refactor mergePopulations. now much more efficient

R/makePopulation.R

@@ -14,22 +14,3 @@ makePopulation = function(individuals, fitness = NULL) {
     classes = c("ecrPopulation", "setOfIndividuals")
   )
 }
-
-# Helper for merging populations.
-#
-# @param .. [\code{list}]\cr
-#  List of objects of type \code{setOfIndividuals}.
-# @return [\code{setOfIndividuals}]
-mergePopulations = function(...) {
-  populations = list(...)
-  individuals = data.frame()
-  fitness = c()
-  for (i in 1:length(populations)) {
-    individuals = rbind(individuals, populations[[i]]$individuals)
-    fitness = c(fitness, populations[[i]]$fitness)
-  }
-  makePopulation(
-    individuals = as.matrix(individuals),
-    fitness = fitness
-  )
-}

R/mergePopulations.R

@@ -0,0 +1,31 @@
+# Helper for merging populations.
+#
+# @param ... [\code{list}]\cr
+#  List of objects of type \code{setOfIndividuals}.
+# @return [\code{setOfIndividuals}]
+mergePopulations = function(...) {
+  populations = list(...)
+  # get n.params
+  n.params = ncol(populations[[1]]$individuals)
+
+  # summarize over all population sizes
+  n.pop = sum(sapply(populations, function(x) length(x$fitness)))
+
+  # allocate space
+  fitness = numeric(n)
+  individuals = matrix(NA, ncol = m, nrow = n)
+
+  # now iterate over populations and generate merged population
+  #FIXME: maybe better implement this in C++
+  start = 1L
+  for (i in 1:length(populations)) {
+    j = nrow(populations[[i]]$individuals)
+    individuals[start:(start + j - 1), ] = populations[[i]]$individuals
+    fitness[start:(start + j - 1)] = populations[[i]]$fitness
+    start = start + j
+  }
+  makePopulation(
+    individuals = individuals,
+    fitness = fitness
+  )
+}