Search Results for "clustering"
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Showing 17 open source projects for "clustering"
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Faiss
Library for efficient similarity search and clustering dense vectors
Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research. -
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Google Maps SDK for iOS Utility Library
Google Maps SDK for iOS Utility Library
google-maps-ios-utils is a collection of open-source utilities that extend the functionality of the Google Maps SDK for iOS. It provides additional features such as clustering, heatmaps, and geometry utilities to enhance map-based applications. -
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PHPFastCache
A high-performance backend cache system
PHPFastCache is a caching library that provides a simple and efficient way to manage data caching in PHP applications. It supports a variety of cache backends, including Redis, Memcached, APCu, and filesystem. The library is designed to enhance performance by reducing database load and speeding up content delivery. PHPFastCache’s flexible API and multi-driver support make it easy to implement caching in both small and large projects. -
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Cachex
A powerful caching library for Elixir with support for transactions
Cachex is a high-performance in-memory caching library for Elixir, offering a robust feature set including expirations, size limits, hooks, fallbacks, async operations, and clustering capabilities. The version 4.x release brought optimized janitor routines, modular streaming and querying, runtime cache warming, size pruning (LRW/LRU), distributed routing mechanisms, and a major documentation overhaul. It integrates seamlessly with Elixir applications via mix dependencies, supports advanced transactional use cases, and includes utilities for distributed node clusters. -
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Homemade Machine Learning
Python examples of popular machine learning algorithms
...Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is pedagogical: you’ll see linear regression, logistic regression, k-means clustering, neural nets, decision trees, etc., built in Python using fundamentals like NumPy and Matplotlib, not hidden behind API calls. It is well suited for learners who want to move beyond library usage to understand how algorithms operate internally—how cost functions, gradients, updates and predictions work. -
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Horde
Horde is a distributed Supervisor and Registry
...Because everything runs under OTP supervision, failures are isolated and recoveries are automatic, even during network partitions or rolling deploys. It integrates naturally with common clustering tools and plays well with PubSub, job systems, and presence tracking. The result is predictable, configuration-driven distribution that removes a lot of custom glue typically needed for multi-node Elixir systems. -
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Tribuo
Tribuo - A Java machine learning library
Tribuo* is a machine learning library written in Java. It provides tools for classification, regression, clustering, model development, and more. It provides a unified interface to many popular third-party ML libraries like xgboost and liblinear. With interfaces to native code, Tribuo also makes it possible to deploy models trained by Python libraries (e.g. scikit-learn, and pytorch) in a Java program. Tribuo is licensed under Apache 2.0. Remove the uncertainty around exactly which artifacts you're using in production. ... -
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SageMaker Spark
A Spark library for Amazon SageMaker
...These pipelines interleave native Spark ML stages and stages that interact with SageMaker training and model hosting. With SageMaker Spark, you can train on Amazon SageMaker from Spark DataFrames using Amazon-provided ML algorithms like K-Means clustering or XGBoost, and make predictions on DataFrames against SageMaker endpoints hosting your trained models, and, if you have your own ML algorithms built into SageMaker compatible Docker containers, you can use SageMaker Spark to train and infer on DataFrames with your own algorithms -- all at Spark scale. SageMaker Spark depends on hadoop-aws-2.8.1. ... -
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minicle
A Node.js module for easily processing command line switches and args
There are scads of CLI parsers out there, so why another one? Mostly because the others aim to do too much and can be a pain to use when you just need something quick and simple. All Minicle does is parse CLI options. It doesn't handle exotic edge cases, generate usage information, validate arguments, or anything else. It does handle all the usual basics plus git-style subcommands. -
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Quasar
Fibers, channels and actors for the JVM
Quasar is a library that provides high-performance lightweight threads, Go-like channels, Erlang-like actors, and other asynchronous programming tools for Java and Kotlin. Quasar is developed by Parallel Universe and released as free software, dual-licensed under the Eclipse Public License and the GNU Lesser General Public License. Quasar fibers rely on bytecode instrumentation. This can be done at classloading time via a Java Agent, or at compilation time with an Ant task. Quasar’s... -
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node2vec
Learn continuous vector embeddings for nodes in a graph using biased R
...The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. The repository contains reference code accompanying the research paper node2vec: Scalable Feature Learning for Networks (KDD 2016). It allows researchers and practitioners to apply node2vec to various graph datasets and evaluate embedding quality on downstream tasks. By bridging ideas from graph theory and word embedding models, this project demonstrates how graph-based machine learning can be made efficient and flexible. -
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ParaStation is a robust and efficient cluster middleware, consisting of a high-performance communication layer (MPI) and a sophisticated management layer. Please notice, the public development has moved to github: https://github.com/ParaStation
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The JINSECT toolkit is a Java-based toolkit and library that supports and demonstrates the use of n-gram graphs within Natural Language Processing applications, ranging from summarization and summary evaluation to text classification and indexing.
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Fast Matrix for Java
General purpose matrix utilities for Java in Parallel Computing
Fast Matrix for Java (fm4j) is a general-purpose matrix utility library for computing with dense matrices. fm4j encapsulated different underlying implementations and select the optimal one in run-time depending on the size of the input matrix. Moreover, fm4j employs Java (Tm) Concurrency to take advantage of the computation power of multi-cor processors. -
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Statistics modules in Perl Data Language, with a quick-start guide for non-PDL people. They make the PDL shell work like R, but with PDL threading (fast automatic iteration) of procedures including t-test, linear regression, and k-means clustering.
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Fluster is a clustering algorithm for the Google Maps API v3. It manages all your markers on the map and summarizes them to "clustered markers" if there is not enough space around them. Acts like MarkerClusterer for v2.
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Hedera provides common interfaces for popular group communications packages like JGroups, Appia, and Spread. Hedera allows applications to switch easily between group communications without code changes and with minimal reconfiguration.
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