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‎.github/workflows/ant.yml

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name: Java CI
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on:
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push:
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branches: [ master ]
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tags: [ 'v*.*' ]
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pull_request:
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branches: [ master ]
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jobs:
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test:
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strategy:
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matrix:
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os: [ubuntu-latest, windows-latest, macOS-latest]
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java: [ 8 ]
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runs-on: ${{ matrix.os }}
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steps:
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- uses: actions/checkout@v2
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- uses: actions/setup-java@v1
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with:
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java-version: ${{ matrix.java }}
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java-package: jdk
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- uses: r-lib/actions/setup-r@v1
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with:
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r-version: 3.6
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- if: matrix.os == 'macos-latest'
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run: ln -s /Library/Frameworks/R.framework/Versions/3.6 /Library/Frameworks/R.framework/Versions/3.5
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- uses: r-lib/actions/setup-pandoc@v1
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- run: |
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WD=$PWD
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cd ..
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git clone https://github.com/Funz/funz-profile
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cd $WD
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shell: bash
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- if: matrix.os == 'ubuntu-latest'
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run: sudo apt-get -y install libharfbuzz-dev libfribidi-dev libcurl4-openssl-dev
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- run: ant -noinput -buildfile build.xml test
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id: anttest
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continue-on-error: true
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- if: steps.anttest.outcome != 'success'
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uses: actions/upload-artifact@v2
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with:
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name: artifacts-${{ matrix.os }}-${{ matrix.java }}
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path: |
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*.txt
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*.log
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*.out
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*.Rout
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*.Rmd
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*.md
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*.html
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- if: steps.anttest.outcome != 'success'
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run: exit 1
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release:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2
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- uses: actions/setup-java@v1
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with:
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java-version: 1.8
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java-package: jdk
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- run: |
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WD=$PWD
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cd ..
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git clone https://github.com/Funz/funz-profile
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cd $WD
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- run: |
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ant clean dist
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cd dist; zip -r ../algorithm-MyAlgorithm.zip *; cd ..
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ant install
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zip -r FunzR-MyAlgorithm.zip FunzR-MyAlgorithm
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- uses: actions/upload-artifact@v2
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with:
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path: dist
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- uses: softprops/action-gh-release@v1
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if: startsWith(github.ref, 'refs/tags/')
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with:
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files: ./*-MyAlgorithm.zip
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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‎.gitignore

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/**/*-private
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/**/log.txt
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/**/*history
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/build/
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/tmp/
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/nbproject/
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/dist/
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/test/
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/*.png
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/*.html
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/*.log
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/*.zip
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/*.out
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/*.csv
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/*.txt
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/*.Rout
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/*.Rdata
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/*.Rmd
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/*.exec
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/FunzR-*
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/libR/

‎README.md

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[![.github/workflows/ant.yml](https://github.com/Funz/algorithm-MyAlgorithm/actions/workflows/ant.yml/badge.svg)](https://github.com/Funz/algorithm-MyAlgorithm/actions/workflows/ant.yml)
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___This repositry is intended to be forked as a basis for an new algorithm implementation.___
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___You can search for "MyAlgorithm" as a key to replace everywhere in this directory.___
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# Funz algorithm: MyAlgorithm
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* MyAlgorithm method for ...
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* author: ...
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* ref: ...
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* tags: ...
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* options: ...
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* input: ...
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* output: ...
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![Analytics](https://ga-beacon.appspot.com/UA-109580-20/algorithm-MyAlgorithm)

‎build.xml

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<project name="algorithm-MyAlgorithm" default="test" basedir=".">
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<property name="algorithm.name" value="MyAlgorithm" />
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<property name="install.dir" value="${basedir}/FunzR-${algorithm.name}" />
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<property name="funz.release" value="v1.12" />
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<property name="build_algorithm.xml" location="../funz-profile/build_algorithm.xml" />
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<import file="${build_algorithm.xml}"/>
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<target name="dist" depends="dist-algorithm"/> <!-- dist-ioplugin: copy just the plugin ascii files -->
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<target name="test" depends="test-algorithm"/> <!-- test-ioplugin: test the plugin ascii files -->
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<target name="super.install" depends="install"/>
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<target name="super.clean" depends="clean"/>
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</project>

‎src/main/doe/MyAlgorithm.R

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#help: MyAlgorithm method for ...
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#author: ...
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#ref: ...
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#tags: ...
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#options: ytarget='0.0';ytol='3.e-8';xtol='1.e-8';xsample_size='10';max_iterations='10'
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#input: x=list(min=0,max=1)
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#output: y=0.01
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MyAlgorithm <- function(options) {
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myAlgorithm = new.env()
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myAlgorithm$ytol <- as.numeric(options$ytol)
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myAlgorithm$xtol <- as.numeric(options$xtol)
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myAlgorithm$ytarget <- as.numeric(options$ytarget)
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myAlgorithm$max_iterations <- as.integer(options$max_iterations)
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myAlgorithm$xsample_size <- as.integer(options$xsample_size)
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myAlgorithm$i = NA
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return(myAlgorithm)
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}
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#' first design building.
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#' @param input variables description (min/max, properties, ...)
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#' @param output values of interest description
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getInitialDesign <- function(myAlgorithm, input, output) {
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myAlgorithm$i <- 0
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myAlgorithm$input <- input
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x = matrix(runif(myAlgorithm$xsample_size * length(input)),ncol=length(input))
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names(x) <- names(input)
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return(from01(x,myAlgorithm$input))
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}
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## iterated design building.
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## @param X data frame of current doe variables
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## @param Y data frame of current results
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## @return data frame or matrix of next doe step
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getNextDesign <- function(myAlgorithm, X, Y) {
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names(X) = names(myAlgorithm$input)
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X = to01(X,myAlgorithm$input)
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Y = matrix(Y,ncol=1) - myAlgorithm$ytarget
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if (myAlgorithm$i >= myAlgorithm$max_iterations) {
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return(NULL)
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} else
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myAlgorithm$i = myAlgorithm$i+1
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Xnext = matrix(runif(myAlgorithm$xsample_size * length(myAlgorithm$input)),ncol=length(myAlgorithm$input))
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names(Xnext) <- names(myAlgorithm$input)
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return(from01(Xnext,myAlgorithm$input))
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}
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## final analysis. Return HTML string
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## @param X data frame of doe variables
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## @param Y data frame of results
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## @return HTML string of analysis
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displayResults <- function(myAlgorithm, X, Y) {
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myAlgorithm$files <- paste("result", myAlgorithm$i, ".png", sep = "")
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height <- 500
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width <- 500
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png(file = myAlgorithm$files, height = height, width = width)
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pairs(cbind(X,Y))
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dev.off()
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html <- paste0(' <HTML name="SomeInfo">Here is some information in the end','<br/>',
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'<img src="', myAlgorithm$files, '" width="', width, '" height="', height, '"/></HTML>',collapse=';')
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info <- paste0('<info>',mean(Y),'</info>')
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return(paste0(html,info))
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}
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displayResultsTmp <- displayResults
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from01 = function(X, inp) {
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for (i in 1:ncol(X)) {
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namei = names(X)[i]
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X[,i] = X[,i] * (inp[[ namei ]]$max-inp[[ namei ]]$min) + inp[[ namei ]]$min
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}
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return(X)
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}
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to01 = function(X, inp) {
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for (i in 1:ncol(X)) {
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namei = names(X)[i]
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X[,i] = (X[,i] - inp[[ namei ]]$min) / (inp[[ namei ]]$max-inp[[ namei ]]$min)
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}
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return(X)
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}

‎src/test/cases/cos_pi.R

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## This file should provide following objects, when loaded:
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# f : function
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# input.f : list of input dimensions, contains list of properties like lower & upper bounds of each dimensions
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# output.f : list of output dimensions
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# *.f : list of math properties. To be compared with algorithm results
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# [print.f] : method to print/plot the function for information
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f = function(X) {
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matrix(Vectorize(function(x) {cos(pi*x)})(X),ncol=1)
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}
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input.f = list(
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x=list(min=0,max=1)
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)
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output.f = "cos_pi"
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root.f = 0.5
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test = function(algorithm_file) {
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results = run.algorithm(algorithm_file, options=NULL,fun=list(input=input.f,output=output.f,fun=f))
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library(testthat)
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# Replace following test by something suitable
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#test_that("cos_pi info",{expect_equal(as.numeric(results$info),root.f,tolerance = .0001)})
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}
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‎src/test/cases/poly3.R

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## This file should provide following objects, when loaded:
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# f : function
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# input.f : list of input dimensions, contains list of properties like lower & upper bounds of each dimensions
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# output.f : list of output dimensions
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# *.f : list of math properties. To be compared with algorithm results
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# [print.f] : method to print/plot the function for information
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f = function(X) {
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matrix(Vectorize(function(x) {((x-.75)/3)^3})(X),ncol=1)
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}
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input.f = list(
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x=list(min=0,max=1)
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)
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output.f = "poly3"
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info.f = 0.75
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test = function(algorithm_file) {
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results = run.algorithm(algorithm_file, options=NULL,fun=list(input=input.f,output=output.f,fun=f))
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library(testthat)
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# Replace following test by something suitable
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#test_that("poly3 info",{expect_equal(as.numeric(results$info),info.f,tolerance = .0001)})
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}
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