Ayuda trabajo de python a java

from random import randint
import numpy
import random
import itertools
import time
import sys
import tkinter
from tkinter.ttk import *

start_time = time.clock()

winningsMatrix = numpy.matrix([
[0.00,0.00,0.00,0.00],
[0.28,0.25,0.15,0.20],
[0.45,0.41,0.25,0.33],
[0.65,0.55,0.40,0.42],
[0.78,0.65,0.50,0.48],
[0.90,0.75,0.62,0.53],
[1.02,0.80,0.73,0.56],
[1.13,0.85,0.82,0.58],
[1.23,0.88,0.90,0.60],
[1.32,0.90,0.96,0.60],
[1.38,0.90,1.00,0.60]])

def create_initial_population(numberofIndividuals):
InitialPopulation = list()
i = 0
while(i < numberofIndividuals):
individual = [randint(0,10) for r in range(0,4)]
summing = individual[0] + individual[1] + individual[2] + individual[3]
if summing == 10 :
InitialPopulation.append(individual)
i = i + 1

return  InitialPopulation

def compute_aptitude( individual ) :
sumInversion = individual[0] + individual[1] + individual[2] + individual[3]
#winnings = (((individual[0] * winningsMatrix[individual[0] - 1 ,0]) / 100) + individual[0]) + (((individual[1] * winningsMatrix[individual[1] - 1,1]) / 100) + individual[1]) + (((individual[2] * winningsMatrix[individual[2] - 1,2]) / 100) + individual[2]) + (((individual[3] * winningsMatrix[individual[3] - 1,3]) / 100) + individual[3])
winnings = (1000000 * winningsMatrix[individual[0] ,0]) + (1000000 * winningsMatrix[individual[1] ,1]) + (1000000 * winningsMatrix[individual[2] ,2]) + (1000000 * winningsMatrix[individual[3] ,3])
aptitude = winnings / ((500 * abs(sumInversion - 10)) + 1)
return aptitude

def save_aptitude(population):
aptitude = list()
for ind in population:
aptitude.append(compute_aptitude(ind))
return aptitude

def make_selection(population):
selection = list()
for i in range(0, len(population)) :
firstElement= random.choice(population)
secondElement = random.choice(population)
if compute_aptitude(firstElement) > compute_aptitude(secondElement):
selection.append(firstElement)
else :
selection.append(secondElement)
return selection

def one_point_cross(parent1, parent2, crossPoint) :
lefParent1 = parent1[0:crossPoint]
rigthParent1 = parent1[crossPoint: len(parent1)]
lefParent2 = parent2[0: crossPoint]
rigthParent2 = parent2[crossPoint: len(parent2)]

children1 = lefParent1 + rigthParent2
children2 = lefParent2 + rigthParent1
return children1, children2

def cross_parents(population, crossProbability) :
newPopulation = list()
crossPoint = randint(0,3)
for i in range(0, len(population)) :
parent1 = random.choice(population)
parent2 = random.choice(population)

	probability = random.uniform(0.0,1.0)
	if (probability >= crossProbability) :
		children = one_point_cross(parent1,parent2, crossPoint)
		newPopulation.append(children[0])
		newPopulation.append(children[1])
	else :
		newPopulation.append(parent1)
		newPopulation.append(parent2)

return newPopulation

def mutation(individual, mutationPoint1, mutationPoint2):
m1 = individual[mutationPoint1]
m2 = individual[mutationPoint2]

individual[mutationPoint1] = m2
individual[mutationPoint2] = m1

return individual

def make_mutation(population, mutationProbability) :
newPopulation = list()
mut1 = randint(0,3)
mut2 = randint(0,3)
for ind in population:
probability = random.uniform(0.0, 1.0)
if mutationProbability >= probability :
newPopulation.append(mutation(ind, mut1, mut2))
else :
newPopulation.append(ind)

return newPopulation

def getBest(population) :
aptitude = save_aptitude(population)
bestInd = max(aptitude)
bestIndividual = None
for i in range(0,len(aptitude)):
if aptitude == bestInd:
bestIndividual = population

return bestIndividual

def main(generationsNumber, crossProbability, mutationProbability, numberofIndividuals):
bestIndividuals = list()
population = create_initial_population(numberofIndividuals)

for i in range(0, generationsNumber) :
	selection = make_selection(population)
	cross = cross_parents(selection, crossProbability)
	mutate = make_mutation(cross, mutationProbability)
	bestIndividuals.append(getBest(mutate))


for i in range(0, len(bestIndividuals)):
	print("Best Individual of Generation {}: {}".format( i + 1, bestIndividuals[i] ))

best = getBest(bestIndividuals)

print("DISTRIBUCIÓN DE LA INVERSIÓN CON MAYORES GANANCIAS:")
print("CIUDAD 1: {} MILLONES, GANANCIA: ${}".format(best[0], ((winningsMatrix[best[0],0])*1000000)))
print("CIUDAD 2: {} MILLONES, GANANCIA: ${}".format(best[1], ((winningsMatrix[best[1],1])*1000000)))
print("CIUDAD 3: {} MILLONES, GANANCIA: ${}".format(best[2], ((winningsMatrix[best[2],2])*1000000)))
print("CIUDAD 4: {} MILLONES, GANANCIA: ${}".format(best[3], ((winningsMatrix[best[3],3])*1000000)))
print("GANANCIA TOTAL: ${}00".format(compute_aptitude(best)))

print ("Time to reach the final state: {} seconds".format( time.clock() - start_time))

main(20,0.8,0.01,50)
#root = tkinter.Tk() #Create main window
#root.title("ALGORITMO GENÉTICO")
#root.config (bg = "white") #Set background color
#root.geometry ("400x400") #Set size of window

#individuos = Label(text='INDIVIDUOS DE LA POBLACIÓN').pack(padx=10,pady=10)
#x = Entry(root, width=10).pack(padx=10,pady=10)

#generaciones = Label(text='NÚMERO DE GENERACIONES').pack(padx=10,pady=10)
#y = Entry(root, width=10).pack(padx=10,pady=10)

#probCruza = Label(text='PROBABILIDAD DE CRUZA').pack(padx=10,pady=10)
#z = Entry(root, width=10).pack(padx=10,pady=10)

#probMuta = Label(text='PROBABILIDAD DE MUTACIÓN').pack(padx=10,pady=10)
#a = Entry(root, width=10).pack(padx=10,pady=10)

#buttonCapture = tkinter.ttk.Button(root, text = 'EJECUTAR', command = "") #Create button and assign it a function to execute
#buttonCapture.pack(padx=10,pady=10) #Execute function when click in the button

#root.mainloop()