Data update

Task/Evolutionary-algorithm/Nanoquery/evolutionary-algorithm.nanoquery

@@ -8,77 +8,77 @@ rand   = new(Random)
 
 // returns a random string of the specified length
 def rand_string(length)
-	global tbl
-	global rand
+    global tbl
+    global rand
 
-	ret = ""
+    ret = ""
 
-	for i in range(1, length)
-		ret += tbl[rand.getInt(len(tbl))]
-	end
+    for i in range(1, length)
+        ret += tbl[rand.getInt(len(tbl))]
+    end
 
-	return ret
+    return ret
 end
 
 // gets the fitness of a given string
 def fitness(string)
-	global target
-	fit = 0
+    global target
+    fit = 0
 
-	for i in range(0, len(string) - 1)
-		if string[i] != target[i]
-			fit -= 1
-		end
-	end
+    for i in range(0, len(string) - 1)
+        if string[i] != target[i]
+            fit -= 1
+        end
+    end
 
-	return fit
+    return fit
 end
 
 // mutates the specified string with a chance of 0.09 by default
 def mutate(string)
-	global chance
-	global rand
-	global tbl
-	mutated = string
+    global chance
+    global rand
+    global tbl
+    mutated = string
 
-	for i in range(0, len(mutated) - 1)
-		if rand.getFloat() <= chance
-			mutated[i] = tbl[rand.getInt(len(tbl))]
-		end
-	end
+    for i in range(0, len(mutated) - 1)
+        if rand.getFloat() <= chance
+            mutated[i] = tbl[rand.getInt(len(tbl))]
+        end
+    end
 
-	return mutated
+    return mutated
 end
 
 // a function to find the index of the string with the best fitness
 def most_fit(strlist)
-	global target
-	
-	best_score = -(len(target) + 1)
-	best_index = 0
-	
-	for j in range(0, len(strlist) - 1)
-		fit = fitness(strlist[j])
-		if fit > best_score
-			best_index = j
-			best_score = fit
-		end
-	end
+    global target
 
-	return {best_index, best_score}
+    best_score = -(len(target) + 1)
+    best_index = 0
+
+    for j in range(0, len(strlist) - 1)
+        fit = fitness(strlist[j])
+        if fit > best_score
+            best_index = j
+            best_score = fit
+        end
+    end
+
+    return {best_index, best_score}
 end
 
 parent = rand_string(len(target)); iter = 1
 while parent != target
-	children = {}
+    children = {}
 
-	for i in range(1, 30)
-		children.append(mutate(parent))
-	end
+    for i in range(1, 30)
+        children.append(mutate(parent))
+    end
 
-	fit = most_fit(children)
-	parent = children[fit[0]]
-	print format("iter %d, score %d: %s\n", iter, fit[1], parent)
+    fit = most_fit(children)
+    parent = children[fit[0]]
+    print format("iter %d, score %d: %s\n", iter, fit[1], parent)
 
-	iter += 1
+    iter += 1
 end