#!/usr/bin/python

import random

######################### Configurable options
eta = 1.5
peaks = [1,4,9,16,25,36,49,64,81,100]
max_generations = 10
initial_population = 25
######################### Don't change stuff past here unless you know what you're doing
fitness_array = { }
print "# peaks = " + str(peaks)
print "# eta = " + str(eta)
print "# max_generations = " + str(max_generations)
print "# COLUMNS: Generation || Number || Occurences || % of Population || Average Num Copies (fitness)"

def make_fitness_array():
	for spot in range(101):
		global eta
		global peaks
		global fitness_array

		shortest_distance = 999		# Just some initial value that will be erased over
		for peak in peaks:
			distance = abs(spot - peak)
			if distance < shortest_distance:
				shortest_distance = distance

		if shortest_distance == 0 :
			fitness = eta * 1.0
		else: 
			fitness = eta * (1.0 / shortest_distance)

		fitness_array[spot] = fitness

def make_initial_population():
	global initial_population
	population = [ ]

	for i in range(initial_population):
		spot = int(round(random.random() * 100))
		population.append(spot)

	return population

def how_many_children():
	global population
	global fitness_array

	number_children = [ ]

	for critter in population:
		int_numkids = int(fitness_array[critter])
		decimal_numkids = fitness_array[critter] - int_numkids;
		if random.random() < decimal_numkids:
			int_numkids = int_numkids + 1

		number_children.append(int_numkids)

	return number_children

def mutation(parent):
	random_number = random.random()
	if random_number < 0.25:		# Do a negative mutation
		child = parent - 1
		if child < 0:			# So we don't go below 0
			child = 0

	elif random_number < 0.5:		# Do a positive mutation
		child = parent + 1
		if child > 100:			# So we don't go above 100
			child = 100
	else:
		child = parent			# Don't do any mutation

	return child

def make_next_generation():
	global population
	global numkids_to_make
	children = [ ]

	for i in range(len(population)):			# We're going to make kids for every single critter (assuming they have any)
		for count in range(numkids_to_make[i]):		# Runs for numkids >= 1
			children.append( mutation(population[i]) )
	return children

def list2dict(list):
	dict = { }
	list.sort()

	for item in list:
		if dict.has_key(item):
			dict[item] += 1
		else:
			dict[item] = 1
	return dict

## Main program begins here

population = [ ]

make_fitness_array()
population = make_initial_population()

# print str(fitness_array)

for generation in range(1,max_generations+1):
	population.sort()
	numkids_to_make = how_many_children()
#	print str(numkids_to_make)
	dict_population = list2dict(population)

	population_size = len(population)
	print "####### Size of Population: " + str(population_size)
	for critter in dict_population.keys():
		percent_population = round( dict_population[critter] / float(population_size), 4)
		print str(generation) + "\t" + str(critter) + "\t" + str(dict_population[critter]) + "\t" + str(percent_population) + "\t" + str(fitness_array[critter])
	population = make_next_generation()