abcpy | ABCpy package
kandi X-RAY | abcpy Summary
kandi X-RAY | abcpy Summary
abcpy is a Python library. abcpy has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. However abcpy has 10 bugs. You can install using 'pip install abcpy' or download it from GitHub, PyPI.
ABCpy is a scientific library written in Python for Bayesian uncertainty quantification in absence of likelihood function, which parallelizes existing approximate Bayesian computation (ABC) algorithms and other likelihood-free inference schemes.
ABCpy is a scientific library written in Python for Bayesian uncertainty quantification in absence of likelihood function, which parallelizes existing approximate Bayesian computation (ABC) algorithms and other likelihood-free inference schemes.
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Support
abcpy has a low active ecosystem.
It has 101 star(s) with 30 fork(s). There are 13 watchers for this library.
It had no major release in the last 12 months.
There are 3 open issues and 20 have been closed. On average issues are closed in 35 days. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of abcpy is 0.6.3
Quality
abcpy has 10 bugs (4 blocker, 0 critical, 5 major, 1 minor) and 226 code smells.
Security
abcpy has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
abcpy code analysis shows 0 unresolved vulnerabilities.
There are 1 security hotspots that need review.
License
abcpy is licensed under the BSD-3-Clause-Clear License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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abcpy releases are available to install and integrate.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
abcpy saves you 3328 person hours of effort in developing the same functionality from scratch.
It has 7142 lines of code, 686 functions and 76 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed abcpy and discovered the below as its top functions. This is intended to give you an instant insight into abcpy implemented functionality, and help decide if they suit your requirements.
- Train the model
- Calculates the Fisher divergence loss between two vectors
- Calculates the Fisher divergence loss loss
- Perform a batch - Fisher divergence with c
- Accept parameter version 2
- Resets the flags for all models
- Returns a list of accepted parameters
- Get the mapping from a list of models
- Update covariance matrix
- Forward the model to the given values
- Estimate the marginal likelihood of each observation
- Resample the parameter
- Multi - layer training
- Performs a triplet training
- Generate the model from the input values
- Sample a parameter from the model
- Generate a list of k values from the input values
- Generate k - points from the input values
- Generate a list of k values from input_values
- Calculates the statistics for the training set
- Perform contrastive training
- Accept parameter data
- Infer model parameters
- Sample a parameter
- Simulate the acceptance kernel
- Initialize a StateState from a file
Get all kandi verified functions for this library.
abcpy Key Features
No Key Features are available at this moment for abcpy.
abcpy Examples and Code Snippets
Quick installation and requirements,Troubleshooting
Python
Lines of Code : 3License : Permissive (BSD-3-Clause-Clear)
Lines of Code : 3License : Permissive (BSD-3-Clause-Clear)
Copysudo apt-get install libopenmpi-dev
env MPICC=/path/to/mpicc pip install mpi4py
sudo apt install python3-mpi4py
Quick installation and requirements
PythonLines of Code : 1License : Permissive (BSD-3-Clause-Clear)
Lines of Code : 1License : Permissive (BSD-3-Clause-Clear)Copypip install abcpy
Community Discussions
No Community Discussions are available at this moment for abcpy.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install abcpy
ABCpy can be installed from pip:. Check here for more details. Basic requirements are listed in requirements.txt. That also includes packages required for MPI parallelization there, which is very often used. However, we also provide support for parallelization with Apache Spark (see below).
torch is needed in order to use neural networks to learn summary statistics. It can be installed by running pip install -r requirements/neural_networks_requirements.txt
In order to use Apache Spark for parallelization, findspark and pyspark are required; install them by pip install -r requirements/backend-spark.txt
torch is needed in order to use neural networks to learn summary statistics. It can be installed by running pip install -r requirements/neural_networks_requirements.txt
In order to use Apache Spark for parallelization, findspark and pyspark are required; install them by pip install -r requirements/backend-spark.txt
Support
For more information, check out the. Further, we provide a collection of models for which ABCpy has been applied successfully. This is a good place to look at more complicated inference setups.
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