typos

Author: blaine-tc

Genetic/README.markdown

@@ -29,7 +29,7 @@ The randomization that allows for organisms to change over time. In GAs we build
 ### Problem
 For this quick and dirty example, we are going to produce an optimized string using a simple genetic algorithm. More specifically we are trying to take a randomly generated origin string of a fixed length and evolve it into the most optimized string of our choosing.
 
-We will be creating a bio-inspired world where the absolute existence is the string `Hello, World!`. Nothing in this universe is better and it's our goal to get as close to it as possible to ensure survival. 
+We will be creating a bio-inspired world where the absolute existence is the string `Hello, World!`. Nothing in this universe is better and it's our goal to get as close to it as possible to ensure survival.
 
 ### Define the Universe
 
@@ -73,8 +73,8 @@ let MUTATION_CHANCE = 100
      return lexicon[rand]
  }
  ```
- 
- **Note**: `arc4random_uniform` is strickly used in this example. It would be fun to play around with some of the [randomization in GameKit](https://developer.apple.com/library/content/documentation/General/Conceptual/GameplayKit_Guide/RandomSources.html)
+
+ **Note**: `arc4random_uniform` is strictly used in this example. It would be fun to play around with some of the [randomization in GameKit](https://developer.apple.com/library/content/documentation/General/Conceptual/GameplayKit_Guide/RandomSources.html)
 
  ### Population Zero
 
@@ -83,8 +83,6 @@ Before selecting, crossover and mutation, we need a population to start with. No
  ```swift
  func randomPopulation(from lexicon: [UInt8], populationSize: Int, dnaSize: Int) -> [[UInt8]] {
 
-    let len = UInt32(lexicon.count)
-
     var pop = [[UInt8]]()
 
     (0..<populationSize).forEach { _ in
@@ -96,7 +94,7 @@ Before selecting, crossover and mutation, we need a population to start with. No
         pop.append(dna)
     }
     return pop
-}
+ }
  ```
 
 ### Selection
@@ -124,17 +122,17 @@ Let's take a second and ask why on this one. Why would you not always want to se
 With all that, here is our weight choice function:
 
 ```swift
-func weightedChoice(items:[(item:[UInt8], weight:Double)]) -> (item:[UInt8], weight:Double) {
+func weightedChoice(items:[(dna:[UInt8], weight:Double)]) -> (dna:[UInt8], weight:Double) {
 
     let total = items.reduce(0.0) { return $0 + $1.weight}
 
     var n = Double(arc4random_uniform(UInt32(total * 1000000.0))) / 1000000.0
 
-    for itemTuple in items {
-        if n < itemTuple.weight {
-            return itemTuple
+    for item in items {
+        if n < item.weight {
+            return item
         }
-        n = n - itemTuple.weight
+        n = n - item.weight
     }
     return items[1]
 }
@@ -144,7 +142,7 @@ The above function takes a list of individuals with their calculated fitness. Th
 
 ## Mutation
 
-The all powerful mutation, the thing that introduces otherwise non exisitant fitness variance. It can either hurt of improve a individuals fitness but over time it will cause evolution towards more fit populations. Imagine if our initial random population was missing the charachter `H`, in that case we need to rely on mutation to introduce that character into the population in order to achive the optimal solution.
+The all powerful mutation, the thing that introduces otherwise non existent fitness variance. It can either hurt of improve a individuals fitness but over time it will cause evolution towards more fit populations. Imagine if our initial random population was missing the charachter `H`, in that case we need to rely on mutation to introduce that character into the population in order to achieve the optimal solution.
 
 ```swift
 func mutate(lexicon: [UInt8], dna:[UInt8], mutationChance:Int) -> [UInt8] {
@@ -184,7 +182,7 @@ The above is used to generate a completely new generation based on the current g
 
 ## Putting it all together -- Running the Genetic Algorithm
 
-We now have all the functions we need to kick off the algorthim. Let's start from the beginning, first we need a random population to serve as a starting point. We will also initialize a fittest variable to hold the fittest individual, we will initialize it with the first individual of our random population.
+We now have all the functions we need to kick off the algorithm. Let's start from the beginning, first we need a random population to serve as a starting point. We will also initialize a fittest variable to hold the fittest individual, we will initialize it with the first individual of our random population.
 
 ```swift
 var population:[[UInt8]] = randomPopulation(from: lex, populationSize: POP_SIZE, dnaSize: DNA_SIZE)
@@ -202,7 +200,7 @@ for generation in 0...GENERATIONS {
 Now, for each individual in the population, we need to calculate its fitness and weighted value. Since 0 is the best value we will use `1/fitness` to represent the weight. Note this is not a percent, but just how much more likely the value is to be selected over others. If the highest number was the most fit, the weight calculation would be `fitness/totalFitness`, which would be a percent.
 
 ```swift
-var weightedPopulation = [(item:[UInt8], weight:Double)]()
+var weightedPopulation = [(dna:[UInt8], weight:Double)]()
 
 for individual in population {
   let fitnessValue = calculateFitness(dna: individual, optimal: OPTIMAL)
@@ -224,7 +222,7 @@ The below loop is where we pull everything together. We loop for `POP_SIZE`, sel
     let ind1 = weightedChoice(items: weightedPopulation)
     let ind2 = weightedChoice(items: weightedPopulation)
 
-    let offspring = crossover(dna1: ind1.item, dna2: ind2.item, dnaSize: DNA_SIZE)
+    let offspring = crossover(dna1: ind1.dna, dna2: ind2.dna, dnaSize: DNA_SIZE)
 
     // append to the population and mutate
     nextGeneration.append(mutate(lexicon: lex, dna: offspring, mutationChance: MUTATION_CHANCE))
@@ -237,7 +235,7 @@ The final piece to the main loop is to select the fittest individual of a popula
 fittest = population[0]
 var minFitness = calculateFitness(dna: fittest, optimal: OPTIMAL)
 
-for indv in population {lex
+population.forEach { indv in
     let indvFitness = calculateFitness(dna: indv, optimal: OPTIMAL)
     if indvFitness < minFitness {
         fittest = indv

Genetic/gen.playground/Contents.swift

@@ -3,16 +3,16 @@
 import Foundation
 
 extension String {
-    var asciiArray: [UInt8] {
+    var unicodeArray: [UInt8] {
         return [UInt8](self.utf8)
     }
 }
 
 
-let lex: [UInt8] = " !\"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~".asciiArray
+let lex: [UInt8] = " !\"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~".unicodeArray
 
 // This is the end goal and what we will be using to rate fitness. In the real world this will not exist
-let OPTIMAL:[UInt8] = "Hello, World".asciiArray
+let OPTIMAL:[UInt8] = "Hello, World".unicodeArray
 
 // The length of the string in our population. Organisms need to be similar
 let DNA_SIZE = OPTIMAL.count
@@ -34,13 +34,11 @@ func randomChar(from lexicon: [UInt8]) -> UInt8 {
 
 func randomPopulation(from lexicon: [UInt8], populationSize: Int, dnaSize: Int) -> [[UInt8]] {
 
-    let len = UInt32(lexicon.count)
-    
     var pop = [[UInt8]]()
 
-    for _ in 0..<populationSize {
+    (0..<populationSize).forEach { _ in
         var dna = [UInt8]()
-        for _ in 0..<dnaSize {
+        (0..<dnaSize).forEach { _ in
             let char = randomChar(from: lexicon)
             dna.append(char)
         }
@@ -49,38 +47,33 @@ func randomPopulation(from lexicon: [UInt8], populationSize: Int, dnaSize: Int)
     return pop
 }
 
-randomPopulation(from: lex, populationSize: POP_SIZE, dnaSize: DNA_SIZE)
-
 func calculateFitness(dna:[UInt8], optimal:[UInt8]) -> Int {
-    
     var fitness = 0
-    for c in 0...dna.count-1 {
+    (0...dna.count-1).forEach { c in
         fitness += abs(Int(dna[c]) - Int(optimal[c]))
     }
     return fitness
 }
 
-calculateFitness(dna: "Gello, World".asciiArray, optimal: "Hello, World".asciiArray)
-
-func weightedChoice(items:[(item:[UInt8], weight:Double)]) -> (item:[UInt8], weight:Double) {
+func weightedChoice(items:[(dna:[UInt8], weight:Double)]) -> (dna:[UInt8], weight:Double) {
 
     let total = items.reduce(0.0) { return $0 + $1.weight}
 
     var n = Double(arc4random_uniform(UInt32(total * 1000000.0))) / 1000000.0
 
-    for itemTuple in items {
-        if n < itemTuple.weight {
-            return itemTuple
+    for item in items {
+        if n < item.weight {
+            return item
         }
-        n = n - itemTuple.weight
+        n = n - item.weight
     }
     return items[1]
 }
 
 func mutate(lexicon: [UInt8], dna:[UInt8], mutationChance:Int) -> [UInt8] {
     var outputDna = dna
 
-    for i in 0..<dna.count {
+    (0..<dna.count).forEach { i in
         let rand = Int(arc4random_uniform(UInt32(mutationChance)))
         if rand == 1 {
             outputDna[i] = randomChar(from: lexicon)
@@ -108,9 +101,8 @@ func main() {
     var fittest = [UInt8]()
 
     for generation in 0...GENERATIONS {
-        // print("Generation \(generation) with random sample: \(String(bytes: population[0], encoding:.ascii)!)")
 
-        var weightedPopulation = [(item:[UInt8], weight:Double)]()
+        var weightedPopulation = [(dna:[UInt8], weight:Double)]()
 
         // calulcated the fitness of each individual in the population
         // and add it to the weight population (weighted = 1.0/fitness)
@@ -129,7 +121,7 @@ func main() {
             let ind1 = weightedChoice(items: weightedPopulation)
             let ind2 = weightedChoice(items: weightedPopulation)
 
-            let offspring = crossover(dna1: ind1.item, dna2: ind2.item, dnaSize: DNA_SIZE)
+            let offspring = crossover(dna1: ind1.dna, dna2: ind2.dna, dnaSize: DNA_SIZE)
 
             // append to the population and mutate
             population.append(mutate(lexicon: lex, dna: offspring, mutationChance: MUTATION_CHANCE))
@@ -139,7 +131,7 @@ func main() {
         var minFitness = calculateFitness(dna: fittest, optimal: OPTIMAL)
 
         // parse the population for the fittest string
-        for indv in population {lex
+        population.forEach { indv in
             let indvFitness = calculateFitness(dna: indv, optimal: OPTIMAL)
             if indvFitness < minFitness {
                 fittest = indv
@@ -154,4 +146,3 @@ func main() {
 }
 
 main()
-

Genetic/gen.swift

@@ -3,16 +3,16 @@
 import Foundation
 
 extension String {
-    var asciiArray: [UInt8] {
+    var unicodeArray: [UInt8] {
         return [UInt8](self.utf8)
     }
 }
 
 
-let lex: [UInt8] = " !\"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~".asciiArray
+let lex: [UInt8] = " !\"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~".unicodeArray
 
 // This is the end goal and what we will be using to rate fitness. In the real world this will not exist
-let OPTIMAL:[UInt8] = "Hello, World".asciiArray
+let OPTIMAL:[UInt8] = "Hello, World".unicodeArray
 
 // The length of the string in our population. Organisms need to be similar
 let DNA_SIZE = OPTIMAL.count
@@ -34,53 +34,46 @@ func randomChar(from lexicon: [UInt8]) -> UInt8 {
 
 func randomPopulation(from lexicon: [UInt8], populationSize: Int, dnaSize: Int) -> [[UInt8]] {
 
-    let len = UInt32(lexicon.count)
-
-    var pop = [[UInt8]]()
-
-    for _ in 0..<populationSize {
-        var dna = [UInt8]()
-        for _ in 0..<dnaSize {
-            let char = randomChar(from: lexicon)
-            dna.append(char)
-        }
-        pop.append(dna)
-    }
-    return pop
+   var pop = [[UInt8]]()
+
+   (0..<populationSize).forEach { _ in
+       var dna = [UInt8]()
+       (0..<dnaSize).forEach { _ in
+           let char = randomChar(from: lexicon)
+           dna.append(char)
+       }
+       pop.append(dna)
+   }
+   return pop
 }
 
-randomPopulation(from: lex, populationSize: POP_SIZE, dnaSize: DNA_SIZE)
-
 func calculateFitness(dna:[UInt8], optimal:[UInt8]) -> Int {
-
     var fitness = 0
-    for c in 0...dna.count-1 {
+    (0...dna.count-1).forEach { c in
         fitness += abs(Int(dna[c]) - Int(optimal[c]))
     }
     return fitness
 }
 
-calculateFitness(dna: "Gello, World".asciiArray, optimal: "Hello, World".asciiArray)
-
-func weightedChoice(items:[(item:[UInt8], weight:Double)]) -> (item:[UInt8], weight:Double) {
+func weightedChoice(items:[(dna:[UInt8], weight:Double)]) -> (dna:[UInt8], weight:Double) {
 
     let total = items.reduce(0.0) { return $0 + $1.weight}
 
     var n = Double(arc4random_uniform(UInt32(total * 1000000.0))) / 1000000.0
 
-    for itemTuple in items {
-        if n < itemTuple.weight {
-            return itemTuple
+    for item in items {
+        if n < item.weight {
+            return item
         }
-        n = n - itemTuple.weight
+        n = n - item.weight
     }
     return items[1]
 }
 
 func mutate(lexicon: [UInt8], dna:[UInt8], mutationChance:Int) -> [UInt8] {
     var outputDna = dna
 
-    for i in 0..<dna.count {
+    (0..<dna.count).forEach { i in
         let rand = Int(arc4random_uniform(UInt32(mutationChance)))
         if rand == 1 {
             outputDna[i] = randomChar(from: lexicon)
@@ -108,9 +101,8 @@ func main() {
     var fittest = [UInt8]()
 
     for generation in 0...GENERATIONS {
-        // print("Generation \(generation) with random sample: \(String(bytes: population[0], encoding:.ascii)!)")
 
-        var weightedPopulation = [(item:[UInt8], weight:Double)]()
+        var weightedPopulation = [(dna:[UInt8], weight:Double)]()
 
         // calulcated the fitness of each individual in the population
         // and add it to the weight population (weighted = 1.0/fitness)
@@ -129,7 +121,7 @@ func main() {
             let ind1 = weightedChoice(items: weightedPopulation)
             let ind2 = weightedChoice(items: weightedPopulation)
 
-            let offspring = crossover(dna1: ind1.item, dna2: ind2.item, dnaSize: DNA_SIZE)
+            let offspring = crossover(dna1: ind1.dna, dna2: ind2.dna, dnaSize: DNA_SIZE)
 
             // append to the population and mutate
             population.append(mutate(lexicon: lex, dna: offspring, mutationChance: MUTATION_CHANCE))
@@ -139,7 +131,7 @@ func main() {
         var minFitness = calculateFitness(dna: fittest, optimal: OPTIMAL)
 
         // parse the population for the fittest string
-        for indv in population {lex
+        population.forEach { indv in
             let indvFitness = calculateFitness(dna: indv, optimal: OPTIMAL)
             if indvFitness < minFitness {
                 fittest = indv