Support for multiple training datasets (#38)

* Multiple training data sets allowed

* Tests with multiple training data sets

* Updating docs according to #38

Documenting all models which predictions will be based on all
training data provided.

Some models already supported multiple training data sets.

docs/machine-learning/association/apriori.md

@@ -27,6 +27,8 @@ $associator = new Apriori($support = 0.5, $confidence = 0.5);
 $associator->train($samples, $labels);
 ```
 
+You can train the associator using multiple data sets, predictions will be based on all the training data.
+
 ### Predict
 
 To predict sample label use `predict` method. You can provide one sample or array of samples:

docs/machine-learning/classification/k-nearest-neighbors.md

@@ -24,6 +24,8 @@ $classifier = new KNearestNeighbors();
 $classifier->train($samples, $labels);
 ```
 
+You can train the classifier using multiple data sets, predictions will be based on all the training data.
+
 ## Predict
 
 To predict sample label use `predict` method. You can provide one sample or array of samples:

docs/machine-learning/classification/naive-bayes.md

@@ -14,6 +14,8 @@ $classifier = new NaiveBayes();
 $classifier->train($samples, $labels);
 ```
 
+You can train the classifier using multiple data sets, predictions will be based on all the training data.
+
 ### Predict
 
 To predict sample label use `predict` method. You can provide one sample or array of samples:

docs/machine-learning/classification/svc.md

@@ -34,6 +34,8 @@ $classifier = new SVC(Kernel::LINEAR, $cost = 1000);
 $classifier->train($samples, $labels);
 ```
 
+You can train the classifier using multiple data sets, predictions will be based on all the training data.
+
 ### Predict
 
 To predict sample label use `predict` method. You can provide one sample or array of samples:

docs/machine-learning/neural-network/backpropagation.md

@@ -27,3 +27,4 @@ $training->train(
     $maxIteraions = 30000
 );
 ```
+You can train the neural network using multiple data sets, predictions will be based on all the training data.

docs/machine-learning/regression/least-squares.md

@@ -14,6 +14,8 @@ $regression = new LeastSquares();
 $regression->train($samples, $targets);
 ```
 
+You can train the model using multiple data sets, predictions will be based on all the training data.
+
 ### Predict
 
 To predict sample target value use `predict` method with sample to check (as `array`). Example:

docs/machine-learning/regression/svr.md

@@ -34,6 +34,8 @@ $regression = new SVR(Kernel::LINEAR);
 $regression->train($samples, $targets);
 ```
 
+You can train the model using multiple data sets, predictions will be based on all the training data.
+
 ### Predict
 
 To predict sample target value use `predict` method. You can provide one sample or array of samples:

src/Phpml/Classification/DecisionTree.php

@@ -64,12 +64,13 @@ class DecisionTree implements Classifier
      */
     public function train(array $samples, array $targets)
     {
-        $this->featureCount = count($samples[0]);
-        $this->columnTypes = $this->getColumnTypes($samples);
-        $this->samples = $samples;
-        $this->targets = $targets;
-        $this->labels = array_keys(array_count_values($targets));
-        $this->tree = $this->getSplitLeaf(range(0, count($samples) - 1));
+        $this->samples = array_merge($this->samples, $samples);
+        $this->targets = array_merge($this->targets, $targets);
+
+        $this->featureCount = count($this->samples[0]);
+        $this->columnTypes = $this->getColumnTypes($this->samples);
+        $this->labels = array_keys(array_count_values($this->targets));
+        $this->tree = $this->getSplitLeaf(range(0, count($this->samples) - 1));
     }
 
     protected function getColumnTypes(array $samples)

src/Phpml/Classification/NaiveBayes.php

@@ -63,12 +63,12 @@ class NaiveBayes implements Classifier
      */
     public function train(array $samples, array $targets)
     {
-        $this->samples = $samples;
-        $this->targets = $targets;
-        $this->sampleCount = count($samples);
-        $this->featureCount = count($samples[0]);
+        $this->samples = array_merge($this->samples, $samples);
+        $this->targets = array_merge($this->targets, $targets);
+        $this->sampleCount = count($this->samples);
+        $this->featureCount = count($this->samples[0]);
 
-        $labelCounts = array_count_values($targets);
+        $labelCounts = array_count_values($this->targets);
         $this->labels = array_keys($labelCounts);
         foreach ($this->labels as $label) {
             $samples = $this->getSamplesByLabel($label);

src/Phpml/Helper/Trainable.php

@@ -9,12 +9,12 @@ trait Trainable
     /**
      * @var array
      */
-    private $samples;
+    private $samples = [];
 
     /**
      * @var array
      */
-    private $targets;
+    private $targets = [];
 
     /**
      * @param array $samples
@@ -22,7 +22,7 @@ trait Trainable
      */
     public function train(array $samples, array $targets)
     {
-        $this->samples = $samples;
-        $this->targets = $targets;
+        $this->samples = array_merge($this->samples, $samples);
+        $this->targets = array_merge($this->targets, $targets);
     }
 }

src/Phpml/Regression/LeastSquares.php

@@ -13,12 +13,12 @@ class LeastSquares implements Regression
     /**
      * @var array
      */
-    private $samples;
+    private $samples = [];
 
     /**
      * @var array
      */
-    private $targets;
+    private $targets = [];
 
     /**
      * @var float
@@ -36,8 +36,8 @@ class LeastSquares implements Regression
      */
     public function train(array $samples, array $targets)
     {
-        $this->samples = $samples;
-        $this->targets = $targets;
+        $this->samples = array_merge($this->samples, $samples);
+        $this->targets = array_merge($this->targets, $targets);
 
         $this->computeCoefficients();
     }

tests/Phpml/Classification/DecisionTreeTest.php

@@ -8,7 +8,7 @@ use Phpml\Classification\DecisionTree;
 
 class DecisionTreeTest extends \PHPUnit_Framework_TestCase
 {
-    public $data = [
+    private $data = [
         ['sunny',        85,        85,    'false',    'Dont_play'    ],
         ['sunny',        80,    90,    'true',        'Dont_play'    ],
         ['overcast',    83,    78,    'false',    'Play'        ],
@@ -25,34 +25,39 @@ class DecisionTreeTest extends \PHPUnit_Framework_TestCase
         ['rain',        71,    80,    'true',    'Dont_play'    ]
     ];
 
-    public function getData()
+    private $extraData = [
+        ['scorching',   90,     95,     'false',   'Dont_play'],
+        ['scorching',  100,     93,     'true',    'Dont_play'],
+    ];
+
+    private function getData($input)
     {
-        static $data = null, $targets = null;
-        if ($data == null) {
-            $data = $this->data;
-            $targets = array_column($data, 4);
-            array_walk($data, function (&$v) {
-                array_splice($v, 4, 1);
-            });
-        }
-        return [$data, $targets];
+        $targets = array_column($input, 4);
+        array_walk($input, function (&$v) {
+            array_splice($v, 4, 1);
+        });
+        return [$input, $targets];
     }
 
     public function testPredictSingleSample()
     {
-        list($data, $targets) = $this->getData();
+        list($data, $targets) = $this->getData($this->data);
         $classifier = new DecisionTree(5);
         $classifier->train($data, $targets);
         $this->assertEquals('Dont_play', $classifier->predict(['sunny', 78, 72, 'false']));
         $this->assertEquals('Play', $classifier->predict(['overcast', 60, 60, 'false']));
         $this->assertEquals('Dont_play', $classifier->predict(['rain', 60, 60, 'true']));
 
+        list($data, $targets) = $this->getData($this->extraData);
+        $classifier->train($data, $targets);
+        $this->assertEquals('Dont_play', $classifier->predict(['scorching', 95, 90, 'true']));
+        $this->assertEquals('Play', $classifier->predict(['overcast', 60, 60, 'false']));
         return $classifier;
     }
 
     public function testTreeDepth()
     {
-        list($data, $targets) = $this->getData();
+        list($data, $targets) = $this->getData($this->data);
         $classifier = new DecisionTree(5);
         $classifier->train($data, $targets);
         $this->assertTrue(5 >= $classifier->actualDepth);

tests/Phpml/Classification/NaiveBayesTest.php

@@ -34,5 +34,15 @@ class NaiveBayesTest extends \PHPUnit_Framework_TestCase
         $predicted = $classifier->predict($testSamples);
 
         $this->assertEquals($testLabels, $predicted);
+
+        // Feed an extra set of training data.
+        $samples = [[1, 1, 6]];
+        $labels = ['d'];
+        $classifier->train($samples, $labels);
+
+        $testSamples = [[1, 1, 6], [5, 1, 1]];
+        $testLabels = ['d', 'a'];
+        $this->assertEquals($testLabels, $classifier->predict($testSamples));
+
     }
 }