create matrix calculation for ls regression for multiple variable

src/Phpml/Exception/InvalidArgumentException.php

@@ -41,4 +41,21 @@ class InvalidArgumentException extends \Exception
     {
         return new self(sprintf('The array must have at least %s elements', $minimumSize));
     }
+
+    /**
+     * @return InvalidArgumentException
+     */
+    public static function matrixDimensionsDidNotMatch()
+    {
+        return new self('Matrix dimensions did not match');
+    }
+
+    /**
+     * @return InvalidArgumentException
+     */
+    public static function inconsistentMatrixSupplied()
+    {
+        return new self('Inconsistent matrix aupplied');
+    }
+
 }

src/Phpml/Exception/MatrixException.php

@@ -0,0 +1,25 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\Exception;
+
+class MatrixException extends \Exception
+{
+    /**
+     * @return MatrixException
+     */
+    public static function notSquareMatrix()
+    {
+        return new self('Matrix is not square matrix');
+    }
+
+    /**
+     * @return MatrixException
+     */
+    public static function columnOutOfRange()
+    {
+        return new self('Column out of range');
+    }
+
+}

src/Phpml/Math/Matrix.php

@@ -0,0 +1,253 @@
+<?php
+declare(strict_types = 1);
+
+namespace Phpml\Math;
+
+use Phpml\Exception\InvalidArgumentException;
+use Phpml\Exception\MatrixException;
+
+class Matrix
+{
+    /**
+     * @var array
+     */
+    private $matrix;
+
+    /**
+     * @var int
+     */
+    private $rows;
+
+    /**
+     * @var int
+     */
+    private $columns;
+
+    /**
+     * @var float
+     */
+    private $determinant;
+
+    /**
+     * @param array $matrix
+     * @param bool $validate
+     *
+     * @throws InvalidArgumentException
+     */
+    public function __construct(array $matrix, bool $validate = true)
+    {
+        $this->rows = count($matrix);
+        $this->columns = count($matrix[0]);
+
+        if($validate) {
+            for ($i = 0; $i < $this->rows; $i++) {
+                if (count($matrix[$i]) !== $this->columns) {
+                    throw InvalidArgumentException::matrixDimensionsDidNotMatch();
+                }
+            }
+        }
+
+        $this->matrix = $matrix;
+    }
+
+    /**
+     * @return array
+     */
+    public function toArray()
+    {
+        return $this->matrix;
+    }
+
+    /**
+     * @return int
+     */
+    public function getRows()
+    {
+        return $this->rows;
+    }
+
+    /**
+     * @return int
+     */
+    public function getColumns()
+    {
+        return $this->columns;
+    }
+
+    /**
+     * @param $column
+     *
+     * @return array
+     *
+     * @throws MatrixException
+     */
+    public function getColumnValues($column)
+    {
+        if($column >= $this->columns) {
+            throw MatrixException::columnOutOfRange();
+        }
+
+        $values = [];
+        for ($i = 0; $i < $this->rows; $i++) {
+            $values[] = $this->matrix[$i][$column];
+        }
+
+        return $values;
+    }
+
+    /**
+     * @return float|int
+     * 
+     * @throws MatrixException
+     */
+    public function getDeterminant()
+    {
+        if($this->determinant) {
+            return $this->determinant;
+        }
+
+        if (!$this->isSquare()) {
+            throw MatrixException::notSquareMatrix();
+        }
+
+        $determinant = 0;
+        if ($this->rows == 1 && $this->columns == 1) {
+            $determinant = $this->matrix[0][0];
+        } else if ($this->rows == 2 && $this->columns == 2) {
+            $determinant = $this->matrix[0][0] * $this->matrix[1][1] -
+                $this->matrix[0][1] * $this->matrix[1][0];
+        } else {
+            for ($j = 0; $j < $this->columns; $j++) {
+                $subMatrix = $this->crossOut(0, $j);
+                if (fmod($j, 2) == 0) {
+                    $determinant += $this->matrix[0][$j] * $subMatrix->getDeterminant();
+                } else {
+                    $determinant -= $this->matrix[0][$j] * $subMatrix->getDeterminant();
+                }
+            }
+        }
+
+        return $this->determinant = $determinant;
+    }
+
+        /**
+     * @return bool
+     */
+    public function isSquare()
+    {
+        return $this->columns === $this->rows;
+    }
+
+    /**
+     * @return Matrix
+     */
+    public function transpose()
+    {
+        $newMatrix = [];
+        for ($i = 0; $i < $this->rows; $i++) {
+            for ($j = 0; $j < $this->columns; $j++) {
+                $newMatrix[$j][$i] = $this->matrix[$i][$j];
+            }
+        }
+
+        return new self($newMatrix, false);
+    }
+
+    /**
+     * @param Matrix $matrix
+     *
+     * @return Matrix
+     *
+     * @throws InvalidArgumentException
+     */
+    public function multiply(Matrix $matrix)
+    {
+        if ($this->columns != $matrix->getRows()) {
+            throw InvalidArgumentException::inconsistentMatrixSupplied();
+        }
+
+        $product = [];
+        $multiplier = $matrix->toArray();
+        for ($i = 0; $i < $this->rows; $i++) {
+            for ($j = 0; $j < $matrix->getColumns(); $j++) {
+                $product[$i][$j] = 0;
+                for ($k = 0; $k < $this->columns; $k++) {
+                    $product[$i][$j] += $this->matrix[$i][$k] * $multiplier[$k][$j];
+                }
+            }
+        }
+        return new self($product, false);
+    }
+
+    /**
+     * @param $value
+     *
+     * @return Matrix
+     */
+    public function divideByScalar($value)
+    {
+        $newMatrix = array();
+        for ($i = 0; $i < $this->rows; $i++) {
+            for ($j = 0; $j < $this->columns; $j++) {
+                $newMatrix[$i][$j] = $this->matrix[$i][$j] / $value;
+            }
+        }
+
+        return new self($newMatrix, false);
+    }
+
+    /**
+     * @return Matrix
+     *
+     * @throws MatrixException
+     */
+    public function inverse()
+    {
+        if (!$this->isSquare()) {
+            throw MatrixException::notSquareMatrix();
+        }
+
+        $newMatrix = array();
+        for ($i = 0; $i < $this->rows; $i++) {
+            for ($j = 0; $j < $this->columns; $j++) {
+                $subMatrix = $this->crossOut($i, $j);
+                if (fmod($i + $j, 2) == 0) {
+                    $newMatrix[$i][$j] = ($subMatrix->getDeterminant());
+                } else {
+                    $newMatrix[$i][$j] = -($subMatrix->getDeterminant());
+                }
+            }
+        }
+
+        $cofactorMatrix = new self($newMatrix, false);
+
+        return $cofactorMatrix->transpose()->divideByScalar($this->getDeterminant());
+    }
+
+    /**
+     * @param int $row
+     * @param int $column
+     *
+     * @return Matrix
+     */
+    public function crossOut(int $row, int $column)
+    {
+        $newMatrix = [];
+        $r = 0;
+        for ($i = 0; $i < $this->rows; $i++) {
+            $c = 0;
+            if ($row != $i) {
+                for ($j = 0; $j < $this->columns; $j++) {
+                    if ($column != $j) {
+                        $newMatrix[$r][$c] = $this->matrix[$i][$j];
+                        $c++;
+                    }
+                }
+                $r++;
+            }
+        }
+
+        return new self($newMatrix, false);
+    }
+
+}

src/Phpml/Regression/LeastSquares.php

@@ -4,9 +4,7 @@ declare (strict_types = 1);
 
 namespace Phpml\Regression;
 
-use Phpml\Math\Statistic\Correlation;
+use Phpml\Math\Matrix;
-use Phpml\Math\Statistic\StandardDeviation;
-use Phpml\Math\Statistic\Mean;
 
 class LeastSquares implements Regression
 {
@@ -15,26 +13,21 @@ class LeastSquares implements Regression
      */
     private $samples;
 
-    /**
-     * @var array
-     */
-    private $features;
-
     /**
      * @var array
      */
     private $targets;
 
-    /**
-     * @var array
-     */
-    private $slopes;
-
     /**
      * @var float
      */
     private $intercept;
 
+    /**
+     * @var array
+     */
+    private $coefficients;
+
     /**
      * @param array $samples
      * @param array $targets
@@ -43,22 +36,20 @@ class LeastSquares implements Regression
     {
         $this->samples = $samples;
         $this->targets = $targets;
-        $this->features = [];
 
-        $this->computeSlopes();
+        $this->computeCoefficients();
-        $this->computeIntercept();
     }
 
     /**
-     * @param float $sample
+     * @param array $sample
      *
      * @return mixed
      */
     public function predict($sample)
     {
         $result = $this->intercept;
-        foreach ($this->slopes as $index => $slope) {
+        foreach ($this->coefficients as $index => $coefficient) {
-            $result += ($slope * $sample[$index]);
+            $result += $coefficient * $sample[$index];
         }
 
         return $result;
@@ -67,45 +58,23 @@ class LeastSquares implements Regression
     /**
      * @return array
      */
-    public function getSlopes()
+    public function getCoefficients()
     {
-        return $this->slopes;
+        return $this->coefficients;
-    }
-
-    private function computeSlopes()
-    {
-        $features = count($this->samples[0]);
-        $sdY = StandardDeviation::population($this->targets);
-
-        for($i=0; $i<$features; $i++) {
-            $correlation = Correlation::pearson($this->getFeatures($i), $this->targets);
-            $sdXi = StandardDeviation::population($this->getFeatures($i));
-            $this->slopes[] = $correlation * ($sdY / $sdXi);
-        }
-    }
-
-    private function computeIntercept()
-    {
-        $this->intercept = Mean::arithmetic($this->targets);
-        foreach ($this->slopes as $index => $slope) {
-            $this->intercept -= $slope * Mean::arithmetic($this->getFeatures($index));
-        }
     }
 
     /**
-     * @param $index
+     * coefficient(b) = (X'X)-1X'Y
-     *
-     * @return array
      */
-    private function getFeatures($index)
+    private function computeCoefficients()
     {
-        if(!isset($this->features[$index])) {
+        $samplesMatrix = new Matrix($this->samples);
-            $this->features[$index] = [];
+        $targetsMatrix = new Matrix($this->targets);
-            foreach ($this->samples as $sample) {
-                $this->features[$index][] = $sample[$index];
-            }
-        }
 
-        return $this->features[$index];
+        $ts = $samplesMatrix->transpose()->multiply($samplesMatrix)->inverse();
+        $tf = $samplesMatrix->transpose()->multiply($targetsMatrix);
+
+        $this->coefficients = $ts->multiply($tf)->getColumnValues(0);
+        $this->intercept = array_shift($this->coefficients);
     }
 }

tests/Phpml/Regression/LeastSquaresTest.php

@@ -13,8 +13,8 @@ class LeastSquaresTest extends \PHPUnit_Framework_TestCase
         $delta = 0.01;
 
         //https://www.easycalculation.com/analytical/learn-least-square-regression.php
-        $samples = [[60], [61], [62], [63], [65]];
+        $samples = [[1, 60], [1, 61], [1, 62], [1, 63], [1, 65]];
-        $targets = [3.1, 3.6, 3.8, 4, 4.1];
+        $targets = [[3.1], [3.6], [3.8], [4], [4.1]];
 
         $regression = new LeastSquares();
         $regression->train($samples, $targets);
@@ -22,8 +22,8 @@ class LeastSquaresTest extends \PHPUnit_Framework_TestCase
         $this->assertEquals(4.06, $regression->predict([64]), '', $delta);
 
         //http://www.stat.wmich.edu/s216/book/node127.html
-        $samples = [[9300], [10565], [15000], [15000], [17764], [57000], [65940], [73676], [77006], [93739], [146088], [153260]];
+        $samples = [[1 ,9300], [1, 10565], [1, 15000], [1, 15000], [1, 17764], [1, 57000], [1, 65940], [1, 73676], [1, 77006], [1, 93739], [1, 146088], [1, 153260]];
-        $targets = [7100, 15500, 4400, 4400, 5900, 4600, 8800, 2000, 2750, 2550,  960, 1025];
+        $targets = [[7100], [15500], [4400], [4400], [5900], [4600], [8800], [2000], [2750], [2550],  [960], [1025]];
 
         $regression = new LeastSquares();
         $regression->train($samples, $targets);
@@ -37,16 +37,16 @@ class LeastSquaresTest extends \PHPUnit_Framework_TestCase
 
     public function testPredictMultiFeaturesSamples()
     {
-        $delta = 0.01;
+        $delta = 1;
 
         //http://www.stat.wmich.edu/s216/book/node129.html
-        $samples = [[73676, 1996],[77006,1998],[ 10565, 2000],[146088, 1995],[ 15000, 2001],[ 65940, 2000],[  9300, 2000],[ 93739, 1996],[153260, 1994],[ 17764, 2002],[ 57000, 1998],[ 15000, 2000]];
+        $samples = [[1,  73676, 1996],[1,  77006, 1998],[1,  10565, 2000],[1, 146088, 1995],[1,  15000, 2001],[1,  65940, 2000],[1,   9300, 2000],[1,  93739, 1996],[1, 153260, 1994],[1,  17764, 2002],[1,  57000, 1998],[1,  15000, 2000]];
-        $targets = [2000, 2750, 15500,  960, 4400, 8800, 7100, 2550, 1025, 5900, 4600, 4400];
+        $targets = [[2000], [ 2750], [15500], [  960], [ 4400], [ 8800], [ 7100], [ 2550], [ 1025], [ 5900], [ 4600], [ 4400]];
 
         $regression = new LeastSquares();
         $regression->train($samples, $targets);
 
-        $this->assertEquals(3807, $regression->predict([60000, 1996]), '', $delta);
+        $this->assertEquals(4094, $regression->predict([60000, 1996]), '', $delta);
     }
 
 }