Search Results for "data collection algorithm" - Page 2
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Showing 1706 open source projects for "data collection algorithm"
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MIRT.jl
MIRT: Michigan Image Reconstruction Toolbox (Julia version)
MIRT.jl is a collection of Julia functions for performing image reconstruction and solving related inverse problems. It is very much still under construction, although there are already enough tools to solve useful problems like compressed sensing MRI reconstruction. Trying the demos is a good way to get started. The documentation is even more still under construction. -
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Brotli
Brotli compression format
...This happens when "one-shot" decoding API is used (or input chunk for streaming API is not limited), input size (chunk size) is larger than 2GiB, and input contains uncompressed blocks. After the overflow happens, memcpy is invoked with a gigantic num value, that will likely cause the crash. Brotli is a generic-purpose lossless compression algorithm that compresses data using a combination of a modern variant of the LZ77 algorithm, Huffman coding and 2nd order context modeling, with a compression ratio comparable to the best currently available general-purpose compression methods. It is similar in speed with deflate but offers more dense compression. The specification of the Brotli Compressed Data Format is defined in RFC 7932. ... -
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Clustering.jl
A Julia package for data clustering
Methods for data clustering and evaluation of clustering quality. -
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LAStools
efficient tools for LiDAR processing
LAStools is a collection of efficient, multi-core, scriptable tools for processing LiDAR data. It supports various formats, including LAS, LAZ, Terrasolid BIN, and ESRI Shapefiles, providing a comprehensive suite for LiDAR data management and analysis. -
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WaterfallGrid
A waterfall grid layout view for SwiftUI
A waterfall grid layout view for SwiftUI. You can create a grid that displays the elements of the collection by passing your collection of data and a closure that provides a view for each element in the collection. The grid transforms each element in the collection into a child view by using the supplied closure. WaterfallGrid works with identifiable data (like SwiftUI.List). You can make your data identifiable in one of two ways: by passing along with your data a key path to a property that uniquely identifies each element, or by making your data type conform to the Identifiable protocol. ... -
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Coluna.jl
Branch-and-Price-and-Cut in Julia
Coluna is a branch-and-price-and-cut framework written in Julia. You write an original MIP that models your problem using the JuMP modeling language and our specific extension BlockDecomposition offers a syntax to specify the problem decomposition. Then, Coluna reformulates the original MIP and optimizes the reformulation using the algorithms you choose. Coluna aims to be very modular and tweakable so that you can define the behavior of your customized branch-and-price-and-cut algorithm. -
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Parsers.jl
fast parsing machinery for basic types in Julia
A collection of type parsers and utilities for Julia. Installation: at the Julia REPL, import Pkg; Pkg.add("Parsers") Parsers is maintained collectively by the JuliaData collaborators. Responsiveness to pull requests and issues can vary, depending on the availability of key collaborators. -
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CPlusPlusThings
Collection of various C++ code samples, utilities, patterns
CPlusPlusThings is a repository collecting various C++ code samples, utilities, patterns, and small example projects. It is less a polished product and more a learning/reference collection of snippets and usages of C++ idioms, data structures, algorithms, utilities, and perhaps tricks or meta-programming exercises. (No prominent README or detailed docs were available from my quick search.) Example implementations of data structures and algorithms. Organized as a learning repository (rather than a production framework). ... -
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Yggdrasil
Collection of builder repositories for BinaryBuilder.jl
This repository contains recipes for building binaries for Julia packages using BinaryBuilder.jl. -
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CodeGuide
This repository is a collection of the author, Xiao Fuge
CodeGuide is a curated collection of Java development resources built from years of real-world engineering experience by the author, who has worked extensively in large-scale internet companies. The project serves as a structured and comprehensive learning path for developers who want to strengthen their understanding of Java fundamentals and core programming practices. It includes detailed explanations of design patterns, source code analysis, frameworks, algorithms, and real-world project... -
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LeetCode Master
About "Code Thoughts" LeetCode Practice Guide: 200 classic questions
...Each solution is accompanied by clear commentary to explain the thought process, algorithm design, and complexity analysis. The project is continuously updated and widely used by learners as both a reference and a roadmap to progress from beginner to advanced LeetCode practice. With its thorough coverage and educational style, leetcode-master helps programmers build strong problem-solving skills and confidence for competitive programming. -
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Spotube
Open source Spotify client that doesn't require Premium
An open source, cross-platform Spotify client compatible across multiple platforms utilizing Spotify's data API and YouTube, Piped video or JioSaavn as an audio source, eliminating the need for Spotify Premium. It is still recommended to support creators by engaging with their YouTube channels/Spotify tracks (or preferably by buying their merch/concert tickets/physical media). -
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LZ4
Extremely fast compression algorithm
LZ4 is lossless compression algorithm, providing compression speed > 500 MB/s per core (>0.15 Bytes/cycle). It features an extremely fast decoder, with speed in multiple GB/s per core (~1 Byte/cycle). A high compression derivative, called LZ4_HC, is available, trading customizable CPU time for compression ratio. LZ4 library is provided as open-source software using a BSD license. -
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xxHash
Extremely fast non-cryptographic hash algorithm
...Code is highly portable, and hashes are identical across all platforms (little / big endian). Performance on large data is only one part of the picture. Hashing is also very useful in constructions like hash tables and bloom filters. In these use cases, it's frequent to hash a lot of small data (starting at a few bytes). Algorithm's performance can be very different for such scenarios, since parts of the algorithm, such as initialization or finalization, become fixed cost. ... -
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GMT.jl
Generic Mapping Tools Library Wrapper for Julia
The Generic Mapping Tools, GMT, is an open source collection of tools for manipulating geographic and Cartesian data sets (including filtering, trend fitting, gridding, projecting, etc.) and producing PostScript illustrations ranging from simple x–y plots via contour maps to artificially illuminated surfaces and 3D perspective views. This link will take you to an impressive collection of figures made with GMT. -
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openTSNE
Extensible, parallel implementations of t-SNE
openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE) [1], a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings [2], massive speed improvements [3] [4] [5], enabling t-SNE to scale to millions of data points, and various tricks to improve the global alignment of the resulting visualizations. -
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Go Recipes
Collection of handy tools for Go projects
Visualize the distribution of code coverage in your project. This helps to identify code areas with high and low coverage. Useful when you have a large project with lots of files and packages. This 2D image-hash of your project should be more representative than a single number. For each module, the node representing the greatest version (i.e., the version chosen by Go's minimal version selection algorithm) is colored green. Other nodes, which aren't in the final build list, are colored grey... -
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Immutable.js
Immutable collections for JavaScript
Immutable.js offers a collection of Persistent Immutable data structures for JavaScript. Immutable data is unchangeable once created, which makes application development so much simpler. There’s no defensive copying, and you get advanced memoization and change detection techniques with simple logic. Persistent data gives you a mutative API, one that doesn’t update data in-place but always produces new and updated data. -
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Live-Forensicator
A suite of Tools to aid Incidence Response and Live Forensics
Live-Forensicator is a toolkit intended for live forensic collection and initial triage on Windows machines. It automates the capture of volatile information—running processes, network connections, loaded drivers, account sessions, and in-memory artifacts—into a consistent artifact set that investigators can analyze offline. The tool tries to be non-invasive while collecting sensitive data quickly and logs the collection steps to preserve chain-of-custody details and to help auditors understand potential collection side effects. ... -
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IGListKit
A data-driven UICollectionView framework for building fast lists
IGListKit uses an algorithm adapted from a paper titled A technique for isolating differences between files by Paul Heckel. This algorithm uses a technique known as the longest common subsequence to find a minimal diff between collections in linear time O(n). It finds all inserts, deletes, updates, and moves between arrays of data. A working range is a range of section controllers who aren’t yet visible, but are near the screen. -
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CUTLASS
CUDA Templates for Linear Algebra Subroutines
CUTLASS is a collection of CUDA C++ template abstractions for implementing high-performance matrix-multiplication (GEMM) and related computations at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS and cuDNN. CUTLASS decomposes these "moving parts" into reusable, modular software components abstracted by C++ template classes. -
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Complete-Python-3-Bootcamp
Course Files for Complete Python 3 Bootcamp Course on Udemy
The Complete-Python-3-Bootcamp repository is an educational resource created by Pierian Data as part of their popular Python for Data Science and Machine Learning Bootcamp course. It contains a comprehensive collection of Jupyter Notebooks designed to teach Python programming from the ground up. The repository covers a wide range of Python topics, including data types, control flow, functions, object-oriented programming, error handling, modules, and advanced concepts like decorators and generators. ... -
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AI Hedge Fund
An AI Hedge Fund Team
This repository demonstrates how to build a simplified, automated hedge fund strategy powered by AI/ML. It integrates financial data collection, preprocessing, feature engineering, and predictive modeling to simulate decision-making in trading. The code shows workflows for pulling stock or market data, applying machine learning algorithms to forecast trends, and generating buy/sell/hold signals based on the predictions. Its structure is educational: intended more as a proof-of-concept than a ready-to-use financial product, giving learners insight into the mechanics of quantitative finance automation. ... -
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Fluentd
Fluentd: Unified Logging Layer (project under CNCF)
Fluentd is a CNCF‑graduated open-source data collector that unifies log data collection and consumption across diverse systems. It supports robust reliability, buffering, extensible plugin architecture, and real-time log routing. Fluentd serves as a unified logging layer for structured/unstructured data processing. -
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Deepchecks
Test Suites for validating ML models & data
Deepchecks is the leading tool for testing and for validating your machine learning models and data, and it enables doing so with minimal effort. Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. ...
