GeneExpression | 基因表达式编程,java代码实现。主要实现二分类器,和多分类器。 | Machine Learning library

 by   superzhan Java Version: Current License: No License

kandi X-RAY | GeneExpression Summary

kandi X-RAY | GeneExpression Summary

GeneExpression is a Java library typically used in Artificial Intelligence, Machine Learning applications. GeneExpression has no bugs, it has no vulnerabilities and it has low support. However GeneExpression build file is not available. You can download it from GitHub.

基因表达式编程 (Gene Expression Programming,GEP)是C.Ferreira在遗传算法和遗传程序设计的基础上提出的一种新的遗传算法,它结合了遗传算法和遗传程序设计的优点,建立了一种新的基因编码方式和结果变形形式。.

            kandi-support Support

              GeneExpression has a low active ecosystem.
              It has 4 star(s) with 3 fork(s). There are 2 watchers for this library.
              It had no major release in the last 6 months.
              GeneExpression has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of GeneExpression is current.

            kandi-Quality Quality

              GeneExpression has no bugs reported.

            kandi-Security Security

              GeneExpression has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              GeneExpression does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              GeneExpression releases are not available. You will need to build from source code and install.
              GeneExpression has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed GeneExpression and discovered the below as its top functions. This is intended to give you an instant insight into GeneExpression implemented functionality, and help decide if they suit your requirements.
            • Test program
            • Read data from a file
            • Performs test
            • Set values
            • Main entry point
            • Get feature information
            • Get pareto font
            • Generate superset
            • Single feature count
            • Initializes the population
            • Select the population
            • Recomate two points
            • Main method
            • Gets the order
            • Get super set
            • Gets the fitness function
            • Returns the valid length of an indiv
            • Get sub feature set
            • Set individual
            • Return the average fitness for the population
            • Get the fitness function
            • Get fitness
            • Gets fitness
            • Demonstrates the main test program
            Get all kandi verified functions for this library.

            GeneExpression Key Features

            No Key Features are available at this moment for GeneExpression.

            GeneExpression Examples and Code Snippets

            No Code Snippets are available at this moment for GeneExpression.

            Community Discussions


            Faster way to select top values and rowmeans
            Asked 2020-Jul-16 at 09:56

            Hello I am trying to speed up a block of code that is currently working, but is quite slow with the amount of data that I have. I need to identify the top n% highest value in a row and subsequently use this to make an average by subsetting a dataframe and averaging the values of the subset. Any help or suggestions would be appreciated. This is my current approach:



            Answered 2020-Jul-16 at 09:56

            Here's a smaller version of your example along with my base R solution. Chances are there's also a neat tidyverse way of doing this but I wouldn't know.



            Understanding Vectorized Code In R
            Asked 2018-Aug-11 at 01:03

            I'm trying to understand the answer to this question using R and I'm struggling a lot.

            The dataset for the R code can be found with this code



            Answered 2018-Aug-11 at 01:03

            You question seems to be about understanding the function apply().

            For the technical description, see ?apply.

            My quick explanation: the apply() line of code in your question applies the following function to each of the rows of geneExpression


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install GeneExpression

            You can download it from GitHub.
            You can use GeneExpression like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the GeneExpression component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer For Gradle installation, please refer .


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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          • CLI

            gh repo clone superzhan/GeneExpression

          • sshUrl


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