PHP LL_GradientDescent_Regression Examples

Example #1

File: gradient_descent_test.php Project: vinamilvinamil/Learning-Library-for-PHP

 public function testGradientDescent()
 {
     $xs = array(array(1, 2, 2, 2), array(1, 3, 3, 3), array(1, 4, 4, 4), array(1, 5, 5, 5));
     $ys = array(6, 9, 12, 15);
     $parameters = array(0, 0, 0, 0);
     $gd = new LL_GradientDescent_Regression($xs, $ys, $parameters);
     $gd->setLearningRate(0.01);
     $gd->setRepetitions(10000);
     $gd->train();
     $results = $gd->getParameters();
     $this->assertLessThan(0.01, array_shift($results));
     foreach ($results as $coefficient) {
         $this->assertGreaterThan(0.999, $coefficient);
     }
     $gd->reset();
     $gd->setLearningRate(0.03);
     $gd->setRepetitions(5);
     $results = $gd->train();
     $this->assertLessThan(0.3, array_shift($results));
     $this->assertGreaterThan(0.2, array_shift($results));
     foreach ($results as $coefficient) {
         $this->assertGreaterThan(0.99, $coefficient);
     }
 }

Example #2

File: logistic.php Project: vinamilvinamil/Learning-Library-for-PHP

 protected function hypothesis($row = null)
 {
     return $this->sigmoid(parent::hypothesis($row));
 }

Example #3

File: stochastic_gradient_descent.php Project: vinamilvinamil/Learning-Library-for-PHP

 function __construct($xs, $ys, $initialParameters = null)
 {
     parent::__construct($xs, $ys, $initialParameters);
     $this->setBadIterationsThreshold(100);
     // set a higher default ootb.
 }