Search Results for "data structures" - Page 4
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Showing 139 open source projects for "data structures"
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KvFront
A GUI tool for redis & memcached
...KvFront features an easy-to -use and straightforward graphical user interface, allowing users to easily work with Redis and Memcached in Linux based GTK+ 3.0 desktop environment. Its highlights include the ability to manage Redis and Memcached. It supports the most of data structures of Redis and Memcached, as well as to view the status of Redis and Memcached instances. In additon, KvFront also provides the scan alility of Redis , which can scan all keys in Redis. Installation: ----------------------------- Linux: KvFront is now available in PyPI. KvFront could be installed using pip: pip install KvFront Windows KvFront is supported running on Windows64. 1. download KvFront-x.x.x-win64.7z to local 2. un7z it to the local directory , the local dir is supposed to be "d:\KvFront" 3. double click "d:\KvFront\bin\KvFront.exe" -
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MeshCNN in PyTorch
Convolutional Neural Network for 3D meshes in PyTorch
MeshCNN is a deep learning framework designed specifically for processing 3D triangular mesh data using convolutional neural networks. Unlike traditional CNNs that operate on images or voxel grids, MeshCNN performs convolution operations directly on the edges of mesh structures. This design allows the model to capture geometric relationships between mesh elements while preserving the underlying topology of 3D shapes. The framework introduces specialized layers such as edge-based convolution, mesh pooling, and mesh unpooling operations that enable hierarchical feature learning on mesh surfaces. ... -
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Easy Upscale
A simple image upscaler application using EDSR, ESPCN, FSRCNN, etc.
This application was made to fulfill the assignment for the Data Structures course. The concept of the application is an application to upgrade/enhance image quality. The main theme is queues, we implement circular queues for pooling/storing a list of images to be upscaled. Gui creation is made manually using the tkinter library. For the upscale process itself, it uses the OpenCV library with a model obtained from open source. -
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Machine Learning Financial Laboratory
MlFinLab helps portfolio managers and traders
...The project provides a large collection of tools that implement techniques from academic research on financial machine learning. It covers the full lifecycle of developing data-driven trading strategies, including data preprocessing, feature engineering, labeling techniques, model training, and performance evaluation. Many of the algorithms implemented in the library are based on concepts introduced in advanced quantitative finance literature and peer-reviewed research. The library also includes tools for constructing specialized financial data structures, generating predictive features, and evaluating trading strategies through backtesting. ... -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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tensorflow_template_application
TensorFlow template application for deep learning
tensorflow_template_application is a template project that demonstrates how to structure scalable applications built with TensorFlow. The repository provides a standardized architecture that helps developers organize machine learning code into clear components such as data processing, model training, evaluation, and deployment. Instead of focusing on a specific algorithm, the project emphasizes software engineering practices that make machine learning systems easier to maintain and extend. The template includes configuration files, scripts, and project structures that help teams build reproducible experiments and production-ready pipelines. ... -
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algo
50 Code Implementations You Must Know About Data Structures
algo is an educational repository containing practical implementations of essential data structures and algorithms. It organizes roughly fifty core exercises by topic rather than presenting a single reusable software library. The material covers arrays, linked lists, stacks, queues, recursion, sorting, binary search, hash tables, strings, trees, heaps, and graphs. More advanced examples address backtracking, divide-and-conquer methods, dynamic programming, shortest paths, topological sorting, caches, and priority queues. ... -
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BeaEngine 5
BeaEngine disasm project
BeaEngine is a C library designed to decode instructions from 16-bit, 32-bit and 64-bit intel architectures. It includes standard instructions set and instructions set from FPU, MMX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, VMX, CLMUL, AES, MPX, AVX, AVX2, AVX512 (VEX & EVEX prefixes), CET, BMI1, BMI2, SGX, UINTR, KL, TDX and AMX extensions. If you want to analyze malicious codes and more generally obfuscated codes, BeaEngine sends back a complex structure that describes precisely the... -
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Gato (Graph Animation Toolbox): Animate graph algorithms for example for computing shortest paths, minimal spanning trees, maximum flows or maximal cardinality or weight matchings. Create your own animations using the Animated Data Structures (ADS).
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D0rganizer
Program to manage data and information
Store collected information and data properly using D0rgnizer (The organizer). Create cases, folders, files, and insert images. Compare information between files, and export data to share with your team. Useful for data collection in investigations, ethical hacking, and more. -
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interactive-coding-challenges
120+ interactive Python coding interview challenges
Interactive Coding Challenges is a collection of practice problems designed to strengthen data structures, algorithms, and problem-solving skills. The repository emphasizes a learn-by-doing approach: you read a prompt, attempt a solution, and verify behavior with tests, often within notebooks or scripts. Problems span arrays, strings, stacks, queues, linked lists, trees, graphs, dynamic programming, and more, mirroring common interview themes. -
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Delta ML
Deep learning based natural language and speech processing platform
...DELTA has been used for developing several state-of-the-art algorithms for publications and delivering real production to serve millions of users. It helps you to train, develop, and deploy NLP and/or speech models. Use configuration files to easily tune parameters and network structures. What you see in training is what you get in serving: all data processing and features extraction are integrated into a model graph. Text classification, named entity recognition, question and answering, text summarization, etc. Uniform I/O interfaces and no changes for new models. -
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CBMPy
PySCeS Constraint Based Modelling
...CBMPy supports the latest standards for encoding CBM models encoding, SBML L3 FBC, COBRA as well as MIRIAM compliant RDF and custom annotations. Its architecture is both extensible and flexible using data structures that are intuitive to the biologist while transparently translating these into the underlying mathematical structures. . CBMPy implements popular analyses such as FBA, FVA, element/charge balancing, network analysis and model editing as well as advanced methods developed for the ecosystem modelling. CBMPy supports user interaction via: - interactive console or as a library for advanced use - GUI, visual representation of the model, analysis methods - a SOAP based webAPI exposes high level functionality via web services -
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An open source framework for LC-MS based proteomics and metabolomics. OpenMS offers data structures and algorithms for the processing of mass spectrometry data. The library is written in C++. Our source code and wiki lives on GitHub (https://github.com/OpenMS/OpenMS).
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Code Catalog in Python
Algorithms and data structures for review for coding interview
code-catalog-python serves as a grab-bag of small, readable Python examples that illustrate common algorithms, data structures, and utility patterns. Each snippet aims to be self-contained and easy to study, with clear inputs, outputs, and the essential logic on display. The catalog format lets you scan for an example, copy it, and adapt it to your use case without wading through a large framework. It favors clarity over micro-optimizations so learners can grasp the idea before worrying about edge performance. ... -
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Data Algorithm/leetcode/lintcode
Data Structure and Algorithm notes
This work is some notes of learning and practicing data structures and algorithms. Part I is a brief introduction of basic data structures and algorithms, such as, linked lists, stack, queues, trees, sorting and etc. This book notes about learning data structure and algorithms. It was written in Simplified Chinese but other languages such as English and Traditional Chinese are also working in progress. -
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Dynamic Routing Between Capsules
A PyTorch implementation of the NIPS 2017 paper
...Instead of scalar neuron activations, capsules output vectors that encode both the presence of features and their spatial properties such as orientation or pose. The repository implements the dynamic routing algorithm between capsules, which allows lower-level features to route their outputs to higher-level structures that best represent the detected patterns. This approach enables the model to capture part-to-whole relationships in visual data more effectively than standard CNNs. The project serves primarily as a research implementation that demonstrates how capsule networks can be built and trained using modern deep learning frameworks. -
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Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). The concept of model interpretability in the field of machine learning is still new, largely subjective, and, at times, controversial. Model interpretation is the ability to explain and validate the decisions of a predictive model to enable fairness, accountability, and transparency in algorithmic decision-making. ... -
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Learn Python the Hard Way
Concise study notes derived from “Learn Python the Hard Way”
This repository contains concise study notes derived from “Learn Python the Hard Way,” organized to reinforce core Python concepts through small, targeted examples. It emphasizes hands-on practice—short scripts, exercises, and explanations that help cement syntax, data structures, functions, and modules. The notes call out common gotchas, idioms, and style preferences so learners form good habits early. Because the content is intentionally compact, it’s easy to revisit a topic quickly when preparing for interviews or refreshing fundamentals. The material favors clarity over abstraction, keeping examples runnable and easy to modify in any editor. ... -
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jsondata
Modular JSON by trees and branches, pointers and patches
The 'jsondata' package provides for the modular in-memory processing of JSON data by trees, branches, pointers, and patches. The main interface classes are: - JSONData - Core for RFC7159 based data structures. Provides modular data components. - JSONDataSerializer - Core for RFC7159 based data persistence. Provides modular data serialization. - JSONPointer - RFC6901 for addressing by pointer paths. Provides pointer arithmetics... -
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PyXB (“pixbee”) is a pure Python package that generates Python source code for classes that correspond to data structures defined by XMLSchema. In concept it is similar to JAXB for Java and CodeSynthesis XSD for C++.
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FastVersion
FastVersion is a Qgis plugin for data versioning in a Postgis database
...The system uses algorithms and data structures so that information is not duplicated each time a version is created. We recommend downloading it from the Qgis app. Plugins->Manage and Install Plugins->Settings->Show also experimental plugins Plugins->Manage and Install Plugins->Search FastVersion If downloaded from sourceforge, it must be unzipped in \.qgis2\python\plugins -
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X-SOCS
X-ray Strain Orientation Calculation Software
The X-ray Strain Orientation Calculation Software (X-SOCS) is a user-friendly software, developed for automatic analysisof 5D sets of data recorded during continuous mapping measurements. X-SOCS aims at retrieving strain and tilt maps of nanostructures, films, surfaces or even embedded structures. For more information: http://scripts.iucr.org/cgi-bin/paper?S1600576714004506 To download the latest version of XSOCS (D. Naudet, T. Vincent, M.I. Richard, G. Chahine), follow the instruction in the following link: https://gitlab.esrf.fr/kmap/xsocs You can find on youtube video tutorials explaining the main steps to follow for a standard KMAP data analysis. ... -
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Algorithms in Python
Data Structures and Algorithms in Python
Algorithms in Python is a collection of algorithm and data structure implementations (primarily in Python) meant to serve as both learning material and reference code for engineers. It includes code for graph algorithms, heap data structures, stacks, queues, and more — each implemented cleanly so learners can trace logic and adapt for their problems. The repository is particularly useful for people preparing for competitive programming, job interviews, or building a foundational understanding of algorithmic patterns. ... -
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RDKit
Open-Source Cheminformatics and Machine Learning
A collection of cheminformatics and machine-learning software written in C++ and Python. NOTE: the RDKit source code and downloads are now in github: https://github.com/rdkit/rdkit The core algorithms and data structures are written in C++. Wrappers are provided to use the toolkit from either Python or Java. Additionally, the RDKit distribution includes a PostgreSQL-based cartridge that allows molecules to be stored in relational database and retrieved via substructure and similarity searches. -
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Py3DN
Morphometric analysis and visualization of the 3D structure of neurons
Py3DN, is an open-source solution providing innovative tools to analyze 3D data collected with the widely used commercial system Neurolucida (MBF). It allows the construction of mathematical representations of neuronal topology, visualization and a variety of morphometric analysis on the neuronal structures. Above all, it provides a flexible environment where new types of analyses can be easily set up allowing total freedom to test new hypotheses.
