Python Extension Packages for Windows - Christoph Gohlke. By Christoph Gohlke, Laboratory for Fluorescence Dynamics, University of California, Irvine. This page provides 32- and 64-bit Windows binaries of many scientific open-source extension packages for the official CPython distribution of the Python programming language. The files are unofficial (meaning: informal, unrecognized, personal, unsupported, no warranty, no liability, provided "as is") and made available for testing and evaluation purposes. If downloads fail reload this page, enable JavaScript, disable download managers, disable proxies, clear cache, and use Firefox. Please only download files manually as needed. Most binaries are built from source code found on PyPI or in the projects public revision control systems. Source code changes, if any, have been submitted to the project maintainers or are included in the packages. Refer to the documentation of the individual packages for license restrictions and dependencies.
Use pip version 8 or newer to install the downloaded .whl files. Statistical Modeling, Causal Inference, and Social Science. About PASCAL | PASCAL 2. Weka - home. Sentiment Mining for Business and Research. Project details:WEKA. The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this functionality.
The main strengths of Weka are that it is freely available under the GNU General Public License, very portable because it is fully implemented in the Java programming language and thus runs on almost any computing platform, contains a comprehensive collection of data preprocessing and modeling techniques, and is easy to use by a novice due to the graphical user interfaces it contains.
Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection. Weka's main user interface is the Explorer, but essentially the same functionality can be accessed through the component-based Knowledge Flow interface and from the command line.
What's new since version 3.6.0? [1] Ian H. . [2] G. Holmes; A. Boost C++ Libraries. Data Mining and machine learning used to help you understand the business better and also improve future performance through predictive analytics. | Weka Project: Pentaho Data Integration. Community Wiki Home - Pentaho Community - Pentaho Wiki. Primer. WEKA is a comprehensive toolbench for machine learning and data mining. Its main strengths lie in the classification area, where all current ML approaches -- and quite a few older ones -- have been implemented within a clean, object-oriented Java class hierarchy. Regression, Association Rules and clustering algorithms have also been implemented. However, WEKA is also quite complex to handle -- amply demonstrated by many questions on the WEKA mailing list.
Concerning the graphical user interface, the WEKA development group offers documentation for the Explorer and the Experimenter. However, there is little documentation on using the command line interface to WEKA, although it is essential for realistic learning tasks. This document serves as a practical introduction to the command line interface. Dataset % This is a toy example, the UCI weather dataset. % Any relation to real weather is purely coincidental.}}
@relation golfWeatherMichigan_1988/02/10_14days @attribute play {yes, no} instance. 11F: Homework 03. Sam roweis : notes. I have written a few small tutorial notes on various topics that were of interest to me. You can get them below. Please send me any comments or corrections you have! NB: Until I get around to making progress on my barely started book, these are the best I've got to offer. But some are quite old, and none have been carefully checked over, so use at your own risk. Also, I would be remiss if I didn't point you to Kevin Murphy, who has many excellent tutorials and research notes on his page Thomas Minka who is seemingly unstoppable and will fill you in on matrix calculus all things Bayesian Adam Berger who has some good introductory notes on MaxEnt modeling Other pages listing matrix identities:[ Imperial, Resa, CMU, Utah] Enjoy! Possibly Useful Notes Useful Matrix and Gaussian formulae Two "cheat-sheets" of useful matrix and Gaussian formulae for anyone who needs to take inverses or derivatives or conditional expectations using linear operators and normal densities.
Tutorials. Matlab Code by Mark Schmidt (optimization, graphical models, machine learning) Summary This package contains the most recent version of various Matlab codes I released during my PhD work. I would recommend downloading and using this package if you plan on using more than one of my Matlab codes. This is because this package includes all the more recent bug-fixes and efficiency-improvements, while in making this package I have updated my old code to make it compatible with the new code and newer versions of Matlab.
Further, I typically do not update the individual packages unless I am making a major change (such as the updates of minFunc and UGM). The particular packages included (from oldest to newest) are: minFunc - Function for unconstrained optimization of differentiable real-valued multivariate functions. Examples Each of the packages includes one or more demos that show how to use the code. MinFunc Updates.