jakevdp/sklearn_scipy2013 enthought/pyql GUESS: The Graph Exploration System jakevdp/sklearn_pycon2013 a free/open-source library for quantitative finance GitHub - airbnb/caravel: Caravel is a data exploration platform designed to be visual, intuitive, and interactive Multi-armed bandit Resource problem in machine learning In probability theory and machine learning, the multi-armed bandit problem (sometimes called the K-[1] or N-armed bandit problem[2]) is a problem in which a fixed limited set of resources must be allocated between competing (alternative) choices in a way that maximizes their expected gain, when each choice's properties are only partially known at the time of allocation, and may become better understood as time passes or by allocating resources to the choice.[3][4] This is a classic reinforcement learning problem that exemplifies the exploration–exploitation tradeoff dilemma. In the problem, each machine provides a random reward from a probability distribution specific to that machine, that is not known a-priori. Herbert Robbins in 1952, realizing the importance of the problem, constructed convergent population selection strategies in "some aspects of the sequential design of experiments".[6] A theorem, the Gittins index, first published by John C. .
StatsModels: Statistics in Python — statsmodels 0.6.0.dev-455510c documentation statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are avalable for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. Since version 0.5.0 of statsmodels, you can use R-style formulas together with pandas data frames to fit your models. import numpy as npimport statsmodels.api as smimport statsmodels.formula.api as smf # Load datadat = sm.datasets.get_rdataset("Guerry", "HistData").data # Fit regression model (using the natural log of one of the regressors)results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=dat).fit() # Inspect the resultsprint results.summary() You can also use numpy arrays instead of formulas:
Blog Le dessous des "labs" Billet publié par Stéphane Vincent Vous en avez assez des "labs" ? Difficile de vous le reprocher... Labs2, une initiative de Kennisland C’est pourquoi la rencontre Labs2 initiée les 24 et 25 avril dernier à Amsterdam par le think-tank hollandais Kennisland et l’organisme de coopération Hivos venait à point nommé. Des labs sur tous les thèmes Les participants venaient du monde entier et reflétaient une très grande diversité d’objectifs et de thèmes : en Afrique du Sud, Ushahidi anime un logiciel de crowdsourcing, né après la crise au Kenya en 2007 à la suite de l’élection présidentielle pour collecter les témoignages de violence envoyés par email et SMS et les placer sur Google Maps ; en Grande-Bretagne, le Finance Lab explore de nouveaux modèles de systèmes financiers au services des populations ; au Cambodge, le Human Centered Innovation Lab co-conçoit des équipements du quotidien avec les habitants. Quel est le core-business des labs ? Ethno, proto, carto
Hyperopt by hyperopt hyperopt is a Python library for optimizing over awkward search spaces with real-valued, discrete, and conditional dimensions. Currently two algorithms are implemented in hyperopt: Random SearchTree of Parzen Estimators (TPE) Hyperopt has been designed to accommodate Bayesian optimization algorithms based on Gaussian processes and regression trees, but these are not currently implemented. All algorithms can be run either serially, or in parallel by communicating via MongoDB. User installation: Developer installation: git clone (cd hyperopt && python setup.py develop)(cd hyperopt && nosetests) For more information see Installation Notes. Documentation is currently hosted on the wiki, but here are some quick links to the most relevant pages: See projects using hyperopt on the wiki.