Improvement and feature addition and Update from deprecated libraries to new ones

Cargo.toml

@@ -1,21 +1,33 @@
 [package]
-
 name = "nn"
-version = "0.1.6"
-authors = ["Jack Montgomery <jackm321@gmail.com>"]
-repository = "https://github.com/jackm321/RustNN"
-documentation = "https://jackm321.github.io/RustNN/doc/nn/"
-license = "Apache-2.0"
-readme = "README.md"
+version = "0.6.0"
+authors = ["https://github.com/jackm321/RustNN"]
 
-description = """
-A multilayer feedforward backpropagation neural network library
-"""
+[dependencies]
+rand = "0.3.*"
+serde = "1.*"
+serde_derive = "1.*"
+serde_json = "1.*"
 
-keywords = ["nn", "neural-network", "classifier", "backpropagation",
-            "machine-learning"]
 
-[dependencies]
-rand = "0.3.7"
-rustc-serialize = "0.3.12"
-time = "0.1.24"
+
+[profile.dev]
+opt-level = 3
+lto = true
+panic = "unwind"
+debug = true
+debug-assertions = true
+
+[profile.test]
+opt-level = 0
+lto = false
+panic = "unwind"
+debug = true
+debug-assertions = true
+
+[profile.release]
+opt-level = 3
+lto = true
+panic = "unwind"
+debug = false
+debug-assertions = false

README.md

@@ -1,12 +1,8 @@
 # RustNN
 
-[![Build Status](https://travis-ci.org/jackm321/RustNN.svg?branch=master)](https://travis-ci.org/jackm321/RustNN)
-
 An easy to use neural network library written in Rust.
 
-[Crate](https://crates.io/crates/nn)
-
-[Documentation](https://jackm321.github.io/RustNN/doc/nn/)
+For the documentation take a look at the original library or generate it using "cargo doc".
 
 ## Description
 RustNN is a [feedforward neural network ](http://en.wikipedia.org/wiki/Feedforward_neural_network)
@@ -15,6 +11,10 @@ generates fully connected multi-layer artificial neural networks that
 are trained via [backpropagation](http://en.wikipedia.org/wiki/Backpropagation).
 Networks are trained using an incremental training mode.
 
+## Fork
+This fork adds L2 regularization and several activation functions to the original crate. Additionally, there are a few minor improvements.
+Lambda can be set just like the learning rate. The activation functions for hidden and output gets set in NN::new as second and third parameter respectively.
+
 ## XOR example
 
 This example creates a neural network with `2` nodes in the input layer,
@@ -27,7 +27,7 @@ given examples. See the documentation for the `NN` and `Trainer` structs
 for more details.
 
 ```rust
-use nn::{NN, HaltCondition};
+use nn::{NN, HaltCondition, Activation};
 
 // create examples of the XOR function
 // the network is trained on tuples of vectors where the first vector
@@ -43,7 +43,7 @@ let examples = [
 // that specifies the number of layers and the number of nodes in each layer
 // in this case we have an input layer with 2 nodes, one hidden layer
 // with 3 nodes and the output layer has 1 node
-let mut net = NN::new(&[2, 3, 1]);
+let mut net = NN::new(&[2, 3, 1], Activation::PELU, Activation::Sigmoid);
 
 // train the network on the examples of the XOR function
 // all methods seen here are optional except go() which must be called to begin training

examples/selector.rs

@@ -0,0 +1,43 @@
+extern crate nn;
+
+use nn::{NN, HaltCondition, Activation};
+
+const ACTIONS:u32 = 10;
+
+
+fn main()
+{
+	// create examples of the xor function
+	let mut examples = Vec::new();
+	for i in 0..ACTIONS
+	{
+		let mut result = Vec::new();
+		for j in 0..ACTIONS
+		{
+			if j == i { result.push(1.0); }
+			else { result.push(0.0); }
+		}
+		let example = (vec![i as f64], result);
+		examples.push(example);
+	}
+
+	// create a new neural network
+	let mut nn = NN::new(&[1, 10, ACTIONS], Activation::PELU, Activation::Sigmoid);
+
+	// train the network
+	nn.train(&examples)
+		.log_interval(Some(1000))
+		.halt_condition( HaltCondition::MSE(0.01) )
+		.rate(0.025)
+		.momentum(0.5)
+		.lambda(0.00005)
+		.go();
+
+	// print results of the trained network
+	for &(ref input, _) in examples.iter()
+	{
+		let result = nn.run(input);
+		let print:Vec<String> = result.iter().map(|x:&f64| { format!("{:4.2}", (*x * 100.0).round() / 100.0) }).collect();
+		println!("{:1.0} -> {:?}", input[0], print);
+	}
+}