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124 lines
3.3 KiB
124 lines
3.3 KiB
library(ggplot2)
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func.g <- function(x) x
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func.k <- function(x) sin(x)
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func.h <- function(x) x * sin(x)
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func.l <- function(x) 2 + cos(x) + sin(2*x)
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delta.const <- function(x) {
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return(function() x)
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}
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delta.gaus <- function() {
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return(rnorm(1))
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}
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ea.init_population <- function(range, size, rand_gen) {
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return(rand_gen(size) * (range[2] - range[1]) + range[1])
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}
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ea.trace <- function(range, delta, population_size, fit_func, iterations) {
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population <- ea.init_population(range, population_size, runif)
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df <- data.frame(i=integer()
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,max=numeric()
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,median=numeric()
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,min=numeric())
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for(i in 1:iterations) {
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population <- ea.iterate(delta, population, fit_func)
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df[nrow(df) + 1,] <- c(i
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,population[1]
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,population[length(population) %/% 2]
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,population[length(population)]
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)
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}
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df["max_val"] <- fit_func(df$max)
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df["median_val"] <- fit_func(df$median)
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df["min_val"] <- fit_func(df$min)
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res <- list(population, df)
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names(res) <- c("population", "df_tr")
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return(res)
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}
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ea.traces <- function(range, deltas, population_size, fit_funcs, iterations) {
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df <- data.frame(i=integer()
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,max=numeric()
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,median=numeric()
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,min=numeric()
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,delta_func=character()
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,delta=numeric()
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,fit_func=character())
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for(delta_func in names(deltas)) {
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delta <- deltas[delta_func]
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for(fit_name in names(fit_funcs)) {
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fit <- fit_funcs[fit_name]
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tmp_df <- ea.trace(range, delta, population_size, fit, iterations)$df_tr
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tmp_df["delta_func"] <- delta_func
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if(delta_func == "delta.gaus") {
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tmp_df["delta"] <- NA
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} else {
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tmp_df["delta"] <- delta()
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}
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tmp_df["fit_func"] <- fit_name
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df <- rbind(df, tmp_df)
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}
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}
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return(df)
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}
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ea.plot <- function(range, delta, population_size, fit_func, iterations) {
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res <- ea.trace(range, delta, population_size, fit_func, iterations)
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df_vals <- melt(res$df_tr[c("i", "min_val", "median_val", "max_val")], id.vars="i")
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p <- ggplot(data=df_vals, aes(x=i)) +
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geom_line(aes(y=value, linetype=variable))
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return(p)
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}
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ea.run <- function(range, delta, population_size, fit_func, iterations) {
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population <- ea.init_population(range, population_size, runif)
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for(i in 1:iterations) {
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population <- ea.iterate(delta, population, fit_func)
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}
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return(population)
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}
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ea.iterate <- function(delta, population, fit_func) {
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children <- c()
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for(individual in population) {
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children <- append(children, ea.mutate(individual, delta))
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}
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population <- append(population, children)
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return(ea.select(population, fit_func))
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}
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ea.mutate <- function(individual, delta) {
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sign <- sample(c(-1,1), 1)
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return(individual + sign * delta())
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}
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ea.select <- function(population, fit_func) {
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sorted_popul <- population[order(sapply(population, fit_func), decreasing=TRUE)]
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return(sorted_popul[1 : (length(sorted_popul) %/% 2)])
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}
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