgtkglext1-ruby libboost-wave1.39-dev drgenius bfbtester libchromexvmcpro1 isdnutils-xtools ubuntuone-client openoffice.org2-math openoffice.org-l10n-lt lsb-cxx-ia32 kdeartwork-emoticons-kde4 wmpuzzle trafshow python-plplot lx-gdb link-monitor-applet libscm-dev liblog-agent-logger-perl libccrtp-doc libclass-throwable-perl kde-i18n-csb jack-jconv hamradio-menus coinor-libvol-doc msx-emulator bitbake nabi language-pack-gnome-zh libpaperg popularity-contest xracer-tools xfont-nexus opendrim-lmp-baseserver libvorbisfile-ruby liblinebreak-doc libgfcui-2.0-0c2a-dbg libblacs-mpi-dev dict-freedict-spa-eng blender-ogrexml aspell-da x11-apps openoffice.org-l10n-lv openoffice.org-l10n-nl pnmtopng libodbcinstq1 libhsqldb-java-doc libmono-addins-gui0.2-cil sg3-utils linux-backports-modules-alsa-2.6.31-19-generic yorick-yeti-gsl python-pymssql plasma-widget-cpuload mcpp gpsim-lcd cl-csv libhtml-clean-perl asterisk-dbg apt-dater-dbg libgnome-mag1-dev language-pack-gnome-yo python-crypto svn-autoreleasedeb sugar-terminal-activity mii-diag maria-doc libplexus-component-api-java-doc libhugs-hgl-bundled libchipcard-libgwenhywfar47-plugins libghc6-random-dev freefem3d ezmlm cakephp-scripts aspell-ar ara-byte not+sparc openoffice.org-l10n-nn linux-backports-modules-karmic-generic-pae -
Lab 1 Intro to MATLAB and Freemat
Lab 1 Intro to MATLAB and FreeMat Objectives concepts 1. Variables, vectors, and arrays 2. Plotting data 3. Script files skills 1. Use MATLAB to solve homework problems 2. Plot lab data and mathematical models 3. Write script files to save, edit and debug MATLAB commands 4. Solve systems of equations Key Prerequisites None Required Resources Access to MATLAB (buy here) or FreeMat (download here) Circuit analysis is often a computationally challenging activity. Even powerful programmable calculators can be inadequate to the task. In this lab we develop some basic proficiency with MATLAB and its red-haired cousin FreeMat, so that you can use these tools for your homework and future lab activities. You are welcome to use MATLAB or FreeMat for this lab. Since most people will probably be using FreeMat on their home computers, this lab will illustrate concepts with FreeMat. For what we will be using them for, however, the two are almost identical. 1 Lab 1: Intro to MATLAB and FreeMat Vocabulary All key vocabulary used in this lab are listed below, with closely related words listed together: variable, function vector, array plot, axes, legend script file Discussion and Procedure Part 1. Basics and Simple Arithmetic Before we begin, we’ll need to identify the main components of the MATLAB/FreeMat environment, shown in figure 1. These include the Current Directory, indicating what folder you are looking at in the “Listing of Current Directory” window. You can change the current directory by clicking the manila folder to the right of the Current Directory. Figure 1. The FreeMat Environment 2 Lab 1: Intro to MATLAB and FreeMat We spend much of our time in MATLAB working in the Command Window, which is where we type instructions. -
A Comparative Evaluation of Matlab, Octave, Freemat, and Scilab for Research and Teaching
A COMPARATIVE EVALUATION OF MATLAB, OCTAVE, FREEMAT, AND SCILAB FOR RESEARCH AND TEACHING Neeraj Sharma and Matthias K. Gobbert Department of Mathematics and Statistics, University of Maryland, Baltimore County {nsharma1,gobbert}@umbc.edu Technical Report HPCF–2010–7, www.umbc.edu/hpcf > Publications Abstract: The commercial software package Matlab is the most widely used numerical compu- tational package. GNU Octave, FreeMat, and Scilab are other numerical computational packages that have many of the same features as Matlab. Unlike Matlab, the other three packages are free of charge to the user and easily available to download to Linux, Windows, or Mac OS X operating systems. Are these packages viable alternatives to Matlab for uses in research or teaching? We review in depth the basic operations of Matlab, such as solving a system of linear equations, com- puting eigenvalues and eigenvectors, and plotting in two-dimensions. In addition, we research a more complex test problem resulting from a finite difference discretization of the Poisson equation in two spatial dimensions. Then, we compare the results we receive from GNU Octave, FreeMat, and Scilab to our previously found Matlab results. All packages gave identical numerical results, though Scilab exhibited a limitation in the size of the linear system it could solve in the complex test problem and FreeMat was hampered by the lack of a conjugate gradient function. The available graphical functions differ in functionality, but give equivalent plots, though FreeMat has limited three-dimensional graphics capabilities at present. Based on these tests, we conclude that GNU Octave is the most compatible with Matlab due to its numerical abilities and the similarity of its syntax. -
Mathematical Software Tools Applicable to Remote Learning and Scientific Research in Case of Isolation
INTERNATIONAL SCIENTIFIC JOURNAL "MATHEMATICAL MODELING" WEB ISSN 2603-2929; PRINT ISSN 2535-0986 Mathematical software tools applicable to remote learning and scientific research in case of isolation Svilen Rachev1, Milena Racheva1, Andrey Andreev1,2, Dobrin Ganchev1 Technical University of Gabrovo, Bulgaria1 Bulgarian Academy of Science, Sofia, Bulgaria2 [email protected] Abstract: This paper is devoted to some up-to-date computational tools, which are on-line available and appropriate to remote learning as well as to scientific research implementation relevant to mathematics and their applications. The following aspects are concerned and discussed: some of the opportunities and benefits afforded by the software tools which are taken into consideration; necessary requirements which have to be met in order to use these tools; some disadvantages and drawbacks which may arise. Comparison of fees and prices for different mathematical software tools is done. For purpose of illustration realistic examples are also given. Keywords: MATHEMATICAL SOFTWARE, COMPUTER ALGEBRA SYSTEMS, FREE SOFTWARE, CLOUD COMPUTING 1. Introduction implementation and an environment in which the language to be used. Nowadays, the mathematical software tools used in research This means that, using CAS, one may manipulate mathematical and engineering play a crucial role in the development of expressions in a way as if he do it manually. technology, education and science. For example, the treatment of complex numerical analysis with the aid of mathematical software Computer algebra systems can be conditionally divided into two is currently used in all branches of physical, medical and social types: sciences. For that matter, by means of Computer aided design (CAD) software the level of scientific research and in particular of specialized, which are devoted to a specific part of engineering science can be significantly improved. -
A Review on the Comparative Roles of Mathematical Softwares in Fostering Scientific and Mathematical Research
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Covenant University Repository Global Journal of Pure and Applied Mathematics. ISSN 0973-1768 Volume 11, Number 6 (2015), pp. 4937-4948 © Research India Publications http://www.ripublication.com A Review On The Comparative Roles Of Mathematical Softwares In Fostering Scientific And Mathematical Research Emetere Moses Eterigho1 and Sanni Eshorame Samuel2 1Physics Department, Covenant University, P.M.B. 1023, Ota, Nigeria 2Chemical Engineering Department, Covenant University, P.M.B. 1023, Ota, Nigeria [email protected] Abstract Mathematical software tools used in science, research and engineering have a developmental trend. Various subdivisions for mathematical software applications are available in the aforementioned areas but the research intent or problem under study, determines the choice of software required for mathematical analyses. Since these software applications have their limitations, the features present in one type are often augmented or complemented by revised versions of the original versions in order to increase their abilities to multi-task. For example, the dynamic mathematics software was designed with integrated advantages of different types of existing mathematics software as an improved version for understanding numerical related problems for advanced mathematical content (advanced simulation). In recent times, science institutions have adopted the use of computer codes in solving mathematics related problems. The treatment of complex numerical analysis with the aid of mathematical software is currently used in all branches of physical, biological and social sciences. However, the programming language for mathematics related software varies with their functionalities. Many invaluable researches have been compromised within the confines of unacceptable but expedient standards because of insufficient understanding of the valuable services the available variety of mathematical software could offer.