An Application in Python
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The Shogun Machine Learning Toolbox
The Shogun Machine Learning Toolbox Heiko Strathmann, Gatsby Unit, UCL London Open Machine Learning Workshop, MSR, NY August 22, 2014 A bit about Shogun I Open-Source tools for ML problems I Started 1999 by SÖren Sonnenburg & GUNnar Rätsch, made public in 2004 I Currently 8 core-developers + 20 regular contributors I Purely open-source community driven I In Google Summer of Code since 2010 (29 projects!) Ohloh - Summary Ohloh - Code Supervised Learning Given: x y n , want: y ∗ x ∗ I f( i ; i )gi=1 j I Classication: y discrete I Support Vector Machine I Gaussian Processes I Logistic Regression I Decision Trees I Nearest Neighbours I Naive Bayes I Regression: y continuous I Gaussian Processes I Support Vector Regression I (Kernel) Ridge Regression I (Group) LASSO Unsupervised Learning Given: x n , want notion of p x I f i gi=1 ( ) I Clustering: I K-Means I (Gaussian) Mixture Models I Hierarchical clustering I Latent Models I (K) PCA I Latent Discriminant Analysis I Independent Component Analysis I Dimension reduction I (K) Locally Linear Embeddings I Many more... And many more I Multiple Kernel Learning I Structured Output I Metric Learning I Variational Inference I Kernel hypothesis testing I Deep Learning (whooo!) I ... I Bindings to: LibLinear, VowpalWabbit, etc.. http://www.shogun-toolbox.org/page/documentation/ notebook Some Large-Scale Applications I Splice Site prediction: 50m examples of 200m dimensions I Face recognition: 20k examples of 750k dimensions ML in Practice I Modular data represetation I Dense, Sparse, Strings, Streams, ... I Multiple types: 8-128 bit word size I Preprocessing tools I Evaluation I Cross-Validation I Accuracy, ROC, MSE, .. -
SWIG and Ruby
SWIG and Ruby David Grayson Las Vegas Ruby Meetup 2014-09-10 SWIG ● SWIG stands for: Simplified Wrapper and Interface Generator ● SWIG helps you access C or C++ code from 22 different languages, including Ruby SWIG inputs and outputs Ruby C extension SWIG interface file (.i) source (.c or .cxx) Simple C++ example libdavid.h: libdavid.i #include <stdio.h> %module "david" class David %{ { #include <libdavid.h> public: %} David(int x) { class David this->x = x; { } public: David(int x); void announce() void announce(); { int x; }; printf("David %d\n", x); } int x; }; Compiling Simple C++ example extconf.rb require 'mkmf' system('swig -c++ -ruby libdavid.i') or abort create_makefile('david') Commands to run: $ ruby extconf.rb # create libdavid_wrap.cxx and Makefile $ make # compile david.so $ irb -r./david # try it out irb(main):001:0> d = David::David.new(4) => #<David::David:0x007f40090a5280 @__swigtype__="_p_David"> irb(main):002:0> d.announce David 4 => nil (This example worked for me with SWIG 3.0.2 and Ruby 2.1.2.) That example was pretty simple ● All code was in a .h file ● No external libraries ● Simple data types ● No consideration of deployment ...but SWIG has tons of features C: C++: ● All ISO C datatypes ● All C++ datatypes ● Global functions ● References ● Global variables, constants ● Pointers to members ● Structures and unions ● Classes ● Pointers ● (Multiple) inheritance ● (Multidimensional) arrays ● Overloaded functions ● Pointers to functions ● Overloaded methods ● Variable length arguments ● Overloaded operators ● Typedefs ● Static members ● Enums ● Namespaces ● Templates ● Nested classes ... SWIG Typemaps ● Define custom ways to map between scripting- language types and C++ types. ● Can be used to add and remove parameters from of exposed functions. -
PHP Beyond the Web Shell Scripts, Desktop Software, System Daemons and More
PHP Beyond the web Shell scripts, desktop software, system daemons and more Rob Aley This book is for sale at http://leanpub.com/php This version was published on 2013-11-25 This is a Leanpub book. Leanpub empowers authors and publishers with the Lean Publishing process. Lean Publishing is the act of publishing an in-progress ebook using lightweight tools and many iterations to get reader feedback, pivot until you have the right book and build traction once you do. ©2012 - 2013 Rob Aley Tweet This Book! Please help Rob Aley by spreading the word about this book on Twitter! The suggested hashtag for this book is #phpbeyondtheweb. Find out what other people are saying about the book by clicking on this link to search for this hashtag on Twitter: https://twitter.com/search?q=#phpbeyondtheweb Contents Welcome ............................................ i About the author ...................................... i Acknowledgements ..................................... ii 1 Introduction ........................................ 1 1.1 “Use PHP? We’re not building a website, you know!”. ............... 1 1.2 Are you new to PHP? ................................. 2 1.3 Reader prerequisites. Or, what this book isn’t .................... 3 1.4 An important note for Windows and Mac users ................... 3 1.5 About the sample code ................................ 4 1.6 External resources ................................... 4 1.7 Book formats/versions available, and access to updates ............... 5 1.8 English. The Real English. .............................. 5 2 Getting away from the Web - the basics ......................... 6 2.1 PHP without a web server .............................. 6 2.2 PHP versions - what’s yours? ............................. 7 2.3 A few good reasons NOT to do it in PHP ...................... 8 2.4 Thinking about security ............................... -
Analytic Center Cutting Plane Method for Multiple Kernel Learning
Ann Math Artif Intell DOI 10.1007/s10472-013-9331-4 Analytic center cutting plane method for multiple kernel learning Sharon Wulff · Cheng Soon Ong © Springer Science+Business Media Dordrecht 2013 Abstract Multiple Kernel Learning (MKL) is a popular generalization of kernel methods which allows the practitioner to optimize over convex combinations of kernels. We observe that many recent MKL solutions can be cast in the framework of oracle based optimization, and show that they vary in terms of query point generation. The popularity of such methods is because the oracle can fortuitously be implemented as a support vector machine. Motivated by the success of centering approaches in interior point methods, we propose a new approach to optimize the MKL objective based on the analytic center cutting plane method (accpm). Our experimental results show that accpm outperforms state of the art in terms of rate of convergence and robustness. Further analysis sheds some light as to why MKL may not always improve classification accuracy over naive solutions. Keywords Multiple kernel learning · accpm · Oracle methods · Machine learning Mathematics Subject Classification (2010) 68T05 1 Introduction Kernel methods, for example the support vector machine (SVM), are a class of algorithms that consider only the similarity between examples [1]. A kernel function S. Wulff Department of Computer Science, ETH, Zürich, Switzerland e-mail: [email protected] C. S. Ong (B) NICTA, The University of Melbourne, Melbourne, Australia e-mail: [email protected] S. Wulff, C.S. Ong k implicitly maps examples x to a feature space given by a feature map via the identity k(xi, x j) = (xi), (x j) . -
Python and Epics: Channel Access Interface to Python
Python and Epics: Channel Access Interface to Python Matthew Newville Consortium for Advanced Radiation Sciences University of Chicago October 12, 2010 http://cars9.uchicago.edu/software/python/pyepics3/ Matthew Newville (CARS, Univ Chicago) Epics and Python October 12, 2010 Why Python? The Standard Answers Clean Syntax Easy to learn, remember, and read High Level Language No pointers, dynamic memory, automatic memory Cross Platform code portable to Unix, Windows, Mac. Object Oriented full object model, name spaces. Also: procedural! Extensible with C, C++, Fortran, Java, .NET Many Libraries GUIs, Databases, Web, Image Processing, Array math Free Both senses of the word. No, really: completely free. Matthew Newville (CARS, Univ Chicago) Epics and Python October 12, 2010 Why Python? The Real Answer Scientists use Python. Matthew Newville (CARS, Univ Chicago) Epics and Python October 12, 2010 All of these tools use the C implementation of Python. NOT Jython (Python in Java) or IronPython (Python in .NET): I am not talking about Jython. Why Do Scientists Use Python? Python is great. The tools are even better: numpy Fast arrays. matplotlib Excellent Plotting library scipy Numerical Algorithms (FFT, lapack, fitting, . ) f2py Wrapping Fortran for Python sage Symbolic math (ala Maple, Mathematica) GUI Choices Tk, wxWidgets, Qt, . Free Python is Free. All these tools are Free (BSD). Matthew Newville (CARS, Univ Chicago) Epics and Python October 12, 2010 Why Do Scientists Use Python? Python is great. The tools are even better: numpy Fast arrays. matplotlib Excellent Plotting library scipy Numerical Algorithms (FFT, lapack, fitting, . ) f2py Wrapping Fortran for Python sage Symbolic math (ala Maple, Mathematica) GUI Choices Tk, wxWidgets, Qt, . -
Mobilní Telefony V Linuxu
Michal Čihař <[email protected]> Mobilní telefony v Linuxu – Gammu a Wammu Mobilní telefon má dnes téměř každý a přístup k mnoha informacím, které v něm dnes mohou být uloženy z počítače se jeví jako logický požadavek. Pro Windows nám většinou výrobce poskytne více či méně kvalitní program, ale pod ostatními systémy si obvykle musíme poradit pomocí jiných nástrojů. V této přednášce se dozvíte jak je na tom v tomto ohledu Linux. Připojení telefonu Telefonem můžeme připojit mnoha způsoby, a každý z nich má své výhody i nevýhody. Hlavním rozdílem je jestli se budeme připojovat prostřednictvím kabelu, nebo využijeme bezdrátového připojení. První telefony, které umožňovaly připojení k počítači, používaly sériový kabel. Toto připojení bylo bezproblémové, ale jak s postupem doby začaly z počítačů mizet sériové porty, výrobci od něho začali ustupovat. V dnešní době je v podstatě nahrazeno různými USB kabely, které dost často opět obsahují konvertor USB/sériový port. Takže stále používáme tu samou technologii, jen k tomu používáme řádově dražší kabel. U těchto kabelů jsou občas problémy s ovladači, protože standardizace do této oblasti ještě nedorazila. Nicméně většina z nich vám bude na Linuxu fungovat. Z bezdrátových připojení je zdaleka nejrozšířenější Bluetooth a IrDA. IrDA pracuje v infračerveném pásmu a má relativně krátký dosah. Oproti tomu Bluetooth pracující v mikrovlnném pásmu (stejné jako WiFi) má dosah větší. Z toho ovšem plynou i možná bezpečnostní rizika. Toto připojení je všesměrové a tudíž kdokoliv, kdo se nachází ve vašem okolí se může pokusit k telefonu připojit. A nutno podotknout, že díky špatnému zabezpečení nebo chybám v implementaci v telefonu se mu to může zdárně podařit. -
Advanced Wxpython Nuts and Bolts Robin Dunn O'reilly Open
Advanced wxPython Nuts and Bolts Robin Dunn Software Craftsman O’Reilly Open Source Convention July 21–25, 2008 Slides available at http://wxPython.org/OSCON2008/ wxPython: Cross Platform GUI Toolkit 1 Presentation Overview • Introduction • Widget Inspection Tool • wx.ListCtrl • Keeping the UI Updated • Virtual wx.ListCtrl • Sizers and more sizers • wx.TreeCtrl • XML based resource system • wx.gizmos.TreeListCtrl • Data transfer • CustomTreeCtrl – data objects • wx.grid.Grid – clipboard • ScrolledPanel – drag and drop • wx.HtmlWindow • Creating custom widgets • Double buffered drawing wxPython: Cross Platform GUI Toolkit 2 Introduction to wxPython • wxPython is a GUI toolkit for Python, built upon the wxWidgets C++ toolkit. (See http://wxWidgets.org/) – Cross platform: Windows, Linux, Unix, OS X. – Uses native widgets/controls, plus many platform independent widgets. • Mature, well established projects. – wxWidgets: 1992 – wxPython: 1996 wxPython: Cross Platform GUI Toolkit 3 Introduction: architecture wxPython Library Proxy classes wxPython Extension Modules wxWidgets Toolkit Platform GUI Operating System wxPython: Cross Platform GUI Toolkit 4 Introduction: partial class hierarchy wx.Object wx.EvtHandler wx.Window wx.TopLevelWindow wx.Panel wx.Control wx.Frame wx.Dialog wx.ScrolledWindow wxPython: Cross Platform GUI Toolkit 5 wx.ListCtrl • Presents a list of items with one of several possible views – List – Report – Icon • Supports various attributes and operations on the list data – Icons, and colors – Sorting – multiple selection • -
Raritas: a Program for Counting High Diversity Categorical Data with Highly Unequal Abundances
Raritas: a program for counting high diversity categorical data with highly unequal abundances David B. Lazarus1, Johan Renaudie1, Dorina Lenz2, Patrick Diver3 and Jens Klump4 1 Museum für Naturkunde, Berlin, Germany 2 Leibniz-Institut für Zoo- und Wildtierforschung, Berlin, Germany 3 Divdat Consulting, Wesley, AR, USA 4 CSIRO, Mineral Resources, Kensington, NSW, Australia ABSTRACT Acquiring data on the occurrences of many types of difficult to identify objects are often still made by human observation, for example, in biodiversity and paleontologic research. Existing computer counting programs used to record such data have various limitations, including inflexibility and cost. We describe a new open-source program for this purpose—Raritas. Raritas is written in Python and can be run as a standalone app for recent versions of either MacOS or Windows, or from the command line as easily customized source code. The program explicitly supports a rare category count mode which makes it easier to collect quantitative data on rare categories, for example, rare species which are important in biodiversity surveys. Lastly, we describe the file format used by Raritas and propose it as a standard for storing geologic biodiversity data. ‘Stratigraphic occurrence data’ file format combines extensive sample metadata and a flexible structure for recording occurrence data of species or other categories in a series of samples. Subjects Biodiversity, Bioinformatics, Ecology, Paleontology Keywords Software, Point-counting, Rarity, Data standards, Micropaleontology, Biostratigraphy, Submitted 5 April 2018 Biodiversity, Ecology, Python, Range chart Accepted 26 July 2018 Published 9 October 2018 Corresponding author INTRODUCTION David B. Lazarus, Human observations as a source of scientific data [email protected] Quantitative data about many aspects of the natural world are collected in modern Academic editor Donald Baird science with the use of instruments, but a substantial amount of observational data is still Additional Information and collected by human observation. -
Foreign Library Interface by Daniel Adler Dia Applications That Can Run on a Multitude of Plat- Forms
30 CONTRIBUTED RESEARCH ARTICLES Foreign Library Interface by Daniel Adler dia applications that can run on a multitude of plat- forms. Abstract We present an improved Foreign Function Interface (FFI) for R to call arbitary na- tive functions without the need for C wrapper Foreign function interfaces code. Further we discuss a dynamic linkage framework for binding standard C libraries to FFIs provide the backbone of a language to inter- R across platforms using a universal type infor- face with foreign code. Depending on the design of mation format. The package rdyncall comprises this service, it can largely unburden developers from the framework and an initial repository of cross- writing additional wrapper code. In this section, we platform bindings for standard libraries such as compare the built-in R FFI with that provided by (legacy and modern) OpenGL, the family of SDL rdyncall. We use a simple example that sketches the libraries and Expat. The package enables system- different work flow paths for making an R binding to level programming using the R language; sam- a function from a foreign C library. ple applications are given in the article. We out- line the underlying automation tool-chain that extracts cross-platform bindings from C headers, FFI of base R making the repository extendable and open for Suppose that we wish to invoke the C function sqrt library developers. of the Standard C Math library. The function is de- clared as follows in C: Introduction double sqrt(double x); We present an improved Foreign Function Interface The .C function from the base R FFI offers a call (FFI) for R that significantly reduces the amount of gate to C code with very strict conversion rules, and C wrapper code needed to interface with C. -
Python Guide Documentation Publicación 0.0.1
Python Guide Documentation Publicación 0.0.1 Kenneth Reitz 17 de May de 2018 Índice general 1. Empezando con Python 3 1.1. Eligiendo un Interprete Python (3 vs. 2).................................3 1.2. Instalando Python Correctamente....................................5 1.3. Instalando Python 3 en Mac OS X....................................6 1.4. Instalando Python 3 en Windows....................................8 1.5. Instalando Python 3 en Linux......................................9 1.6. Installing Python 2 on Mac OS X.................................... 10 1.7. Instalando Python 2 en Windows.................................... 12 1.8. Installing Python 2 on Linux....................................... 13 1.9. Pipenv & Ambientes Virtuales...................................... 14 1.10. Un nivel más bajo: virtualenv...................................... 17 2. Ambientes de Desarrollo de Python 21 2.1. Your Development Environment..................................... 21 2.2. Further Configuration of Pip and Virtualenv............................... 26 3. Escribiendo Buen Código Python 29 3.1. Estructurando tu Proyecto........................................ 29 3.2. Code Style................................................ 40 3.3. Reading Great Code........................................... 49 3.4. Documentation.............................................. 50 3.5. Testing Your Code............................................ 53 3.6. Logging.................................................. 57 3.7. Common Gotchas........................................... -
Dynamic Message-Oriented Middleware with Open Sound Control and Odot
http://www.cnmat.berkeley.edu/publications/dynamic-message-oriented-middleware-open-sound-control-and-odot Dynamic Message-Oriented Middleware with Open Sound Control and Odot John MacCallum1, Rama Gottfried1, Ilya Rostovtsev1, Jean Bresson2, and Adrian Freed1 1Center for New Music and Audio Technologies, Department of Music, University of California, Berkeley, [email protected], [email protected], [email protected], [email protected] 2UMR STMS: IRCAM-CNRS-UPMC, Paris, [email protected] ABSTRACT coding, and the odot framework provides a set of tools for constructing such a layer in a dynamic and agile fashion. We present recent work on odot, a system that extends Open We begin with brief descriptions of Message-Oriented Mid- Sound Control and facilitates the rapid and dynamic con- dleware, OSC, and odot. We then discuss our ongoing work struction of Message-Oriented Middleware providing an in- to provide support for a variety of host and nested host envi- teroperability layer for communication between applications. ronments that can be made to communicate by passing OSC Unlike traditional middleware systems, odot, when embedded between them. Finally, we conclude by discussing a number in a host environment, provides a node where computation of examples and future work. can take place, allowing middleware to take shape dynami- cally as the needs of the system develop. 2. OPEN SOUND CONTROL AND ODOT 2.1 Open Sound Control (OSC) 1. INTRODUCTION Open Sound Control[1][2][3] is a popular encoding in the In the course of a complex design project we often encounter music and media/arts programming communities for moving the need to use multiple applications to satisfy a diverse set data between sensors and actuators, as well as processes and of requirements. -
Up & Running with Wxpython Robin Dunn Patrick O'brien O'reill
wxPython in a Nutshell Robin Dunn http://wxPython.org/ O’Reilly Open Source Convention July 26–30, 2004 wxPython: Cross Platform GUI Toolkit 1 The best way to eat an elephant… wxPython: Cross Platform GUI Toolkit 2 …is one bite at a time wxPython: Cross Platform GUI Toolkit 3 Introduction to wxPython • wxPython is a GUI toolkit for Python, built upon the wxWidgets C++ toolkit. – Cross platform: Windows, Linux, Unix, OS X. – Uses native widgets/controls, plus many platform independent widgets. • Mature, well established projects. – wxWidgets: 1992 – wxPython: 1996 wxPython: Cross Platform GUI Toolkit 4 Introduction: architecture wxPython Library Proxy classes wxPython Extension Modules wxWidgets Toolkit Platform GUI Operating System wxPython: Cross Platform GUI Toolkit 5 Introduction: partial class hierarchy wx.Object wx.EvtHandler wx.Window wx.TopLevelWindow wx.Panel wx.Control wx.Frame wx.Dialog wx.ScrolledWindow wxPython: Cross Platform GUI Toolkit 6 Getting started with wxPython • Installation is simple -- binary installers are available at SourceForge and via http://wxPython.org/download.php for: – Windows: *.exe – Linux: *.rpm (and *.deb’s are available separately.) – OS X: *.dmg, a disk image that contains an Installer package. • Can be built from source for other Unix-like systems. wxPython: Cross Platform GUI Toolkit 7 Getting started with wxPython • Choose an installer. • Which version of Python do you use? – 2.2, or 2.3 • Unicode? – Windows, but be careful with Win9x/ME – Linux/Unix, with the GTK2 build – OS X, soon • or ANSI? – All platforms wxPython: Cross Platform GUI Toolkit 8 Getting started with wxPython • Choose an editor or development environment: – Boa Constructor – WingIDE – PyAlaMode – SCiTE – Emacs, vi, etc.