IntegratingPythonWithOtherLanguages
[Hint: The idea is to create pages for the stuff, not just link it.] There a various tools which make it easier to bridge the gap between Python and C/C++: Pyrex - write your extension module on Python Cython -- Cython -- an improved version of Pyrex CXX - PyCXX - helper lib for writing Python extensions in C++ SCXX ctypes is a Python module allowing to create and manipulate C data types in Python. These can then be passed to C-functions loaded from dynamic link libraries. elmer - compile and run python code from C, as if it was written in C PicklingTools is a collection of libraries for exchanging Python Dictionaries between C++ and Python. weave - include C code lines in Python program ackward exposes parts of Python's standard library as idiomatic C++ C/C++ Binding Generators Tools to make C/C++ functions/methods accessible from Python by generating binding (Python extension or module) from header files. Articles Related See also to name a few.
Python - Quick Guide
Python is a high-level, interpreted, interactive and object oriented-scripting language. Python is InterpretedPython is InteractivePython is Object-OrientedPython is Beginner's Language Python was developed by Guido van Rossum in the late eighties and early nineties at the National Research Institute for Mathematics and Computer Science in the Netherlands. Python's feature highlights include: Easy-to-learnEasy-to-readEasy-to-maintainA broad standard libraryInteractive ModePortableExtendableDatabasesGUI ProgrammingScalable The most up-to-date and current source code, binaries, documentation, news, etc. is available at the official website of Python: Python Official Website : You can download the Python documentation from the following site. Python Documentation Website : www.python.org/doc/ Interactive Mode Programming: Invoking the interpreter without passing a script file as a parameter brings up the following prompt: >>> print "Hello, Python!" Hello, Python! #! Example: #! #!
Templating
Templating, and in particular web templating is a way to represent data in different forms. These forms often (but not always) intended to be readable, even attractive, to a human audience. Frequently, templating solutions involve a document (the template) and data. Template usually looks much like the final output, with placeholders instead of actual data (or example data in simplified form), bears common style and visual elements. Templating Engines There are many, many different HTML/XML templating packages and modules for Python that provide different feature sets and syntaxes. The number of templating engines is so great because the mechanisms involved are pretty easy to write in Python, at least for a fairly basic template engine; this recipe from the Python Cookbook shows how easy it is. Engines using Value Substitution The simplest form of templating engine is that which merely substitutes values into a template in order to produce the final output. HTML Shorthand Processors
The Python Tutorial
Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms. The Python interpreter and the extensive standard library are freely available in source or binary form for all major platforms from the Python Web site, and may be freely distributed. The same site also contains distributions of and pointers to many free third party Python modules, programs and tools, and additional documentation. The Python interpreter is easily extended with new functions and data types implemented in C or C++ (or other languages callable from C). This tutorial introduces the reader informally to the basic concepts and features of the Python language and system.
Welcome to PyBrain’s documentation! — PyBrain v0.3 documentation
The documentation is build up in the following parts: first, there is the quickstart tutorial which aims at getting you started with PyBrain as quickly as possible. This is the right place for you if you just want get a feel for the library or if you never used PyBrain before. Although the quickstart uses supervised learning with neural networks as an example, this does not mean that that’s it. PyBrain is not only about supervised learning and neural networks. While the quickstart should be read sequentially, the tutorial chapters can mostly be read independently of each other. In case this does not suffice, we also have an API reference, the Module Index. If you want to develop for PyBrain and contribute, check out our guidelines in our wiki: If at any point the documentation does not suffice, you can always get help at the pybrain Google Group at Installation Quick answer:
Principal Component Analysis step by step
In this article I want to explain how a Principal Component Analysis (PCA) works by implementing it in Python step by step. At the end we will compare the results to the more convenient Python PCA()classes that are available through the popular matplotlib and scipy libraries and discuss how they differ. The main purposes of a principal component analysis are the analysis of data to identify patterns and finding patterns to reduce the dimensions of the dataset with minimal loss of information. Here, our desired outcome of the principal component analysis is to project a feature space (our dataset consisting of n x d-dimensional samples) onto a smaller subspace that represents our data "well". About the notation: In the following sections, we will use a bold-face and lower-case letters for denoting column vectors vectors (e.g., e) and bold-face upper-case letters for matrices (e.g., W) Principal Component Analysis (PCA) Vs. What is a "good" subspace? Summarizing the PCA approach
