Borrar filtros
Borrar filtros

i want to change this python program to matlab

1 visualización (últimos 30 días)
yared Zeleke
yared Zeleke el 3 de Abr. de 2018
Comentada: yared Zeleke el 10 de Abr. de 2018
import random
import string
target = "Hello, World!"
def calc_fitness(source, target):
fitval = 0
for i in range(0, len(source)):
fitval += (ord(target[i]) - ord(source[i])) ** 2
return(fitval)
def mutate(parent1, parent2):
child_dna = parent1['dna'][:]
# Mix both DNAs
start = random.randint(0, len(parent2['dna']) - 1)
stop = random.randint(0, len(parent2['dna']) - 1)
if start > stop:
stop, start = start, stop
child_dna[start:stop] = parent2['dna'][start:stop]
# Mutate one position
charpos = random.randint(0, len(child_dna) - 1)
child_dna[charpos] = chr(ord(child_dna[charpos]) + random.randint(-1,1))
child_fitness = calc_fitness(child_dna, target)
return({'dna': child_dna, 'fitness': child_fitness})
def random_parent(genepool):
wRndNr = random.random() * random.random() * (GENSIZE - 1)
wRndNr = int(wRndNr)
return(genepool[wRndNr])
def dump_genepool(generation, genepool):
for candidate in genepool:
print "%6i %6i %15s" % (
generation,
candidate['fitness'],
''.join(candidate['dna'])
)
print
GENSIZE = 20
genepool = []
for i in range(0, GENSIZE):
dna = [random.choice(string.printable[:-5]) for j in range(0, len(target))]
fitness = calc_fitness(dna, target)
candidate = {'dna': dna, 'fitness': fitness }
genepool.append(candidate)
generation = 0
while True:
generation += 1
genepool.sort(key=lambda candidate: candidate['fitness'])
dump_genepool(generation, genepool)
if genepool[0]['fitness'] == 0:
# Target reached
break
parent1 = random_parent(genepool)
parent2 = random_parent(genepool)
child = mutate(parent1, parent2)
if child['fitness'] < genepool[-1]['fitness']:
genepool[-1] = child

Respuestas (1)

yared Zeleke
yared Zeleke el 4 de Abr. de 2018
Editada: yared Zeleke el 4 de Abr. de 2018
clc
clear all
target="hello,world!";
function fitval = fitness(source, target)%def calc_fitness;
fitval = 0;
for i = 1 : length(source)
fitval = fitval + (double(target(i)) - double(source(i))) ^ 2;
end
end
function [child_dna,child_fitness]=mutate(parent1,parent2)%def mutate(parent1,
parent2):
child_dna=parent1(1,:);
start=randi(size(parent2(1,:)),1,1);
stop=randi(size(parent2(1,:)),1,1);
if(start>stop) tmp=start; start=stop; stop=start; end
child_dna(start:stop)=parent2(1,start:stop);
charpos=randi(size(child_dna),1,1);
child_dna(charpos)=char(uint8(child_dna(charpos))+randi(3,1,1)-2);
%child_fitness=calc_fitness(child_dna,target);
child_fitness=0;
end
function [x] = random_parent(genepool , GENSIZE)
wRndNr = rand() * rand() * ( GENSIZE - 1 )
wRndNr = int32(wRndNr); x = genepool(wRndNr);
  1 comentario
yared Zeleke
yared Zeleke el 10 de Abr. de 2018
this is some part of it any one would you finish it up please?

Iniciar sesión para comentar.

Categorías

Más información sobre Statistics and Machine Learning Toolbox en Help Center y File Exchange.

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by