CTGAN VS Mimesis

Compare CTGAN vs Mimesis and see what are their differences.

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CTGAN Mimesis
2 3
1,273 4,437
2.4% -
8.3 8.4
5 days ago 10 days ago
Python Python
GNU General Public License v3.0 or later MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

CTGAN

Posts with mentions or reviews of CTGAN. We have used some of these posts to build our list of alternatives and similar projects.

Mimesis

Posts with mentions or reviews of Mimesis. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-12.

What are some alternatives?

When comparing CTGAN and Mimesis you can also consider the following projects:

SDV - Synthetic data generation for tabular data

faker - Faker is a Python package that generates fake data for you.

Copulas - A library to model multivariate data using copulas.

fake2db - create custom test databases that are populated with fake data

FAST-RIR - This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.

radar

gretel-synthetics - Synthetic data generators for structured and unstructured text, featuring differentially private learning.

FauxFactory - Generates random data for your tests.

DeepEcho - Synthetic Data Generation for mixed-type, multivariate time series.

callee - Argument matchers for unittest.mock

aiounittest - Test python asyncio-based code with ease.

pytest-fastapi-deps - This library allows you to easily replace FastAPI dependencies in your tests. Regular mocking techniques do not work due to the inner working of FastAPI.

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