Displaying 1 to 20 from 116 results

Koloboke - Java Collections till the last breadcrumb of memory and performance

  •    Java

Koloboke aims to replace the standard Java collections and streams with more efficient implementations. The current version of Koloboke focuses on replacing java.util.HashSet and java.util.HashMap. It provides a complete set of primitive type implementations for each collection. Its able to avoid the expensive boxing/unboxing of primitives and saves memory for boxed primitive objects. It is the fastest and the most memory efficient library implementing hash maps and sets.

gods - GoDS (Go Data Structures)

  •    Go

Implementation of various data structures and algorithms in Go.Containers are either ordered or unordered. All ordered containers provide stateful iterators and some of them allow enumerable functions.

go-datastructures

  •    Go

Go-datastructures is a collection of useful, performant, and threadsafe Go datastructures.Interval tree for collision in n-dimensional ranges. Implemented via a red-black augmented tree. Extra dimensions are handled in simultaneous inserts/queries to save space although this may result in suboptimal time complexity. Intersection determined using bit arrays. In a single dimension, inserts, deletes, and queries should be in O(log n) time.




Guava - Google Core Libraries for Java

  •    Java

Guava is a set of core libraries that includes new collection types (such as multimap and multiset), immutable collections, a graph library, functional types, an in-memory cache, and APIs/utilities for concurrency, I/O, hashing, primitives, reflection, string processing, and much more.

BoomFilters - Probabilistic data structures for processing continuous, unbounded streams.

  •    Go

Boom Filters are probabilistic data structures for processing continuous, unbounded streams. This includes Stable Bloom Filters, Scalable Bloom Filters, Counting Bloom Filters, Inverse Bloom Filters, Cuckoo Filters, several variants of traditional Bloom filters, HyperLogLog, Count-Min Sketch, and MinHash.Classic Bloom filters generally require a priori knowledge of the data set in order to allocate an appropriately sized bit array. This works well for offline processing, but online processing typically involves unbounded data streams. With enough data, a traditional Bloom filter "fills up", after which it has a false-positive probability of 1.

awesome-python - A curated list of awesome Python frameworks, libraries, software and resources

  •    Python

A curated list of awesome Python frameworks, libraries, software and resources.Inspired by awesome-php.


transducers-js - Transducers for JavaScript

  •    Javascript

A high performance Transducers implementation for JavaScript.Transducers are composable algorithmic transformations. They are independent from the context of their input and output sources and specify only the essence of the transformation in terms of an individual element. Because transducers are decoupled from input or output sources, they can be used in many different processes - collections, streams, channels, observables, etc. Transducers compose directly, without awareness of input or creation of intermediate aggregates.

Koloboke - Java Collections till the last breadcrumb of memory and performance

  •    Java

A family of projects around collections in Java (so far). A carefully designed extension of the Java Collections Framework with primitive specializations and more. Java 6+. Apache 2.0 license.

collections - This package contains JavaScript implementations of common data structures with idiomatic interfaces

  •    Javascript

This package contains JavaScript implementations of common data structures with idiomatic iterfaces, including extensions for Array and Object. You can use these Node Packaged Modules with Node.js, Browserify, Mr, or any compatible CommonJS module loader. Using a module loader or bundler when using Collections in web browsers has the advantage of only incorporating the modules you need. However, you can just embed <script src="collections/collections.min.js"> and all of the collections will be introduced as globals. ⚠️ require("collections") is not supported.

collect.js - 💎 Convenient and dependency free wrapper for working with arrays and objects

  •    Javascript

Using Laravel as your backend? Collect.js offers an (almost) identical api to Laravel Collections 5.5. See differences. All comparisons in collect.js are done using strict equality. Using loose equality comparisons are generally frowned upon in JavaScript. Laravel only performs "loose" comparisons by default and offer several "strict" comparison methods. These methods have not been implemented in collect.js because all methods are strict by default.

vvedenie-mashinnoe-obuchenie - :memo: Подборка ресурсов по машинному обучению

  •    

Постоянно обновляемая подборка ресурсов по машинному обучению. Обсуждение машинного обучения в мессенджерах (группы, каналы, чаты, сообщества).

backbone-react-component - A bit of nifty glue that automatically plugs your Backbone models and collections into your React components, on the browser and server

  •    Javascript

Backbone.React.Component is a mixin and API that glues Backbone models and collections into React components. When used as a mixin the component is mounted, a wrapper starts listening to models and collections changes to automatically set your component state and achieve UI binding through reactive updates.

Redisson - Redis based In-Memory Data Grid for Java

  •    Java

Redisson - Distributed and Scalable Java data structures (Set, SortedSet, Map, ConcurrentMap, List, Queue, Deque, Lock, AtomicLong, CountDownLatch, Publish / Subscribe, HyperLogLog) on top of Redis server. Advanced redis java client. It supports over 28+ data structures and services, Synchronous / asynchronous / reactive interfaces and lot more.

Tape - A lightning fast, transactional, file-based FIFO for Android and Java.

  •    Java

A lightning fast, transactional, file-based FIFO for Android and Java. QueueFile is a lightning-fast, transactional, file-based FIFO. Addition and removal from an instance is an O(1) operation and is atomic. Writes are synchronous; data will be written to disk before an operation returns. The underlying file is structured to survive process and even system crashes and if an I/O exception is thrown during a mutating change, the change is aborted.

hyperloglog - HyperLogLog with lots of sugar (Sparse, LogLog-Beta bias correction and TailCut space reduction)

  •    Go

An improved version of HyperLogLog for the count-distinct problem, approximating the number of distinct elements in a multiset using 20-50% less space than other usual HyperLogLog implementations.This work is based on "Better with fewer bits: Improving the performance of cardinality estimation of large data streams - Qingjun Xiao, You Zhou, Shigang Chen".