Search Results for "data collection algorithm" - Page 31
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Showing 1563 open source projects for "data collection algorithm"
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1
Pascalloid Calc
Hui or Pascal's Triangle into shaped Geometry, 3D
Pascal's Triangle has been taken into higher dimensions. The Five Platonic Solids are included in this software, along with various geometric algorithms to create a generative geometry system. Equations in combinatoric and algebraic forms are given. Tools to play with the data are given. There is now a way to create user-defined algorithms. Dodecahedral and Icosahedral Pascalloids are now geometrically perfect and include no 'folding errors'. A 'data only' data output mode allows users... -
2
Electronic Circuit Optimization
Optimer: a SPICE base electrical/ electronic circuit optimization tool
This project is dedicated to the optimization of (any) electrical and electronic circuits and components using evolutionary and heuristic algorithms incorporated with SPICE simulators (such as HSPICE, ngSPICE, etc.). We provide Optimer, which is a user graphical interface for circuit design and optimization. Website: https://www.circuitoptimization.com/ E-mail: contact@circuitoptimization.com/ -
3
CTS Surveyor
Foot traffic and facial analytics for your business and home
Surveyor is a software solution that monitors its environment via camera and gathers demographic information about the public in the surrounding area, providing important statistics such as number of people passing by as well as providing facial analytics to classify the pedestrians based on their age and gender. The statistical data is stored in a local database and is made available via RESTful API’s, and easy integration with other applications can be accomplished via a WebSocket... -
4
Elasticquent
Maps Laravel Eloquent models to Elasticsearch types
Elasticsearch for Eloquent Laravel Models. Elasticquent makes working with Elasticsearch and Eloquent models easier by mapping them to Elasticsearch types. You can use the default settings or define how Elasticsearch should index and search your Eloquent models right in the model. Elasticquent uses the official Elasticsearch PHP API. To get started, you should have a basic knowledge of how Elasticsearch works (indexes, types, mappings, etc). When using a database, Eloquent models are... -
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5
pytorch-examples
Simple examples to introduce PyTorch
The pytorch-examples project is a collection of concise and practical examples demonstrating how to use PyTorch for machine learning and deep learning tasks. It focuses on clarity and minimalism, providing small, self-contained scripts that illustrate key concepts such as neural network training, optimization, and data handling. The examples cover a range of topics including supervised learning, generative models, and reinforcement learning, making it a valuable resource for both beginners and experienced practitioners. ... -
6
PyRobot
An Open Source Robotics Research Platform
...It also plugs into simulation backends, letting you prototype perception and control algorithms before deploying on physical robots. By standardizing common operations and providing examples, PyRobot reduces boilerplate and encourages best practices in perception-control loops, data collection, and benchmarking. -
7
OpenUnderwriter (Insurance Distribution)
Feature rich insurance quote & buy system for underwriters and brokers
OpenUnderwriter is an open source software house specialising in the development of IT solutions for the insurance market. Specialists in the areas of eBusiness and component based development, the team has developed technology for a number of major insurance companies. The OpenUnderwriter platform has been collaboratively developed by some of the best technical brains in the Insurance Industry and offers a viable alternative to traditional solutions. To date, OpenUnderwriter has been... -
8
Enlive
Selector-based templating and transformation system for Clojure
Enlive is a Clojure library for HTML templating, transformation, and scraping, supporting composable manipulation of HTML/XML in a functional style. It allows selecting, transforming, and generating HTML fragments using CSS selectors, and supports server-side template composition, dynamic pages, and content rewriting. By default selector-transformation pairs are run sequentially. When you know that several transformations are independent, you can now specify (as an optimization) to process... -
9
Active Learning
Framework and examples for active learning with machine learning model
...It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. The main experiment runner (run_experiment.py) supports a wide range of configurations, including batch sizes, dataset subsets, model selection, and data preprocessing options. It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. ... -
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10
GVAR
The GVAR Toolbox 1.1 is designed for the purpose of GVAR modelling.
The GVAR Toolbox was originally launched in December 2010 with the release of version 1.0, sponsored by the European Central Bank. Version 1.1 was released in July 2011 and is available to download, free of charge, from this website. Derived from Dr. L.Vanessa Smith's project Exploring International Economic Linkages Using a Global Model, the GVAR Toolbox 1.1 is the second release of a collection of MatLab procedures with an Excel-based interface, designed for the purpose of GVAR... -
11
wisp
Wisp - A preference based location finder app
...It needs manual research and lot of intuitive processes to find the best location according to some earlier knowledge about the place. It is mainly about accessing publicly available spatial data, applying a simple algorithm to summarise the data according to given preferences, and visualising the result on a map. We introduced JJCluster to eliminate the rigorous way of researching about a place and visualizing the location in real time. This algorithm successfully finds the heart of a city when used in Wisp application. The main purpose of designing Wisp is to find the best location according to a set of preferences in a map. -
12
data-science-ipython-notebooks
Data science Python notebooks: Deep learning
Data Science IPython Notebooks is a broad, curated set of Jupyter notebooks covering Python, data wrangling, visualization, machine learning, deep learning, and big data tools. It aims to be a practical map of the ecosystem, showing hands-on examples with libraries such as NumPy, pandas, matplotlib, scikit-learn, and others. Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. -
13
PythonRobotics
Python sample codes and textbook for robotics algorithms
PythonRobotics is a Python code collection and textbook for learning robotics algorithms through readable examples. It covers practical topics such as localization, mapping, path planning, path tracking, control, SLAM, and autonomous navigation. The project is written to make each algorithm’s core idea easy to understand, rather than hiding the logic behind large frameworks. It keeps dependencies minimal so learners can focus on the math, implementation, and behavior of each robotics method.... -
14
DS-Take-Home
Solution to the book A Collection of Data Science Take-Home Challenge
DS-Take-Home is a repository that provides practical solutions to a series of real-world data science challenges inspired by the book A Collection of Data Science Take-Home Challenges. The project is designed as a learning resource where aspiring data scientists can study how typical industry-style take-home assignments are solved using data analysis and machine learning techniques. Each challenge is implemented in a separate Jupyter notebook that walks through the process of analyzing datasets, performing exploratory data analysis, building predictive models, and interpreting results. ... -
15
spark-ml-source-analysis
Spark ml algorithm principle analysis and specific source code
spark-ml-source-analysis is a technical repository that analyzes the internal implementation of machine learning algorithms within Apache Spark’s MLlib library. The project aims to help developers and data scientists understand how distributed machine learning algorithms are implemented and optimized inside the Spark ecosystem. Instead of providing a runnable software system, the repository focuses on explaining algorithm principles and examining the underlying source code used in Spark’s machine learning package. The repository contains detailed analyses of various algorithms including classification, regression, clustering, dimensionality reduction, and recommendation systems. ... -
16
Django REST Pandas
Serves up Pandas dataframes via the Django REST Framework
...The resulting API can serve up CSV (and a number of other formats for consumption by a client-side visualization tool like d3.js. The design philosophy of DRP enforces a strict separation between data and presentation. This keeps the implementation simple, but also has the nice side effect of making it trivial to provide the source data for your visualizations. This capability can often be leveraged by sending users to the same URL that your visualization code uses internally to load the data. While DRP is primarily a data API, it also provides a default collection of interactive visualizations through the @wq/chart library, and a @wq/pandas loader to facilitate custom JavaScript charts that work well with CSV output served by DRP. ... -
17
Python Data Science Tutorials
Common data analysis and machine learning tasks using python
Python Data Science Tutorials is a curated learning repository for common data analysis, machine learning, and natural language processing tasks in Python. It combines external tutorials, courses, reference guides, notebooks, articles, and selected code examples. The collection begins with Python fundamentals and then moves into scientific computing, statistics, NumPy, pandas, data exploration, and visualization. -
18
lazynlp
Library to scrape and clean web pages to create massive datasets
LazyNLP is a lightweight tool for collecting and curating large-scale text datasets for machine learning and NLP applications with minimal manual effort. -
19
ELF (Extensive Lightweight Framework)
An End-To-End, Lightweight and Flexible Platform for Game Research
ELF (Extensive, Lightweight, and Flexible) is a high-performance platform for reinforcement learning research that unifies simulation, data collection, and distributed training. A C++ core provides fast environments and concurrent actors, while Python bindings expose simple APIs for agents, replay, and optimization loops. It supports both single-agent and multi-agent settings, with batched stepping and shared-memory queues that keep GPUs saturated during training. ELF introduced widely used reference systems, most notably ELF OpenGo, demonstrating at-scale self-play with strong analysis tooling and public checkpoints. ... -
20
SafeBox
Truly anonymous and secure file sharing
...Immediately after installing the application, you can start sharing files. Strong cryptography Proven standards of cryptography (PGP, RSA 2048). There is not a single case of hacking data encrypted with PGP using full brute force or cryptographic algorithm vulnerability. Files are not stored in the cloud as soon as the recipient receives the file, it is instantly removed from the cloud. Thus, it is impossible to get files to anyone other than the addressee. Convenient application Minimalistic application with an intuitive interface. ... -
21
OpenDino
Open Source Java platform for Optimization, DoE, and Learning.
...It provides a graphical user interface (GUI) and a platform which simplifies integration of new algorithms as "Modules". Implemented Modules Evolutionary Algorithms: - CMA-ES - (1+1)-ES - Differential Evolution Deterministic optimization algorithm: - SIMPLEX Learning: - a simple Artificial Neural Net Optimization problems: - test functions - interface for executing other programs (solvers) - parallel execution of problems - distributed execution of problems via socket connection between computers Others: - data storage - data analyser and viewer -
22
Tera
An Internet-scale database
Tera is a high-performance distributed NoSQL database, which is inspired by google's BigTable and designed for real-time applications. Tera can easily scale to petabytes of data across thousands of commodity servers. Besides, Tera is widely used in many Baidu products with varied demands， which range from throughput-oriented applications to latency-sensitive services, including web indexing, WebPage DB, LinkBase DB, etc. Tera is the collection of many sparse, distributed, multidimensional tables. The table is indexed by a row key, column key, and a timestamp; each value in the table is an uninterpreted array of bytes. ... -
23
Play-With-Sort-OC
Repository implemented in Objective-C with sorting algorithms
Play-With-Sort-OC is a learning-oriented repository implemented in Objective-C that demonstrates several classic sorting algorithms with code examples (selection sort, bubble sort, insertion sort, quick sort variants, heap sort, etc). The goal is educational; by showing how each algorithm works with animations or clear visualizations in an iOS/Objective-C context, the author helps developers understand not just the “how” but also the “why” behind each algorithm. The repo is especially suited for developers working in the Apple ecosystem (Objective-C, Xcode) who want to build foundational knowledge in data structures and algorithms. ... -
24
VJTools
The vip.com's java coding standard, libraries and tools
...The implementation of the rules mainly relies on the code format template and Sonar code rule checking. The core class library for basic functions such as text, collection, concurrency, etc. Best practices regarding the performance and availability of back-end applications. Observe the JVM process indicators and their busy threads. Generational printing version of JMAP. Online emergency collection of JVM data script. -
25
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.