ls reg with error :(

src/Phpml/Regression/LeastSquares.php

@@ -10,6 +10,11 @@ use Phpml\Math\Statistic\Mean;
 
 class LeastSquares implements Regression
 {
+    /**
+     * @var array
+     */
+    private $samples;
+
     /**
      * @var array
      */
@@ -21,52 +26,86 @@ class LeastSquares implements Regression
     private $targets;
 
     /**
-     * @var float
+     * @var array
      */
-    private $slope;
+    private $slopes;
 
     /**
-     * @var
+     * @var float
      */
     private $intercept;
 
     /**
-     * @param array $features
+     * @param array $samples
      * @param array $targets
      */
-    public function train(array $features, array $targets)
+    public function train(array $samples, array $targets)
     {
-        $this->features = $features;
+        $this->samples = $samples;
         $this->targets = $targets;
+        $this->features = [];
 
-        $this->computeSlope();
+        $this->computeSlopes();
         $this->computeIntercept();
     }
 
     /**
-     * @param float $feature
+     * @param float $sample
      *
      * @return mixed
      */
-    public function predict($feature)
+    public function predict($sample)
     {
-        return $this->intercept + ($this->slope * $feature);
+        $result = $this->intercept;
+        foreach ($this->slopes as $index => $slope) {
+            $result += ($slope * $sample[$index]);
         }
 
-    private function computeSlope()
+        return $result;
+    }
+
+    /**
+     * @return array
+     */
+    public function getSlopes()
     {
-        $correlation = Correlation::pearson($this->features, $this->targets);
+        return $this->slopes;
-        $sdX = StandardDeviation::population($this->features);
+    }
+
+    private function computeSlopes()
+    {
+        $features = count($this->samples[0]);
         $sdY = StandardDeviation::population($this->targets);
 
-        $this->slope = $correlation * ($sdY / $sdX);
+        for($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()
     {
-        $meanY = Mean::arithmetic($this->targets);
+        $this->intercept = Mean::arithmetic($this->targets);
-        $meanX = Mean::arithmetic($this->features);
+        foreach ($this->slopes as $index => $slope) {
+            $this->intercept -= $slope * Mean::arithmetic($this->getFeatures($index));
+        }
+    }
 
-        $this->intercept = $meanY - ($this->slope * $meanX);
+    /**
+     * @param $index
+     *
+     * @return array
+     */
+    private function getFeatures($index)
+    {
+        if(!isset($this->features[$index])) {
+            $this->features[$index] = [];
+            foreach ($this->samples as $sample) {
+                $this->features[$index][] = $sample[$index];
+            }
+        }
+
+        return $this->features[$index];
     }
 }

src/Phpml/Regression/Regression.php

@@ -7,15 +7,15 @@ namespace Phpml\Regression;
 interface Regression
 {
     /**
-     * @param array $features
+     * @param array $samples
      * @param array $targets
      */
-    public function train(array $features, array $targets);
+    public function train(array $samples, array $targets);
 
     /**
-     * @param float $feature
+     * @param float $sample
      *
      * @return mixed
      */
-    public function predict($feature);
+    public function predict($sample);
 }

tests/Phpml/Math/Statistic/CorrelationTest.php

@@ -21,6 +21,11 @@ class CorrelationTest extends \PHPUnit_Framework_TestCase
         $x = [43, 21, 25, 42, 57, 59];
         $y = [99, 65, 79, 75, 87, 82];
         $this->assertEquals(0.549, Correlation::pearson($x, $y), '', $delta);
+
+        $delta = 0.001;
+        $x = [60, 61, 62, 63, 65];
+        $y = [3.1, 3.6, 3.8, 4, 4.1];
+        $this->assertEquals(0.911, Correlation::pearson($x, $y), '', $delta);
     }
 
     /**

tests/Phpml/Regression/LeastSquaresTest.php

@@ -8,30 +8,45 @@ use Phpml\Regression\LeastSquares;
 
 class LeastSquaresTest extends \PHPUnit_Framework_TestCase
 {
-    public function testPredictSingleFeature()
+    public function testPredictSingleFeatureSamples()
     {
         $delta = 0.01;
 
         //https://www.easycalculation.com/analytical/learn-least-square-regression.php
-        $features = [60, 61, 62, 63, 65];
+        $samples = [[60], [61], [62], [63], [65]];
         $targets = [3.1, 3.6, 3.8, 4, 4.1];
 
         $regression = new LeastSquares();
-        $regression->train($features, $targets);
+        $regression->train($samples, $targets);
 
-        $this->assertEquals(4.06, $regression->predict(64), '', $delta);
+        $this->assertEquals(4.06, $regression->predict([64]), '', $delta);
 
         //http://www.stat.wmich.edu/s216/book/node127.html
-        $features = [9300,  10565,  15000,  15000,  17764,  57000,  65940,  73676,  77006,  93739, 146088, 153260];
+        $samples = [[9300], [10565], [15000], [15000], [17764], [57000], [65940], [73676], [77006], [93739], [146088], [153260]];
         $targets = [7100, 15500, 4400, 4400, 5900, 4600, 8800, 2000, 2750, 2550,  960, 1025];
 
         $regression = new LeastSquares();
-        $regression->train($features, $targets);
+        $regression->train($samples, $targets);
 
-        $this->assertEquals(7659.35, $regression->predict(9300), '', $delta);
+        $this->assertEquals(7659.35, $regression->predict([9300]), '', $delta);
-        $this->assertEquals(5213.81, $regression->predict(57000), '', $delta);
+        $this->assertEquals(5213.81, $regression->predict([57000]), '', $delta);
-        $this->assertEquals(4188.13, $regression->predict(77006), '', $delta);
+        $this->assertEquals(4188.13, $regression->predict([77006]), '', $delta);
-        $this->assertEquals(7659.35, $regression->predict(9300), '', $delta);
+        $this->assertEquals(7659.35, $regression->predict([9300]), '', $delta);
-        $this->assertEquals(278.66, $regression->predict(153260), '', $delta);
+        $this->assertEquals(278.66, $regression->predict([153260]), '', $delta);
     }
+
+    public function testPredictMultiFeaturesSamples()
+    {
+        $delta = 0.01;
+
+        //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]];
+        $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);
+    }
+
 }