operon | C++ Large Scale Genetic Programming | Machine Learning library
kandi X-RAY | operon Summary
kandi X-RAY | operon Summary
operon is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. operon has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.
Operon is a modern C++ framework for symbolic regression that uses genetic programming to explore a hypothesis space of possible mathematical expressions in order to find the best-fitting model for a given regression target. Its main purpose is to help develop accurate and interpretable white-box models in the area of system identification. More in-depth documentation available at
Operon is a modern C++ framework for symbolic regression that uses genetic programming to explore a hypothesis space of possible mathematical expressions in order to find the best-fitting model for a given regression target. Its main purpose is to help develop accurate and interpretable white-box models in the area of system identification. More in-depth documentation available at
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operon has a low active ecosystem.
It has 76 star(s) with 19 fork(s). There are 8 watchers for this library.
It had no major release in the last 6 months.
There are 4 open issues and 19 have been closed. On average issues are closed in 46 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of operon is current.
Quality
operon has 0 bugs and 0 code smells.
Security
operon has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
operon code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
operon is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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operon releases are not available. You will need to build from source code and install.
Installation instructions, examples and code snippets are available.
It has 14 lines of code, 0 functions and 1 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of operon
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of operon
operon Key Features
No Key Features are available at this moment for operon.
operon Examples and Code Snippets
No Code Snippets are available at this moment for operon.
Community Discussions
Trending Discussions on operon
QUESTION
Is there a way to omit variables with NA values from facet wrap plots?
Asked 2021-Aug-14 at 18:31
# A tibble: 8 × 4
measurement log2_fc locus operon
1 transcriptome 1 PA3552 arn
2 transcriptome 1.5 PA3553 arn
3 proteome NA PA3552 arn
4 proteome 2 PA3553 arn
5 transcriptome 2.5 PA1179 opr
6 transcriptome 3 PA1180 opr
7 proteome NA PA1179 opr
8 proteome NA PA1180 opr
plot <- ggplot(data=x,aes(x=locus,y=log2_fc,color=measurement)) +
geom_jitter()
plot + facet_wrap(~operon, ncol=2)
ANSWER
Answered 2021-Aug-14 at 00:09You just need to free the x axis:
QUESTION
Grouping the rows until meet certain conditions
Asked 2021-Mar-23 at 07:05
I have question on grouping rows together until meet certain conditions. Here is my dataframe.
...ANSWER
Answered 2021-Mar-23 at 06:47You can increment the operon value by 1 when :
intergenic_distanceis greater than 50 AND it is notNAOR- current
directonvalue is not same as the previousdirectonvalue.
QUESTION
How to split data groups into quartiles by group's size
Asked 2020-Dec-20 at 03:28
I have a dataset for credit card transaction.
I split this dataset by group using below code
...ANSWER
Answered 2020-Dec-20 at 03:28- See inline notation for code explanation
pandas.core.groupby.GroupBy.sizepandas.Series.reset_indexpandas.Series.quantilepandas.cutpandas.DataFrame.mergepathlibpandas.DataFrame.ilocpandas.DataFrame.to_csv
QUESTION
Comparing 2 columns from different files print matching columns
Asked 2020-Feb-28 at 18:45
I know similar questions have been asked which has led me write the current code but I am still not able to get the correct output. Question: If Column 1 (in file 1) matches Column 5 (in file 2), print all columns in file 2 and columns 3 and 4 (in file 1) to a new file.
File 1 (tab-delimited)
...ANSWER
Answered 2020-Feb-28 at 17:59Could you please try following.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install operon
The project requires CMake and a C++17 compliant compiler. On Windows we recommend building with MinGW or with your WSL distro. We recommend using the latest versions of Eigen and Ceres.
The following options can be passed to CMake: | Option | Description | |:----------------------------|:------------| | -DCERES_TINY_SOLVER=ON | Use the very small and self-contained tiny solver from the Ceres suite for solving non-linear least squares problem. | | -DUSE_SINGLE_PRECISION=ON | Perform model evaluation using floats (single precision) instead of doubles. Great for reducing runtime, might not be appropriate for all purposes. | | -DUSE_OPENLIBM=ON | Link against Julia's openlibm, a high performance mathematical library (recommended to improve consistency across compilers and operating systems). | | -DBUILD_TESTS=ON | Build the unit tests. | | -DBUILD_PYBIND=ON | Build the Python bindings. | | -DUSE_JEMALLOC=ON | Link against jemalloc, a general purpose malloc(3) implementation that emphasizes fragmentation avoidance and scalable concurrency support (mutually exclusive with tcmalloc). | | -DUSE_TCMALLOC=ON | Link against tcmalloc (thread-caching malloc), a malloc(3) implementation that reduces lock contention for multi-threaded programs (mutually exclusive with jemalloc). | | -DUSE_MIMALLOC=ON | Link against mimalloc a compact general purpose malloc(3) implementation with excellent performance (mutually exclusive with jemalloc or tcmalloc). |.
The following options can be passed to CMake: | Option | Description | |:----------------------------|:------------| | -DCERES_TINY_SOLVER=ON | Use the very small and self-contained tiny solver from the Ceres suite for solving non-linear least squares problem. | | -DUSE_SINGLE_PRECISION=ON | Perform model evaluation using floats (single precision) instead of doubles. Great for reducing runtime, might not be appropriate for all purposes. | | -DUSE_OPENLIBM=ON | Link against Julia's openlibm, a high performance mathematical library (recommended to improve consistency across compilers and operating systems). | | -DBUILD_TESTS=ON | Build the unit tests. | | -DBUILD_PYBIND=ON | Build the Python bindings. | | -DUSE_JEMALLOC=ON | Link against jemalloc, a general purpose malloc(3) implementation that emphasizes fragmentation avoidance and scalable concurrency support (mutually exclusive with tcmalloc). | | -DUSE_TCMALLOC=ON | Link against tcmalloc (thread-caching malloc), a malloc(3) implementation that reduces lock contention for multi-threaded programs (mutually exclusive with jemalloc). | | -DUSE_MIMALLOC=ON | Link against mimalloc a compact general purpose malloc(3) implementation with excellent performance (mutually exclusive with jemalloc or tcmalloc). |.
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