Sign in

Repository Index

47,133 repositories indexed

Tags: All Tags
Tags

A repository shows when it has any selected Tag.

Filters (1)

Filters

Clear all filters

25–48 of 55 · Most stars

A C++ static library offering a clean and simple interface to the 7-zip shared libraries.

  • compression
  • extraction
  • 7z
  • zip

lightweight lossless file minifier/optimizer

  • recompress
  • lossless
  • minifier
  • optimizer

cmix is a lossless data compression program aimed at optimizing compression ratio at the cost of high CPU/memory usage.

VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP

  • opencl
  • cuda
  • c-plus-plus
  • gpgpu

TokuMX is a high-performance, concurrent, compressing, drop-in replacement engine for MongoDB | Issue tracker: https://tokutek.atlassian.net/browse/MX/ |

Fast In-Memory Data Compression Algorithm (header-only C/C++) 540+MB/s compress, 3000+MB/s decompress, ratio% better than LZ4, Snappy, and Zstd@--1

  • data-compression
  • lz77
  • compression
  • compression-algorithm

Fast Static Symbol Table (FSST): efficient random-access string compression

Small C++ header to display async animations, counters, progress bars, and status messages

  • cpp
  • progress-bar
  • counter
  • animation

SimplE Lossless Audio

  • lossless-compression-algorithm
  • audio-processing
  • codec

Open source Windows x64 PE packer and crypter. Compresses and encrypts executables with a custom virtual machine into a self extracting stub.

  • compression
  • packer
  • pe-packer
  • pe-compress

Alternating Minimization Based Trajectory Generation for Quadrotor Aggressive Flight

  • robotics
  • quadrotor
  • trajectory-optimization
  • motion-planning

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.