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INFOTOPO false
Programs for Information Topology Data Analysis Information Topology is a program written in Python (compatible with Python 3.4.x), with a graphic interface built using TKinter [1], plots drawn using Matplotlib [2], calculations made using NumPy [3], and scaffold representations drawn using NetworkX [4]. It computes all the results on information presented in the study [5], that is all the usual information functions: entropy, joint entropy between k random variables (Hk), mutual informations between k random variables (Ik), conditional entropies and mutual informations and provides their cohomological (and homotopy) visualisation in the form of information landscapes and information paths together with an approximation of the minimum information energy complex [5]. It is applicable on any set of empirical data that is data with several trials-repetitions-essays (parameter m), and also allows to compute the undersampling regime, the degree k above which the sample size m is to small to provide good estimations of the information functions [5]. The computational exploration is restricted to the simplicial sublattice of random variable (all the subsets of k=n random variables) and has hence a complexity in O(2^n). In this simplicial setting we can exhaustively estimate information functions on the simplicial information structure, that is joint-entropy Hk and mutual-informations Ik at all degrees k=<n and for every k-tuple, with a standard commercial personal computer (a laptop with processor Intel Core i7-4910MQ CPU @ 2.90GHz * 8) up to k=n=21 in reasonable time (about 3 hours). Using the expression of joint-entropy and the probability obtained using equation and marginalization [5], it is possible to compute the joint-entropy and marginal entropy of all the variables. The alternated expression of n-mutual information given by equation then allows a direct evaluation of all of these quantities. The definitions, formulas and theorems are sufficient to obtain the algorithm [5]. We will further develop a refined interface (help welcome) but for the moment it works like this, and requires minimum Python use knowledge. Please contact pierre.baudot [at] gmail.com for questions, request, developments (etc.): [1] J.W. Shipman. Tkinter reference: a gui for python. . New Mexico Tech Computer Center, Socorro, New Mexico, 2010. [2] J.D. Hunter. Matplotlib: a 2d graphics environment. Comput. Sci. Eng., 9:22–30, 2007. [3] S. Van Der Walt, C. Colbert, and G. Varoquaux. The numpy array: a structure for efficient numerical computation. Comput. Sci. Eng., 13:22– 30, 2011. [4] A.A. Hagberg, D.A. Schult, and P.J. Swart. Exploring network structure, dynamics, and function using networkx. Proceedings of the 7th Python in Science Conference (SciPy2008). Gel Varoquaux, Travis Vaught, and Jarrod Millman (Eds), (Pasadena, CA USA), pages 11–15, 2008. [5] M. Tapia, P. Baudot, M. Dufour, C. Formisano-Tréziny, S. Temporal, M. Lasserre, J. Gabert, K. Kobayashi, JM. Goaillard . Information topology of gene expression profile in dopaminergic neurons doi: https://doi.org/10.1101/168740 http://www.biorxiv.org/content/early/2017/07/26/168740
KNIME false
KNIME [naim] is a user-friendly graphical workbench for the entire analysis process: data access, data transformation, initial investigation, powerful predictive analytics, visualisation and reporting. The open integration platform provides over 1000 modules (nodes), including those of the KNIME community and its extensive partner network.
MLPACK false
MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. MLPACK contains the following algorithms: Collaborative Filtering, Density Estimation Trees, Euclidean Minimum Spanning Trees, Fast Exact Max-Kernel Search (FastMKS), Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), Kernel Principal Component Analysis (KPCA), K-Means Clustering, Least-Angle Regression (LARS/LASSO), Local Coordinate Coding, Locality-Sensitive Hashing (LSH), Logistic regression, Naive Bayes Classifier, Neighbourhood Components Analysis (NCA), Non-negative Matrix Factorization (NMF), Principal Components Analysis (PCA), Independent component analysis (ICA), Rank-Approximate Nearest Neighbor (RANN), Simple Least-Squares Linear Regression (and Ridge Regression), Sparse Coding, Tree-based Neighbor Search (all-k-nearest-neighbors, all-k-furthest-neighbors), Tree-based Range Search.
Mastrave false
Mastrave is a free software library written to perform vectorized scientific computing and to be as compatible as possible with both GNU Octave and Matlab computing frameworks, offering general purpose, portable and freely available features for the scientific community. Mastrave is mostly oriented to ease complex modeling tasks such as those typically needed within environmental models, even when involving irregular and heterogeneous data series. Semantic array programming The Mastrave project attempts to allow a more effective, quick interoperability between GNU Octave and Matlab users by using a reasonably well documented wrap around the main incompatibilities between those computing environments and by promoting a reasonably general idiom based on their common, stable syntagms. It also promotes the systematic adoption of data-transformation abstractions and lightweight semantic constraints to enable concise and reliable implementations of models following the paradigm of semantic array programming. There are a couple of underlying ideas: library design is language design and vice versa (Bell labs); language notation is definitely a "tool of thought" (version), in the sense that there is a feedback between programming/mathematical notation and the ability to think new scientific insights. And perhaps ethic ones. Science and society Mastrave is free software, which is software respecting your freedom. As many other free scientific software packages, it is offered to the scientific community to also promote the development of a free society more concerned about cooperation rather than competitiveness, heading toward knowledge and culture freedom. Such a vision implies the possibility for motivated individuals to freely access, review and contribute even to the cutting-edge academic culture. This possibility relies on the development of tools and methodologies helping to overcome economic, organizational and institutional barriers (i.e. knowledge oligopolies) while systematically promoting reproducible research. This is a long-term goal to which the free software paradigm can and has been able to actively cooperate.
MathGene false
MathGene is a comprehensive JavaScript mathematics engine that delivers the ability to perform advanced numerical and symbolic mathematics processing of LaTeX expressions and send the output to pure HTML for rendering on a conventional web browser or via web server. MathGene has two modules: •mg_translate.js, which translates between LaTeX, HTML, and native MG format. •mg_calculate.js, which performs the calculations. mg_translate.js can be used without mg_calculate.js to perform mathematics rendering only. Both modules are required to perform calculations.
Mcsim true
MCSim is a simulation and statistical inference tool for algebraic or differential equation systems. While other programs have been created to the same end, many of them are not optimal for performing computer intensive and sophisticated Monte Carlo analyses. MCSim was created specifically to perform Monte Carlo analyses in an optimized, and easy to maintain environment.
MedianTracker false
MedianTracker supports efficient median queries on and dynamic additions to a list of values. It provides both the lower and upper median of all values seen so far. Any __cmp__()-able object can be tracked, in addition to numeric types. add() takes log(n) time for a tracker with n items; lower_median() and upper_median() run in constant time. Since all values must be stored, memory usage is proportional to the number of values added (O(n)).
Opennn false
OpenNN is a class library written in C++ which implements neural networks. The library is intended for advanced users, with high C++ and machine learning skills. OpenNN provides an effective framework for the research and development of data mining and predictive analytics algorithms and applications. OpenNN is based on the most popular neural network model, the multilayer perceptron. The package comes with unit testing, many examples and extensive documentation. The library has been designed to learn from data sets. Some typical applications here are function regression (modelling), pattern recognition (classification) and time series prediction (forecasting). OpenNN is being developed by Intelnics, a company specialized in the development and application of neural networks.
OptionMatrix false
These calculators are real-time multi-model option chain pricers with analytics and interactive controls. optionmatrix is the GTK+ graphical user interface version and optionmatrix_console is the Curses version. Both programs feature: greeks, decimal date to real-date translations, real-date to decimal date translations, real-time time bleeding, configurable option expiration date engines, calendars, strike control systems, tickers and over 168+ option models. optionmatrix also supports: spreads, bonds, term structures, cash flow editing, source code viewing and text exporting.
Papertrail false
Papertrail is a Ballot Counting Software. It helps scanning and counting regular paper ballots as known from common election situations. It is Free Software, licensed via the GPLv3, to make the election process as dependable as possible, but speeding up the manual counting process a lot.


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