Search Results for "k-stor"
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Showing 24 open source projects for "k-stor"
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Add Two Lines of Code. Get Full APM.Works out of the box for Rails, Django, Express, Phoenix, and more. Monitoring exceptions and performance in no time.
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Machine Learning Octave
MatLab/Octave examples of popular machine learning algorithms
This repository contains MATLAB / Octave implementations of popular machine learning algorithms, along with explanatory code and mathematical derivations, intended as educational material rather than production code. Implementations of supervised learning algorithms (linear regression, logistic regression, neural nets). The author’s goal is to help users understand how each algorithm works “from scratch,” avoiding black-box library calls. Code written so as to expose and comment on... -
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Armadillo
fast C++ library for linear algebra & scientific computing
* Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads:... -
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Dynamic Arrays and Hashes (Delphi, C++)
Very fast classes for working with Dynamic Arrays in Delphi and C++
(New version 1.04 is released) Dynamic Arrays is a set of useful very fast classes for data manipulating in memory. Flexible memory control, functionality that standard containers do not have, fast operations (assembler implementation for x86 and x64 platforms). Available for Delphi all latest versions and for C++. Powerful Hash and Double Hash classes to work with pairs of values (key and value) and with values that have two keys (key1, key2, value). Give it a try and let me know how... -
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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. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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Active Learning
Framework and examples for active learning with machine learning model
...The main experiment runner (run_experiment.py) supports a wide range of configurations, including batch sizes, dataset subsets, model selection, and data preprocessing options. It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks. -
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GAKNN is a data mining software for gene annotation data. GAKNN is built with k- Nearest Neighbour algorithm optimized by the genetic algorithm. Gene annotation datasets saved under .csv or .arff formats with Gene Ontology or FunCat categorization can use GAKNN to predict gene functions.
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Hyacc is an efficient and practical Yacc/Bison-compatible full LR(1)/LALR(1)/LR(0) and partial LR(k) parser generator in ANSI C based on Knuth and Pager's LR(1) algorithms. Generated parser can be used in open-source or commercial software.
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...In case you use the package in your work, we do appreciate a citation to the publications below. Citation: Yadav B, Peddinti G, Pemovska T, Khan SA, Szwajda A, Tang J, Wennerberg K and Aittokallio T. From drug response profiling to target addiction scoring in cancer cell models. (Submitted)
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COPEread
Connecting Overlapped Pair-End Reads
COPE (Connecting Overlapped Pair-End reads) is a method to align and connect the illumina sequenced Pair-End reads of which the insert size is smaller than the sum of the two read length.The connected reads can be used in genome assembly, resequencing and transcriptome research. The full citation: COPE: An accurate k-mer based pair-end reads connection tool to facilitate genome assembly Binghang Liu; Jianying Yuan; Siu-Ming Yiu; Zhenyu Li; Yinlong Xie; Yanxiang Chen; Yujian Shi; Hao Zhang; Yingrui Li; Tak-Wah Lam; Ruibang Luo Bioinformatics (2012) 28(22): 2870-2874; doi: 10.1093/bioinformatics/bts563 -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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GTkNN
GPU-based Textual kNN (GT-kNN)
The following code is a parallel kNN implementation that uses GPUs for the high dimensional data in text classification. You can use it to classify documents using kNN or to generate meta-features based on the distances between a query document and its k nearest neigbors -
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Ginors Sort
sorting algorithm for binary keys
in-place sorting algorithm with O(n*log(k)) time komplexity. The data have to be the structure: struct { unsigned (char/short/int/long); [...] } -
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Weka4OC GUI for Overlapping clustering
Weka4OC: Weka for Overlapping Clustering is a GUI extending WEKA
This is a GUI application for learning non disjoint groups based on Weka machine learning framework. It offers a variety of learning methods, based on k-means, able to produce overlapping clusters. The application also contains an evaluation framework that calculates several external validation measures. The application offers a visualization tool to discover overlapping groups. -
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drvq
dimensionality-recursive vector quantization
drvq is a C++ library implementation of dimensionality-recursive vector quantization, a fast vector quantization method in high-dimensional Euclidean spaces under arbitrary data distributions. It is an approximation of k-means that is practically constant in data size and applies to arbitrarily high dimensions but can only scale to a few thousands of centroids. As a by-product of training, a tree structure performs either exact or approximate quantization on trained centroids, the latter being not very precise but extremely fast. A detailed README file describes the usage of the software, including license, requirements, installation, file formats, sample data, tools, and options. ... -
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...These functions are alternative functions to the existing standard C library that promote safer, more secure programming. The ISO/IEC Programming languages — C spec, C11, now includes the bounded APIs in Appendix K, "Bounds-checking interfaces". This latest upload supports building static library, a shared library and a linux kernel module.
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ISAD
Individual Synaptic Activity Detection (ISAD)
...It computes synaptic signals from automatically segmented regions of interest and detects peaks that represent vesicle fusion events, thus, pre-synaptic activity. ISAD is based on MWA, which is a continuous wavelet transform based algorithm that employs multiple wavelets and is published as: Sokoll, S., Tönnies, K., and Heine, M. Detection of Spontaneous Vesicle Release at Individual Synapses Using Multiple Wavelets in a CWT-Based Algorithm. Med Image Comput Comput Assist Interv (MICCAI). 2012;15(Pt 1):165-72 ISAD is written in MATLAB and comes with a graphical user interface. -
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Exact Subgraph Matching Algorithm
Exact Subgraph Matching Algorithm for Dependency Graphs
...We designed a simple exact subgraph matching (ESM) algorithm for dependency graphs using a backtracking approach. The total worst-case algorithm complexity is O(n^2 * k^n) where n is the number of vertices and k is the vertex degree. We have demonstrated the successful usage of our algorithm in three biomedical relation and event extraction applications: BioNLP 2011 shared tasks on event extraction, Protein-Residue association detection and Protein-Protein interaction identification. This Java implementation implements our ESM algorithm. ... -
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gpufsknn
A GPU-based efficient data parallel formulation of the kNN problem
A GPU-based efficient data parallel formulation of the k-Nearest Neighbor (kNN) search problem which is a popular method for classifying objects in several fields of research, such as- pattern recognition, machine learning, bioinformatics etc. -
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LR(k) parser generator (k>=0) implementation.
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This is a program for compute the dimension of the Hochschild homology k-vector spaces of a k-algebra A.
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K-automaton is a new parsing (syntactic analysis) machine isomorphous to language. Implemented in Java. Can generate Java code from grammars described in EBNF.
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Project Tokaf is an general implementation of top-k algorithm. It provides interfaces for all modules that are needed. It also features user preferences module, for computing new preferences and manipulating existing ones.
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KNN-WEKA provides a implementation of the K-nearest neighbour algorithm for Weka. Weka is a collection of machine learning algorithms for data mining tasks. For more information on Weka, see http://www.cs.waikato.ac.nz/ml/weka/.
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brCluster is a class library, written in java, that implements generic clustering algorithms carefully designed to allow its aplication in any kind of data. The algorithms implemented are K-means and Hierarchical Clustering (Simple and Complete Link).
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