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113 lines
2.8 KiB
113 lines
2.8 KiB
library(ggplot2)
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library(gganimate)
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library(grDevices)
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rand.pupulation <- function(n) {
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return(matrix(runif(2*n), ncol=2))
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}
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alpha.fixed <- function(alpha) {
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return(function() alpha)
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}
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alpha.runif <- function() {
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return(function() runif(1))
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}
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recom.singlearithm <- function(alpha_gen) {
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return(function(parents) {
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alpha <- alpha_gen()
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gene_index <- sample(1:ncol(parents), 1)
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gene_p1 <- parents[1, gene_index]
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gene_p2 <- parents[2, gene_index]
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children <- parents
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children[1,gene_index] <- alpha * gene_p2 + (1 - alpha) * gene_p1
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children[2,gene_index] <- alpha * gene_p1 + (1 - alpha) * gene_p2
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return(children)
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})
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}
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recom.wholearithm <- function(alpha_gen) {
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return(function(parents) {
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alpha <- alpha_gen()
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children <- parents
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children[1,] <- alpha * children[1,] + (1 - alpha) * children[2,]
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children[2,] <- alpha * children[2,] + (1 - alpha) * children[1,]
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return(children)
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})
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}
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next.population <- function(population, recom) {
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next_population <- matrix(, nrow=0, ncol=2)
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parent_pairs <- matrix(sample(1:nrow(population), nrow(population)), ncol=2)
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for(i in 1:nrow(parent_pairs)) {
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parents <- parent_pairs[i,]
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next_population <- rbind(next_population, recom(population[parents,]))
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}
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return(next_population)
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}
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experiment <- function(population, gen, recom) {
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df <- data.frame( x = population[,1]
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,y = population[,2]
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,generation = 0)
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for(g in 1:gen) {
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population <- next.population(population, recom)
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df <- rbind(df, data.frame( x = population[,1]
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,y = population[,2]
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,generation = g))
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}
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return(df)
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}
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plot.experiment <- function(df, filename) {
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pdf(file=filename, onefile=TRUE)
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for(g in unique(df$generation)) {
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tmp_df <- df[df$generation == g,]
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p <- ggplot(data=tmp_df, aes(x=x, y=y)) +
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geom_point() +
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labs(title=sprintf("generation: %d", g)) +
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xlim(0, 1) +
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ylim(0, 1)
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hull <- chull(tmp_df$x, tmp_df$y)
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p <- p + geom_polygon(data=tmp_df[hull,], alpha=0.25)
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print(p)
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}
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dev.off()
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}
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all.experiments <- function() {
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population <- rand.pupulation(20)
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df <- experiment(population, 10, recom.singlearithm(alpha.fixed(0.5)))
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plot.experiment(df, "single_fixed_alpha.pdf")
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df <- experiment(population, 10, recom.singlearithm(alpha.runif()))
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plot.experiment(df, "single_rand_alpha.pdf")
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df <- experiment(population, 10, recom.wholearithm(alpha.fixed(0.5)))
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plot.experiment(df, "whole_fixed_alpha.pdf")
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df <- experiment(population, 10, recom.wholearithm(alpha.runif()))
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plot.experiment(df, "whole_rand_alpha.pdf")
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}
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