Search Results for "algorithm" - Page 6
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Showing 234 open source projects for "algorithm"
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MultiPlink
[UPDATE 28-10-2015] FINAL MULTIPLINK SSH LIFETIME v2.0 UP-5 "FAREWELL"
Download Link Utama: http://sourceforge.net/projects/multiplink/files/Final_MultiPlink_SSHLIFETIME_2.0_UP-5_FAREWELL.rar/download Jika di Halaman Download, Link belum tersedia, klik link alternatif : https://www.dropbox.com/s/znptxp2gxhihpax/Final_MultiPlink_SSHLIFETIME_2.0_UP-5_FAREWELL.rar?dl=0 Lalu Download & Install juga Minor... -
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A toolkit in C++ and python to process both experimental and simulation data of colloidal particles. Includes among others * a multiscale particle tracking algorithm [1] whose C++ implementation is optimised for 3D confocal data. Python implementation is more versatile (2D and 3D data). * a Leica file reader, * Steindhard bond orientational order calculation * a VTK file writer 1. Leocmach, M. & Tanaka, H. A novel particle tracking method with individual particle size measurement and its application to ordering in glassy hard sphere colloids. ...
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Unsupervised Random Forest
On-line Unsupervised Random Forest
...It supports on-line prediction of new observations (no need to retrain); and supports datasets that contain both continuous (e.g. CPU load) and categorical (e.g. VM instance type) features. In particular, we use an unsupervised formulation of the Random Forest algorithm to calculate similarities and provide them as input to a clustering algorithm. For the sake of efficiency and meeting the dynamism requirement of autonomic clouds, our methodology consists of two steps: (i) off-line clustering and (ii) on-line prediction. RF+PAM can: Cluster observations (Unsupervised Learning) Calculate the dissimilarity between 2 or more observations (how different two observations are) -
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binana
BINANA (BINding ANAlyzer) is a python-implemented algorithm for analyz
BINANA (BINding ANAlyzer) is a python-implemented algorithm for analyzing ligand binding. The program identifies key binding characteristics like hydrogen bonds, salt bridges, and pi interactions. As input, BINANA accepts receptor and ligand files in the PDBQT format. PDBQT files can be generated from the more common PDB file format using the free converter provided with AutoDockTools. -
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Neural Libs
Neural network library for developers
This project includes the implementation of a neural network MLP, RBF, SOM and Hopfield networks in several popular programming languages. The project also includes examples of the use of neural networks as function approximation and time series prediction. Includes a special program makes it easy to test neural network based on training data and the optimization of the network. -
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fitGCP
Fitting genome coverage distributions with mixture models
...Besides commonly used distributions, fitGCP uses distributions tailored to account for common artifacts. The mixture models are iteratively fitted based on the Expectation-Maximization algorithm. Please find the accompanying paper here: http://dx.doi.org/10.1093/bioinformatics/btt147 -
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MITRE Common Layer 3
Approach to choose MANET routing peers for existing protocols.
Common Layer 3 presents an approach that allows an algorithm to choose IP routing peers intelligently among the nodes in a MANET but does not involve any modifications to existing IP routing protocols. In addition, our approach works as a pure interface between (any) MANET radio terminal and any IP router, so nodes using our interface interoperate with nodes that do not use this interface or with nodes using different algorithms to select routing peers. -
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Eye candy showing parallel Conway's Life games in the bit planes of the screen, using boolean BitBlt operations. (Independently rediscovered algorithm given already in Smalltalk-80 Blue Book, by the inventors of BitBlt.)
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LWPR
Locally Weighted Projection Regression (LWPR)
Locally Weighted Projection Regression (LWPR) is a fully incremental, online algorithm for non-linear function approximation in high dimensional spaces, capable of handling redundant and irrelevant input dimensions. At its core, it uses locally linear models, spanned by a small number of univariate regressions in selected directions in input space. A locally weighted variant of Partial Least Squares (PLS) is employed for doing the dimensionality reduction. -
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ThermV
Comprehensive thermal analysis software package
ThermV thermal analysis software package aims to provide the most sophisticated automatic analysis of thermal analysis data (TG/DTG, DTA and DSC). It offers new algorithm for concurrent peak deconvolution at different heating rates and provides full kinetic analysis of these data, including isoconversional methods for Ea, determination of reaction model and full kinetic triplet, Avrami coefficients, and dimensionality of crystal growth for reactions in the solid state. The project is currently in alpha stage, where individual modules will be provided for data analysis. ... -
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MusicPlayer
Music player - endlessly plays your music
This music player is supposed to be simple and all centered around an infinite intelligent queue (some other players call this PartyShuffle or DJ mode). You can manually add songs to it - but if you don't or the queue gets too short, it will automatically intelligently fill it with further songs. The intelligent queue decision currently is based on: * song ratings * context-based choices, e.g. related songs more likely Other features of this player: * open source, simplified BSD... -
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MRA
A general recommender system with basic models and MRA
Multi-categorization Recommendation Adjusting (MRA) is to optimize the results of recommendation based on traditional(basic) recommendation models, through introducing objective category information and taking use of the feature that users always get the habits of preferring certain categories. Besides this, there are two advantages of this improved model: 1) it can be easily applied to any kind of existing recommendation models. And 2) a controller is set in this improved model to provide... -
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EducationalLCS
eLCS - Educational Learning Classifier System
Educational Learning Classifier System (eLCS) is a set of learning classifier system (LCS) educational demos designed to introduce students or researchers to the basics of a modern Michigan-style LCS algorithm. This eLCS package includes 5 different implementations of a basic LCS algorithm, as part of a 6 stage set of demos that will be paired with the first introductory LCS textbook. Each eLCS implementations (from demo 2 up to demo 6) progressively add major components of the entire LCS algorithm in order to illustrate how work, how they are coded, and what impact they have on how an LCS algorithm runs. ... -
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ImpraStorage
ImpraStorage provided a private imap access to store large files
...Each part also contains random noise data (lenght depends on a crypt key) to ensure privacy and exclude easy merge without the corresponding key. An index of files stored is encrypt (with the symmetric-key algorithm Kirmah) and regularly updated. Once decrypt, it permit to perform search on the server and download each part. transfert process is transparent. Just vizualize locally the index of stored files and simply select files to download or upload. ImpraStorage automatically launch the parts to download, then merge parts in the appropriate way to rebuild the original file. ... -
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Encryption with symmetric-key algorithm Kirmah - generate keys with exotic chars - redefine key length - key mark to ensure decryption capabilities - mix data - fast with multiprocessing - possible compression - gui on gtk3 - cli tool - python3 DEPENDENCIES : ======== python3, python-gobject, gobject-introspection, pygtk 3 modes are available to encrypt : ======== - compression (full / disabled or only final step) - random (simulate a random order - based on crypted key - to randomize data) - mix (mix data according to a generated map - based on crypted key - with addition of noise) Process is as follow : ======== encrypt : file > [ compress > ] encrypt > [randomiz data > mix data > compress > ] file.kmh decrypt: file.kmh > [ uncompress > unmix data > unrandomiz data] > decrypt > [uncompress > ] file ======== for encrypt/decrypt large binary files, use the fastest alternative : split/merge
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nPuzzle 8 / 15 play with numbers and cropped images! Searching algorithm A* : divide and conquer approach based on Manhattan-distance and linear conflict. Author: Roberto Bracaglia Morante ver 2.0 written in Java, May 2014 // many improvements and bug fixes ver 1.3 written in Jython, May 2014
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PyVision Computer Vision Toolkit
A Python computer vision library
PyVision is a object-oriented Computer Vision Toolkit for researchers that contains vision and machine learning algorithms and algorithm analysis and easily interfaces with scipy/numpy, PIL, opencv and other computer and machine learning libraries. -
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Planar Roof Top Detection in LiDAR
This tool detects and classifies roof tops from raw spatial LiDAR
A new algorithm for extracting roof tops was developed. Using the assumption that roof tops are planar in construction, a new approach was developed using volume of point clouds to determine whether a cluster contains planar points. This approach yields very promising results and with attention applied to its weaknesses, should provide another algorithm which can rival currently available roof top detection methods. -
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ProximityForest
Efficient Approximate Nearest Neighbors for General Metric Spaces
A proximity forest is a data structure that allows for efficient computation of approximate nearest neighbors of arbitrary data elements in a metric space. See: O'Hara and Draper, "Are You Using the Right Approximate Nearest Neighbor Algorithm?", WACV 2013 (best student paper award). One application of a ProximityForest is given in the following CVPR publication: Stephen O'Hara and Bruce A. Draper, "Scalable Action Recognition with a Subspace Forest," IEEE Conference on Computer Vision and Pattern Recognition, 2012. This source code is provided without warranty and is available under the GPL license. ... -
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PyGAO
Genetic Algorithm Optimization for Python
A simple interface for performing genetic algorithm optimization for numerical problems. I am starting with a stripped-down version, where a solution can be described using a single vector of float numbers. Eventually, I will expand to more generic data structures and add multiple-species search options. For the time being, I have no plans of developing a GUI. For now, this is strictly a computational module. -
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TexLexAn is an open source text analyser for Linux, able to estimate the readability and reading time, to classify and summarize texts. It has some learning abilities and accepts html, doc, pdf, ppt, odt and txt documents. Written in C and Python.
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Prime-Number-Finder
Prime Number Finder usign Sieve of Eratosthenes
Program by: Ravi Sharma (rvisharma) Prime-Number-Finder-using-Sieve-of-Eratosthenes =============================================== Finds Prime Numbers using Sieve of Eratosthenes Algorithm This Program gives you the prime number upto N Integers, Sieve of Eratosthenes Algorithm is used to determine the Prime Number To find the Prime Number, in the console Shell call the prime_finder(N) function, prime number will be given upto N integer. At first you will be prompted with an input, after that you can call it using function prime_finder(n) example call: >>>prime_finder(10) #output [2,3,5,7] (Int) -> (list) -
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...I am not able to maintain and update the software due to change in research funding. However I will be happy to answer any queries or provide support and comments if you are interested in extending this algorithm. The method is fully described in Deshpande, V., Fung, E. D., Pham, S., & Bafna, V. (2013). Cerulean: A hybrid assembly using high throughput short and long reads. arXiv preprint arXiv:1307.7933. http://arxiv.org/abs/1307.7933
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This is the former home of pyecm, which has now been moved to github: https://github.com/martingkelly/pyecm Pyecm factors large integers (up to 50 digits) using the Elliptic Curve Method (ECM), a fast factoring algorithm.
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CAPLET
GDS visualization and parallelized capacitance extraction
Project CAPLET is a capacitance extraction toolkit that extract capacitance at field-solver accuracy. CAPLET can directly handle GDS2 layout files into capacitance matrices in both GUI and command line interfaces. The internal extraction algorithm is specialized for VLSI interconnect structures but not exclusively, as long as the structure is of Manhattan geometry and embedded in a uniform dielectric material.
