commit 1a5e352c099fe7d5d280850f35ff64727631d815
Author: Your Name <you@example.com>
Date:   Sun Jun 9 17:40:23 2019 +0200

    done

diff --git a/NeuralNetwork.js b/NeuralNetwork.js
new file mode 100755
index 0000000..c8d2667
--- /dev/null
+++ b/NeuralNetwork.js
@@ -0,0 +1,162 @@
+class NeuralNetwork
+{
+    constructor(numInputs, numOutputs, numHiddenLayers, numNeuronsPerHiddenLayer)
+    {
+
+        this.numInputs = numInputs;
+        this.numOutputs = numOutputs;
+        this.numHiddenLayers = numHiddenLayers;
+        this.numNeuronsPerHiddenLayer = numNeuronsPerHiddenLayer;
+
+        this.bias = 0.0;
+        this.activationResponse = 1.0;
+        this.neuronLayers = [];
+
+        this.createNetwork();
+    }
+
+    createNetwork()
+    {
+
+		//create the layers of the network
+		if (this.numHiddenLayers > 0)
+		{
+			//create first hidden layer
+			var firstHiddenLayer = new NeuronLayer(this.numNeuronsPerHiddenLayer, this.numInputs);
+			this.neuronLayers.push(firstHiddenLayer);
+
+		    for (var i=0; i<this.numHiddenLayers-1; ++i)
+		    {
+		    	var newHiddenLayer = new NeuronLayer(this.numNeuronsPerHiddenLayer, this.numNeuronsPerHiddenLayer);
+				this.neuronLayers.push(newHiddenLayer);
+		    }
+
+		    //create output layer
+		    var outputLayer = new NeuronLayer(this.numOutputs, this.numNeuronsPerHiddenLayer);
+			this.neuronLayers.push(outputLayer);
+		}
+		else
+		{
+			//create output layer
+			var outputLayer = new NeuronLayer(this.numOutputs, this.numInputs);
+			this.neuronLayers.push(outputLayer);
+		}
+	}
+
+	update(inputs)
+	{
+
+		var outputs = [];
+
+		var cWeight = 0;
+
+		// If the number of inputs supplied is incorrect...
+		if (inputs.length!=this.numInputs)
+		{
+			return outputs; // Return empty outputs
+		}
+
+		// Loop through all layers
+		var inputLayer = true;
+		for (var i=0; i < this.numHiddenLayers + 1; ++i)
+		{
+			var neuronLayer = this.neuronLayers[i];
+
+			if (!inputLayer)
+			{
+				inputs = [];
+				inputs = inputs.concat(outputs);
+			}
+			else
+			{
+				inputLayer = false;
+			}
+
+			outputs = [];
+
+			cWeight = 0;
+
+			// For each neuron sum the (inputs * corresponding weights).
+			// Throw the total at our sigmoid function to get the output.
+			for (var j=0; j < neuronLayer.neurons.length; ++j)
+			{
+				var neuron = neuronLayer.neurons[j];
+
+				var totalInput = 0;
+
+				// For each weight...
+				for (var k=0; k < neuron.numInputs ; ++k) // -1 ???
+				{
+					// Multiply it with the input.
+					totalInput += neuron.weights[k] *
+							inputs[cWeight];
+            console.log("cweight "+cWeight);
+            console.log("neuron weight "+neuron.weights[k]);
+            console.log("input: "+inputs[cWeight]);
+            console.log("total input "+totalInput);
+
+
+					cWeight++;
+				}
+
+				// Add in the bias (final weight)
+			//	totalInput += neuron.weights[neuron.weights.length-1] * this.bias;
+
+				// We can store the outputs from each layer as we generate them.
+			    // The combined activation is first filtered through the sigmoid function
+				outputs.push(this.sigmoid(totalInput, this.activationResponse));
+
+				cWeight = 0;
+			}
+		}
+
+		return outputs;
+	}
+
+	sigmoid(totalInput, activationResponse)
+	{
+		return ( 1 / ( 1 + Math.exp(-totalInput / activationResponse)));
+	}
+
+	getWeights()
+	{
+		var weights = [];
+
+		//for each layer
+		for (var i=0; i<this.numHiddenLayers + 1; ++i)
+		{
+
+			//for each neuron
+			for (var j=0; j<this.neuronLayers[i].neurons.length; ++j)
+			{
+				//for each weight
+				for (var k=0; k<this.neuronLayers[i].neurons[j].numInputs; ++k)
+				{
+					weights.push(this.neuronLayers[i].neurons[j].weights[k]);
+				}
+			}
+		}
+
+		return weights;
+	}
+
+	setWeights(weights)
+	{
+		var cWeight = 0;
+
+		//for each layer
+		for (var i=0; i<this.numHiddenLayers + 1; ++i)
+		{
+
+			//for each neuron
+			for (var j=0; j<this.neuronLayers[i].neurons.length; ++j)
+			{
+				//for each weight
+				for (var k=0; k<this.neuronLayers[i].neurons[j].numInputs; ++k)
+				{
+					this.neuronLayers[i].neurons[j].weights[k] = weights[cWeight++];
+				}
+			}
+		}
+	}
+}
diff --git a/Neuron.js b/Neuron.js
new file mode 100755
index 0000000..bf52429
--- /dev/null
+++ b/Neuron.js
@@ -0,0 +1,15 @@
+class Neuron
+{
+	constructor(numInputs)
+	{
+    this.weights = [];
+		this.numInputs = numInputs;
+
+		for (var i=0; i<numInputs+1; ++i)
+		{
+			var newWeight = -1 + (Math.random()*2);
+			//var newWeight = 1;
+			this.weights.push(newWeight);
+		}
+	}
+}
diff --git a/NeuronLayer.js b/NeuronLayer.js
new file mode 100755
index 0000000..21bae47
--- /dev/null
+++ b/NeuronLayer.js
@@ -0,0 +1,14 @@
+class NeuronLayer
+{
+	constructor(numNeuronsPerHiddenLayer, numInputs)
+	{
+        this.neurons = [];
+
+		for (var i = 0; i < numNeuronsPerHiddenLayer; ++i)
+		{
+			var newNeuron = new Neuron(numInputs);
+			
+			this.neurons.push(newNeuron);
+		}
+	}
+}
diff --git a/index.html b/index.html
new file mode 100644
index 0000000..be51d84
--- /dev/null
+++ b/index.html
@@ -0,0 +1,225 @@
+<html>
+<head>
+<script src="NeuralNetwork.js"></script>
+<script src="Neuron.js"></script>
+<script src="NeuronLayer.js"></script>
+
+<style>
+  body{
+    padding:0;
+    margin:0;
+    overflow:hidden;
+    font-family:Helvetica;
+    font-size:10px;
+  }
+  input{
+    padding:0;
+  }
+  p{
+    padding:0;
+    margin:0;
+  }
+
+  #input{
+      position:absolute;
+      top:100px;
+      left:10px;
+  }
+  #first_layer{
+    position:absolute;
+    top: 100px;
+    left: 200px;
+  }
+  #second_layer{
+    position:absolute;
+    top: 100px;
+    left: 400px;
+  }
+  #output{
+    position:absolute;
+    top: 100px;
+    left: 600px;
+  }
+  #network_container{
+    position:absolute;
+    top: 100px;
+    left: 600px;
+    margin-left:-400px;
+    margin-top:-250px;
+  }
+
+</style>
+  </head>
+<body>
+
+  <canvas id="myCanvas" width="100%" height="100%"></canvas>
+<div id="network_container">
+<div id="input">
+
+
+  <input type="range" min="0" max="100" value="1" class="i0" id="i0">
+  <p id="i0_output"></p>
+  <input type="range" min="0" max="100" value="10" class="i1" id="i1">
+  <p id="i1_output"></p>
+</div>
+<div id = "first_layer">
+ <input type="range" min="-400" max="400" value="10" class="w0" id="w0">
+ <p id="w0_output"></p>
+ <input type="range" min="-400" max="400" value="10" class="w1" id="w1">
+ <p id="w1_output"></p><br><br>
+ <input type="range" min="-400" max="400" value="10" class="w2" id="w2">
+ <p id="w2_output"></p>
+ <input type="range" min="-400" max="400" value="10" class="w3" id="w3">
+ <p id="w3_output"></p><br><br>
+ <input type="range" min="-400" max="400" value="10" class="w4" id="w4">
+ <p id="w4_output"></p>
+ <input type="range" min="-400" max="400" value="10" class="w5" id="w5">
+ <p id="w5_output"></p>
+</div>
+<div id = "second_layer">
+ <input type="range" min="-400" max="400" value="10" class="w6" id="w6">
+ <p id="w6_output"></p>
+ <input type="range" min="-400" max="400" value="10" class="w7" id="w7">
+ <p id="w7_output"></p>
+ <input type="range" min="-400" max="400" value="10" class="w8" id="w8">
+ <p id="w8_output"></p><br><br>
+ <input type="range" min="-400" max="400" value="10" class="w9" id="w9">
+ <p id="w9_output"></p>
+ <input type="range" min="-400" max="400" value="10" class="w10" id="w10">
+ <p id="w10_output"></p>
+ <input type="range" min="-400" max="400" value="10" class="w11" id="w11">
+ <p id="w11_output"></p>
+</div>
+
+<div id="output">
+<input id="clickMe" type="button" value="update network" onclick="update();" />
+<input id="random" type="button" value="random network" onclick="random();" />
+
+<p id="network_output"></p>
+</div>
+</div>
+<script>
+
+  var numInputs = 2;
+  var numOutputs = 2;
+  var numHiddenLayers = 1;
+  var numNeuronsPerHiddenLayer = 3;
+
+  var slider = [];
+  var output = [];
+
+  var neuralNetwork = new NeuralNetwork(numInputs, numOutputs, numHiddenLayers, numNeuronsPerHiddenLayer);
+
+
+  var input0 = document.getElementById("i0")
+  var input_display0 = document.getElementById("i0_output")
+  input_display0.innerHTML = input0.value/100;
+  input0.oninput = function() {
+    input_display0.innerHTML = this.value/100;
+  }
+  var input1 = document.getElementById("i1")
+  var input_display1 = document.getElementById("i1_output")
+  input_display1.innerHTML = input1.value/100;
+  input1.oninput = function() {
+    input_display1.innerHTML = this.value/100;
+  }
+
+
+
+
+//slider
+for (var i = 0; i<12; i++){
+  slider.push(document.getElementById("w"+i));
+  output.push(document.getElementById("w"+i+"_output"));
+  output[i].innerHTML = slider[i].value/100;
+  slider[i].oninput = function() {
+    output[slider.indexOf(this)].innerHTML = this.value/100;
+
+  updateCanvas()
+
+  }
+}
+
+document.addEventListener('mousemove', function(e) {
+  console.log("move")
+  var x = e.clientX / window.innerWidth;
+  var y = e.clientY / window.innerHeight;
+input0.value=x*100;
+input1.value=y*100;
+input_display0.innerHTML = x;
+input_display1.innerHTML = y;
+update([x,y])
+})
+
+
+//CANVAS STUFF
+var c = document.getElementById("myCanvas");
+var ctx = c.getContext("2d");
+ctx.canvas.width  = window.innerWidth;
+ctx.canvas.height = window.innerHeight;
+var stepsize = 50;
+for(var x = 0; x < window.innerWidth; x = x+stepsize){
+  for(var y = 0; y < window.innerHeight; y = y+stepsize){
+    var outputs = neuralNetwork.update([x/window.innerWidth,y/window.innerHeight]);
+    ctx.fillStyle = 'hsl(' + 360 * outputs[0] + ', 50%,'+outputs[1]*100 +'%)';
+    //ctx.fillStyle= "rgb(255,20,255);"
+    console.log(outputs[1]*255)
+    ctx.fillRect(x,y,stepsize,stepsize);
+  }
+}
+
+
+function updateCanvas(){
+
+  var stepsize = 50;
+  for(var x = 0; x < window.innerWidth; x = x+stepsize){
+    for(var y = 0; y < window.innerHeight; y = y+stepsize){
+      var outputs = neuralNetwork.update([x/window.innerWidth,y/window.innerHeight]);
+      ctx.fillStyle = 'hsl(' + 360 * outputs[0] + ', 90%,'+outputs[1]*100+'%)';
+      //ctx.fillStyle= "rgb(255,20,255);"
+      console.log(outputs[1]*255)
+      ctx.fillRect(x,y,stepsize,stepsize);
+    }
+  }
+
+}
+
+
+function update(inputs){
+
+
+var weights = neuralNetwork.getWeights();
+var newWeights = [];
+for (var i=0; i < weights.length; i++) {
+    newWeights.push(slider[i].value/100);
+    //newWeights.push(1);
+}
+  neuralNetwork.setWeights(newWeights);
+
+
+//var inputs = [input0.value/100, input1.value/100];
+  var outputs = neuralNetwork.update(inputs);
+  //  console.log(neuralNetwork.getWeights());
+  var output_display = document.getElementById("network_output")
+  output_display.innerHTML = "<br> "+outputs[0] + "<br><br> "+outputs [1];
+
+document.getElementById('network_container').style.left=outputs[0]*window.innerWidth;
+document.getElementById('network_container').style.top=outputs[1]*window.innerHeight;
+
+}
+
+random()
+function random(inputs){
+
+for (var i = 0; i<slider.length; i++){
+
+  slider[i].value = Math.random()*800-400;
+output[slider.indexOf(slider[i])].innerHTML = slider[i].value/100;
+}
+updateCanvas();
+}
+
+
+</script>
+</body>
+</html>