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89 lines
2.2 KiB
89 lines
2.2 KiB
library(ggplot2)
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library(gganimate)
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linscale.initpopulation <- function(n) {
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return(runif(10))
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}
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linscale.scale <- function(population, a, b) {
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return(a * population + b)
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}
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linscale.relfitness <- function(population) {
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return(population / sum(population))
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}
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linscale.tracegens <- function(popul, a, b, n) {
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lenpopul <- length(popul)
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df <- data.frame( generation = 0
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,individual = 1:lenpopul
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,fitness = popul
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,relfitness = linscale.relfitness(popul)
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)
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for(i in 1:n) {
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popul <- linscale.scale(popul, a, b)
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df <- rbind(df, data.frame( generation = i
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,individual = 1:lenpopul
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,fitness = popul
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,relfitness = linscale.relfitness(popul)))
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}
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return(df)
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}
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linscale.experiment <- function(avals, bvals, n, popul=c()) {
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df <- data.frame( generation = integer()
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,individual = integer()
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,fitness = double()
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,relfitness = double()
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,a = double()
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,b = double())
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if(length(popul) == 0) {
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popul <- linscale.initpopulation(10)
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}
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for(a in avals) {
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for(b in bvals) {
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dftmp <- linscale.tracegens(popul, a, b, n)
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dftmp["a"] <- a
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dftmp["b"] <- b
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df <- rbind(df, dftmp)
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}
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}
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return(df)
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}
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linscale.plot <- function(df) {
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pdf(file="linscale.pdf", onefile=TRUE)
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for(g in unique(df$generation)) {
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p <- ggplot(data=df[df$generation == g, ]) +
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geom_col(aes(x=individual, y=relfitness)) +
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facet_grid(b ~ a, labeller = label_both) +
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labs(title=sprintf("generation: %i", g))
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print(p)
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}
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dev.off()
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}
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linscale.animate <- function(df) {
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anim <- ggplot(data=df) +
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geom_col(aes(x=individual, y=relfitness)) +
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facet_grid(b ~ a, labeller = label_both) +
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labs(title="generation: {closest_state}") +
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transition_states(generation)
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anim_save("linescale.gif", animation=anim)
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}
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data.population <- linscale.initpopulation(10)
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