Add docs for neural network

README.md

@@ -10,7 +10,7 @@
 
 ![PHP-ML - Machine Learning library for PHP](docs/assets/php-ml-logo.png)
 
-Fresh approach to Machine Learning in PHP. Algorithms, Cross Validation, Preprocessing, Feature Extraction and much more in one library.
+Fresh approach to Machine Learning in PHP. Algorithms, Cross Validation, Neural Network, Preprocessing, Feature Extraction and much more in one library.
 
 PHP-ML requires PHP >= 7.0.
 
@@ -62,6 +62,9 @@ Example scripts are available in a separate repository [php-ai/php-ml-examples](
     * [Classification Report](http://php-ml.readthedocs.io/en/latest/machine-learning/metric/classification-report/)
 * Workflow
     * [Pipeline](http://php-ml.readthedocs.io/en/latest/machine-learning/workflow/pipeline)
+* Neural Network
+    * [Multilayer Perceptron](http://php-ml.readthedocs.io/en/latest/machine-learning/neural-network/multilayer-perceptron/)
+    * [Backpropagation training](http://php-ml.readthedocs.io/en/latest/machine-learning/neural-network/backpropagation/)
 * 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/)

composer.json

@@ -3,7 +3,7 @@
     "type": "library",
     "description": "PHP-ML - Machine Learning library for PHP",
     "license": "MIT",
-    "keywords": ["machine learning","pattern recognition","computational learning theory","artificial intelligence"],
+    "keywords": ["machine learning","pattern recognition","neural network","computational learning theory","artificial intelligence"],
     "homepage": "https://github.com/php-ai/php-ml",
     "authors": [
         {

docs/index.md

@@ -62,6 +62,9 @@ Example scripts are available in a separate repository [php-ai/php-ml-examples](
     * [Classification Report](machine-learning/metric/classification-report/)
 * Workflow
     * [Pipeline](machine-learning/workflow/pipeline)
+* Neural Network
+    * [Multilayer Perceptron](machine-learning/neural-network/multilayer-perceptron/)
+    * [Backpropagation training](machine-learning/neural-network/backpropagation/)
 * Cross Validation
     * [Random Split](machine-learning/cross-validation/random-split/)
     * [Stratified Random Split](machine-learning/cross-validation/stratified-random-split/)

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

@@ -2,7 +2,7 @@
 
 Classifier implementing the k-nearest neighbors algorithm.
 
-### Constructor Parameters
+## Constructor Parameters
 
 * $k - number of nearest neighbors to scan (default: 3)
 * $distanceMetric - Distance object, default Euclidean (see [distance documentation](math/distance/))
@@ -12,7 +12,7 @@ $classifier = new KNearestNeighbors($k=4);
 $classifier = new KNearestNeighbors($k=3, new Minkowski($lambda=4));
 ```
 
-### Train
+## Train
 
 To train a classifier simply provide train samples and labels (as `array`). Example:
 
@@ -24,7 +24,7 @@ $classifier = new KNearestNeighbors();
 $classifier->train($samples, $labels);
 ```
 
-### Predict
+## Predict
 
 To predict sample label use `predict` method. You can provide one sample or array of samples:
 

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

@@ -0,0 +1,29 @@
+# Backpropagation
+
+Backpropagation, an abbreviation for "backward propagation of errors", is a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. 
+
+## Constructor Parameters
+
+* $network (Network) - network to train (for example MultilayerPerceptron instance)
+* $theta (int) - network theta parameter
+
+```
+use Phpml\NeuralNetwork\Network\MultilayerPerceptron;
+use Phpml\NeuralNetwork\Training\Backpropagation;
+
+$network = new MultilayerPerceptron([2, 2, 1]);
+$training = new Backpropagation($network);
+```
+
+## Training
+
+Example of XOR training:
+
+```
+$training->train(
+    $samples = [[1, 0], [0, 1], [1, 1], [0, 0]],
+    $targets = [[1], [1], [0], [0]],
+    $desiredError = 0.2,
+    $maxIteraions = 30000
+);
+```

docs/machine-learning/neural-network/multilayer-perceptron.md

@@ -0,0 +1,29 @@
+# MultilayerPerceptron
+
+A multilayer perceptron (MLP) is a feedforward artificial neural network model that maps sets of input data onto a set of appropriate outputs.
+
+## Constructor Parameters
+
+* $layers (array) - array with layers configuration, each value represent number of neurons in each layers
+* $activationFunction (ActivationFunction) - neuron activation function
+
+```
+use Phpml\NeuralNetwork\Network\MultilayerPerceptron;
+$mlp = new MultilayerPerceptron([2, 2, 1]);
+
+// 2 nodes in input layer, 2 nodes in first hidden layer and 1 node in output layer 
+```
+
+## Methods
+
+* setInput(array $input)
+* getOutput()
+* getLayers()
+* addLayer(Layer $layer)
+
+## Activation Functions
+
+* BinaryStep
+* Gaussian
+* HyperbolicTangent
+* Sigmoid (default)

mkdocs.yml

@@ -18,6 +18,9 @@ pages:
       - Classification Report: machine-learning/metric/classification-report.md
     - Workflow:
       - Pipeline: machine-learning/workflow/pipeline.md
+    - Neural Network:
+      - Multilayer Perceptron: machine-learning/neural-network/multilayer-perceptron.md
+      - Backpropagation training: machine-learning/neural-network/backpropagation.md
     - Cross Validation:
       - RandomSplit: machine-learning/cross-validation/random-split.md
       - Stratified Random Split: machine-learning/cross-validation/stratified-random-split.md