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Showing 32 open source projects for "development"
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CTGAN
Conditional GAN for generating synthetic tabular data
CTGAN is a collection of Deep Learning based synthetic data generators for single table data, which are able to learn from real data and generate synthetic data with high fidelity. If you're just getting started with synthetic data, we recommend installing the SDV library which provides user-friendly APIs for accessing CTGAN. The SDV library provides wrappers for preprocessing your data as well as additional usability features like constraints. When using the CTGAN library directly, you may... -
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Mimesis
High-performance fake data generator for Python
Mimesis is an open source high-performance fake data generator for Python, able to provide data for various purposes in various languages. It's currently the fastest fake data generator for Python, and supports many different data providers that can produce data related to people, food, transportation, internet and many more. Mimesis is really easy to use, with everything you need just an import away. Simply import an object, called a Provider, which represents the type of data you... -
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Synthea Patient Generator
Synthetic Patient Population Simulator
...Our synthetic populations provide insight into the validity of this research and encourage future studies in population health. Synthetic data establishes a risk-free environment for Health IT development and experimentation. This includes the evaluation of new treatment models, care management systems, clinical decision support, and more. -
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YData Synthetic
Synthetic data generators for tabular and time-series data
A package to generate synthetic tabular and time-series data leveraging state-of-the-art generative models. Synthetic data is artificially generated data that is not collected from real-world events. It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy. This repository contains material related to Generative Adversarial Networks for synthetic data generation, in particular regular tabular data and time-series. It... -
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BlenderProc
Blender pipeline for photorealistic training image generation
A procedural Blender pipeline for photorealistic training image generation. BlenderProc has to be run inside the blender python environment, as only there we can access the blender API. Therefore, instead of running your script with the usual python interpreter, the command line interface of BlenderProc has to be used. In general, one run of your script first loads or constructs a 3D scene, then sets some camera poses inside this scene and renders different types of images (RGB, distance,... -
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SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the... -
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Copulas
A library to model multivariate data using copulas
Copulas is a Python library for modeling multivariate distributions and sampling from them using copula functions. Given a table of numerical data, use Copulas to learn the distribution and generate new synthetic data following the same statistical properties. Choose from a variety of univariate distributions and copulas – including Archimedian Copulas, Gaussian Copulas and Vine Copulas. Compare real and synthetic data visually after building your model. Visualizations are available as 1D... -
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Synthetic Data Kit
Tool for generating high quality Synthetic datasets
Synthetic Data Kit is a CLI-centric toolkit for generating high-quality synthetic datasets to fine-tune Llama models, with an emphasis on producing reasoning traces and QA pairs that line up with modern instruction-tuning formats. It ships an opinionated, modular workflow that covers ingesting heterogeneous sources (documents, transcripts), prompting models to create labeled examples, and exporting to fine-tuning schemas with minimal glue code. The kit’s design goal is to shorten the “data... -
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Gretel Synthetics
Synthetic data generators for structured and unstructured text
Unlock unlimited possibilities with synthetic data. Share, create, and augment data with cutting-edge generative AI. Generate unlimited data in minutes with synthetic data delivered as-a-service. Synthesize data that are as good or better than your original dataset, and maintain relationships and statistical insights. Customize privacy settings so that data is always safe while remaining useful for downstream workflows. Ensure data accuracy and privacy confidently with expert-grade reports.... -
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Bogus
A simple and sane fake data generator for C#, F#, and VB.NET
Bogus is a simple and sane fake data generator for .NET languages like C#, F# and VB.NET. Bogus is fundamentally a C# port of faker.js and inspired by FluentValidation's syntax sugar. Bogus will help you load databases, UI and apps with fake data for your testing needs. When Bogus updates locales from faker.js or issues bug fixes, sometimes deterministic sequences can change. Changes to deterministic outputs are usually highlighted in the release notes. Changes to deterministic outputs is... -
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Synthetic Data Vault (SDV)
Synthetic Data Generation for tabular, relational and time series data
The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset. Synthetic data can then be used to supplement, augment and in some cases replace real data when training Machine Learning models. Additionally, it enables the testing of Machine Learning or other data dependent... -
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Zylthra
Zylthra: A PyQt6 app to generate synthetic datasets with DataLLM.
Welcome to Zylthra, a powerful Python-based desktop application built with PyQt6, designed to generate synthetic datasets using the DataLLM API from data.mostly.ai. This tool allows users to create custom datasets by defining columns, configuring generation parameters, and saving setups for reuse, all within a sleek, dark-themed interface. -
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Twinify
Privacy-preserving generation of a synthetic twin to a data set
twinify is a software package for the privacy-preserving generation of a synthetic twin to a given sensitive tabular data set. On a high level, twinify follows the differentially private data-sharing process introduced by Jälkö et al.. Depending on the nature of your data, twinify implements either the NAPSU-MQ approach described by Räisä et al. or finds an approximate parameter posterior for any probabilistic model you formulated using differentially private variational inference (DPVI).... -
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Synth
The Declarative Data Generator
Synth is an open-source data-as-code tool that provides a simple CLI workflow for generating consistent data in a scalable way. Use Synth to generate correct, anonymized data that looks and quacks like production. Generate test data fixtures for your development, testing, and continuous integration. Generate data that tells the story you want to tell. Specify constraints, relations, and all your semantics. Seed development and environments and CI. Anonymize sensitive production data. Create realistic data to your specifications. Synth uses a declarative configuration language that allows you to specify your entire data model as code. ... -
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TDG - Test data generator
Test dataset generator with export options to database, csv and json
An utility capable of producing simple datasets with test data with accordance to user-provided patterns and settings. Datasets can be afterwards exported to JSON and CSV files as well as to database table. At moment, SQL Server and PostgreSQL export providers implemented. -
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DBFeeder
Highly Customizable Test Data Generator
DBFeeder is a great tool to generate synthetic testdata for Oracle Databases and it is ideal for companies who wants to outsource development. Thanks to his original approach, data can be highly customizable and it even fits primary and foreign keys constraints of tables. -
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Internet of Things RandOm GENerator
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A Synthetic Data Generator for producing mixed datasets described by relevant, irrelevant, and redundant features.
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Tofu
Tofu is a Python tool for generating synthetic UK Biobank data
Tofu is a Python library for generating synthetic UK Biobank data. The UK Biobank is a large open-access prospective research cohort study of 500,000 middle-aged participants recruited in England, Scotland and Wales. The study has collected and continues to collect extensive phenotypic and genotypic detail about its participants, including data from questionnaires, physical measures, sample assays, accelerometry, multimodal imaging, genome-wide genotyping and longitudinal follow-up for a... -
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rpackage conjurer
Synthetic data generation using R
Builds synthetic data applicable across multiple domains. This package also provides flexibility to control data distribution to make it relevant to many industry examples. -
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TGAN
Generative adversarial training for generating synthetic tabular data
...Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid interfering with other software installed in the system where TGAN is run. For development, you can use make install-develop instead in order to install all the required dependencies for testing and code listing. In order to be able to sample new synthetic data, TGAN first needs to be fitted to existing data. -
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benerator is a framework for creating realistic and valid high-volume test data, used for load and performance testing and showcase setup. Data is generated from an easily configurable metadata model and exported to databases, XML, CSV or flat files.
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Test data generator
Random test data generator
It's a free portable win32 application to generate random test data, with support of only even or only odd numbers. It's easy to test your code on large amount of data. For test integers than 10000 it directly copies the test data to clipboard and for greater amount of data it creates a TXT file in d:\ path. -
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Ava: Testdata Xsl
generates Testdata on base of excel: creates xml,excel,csv,html,sql,+
this tool for test-data-generation receives an 'excel-sheet' as primary input. second important paramter is the 'number of test-records to produce'. The excel-data will be reused as long data is needed. This tool is hightly paramatrisazable by the use of 'xsl scripts'. data can be created, updated, modified and finally exported in a format of your choice Main Fuctions: (1) Generates Testdata (excel, xsl, xml) (2) Exports generated testdata in multiple formats (csv, excel, html,... -
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A Data Generator
A tool to generate synthetic test data useful to Record matchers
With growing amount of information from multiple sources it has become very hard to relate information to the correct real life entities. Record matching software try to solve this by machine learning techniques. To do this effectively, its necessary to train the record matcher with proper test data which is identical to real life data. Hence, there is a need for a data generator to create the synthetic data to be used for evaluating the quality and capability of record matching software....
