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<?php
declare(strict_types=1);
namespace Phpml\Regression;
use Phpml\Helper\Predictable;
use Phpml\Math\Matrix;
class LeastSquares implements Regression
{
use Predictable;
/**
* @var array
*/
private $samples = [];
/**
* @var array
*/
private $targets = [];
/**
* @var float
*/
private $intercept;
/**
* @var array
*/
private $coefficients = [];
public function train(array $samples, array $targets): void
{
$this->samples = array_merge($this->samples, $samples);
$this->targets = array_merge($this->targets, $targets);
$this->computeCoefficients();
}
/**
* @return mixed
*/
public function predictSample(array $sample)
{
$result = $this->intercept;
foreach ($this->coefficients as $index => $coefficient) {
$result += $coefficient * $sample[$index];
}
return $result;
}
public function getCoefficients(): array
{
return $this->coefficients;
}
public function getIntercept(): float
{
return $this->intercept;
}
/**
* coefficient(b) = (X'X)-1X'Y.
*/
private function computeCoefficients(): void
{
$samplesMatrix = $this->getSamplesMatrix();
$targetsMatrix = $this->getTargetsMatrix();
$ts = $samplesMatrix->transpose()->multiply($samplesMatrix)->inverse();
$tf = $samplesMatrix->transpose()->multiply($targetsMatrix);
$this->coefficients = $ts->multiply($tf)->getColumnValues(0);
$this->intercept = array_shift($this->coefficients);
}
/**
* Add one dimension for intercept calculation.
*/
private function getSamplesMatrix(): Matrix
{
$samples = [];
foreach ($this->samples as $sample) {
array_unshift($sample, 1);
$samples[] = $sample;
}
return new Matrix($samples);
}
private function getTargetsMatrix(): Matrix
{
if (is_array($this->targets[0])) {
return new Matrix($this->targets);
}
return Matrix::fromFlatArray($this->targets);
}
}