TS_core01

import random
import matplotlib.pyplot

def matrixCreate(rows,columns):
    list = [[0 for x in xrange(columns)] for x in xrange(rows)]
    return list

def matrixRandomize(v):
        for row in range(len(v)):
            for column in range(len(v[row])):
                v[row][column] = random.random()
        return v

def fitness(v):
    total, average = 0.0,0.0
    for row in range(len(v)):
        for column in range(len(v[row])):
            total += v[row][column]
    average = total/(len(v)*len(v[row]))
    return average

def matrixPerturb(p, prob):
    c = matrixCreate(len(p),len(p[0]))
    for row in range(len(p)):
        for column in range(len(p[row])):
            c[row][column] = p[row][column]
            if prob > random.random():
                c[row][column] = random.random()
    return c

def plotVectorAsLine(fits):
    matplotlib.pyplot.plot(fits[0])
    matplotlib.pyplot.show()
    return

genes = matrixCreate(50, 5000)
for i in range(5):
    parent = matrixCreate(1, 50)
    parent = matrixRandomize(parent)
    parentFitness = fitness(parent)
    fits = matrixCreate(1, 5000)
    for currentGeneration in range(5000):
        for j in range(50):
            genes[j][currentGeneration] = parent[0][j]
        print currentGeneration, parentFitness
        child = matrixPerturb(parent, 0.05)
        childFitness = fitness(child)
        fits[0][currentGeneration] = parentFitness
        if childFitness > parentFitness:
            parent = child
            parentFitness = childFitness
    #plotVectorAsLine(fits)

matplotlib.pyplot.imshow(genes, cmap=matplotlib.pyplot.cm.gray, aspect='auto', interpolation='nearest')
matplotlib.pyplot.show()