update classifier docs

docs/machine-learning/classification/knearestneighbors.md

@@ -5,9 +5,11 @@ Classifier implementing the k-nearest neighbors algorithm.
 ### Constructor Parameters
 
 * $k - number of nearest neighbors to scan (default: 3)
+* $distanceMetric - Distance class, default Euclidean (see Distance Metric  documentation)
 
 ```
 $classifier = new KNearestNeighbors($k=4);
+$classifier = new KNearestNeighbors($k=3, new Minkowski($lambda=4));
 ```
 
 ### Train

docs/machine-learning/classification/naivebayes.md

@@ -0,0 +1,27 @@
+# NaiveBayes Classifier
+
+Classifier based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
+
+### Train
+
+To train a classifier simply provide train samples and labels (as `array`):
+
+```
+$samples = [[5, 1, 1], [1, 5, 1], [1, 1, 5]];
+$labels = ['a', 'b', 'c'];
+
+$classifier = new NaiveBayes();
+$classifier->train($samples, $labels);
+```
+
+### Predict
+
+To predict sample class use `predict` method. You can provide one sample or array of samples:
+
+```
+$classifier->predict([3, 1, 1]);
+// return 'a'
+
+$classifier->predict([[3, 1, 1], [1, 4, 1]);
+// return ['a', 'b']
+```

mkdocs.yml

@@ -4,6 +4,7 @@ pages:
   - Machine Learning:
     - Classification:
       - KNearestNeighbors: machine-learning/classification/knearestneighbors.md
+      - NaiveBayes: machine-learning/classification/naivebayes.md
     - Cross Validation:
       - RandomSplit: machine-learning/cross-validation/randomsplit.md
     - Datasets:

src/Phpml/Classifier/NaiveBayes.php

@@ -33,5 +33,4 @@ class NaiveBayes implements Classifier
 
         return key($predictions);
     }
-    
 }

src/Phpml/Classifier/Traits/Predictable.php

@@ -1,4 +1,5 @@
 <?php
+
 declare (strict_types = 1);
 
 namespace Phpml\Classifier\Traits;
@@ -23,5 +24,4 @@ trait Predictable
 
         return $predicted;
     }
-
 }

src/Phpml/Classifier/Traits/Trainable.php

@@ -1,11 +1,11 @@
 <?php
+
 declare (strict_types = 1);
 
 namespace Phpml\Classifier\Traits;
 
 trait Trainable
 {
-
     /**
      * @var array
      */
@@ -25,5 +25,4 @@ trait Trainable
         $this->samples = $samples;
         $this->labels = $labels;
     }
-
 }