vaeesr

Name	vaeesr JSON
Version	0.0.1 JSON
	download
home_page	None
Summary	Using the latent space of a variational autoencoder to perform symbolic regression by sampling equations.
upload_time	2024-06-23 16:27:15
maintainer	None
docs_url	None
author	Lisa Artmann
requires_python	None
license	None
keywords	symbolic regression equation discovery variational autoencoder
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
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Variational Autoencoder Embeddings Symbolic Regression (VAEESR)

This python package performs Symbolic Regression by creating an Embedding where semantically similar equations are close to each other and it uses MCMC sampling to find the equation that is the closest to the oberved data. Therefore, is uses three main functions: 

1. `create_dataset` which creates a customizable dataset that can be ajusted for the specific problem at hand. The main parameters are: The x_values for which the functions are evaluated, the range of constants, the maximum tree depth, the possible operators and functions and the total number of equations in the dataset.

2. `create_autoencoder` which trains an autoencoder with the dataset. The some hyperparameters can be ajdusted as well. 

3. `perform_MCMC` which performs the symbolic regression by sampling equations from the autoencoder embedding. 

            

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