create docs for StratifiedRandomSplit

README.md

@@ -50,6 +50,7 @@ composer require php-ai/php-ml
     * [Accuracy](http://php-ml.readthedocs.io/en/latest/machine-learning/metric/accuracy/)
 * Cross Validation
     * [Random Split](http://php-ml.readthedocs.io/en/latest/machine-learning/cross-validation/random-split/)
+    * [Stratified Random Split](http://php-ml.readthedocs.io/en/latest/machine-learning/cross-validation/stratified-random-split/)
 * Preprocessing
     * [Normalization](http://php-ml.readthedocs.io/en/latest/machine-learning/preprocessing/normalization/)
     * [Imputation missing values](http://php-ml.readthedocs.io/en/latest/machine-learning/preprocessing/imputation-missing-values/)

docs/index.md

@@ -50,6 +50,7 @@ composer require php-ai/php-ml
     * [Accuracy](http://php-ml.readthedocs.io/en/latest/machine-learning/metric/accuracy/)
 * Cross Validation
     * [Random Split](http://php-ml.readthedocs.io/en/latest/machine-learning/cross-validation/random-split/)
+    * [Stratified Random Split](http://php-ml.readthedocs.io/en/latest/machine-learning/cross-validation/stratified-random-split/)
 * Preprocessing
     * [Normalization](http://php-ml.readthedocs.io/en/latest/machine-learning/preprocessing/normalization/)
     * [Imputation missing values](http://php-ml.readthedocs.io/en/latest/machine-learning/preprocessing/imputation-missing-values/)

docs/machine-learning/cross-validation/random-split.md

@@ -1,4 +1,4 @@
-# RandomSplit
+# Random Split
 
 One of the simplest methods from Cross-validation is implemented as `RandomSpilt` class. Samples are split to two groups: train group and test group. You can adjust number of samples in each group.
 
@@ -6,7 +6,7 @@ One of the simplest methods from Cross-validation is implemented as `RandomSpilt
 
 * $dataset - object that implements `Dataset` interface
 * $testSize - a fraction of test split (float, from 0 to 1, default: 0.3)
-* $seed - seed for random generator (for tests)
+* $seed - seed for random generator (e.g. for tests)
  
 ```
 $randomSplit = new RandomSplit($dataset, 0.2);

docs/machine-learning/cross-validation/stratified-random-split.md

@@ -0,0 +1,44 @@
+# Stratified Random Split
+
+Analogously to `RandomSpilt` class samples are split to two groups: train group and test group.
+Distribution of samples takes into account their targets and trying to divide them equally.
+You can adjust number of samples in each group.
+
+### Constructor Parameters
+
+* $dataset - object that implements `Dataset` interface
+* $testSize - a fraction of test split (float, from 0 to 1, default: 0.3)
+* $seed - seed for random generator (e.g. for tests)
+ 
+```
+$split = new StratifiedRandomSplit($dataset, 0.2);
+```
+
+### Samples and labels groups
+
+To get samples or labels from test and train group you can use getters:
+
+```
+$dataset = new StratifiedRandomSplit($dataset, 0.3, 1234);
+
+// train group
+$dataset->getTrainSamples();
+$dataset->getTrainLabels();
+
+// test group
+$dataset->getTestSamples();
+$dataset->getTestLabels();
+```
+
+### Example
+
+```
+$dataset = new ArrayDataset(
+    $samples = [[1], [2], [3], [4], [5], [6], [7], [8]],
+    $targets = ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b']
+);
+
+$split = new StratifiedRandomSplit($dataset, 0.5);
+```
+
+Split will have equals amount of each target. Two of the target `a` and two of `b`.