dogCount; }
public function setDogCount($count) { $this->dogCount = $count; }
public function getCatCount() { return $this->catCount; }
public function setCatCount($count) { $this->catCount = $count; }
public function getMouseCount() { return $this->mouseCount; }
public function setMouseCount($count) { $this->mouseCount = $count; }
public function getTotalCount() {
return $this->getDogCount() +
$this->getCatCount() +
$this->getMouseCount();
}
public function getTotalCost() {
return ($this->getDogCount() * 15) +
$this->getCatCount() +
($this->getMouseCount() * 0.25);
}
/**
* Generate a random set of counts, within reasonable constraints.
*/
public function randomize() {
$this->setDogCount(rand(1,6));
$this->setCatCount(rand(1,85));
$this->setMouseCount(rand(1,336));
}
/**
* Return the fitness of this solution as a score
* from 0 (worst) to 100 (perfect).
* See: http://en.wikipedia.org/wiki/Fitness_function.
*/
public function getFitness() {
// There must be at least one of each animal:
if ($this->getDogCount() == 0 ||
$this->getCatCount() == 0 ||
$this->getMouseCount() == 0)
return 0;
$countError = abs(100 - $this->getTotalCount());
$amountError = abs(100 - $this->getTotalCost());
if ($countError > 50 || $amountError > 50)
return 0;
else
return 100 - ($countError + $amountError);
}
/**
* Set my attributes from those of my parents.
* This is also known as breeding, crossover, or recombination.
*/
public function combine($dad, $mom) {
$this->setDogCount(rand(0,1) == 0 ?
$dad->getDogCount() : $mom->getDogCount());
$this->setCatCount(rand(0,1) == 0 ?
$dad->getCatCount() : $mom->getCatCount());
$this->setMouseCount(rand(0,1) == 0 ?
$dad->getMouseCount() : $mom->getMouseCount());
}
/**
* Mutate my attributes so that I'm not an exact clone of my parents.
*/
public function mutate() {
$this->setDogCount(rand(0,2) == 0 ?
rand(1,6) : $this->getDogCount());
$this->setCatCount(rand(0,2) == 0 ?
rand(1,85) : $this->getCatCount());
$this->setMouseCount(rand(0,2) == 0 ?
rand(1,336) : $this->getMouseCount());
}
/**
* Compare fitness levels for sorting.
*/
public function compareFitness($purchase1, $purchase2) {
if ($purchase1->getFitness() == $purchase2->getFitness())
return 0;
return ($purchase1->getFitness() > $purchase2->getFitness()) ? -1 : 1;
}
/**
* Format myself in table form.
*/
public function printMe() {
echo '';
echo '| Dogs | Cats | Mice | Count | Cost | Fitness | ';
echo '';
printf('| %d | ',$this->getDogCount());
printf('%d | ',$this->getCatCount());
printf('%d | ',$this->getMouseCount());
printf('%d | ',$this->getTotalCount());
printf('%.3f | ',$this->getTotalCost());
printf('%.3f | ',$this->getFitness());
echo '
';
}
// Class (static):
/**
* Create/initialize a population of the given size with random individuals.
*/
public static function createPopulation($size) {
$population = array();
for ($i=0; $i<$size; $i++) {
$purchase = new PetPurchase();
$purchase->randomize();
array_push($population, $purchase);
}
return $population;
}
/**
* Select a subset of the given population for breeding a new generation.
* Return this subset ordered by the most fit first.
*/
public static function select($population, $size) {
usort($population, array('PetPurchase', 'compareFitness'));
return array_slice($population, 0, $size);
}
/**
* Breed the items in the given population to produce a new generation.
* We'll breed 2 kids for each pair of parents.
*/
public static function breed($population) {
$nextGeneration = array();
for ($i=0; $icombine($dad, $mom);
array_push($nextGeneration, $kid1);
$kid2 = new PetPurchase();
$kid2->combine($dad, $mom);
$kid2->mutate(); // evil twin
array_push($nextGeneration, $kid2);
}
return $nextGeneration;
}
/**
* Find the most fit solution by breeding the given number of generations,
* each with the specified population size.
* Return an array with the most fit and the number of generations created.
*/
public static function findMostFit($generations, $populationSize) {
$population = self::createPopulation($populationSize);
$mostFit = new PetPurchase();
for ($i=0; $i<$generations; $i++) {
$population = self::select($population, $populationSize);
if ($population[0]->getFitness() > $mostFit->getFitness())
$mostFit = $population[0];
if ($mostFit->getFitness() == 100)
break;
$population = self::breed($population);
}
return array($mostFit, $i);
}
}
?>