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/* Stage 6: Simple Processing Blink Sketch
   Created by ScottC on the 8/7/2012 
   http://arduinobasics.blogspot.com/   */
   
/* Setup the color variables and switching mechanism */
   color Red=color(255,0,0);
   color White=color(255,255,255);
   Boolean mySwitch=true;
   

/* The setup function only runs once */

   void setup(){
   /* Set the size of the window using size(width,height) */
      size(400,400);
   }
   
 /* The draw function will refresh screen only after it has
    processed all functions within it. Which is why I needed
    a switch to swap between the red and the white background
    The draw() function will run in an endless loop. */
    
    void draw(){
       blink(mySwitch);     //Call to blink function
       delay(2000);         //The blinking speed (2 secs)
    }
   
   
 /* The blink function switches the background from red to white
     or from white to red. */
     
    void blink(Boolean swtch){
    /* If swtch is true, make the background red
       otherwise make it white */
       if(swtch){
         background(Red);     // red
       }else {           
         background(White);   // white
       }
    /* Toggle mySwitch between true and false */  
       mySwitch=!mySwitch;       
   }

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/* Stage 7: Simple Arduino Serial Random Number Generator
Written by ScottC on 09/07/2012
http://arduinobasics.blogspot.com/
*/

  void setup(){
    Serial.begin(9600);      //Begin Serial Communication
  }

  void loop(){
    Serial.println(random(1,1000)); //Send Random # to computer
    delay(200);                    //Delay 200ms between values.
  }

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/* Some of the Serial code was adapted from Tom Igoe's example
   on this site: http://processing.org/reference/libraries/serial/Serial.html
   and http://processing.org/reference/libraries/serial/serialEvent_.html
   
   The rest of this Processing code was written by ScottC on 11/07/2012
   http://arduinobasics.blogspot.com/
*/


import processing.serial.*; /* Needed for Serial Communication */

/* Global variables */
Serial comPort;
String [] comPortList;
String comPortString;

/*--------------------------------------------------*/

void setup(){
  size(100,100);            /* Set the size of the window  */
  background(0);            /* Set the background to black */
  
  
  /* Get the available com ports. If there is at least one
     com port available, then start communicating on it.
     If there are more than one com ports available, we will
     only open the first one (i.e. comPortList[0])
     The bufferUntil('\n'); statement will generate a serial Event
     when it reads a carriage return */
     
  comPortList = Serial.list();  
  if(comPortList.length>0){
    comPort = new Serial(this, comPortList[0], 9600);
    comPort.bufferUntil('\n');
  }
}

/*--------------------------------------------------*/

void draw(){
  /* The serialEvent function will update the display */
}

/*--------------------------------------------------*/

void serialEvent(Serial cPort){
  comPortString = cPort.readStringUntil('\n');
  if(comPortString != null) {
    comPortString=trim(comPortString);
    
    /* Print to the Debug screen in Processing IDE */
    println(comPortString);
  }
}

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  if(comPortList.length>0){
    comPort = new Serial(this, comPortList[0], 9600);
    comPort.bufferUntil('\n');
  }

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  if(comPortList.length>0){
    comPort = new Serial(this, "COM6", 9600);
    comPort.bufferUntil('\n');
  }

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/* Stage 8: Simple Arduino Serial Random Number Generator
Written by ScottC on 09/07/2012
http://arduinobasics.blogspot.com/
*/

  void setup(){
    Serial.begin(9600);      //Begin Serial Communication
  }

  void loop(){
    Serial.println(random(1,1000)); //Send Random # to computer
    delay(100);                    //Delay 100ms between values.
  }

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/* Simple Serial ECHO script : Written by ScottC 03/07/2012 */

/* Use a variable called byteRead to temporarily store
   the data coming from the computer */
byte byteRead;

void setup() {                
// Turn the Serial Protocol ON
  Serial.begin(9600);
}

void loop() {
   /*  check if data has been sent from the computer: */
  if (Serial.available()) {
    /* read the most recent byte */
    byteRead = Serial.read();
    /*ECHO the value that was read, back to the serial port. */
    Serial.write(byteRead);
  }
}

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/* Jumper: Using an Arduino to animate:
   Written by ScottC on 02/06/2012 */

int photoRPin = 0; 
int minLight;
int maxLight;
int lightLevel;
int adjustedLightLevel;
int oldLightLevel;

void setup() {
  Serial.begin(9600);
  
  //Setup the starting light level limits
  lightLevel=analogRead(photoRPin);
  minLight=lightLevel-10;
  maxLight=lightLevel;
  oldLightLevel=lightLevel;
}

void loop(){
   lightLevel=analogRead(photoRPin);
   delay(10);
  
  //auto-adjust the minimum and maximum limits in real time   
   if(minLight>lightLevel){
     minLight=lightLevel;
   }
   if(maxLight<lightLevel){
     maxLight=lightLevel;
   }
   
   //Map the light level to produce a result between 1 and 28.
   adjustedLightLevel = map(lightLevel, (minLight+20), (maxLight-20), 1, 28); 
   adjustedLightLevel = constrain (adjustedLightLevel, 1,28);
   
   /*Only send a new value to the Serial Port if the 
     adjustedLightLevel value changes.*/
   if(oldLightLevel==adjustedLightLevel){
     //do nothing if the old value and the new value are the same.
   }else{
     //Update the oldLightLevel value for the next round
     oldLightLevel=adjustedLightLevel;
     
     /*Send the adjusted Light level result 
       to Serial port (processing)*/
     Serial.println(adjustedLightLevel);
   } 
}

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/* Jumper: Using an Arduino to animate
   Written by ScottC on 02/06/2012

Source code derived from : 
  http://processing.org/learning/topics/sequential.html
  http://processing.org/discourse/beta/num_1267080062.html

Pictures captured from:
  http://www.youtube.com/watch?v=h6nE8m74kDg      
  
======================================================= */

import processing.serial.*;
Serial myPort;
String sensorReading="";

// Create the array that will hold the images
PImage[] movieImage = new PImage[29];

/* The frame variable is  used to control which 
   image is displayed */
int frame = 1;



/* Setup the size of the window. Initialise serial communication with Arduino
   and pre-load the images to be displayed later on. This is done only once.
   I am using COM6 on my computer, you may need replace this value with your
   active COM port being used by the Arduino.*/

void setup(){
  size(700,600);
  
  myPort = new Serial(this, "COM6", 9600);
  myPort.bufferUntil('\n');
  
  for(int i=0;i<28;i++){
    movieImage[i] = loadImage("Jumper" + (i+1) + ".jpg");
  }
}




// The draw function controls the animation sequence.

void draw(){
  
  //this draws the relevant image to the window  
  image(movieImage[frame-1],0,0,width,height);
}

void serialEvent (Serial myPort){
 sensorReading = myPort.readStringUntil('\n');
  if(sensorReading != null){
    sensorReading=trim(sensorReading);
    if (sensorReading.length()<2){
      frame = integerFromChar(sensorReading.charAt(0));
    }else{
      frame = integerFromChar(sensorReading.charAt(0))*10;
      frame += integerFromChar(sensorReading.charAt(1));
    }
  }
}



/* This function used to convert the character received from the
   serial port (Arduino), and converts it to a number */
   
int integerFromChar(char myChar) {
  if (myChar < '0' || myChar > '9') {
    return -1;
  }else{
  return myChar - '0';
  }
}

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/* This program was created by ScottC on 9/5/2012 to receive serial 
signals from a computer to turn on/off  1-9 LEDs */

void setup() {                
  // initialize the digital pins as an output.
  pinMode(2, OUTPUT);
  pinMode(3, OUTPUT);
  pinMode(4, OUTPUT);
  pinMode(5, OUTPUT);
  pinMode(6, OUTPUT);
  pinMode(7, OUTPUT);
  pinMode(8, OUTPUT);
  pinMode(9, OUTPUT);
  pinMode(10, OUTPUT);
// Turn the Serial Protocol ON
  Serial.begin(9600);
}

void loop() {
  byte byteRead;

   /*  check if data has been sent from the computer: */
  if (Serial.available()) {
  
    /* read the most recent byte */
    byteRead = Serial.read();
    //You have to subtract '0' from the read Byte to convert from text to a number.
    byteRead=byteRead-'0';
    
    //Turn off all LEDS
    for(int i=2; i<11; i++){
      digitalWrite(i, LOW);
    }
    
    if(byteRead>0){
      //Turn on the relevant LEDs
      for(int i=1; i<(byteRead+1); i++){
        digitalWrite(i+1, HIGH);
      }
    }
  }
}

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//Created by ScottC on 12/05/2012 to send mouse coordinates to Arduino

import processing.serial.*;

// Global variables
int new_sX, old_sX;
int nX, nY;
Serial myPort;

// Setup the Processing Canvas
void setup(){
  size( 800, 400 );
  strokeWeight( 10 );
 
  //Open the serial port for communication with the Arduino
  //Make sure the COM port is correct
  myPort = new Serial(this, "COM6", 9600);
  myPort.bufferUntil('\n'); 
}

// Draw the Window on the computer screen
void draw(){
  
  // Fill canvas grey
  background( 100 );
    
  // Set the stroke colour to white
  stroke(255); 
  
  // Draw a circle at the mouse location
  ellipse( nX, nY, 10, 10 );

  //Draw Line from the top of the page to the bottom of the page
  //in line with the mouse.
  line(nX,0,nX,height);  
}


// Get the new mouse location and send it to the arduino
void mouseMoved(){
  nX = mouseX;
  nY = mouseY; 
  
  //map the mouse x coordinates to the LEDs on the Arduino.
  new_sX=(int)map(nX,0,800,0,10);

  if(new_sX==old_sX){
    //do nothing
  } else {
    //only send values to the Arduino when the new X coordinates are different.
    old_sX = new_sX;
    myPort.write(""+new_sX);
  }
}

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style="color: blue;">import processing.serial.*;

style="color: blue;">import java.io.*;

style="color: rgb(43, 145, 175);">int mySwitch=0;

style="color: rgb(43, 145, 175);">int counter=0;
String [] subtext;
Serial myPort;



style="color: rgb(43, 145, 175);">void setup(){
 
style="color: green;">//Create a switch that will control the frequency of text file reads.
 
style="color: green;">//When mySwitch=1, the program is setup to read the text file.
 
style="color: green;">//This is turned off when mySwitch = 0
 mySwitch=1;
 
 
style="color: green;">//Open the serial port for communication with the Arduino
 
style="color: green;">//Make sure the COM port is correct
 myPort = 
style="color: blue;">new Serial(this, 
style="color: rgb(163, 21, 21);">"COM6", 9600);
 myPort.bufferUntil(
style="color: rgb(163, 21, 21);">'\n');
}


style="color: rgb(43, 145, 175);">void draw() {
 
style="color: blue;">if (mySwitch>0){
 
style="color: green;">/*The readData function can be found later in the code.

style="color: green;"> This is the call to read a CSV file on the computer hard-drive. */
 readData(
style="color: rgb(163, 21, 21);">"D:/mySensorData.txt");
 
 
style="color: green;">/*The following switch prevents continuous reading of the text file, until

style="color: green;"> we are ready to read the file again. */
 mySwitch=0;
 }
 
style="color: green;">/*Only send new data. This IF statement will allow new data to be sent to

style="color: green;"> the arduino. */
 
style="color: blue;">if(counter<subtext.length){
 
style="color: green;">/* Write the next number to the Serial port and send it to the Arduino 

style="color: green;"> There will be a delay of half a second before the command is

style="color: green;"> sent to turn the LED off : myPort.write('0'); */
 myPort.write(subtext[counter]);
 delay(500);
 myPort.write(
style="color: rgb(163, 21, 21);">'0');
 delay(100);
 
style="color: green;">//Increment the counter so that the next number is sent to the arduino.
 counter++;
 } 
style="color: blue;">else{
 //If the text file has run out of numbers, then read the text file again in 5 seconds.
 delay(5000);
 mySwitch=1;
 }
} 



style="color: green;">/* The following function will read from a CSV or TXT file */

style="color: rgb(43, 145, 175);">void readData(String myFileName){
 
 File file=
style="color: blue;">new File(myFileName);
 BufferedReader br=
style="color: blue;">null;
 
 try{
 br=
style="color: blue;">new BufferedReader(
style="color: blue;">new FileReader(file));
 String text=
style="color: blue;">null;
 
 
style="color: green;">/* keep reading each line until you get to the end of the file */
 
style="color: blue;">while((text=br.readLine())!=
style="color: blue;">null){
 /* Spilt each line up into bits and pieces using a comma as a separator */
 subtext = splitTokens(text,
style="color: rgb(163, 21, 21);">",");
 }
 }
style="color: blue;">catch(FileNotFoundException e){
 e.printStackTrace();
 }
style="color: blue;">catch(IOException e){
 e.printStackTrace();
 }
style="color: blue;">finally{
 try {
 
style="color: blue;">if (br != null){
 br.close();
 }
 } 
style="color: blue;">catch (IOException e) {
 e.printStackTrace();
 }
 }
}

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style="color: green;">/* This program was created by ScottC on 8/5/2012 to receive serial 

style="color: green;">signals from a computer to turn on/off 1-9 LEDs */


style="color: rgb(43, 145, 175);">void setup() { 
 
style="color: green;">// initialize the digital pins as an output.
 pinMode(2, OUTPUT);
 pinMode(3, OUTPUT);
 pinMode(4, OUTPUT);
 pinMode(5, OUTPUT);
 pinMode(6, OUTPUT);
 pinMode(7, OUTPUT);
 pinMode(8, OUTPUT);
 pinMode(9, OUTPUT);
 pinMode(10, OUTPUT);

style="color: green;">// Turn the Serial Protocol ON
 Serial.begin(9600);
}


style="color: rgb(43, 145, 175);">void loop() {
 
style="color: rgb(43, 145, 175);">byte byteRead;

 
style="color: green;">/* check if data has been sent from the computer: */
 
style="color: blue;">if (Serial.available()) {
 
 
style="color: green;">/* read the most recent byte */
 byteRead = Serial.read();
 
style="color: green;">//You have to subtract '0' from the read Byte to convert from text to a number.
 byteRead=byteRead-
style="color: rgb(163, 21, 21);">'0';
 
 
style="color: green;">//Turn off all LEDs if the byte Read = 0
 
style="color: blue;">if(byteRead==0){
 
style="color: green;">//Turn off all LEDS
 digitalWrite(2, LOW);
 digitalWrite(3, LOW);
 digitalWrite(4, LOW);
 digitalWrite(5, LOW);
 digitalWrite(6, LOW);
 digitalWrite(7, LOW);
 digitalWrite(8, LOW);
 digitalWrite(9, LOW);
 digitalWrite(10, LOW);
 }
 
 
style="color: green;">//Turn LED ON depending on the byte Read.
 
style="color: blue;">if(byteRead>0){
 digitalWrite((byteRead+1), HIGH); 
style="color: green;">// set the LED on
 }
 }
 }

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style="color: rgb(0, 128, 0);">/* TextFile Sender: Written by Scott C on 5th April 2013

style="color: rgb(0, 128, 0);"> using Processing Version 2.0b8 */

import processing.serial.*;

Serial comPort;

style="color: rgb(43, 145, 175);">int counter=0; 
style="color: rgb(0, 128, 0);">// Helps to keep track of values sent.

style="color: rgb(43, 145, 175);">int numItems=0; 
style="color: rgb(0, 128, 0);">//Keep track of the number of values in text file
boolean sendStrings=
style="color: rgb(0, 0, 255);">false; 
style="color: rgb(0, 128, 0);">//Turns sending on and off
StringLoader sLoader; 
style="color: rgb(0, 128, 0);">//Used to send values to Arduino


style="color: rgb(43, 145, 175);">void setup(){
 comPort = 
style="color: rgb(0, 0, 255);">new Serial(
style="color: rgb(0, 0, 255);">this, Serial.list()[0], 9600);
 background(255,0,0); 
style="color: rgb(0, 128, 0);">//Start with a Red background
}


style="color: rgb(43, 145, 175);">void draw(){
}



style="color: rgb(43, 145, 175);">void mousePressed() {
 
style="color: rgb(0, 128, 0);">//Toggle between sending values and not sending values
 sendStrings=!sendStrings;
 
 
style="color: rgb(0, 128, 0);">//If sendStrings is True - then send values to Arduino
 
style="color: rgb(0, 0, 255);">if(sendStrings){
 background(0,255,0); 
style="color: rgb(0, 128, 0);">//Change the background to green
 
 
style="color: rgb(0, 128, 0);">/*When the background is green, transmit

style="color: rgb(0, 128, 0);"> text file values to the Arduino */
 sLoader=
style="color: rgb(0, 0, 255);">new StringLoader();
 sLoader.start();
 }
style="color: rgb(0, 0, 255);">else{
 background(255,0,0); 
style="color: rgb(0, 128, 0);">//Change background to red
 
style="color: rgb(0, 128, 0);">//Reset the counter
 counter=0;
 }
}




style="color: rgb(0, 128, 0);">/*============================================================*/

style="color: rgb(0, 128, 0);">/* The StringLoader class imports data from a text file

style="color: rgb(0, 128, 0);"> on a new Thread and sends each value once every half second */

style="color: rgb(0, 0, 255);">public 
style="color: rgb(0, 0, 255);">class 
style="color: rgb(43, 145, 175);">StringLoader extends Thread{
 
 
style="color: rgb(0, 0, 255);">public StringLoader(){
 
style="color: rgb(0, 128, 0);">//default constructor
 }
 
 
style="color: rgb(0, 0, 255);">public 
style="color: rgb(43, 145, 175);">void run() {
 String textFileLines[]=loadStrings(
style="color: rgb(163, 21, 21);">"d:/mySensorData.txt");
 String lineItems[]=splitTokens(textFileLines[0], 
style="color: rgb(163, 21, 21);">",");
 numItems=lineItems.length;
 
style="color: rgb(0, 0, 255);">for(
style="color: rgb(43, 145, 175);">int i = counter; i<numItems; i++){
 comPort.write(lineItems[i]);
 delay(500);
 comPort.write(
style="color: rgb(163, 21, 21);">"0");
 }
 counter=numItems;
 }
}

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/*Neural Network created by ScottC on 15th Aug 2011
 
Please visit my blog for a detailed explanation of my Neural Network
http://arduinobasics.blogspot.com/p/arduinoprojects.html
 
*/
   
   
 
void setup(){
  ArrayList myTrainingInputs = new ArrayList();
  ArrayList myTrainingOutputs = new ArrayList();
   
  float[] myInputsA={0,0};
  float[] myInputsB={0,1};
  float[] myInputsC={1,0};
  float[] myInputsD={1,1};
  float[] myOutputsA={1};
  float[] myOutputsB={0};
   
   
  println("TRAINING DATA");
  println("--------------------------------------------");
  myTrainingInputs.add(myInputsA);
  myTrainingOutputs.add(myOutputsA);
  println("INPUTS= " + myInputsA[0] + ", " + myInputsA[1] + "; Expected output = " + myOutputsA[0]);
  myTrainingInputs.add(myInputsB);
  myTrainingOutputs.add(myOutputsB);
  println("INPUTS= " + myInputsB[0] + ", " + myInputsB[1] + "; Expected output = " + myOutputsB[0]);
  myTrainingInputs.add(myInputsC);
  myTrainingOutputs.add(myOutputsB);
  println("INPUTS= " + myInputsC[0] + ", " + myInputsC[1] + "; Expected output = " + myOutputsB[0]);
  myTrainingInputs.add(myInputsD);
  myTrainingOutputs.add(myOutputsA);
  println("INPUTS= " + myInputsD[0] + ", " + myInputsD[1] + "; Expected output = " + myOutputsA[0]);
  println("--------------------------------------------");
  
  NeuralNetwork NN = new NeuralNetwork();
  NN.addLayer(2,2);
  NN.addLayer(2,1);
  
  println("Before Training");
  float[] myInputDataA1={0,0};
  NN.processInputsToOutputs(myInputDataA1);
  float[] myOutputDataA1={};
  myOutputDataA1=NN.getOutputs();
  println("Feed Forward:  INPUT = 0,0; OUTPUT=" + myOutputDataA1[0]);
   
  float[] myInputDataB1={0,1};
  NN.processInputsToOutputs(myInputDataB1);
  float[] myOutputDataB1={};
  myOutputDataB1=NN.getOutputs();
  println("Feed Forward:  INPUT = 0,1; OUTPUT=" + myOutputDataB1[0]);
   
  float[] myInputDataC1={1,0};
  NN.processInputsToOutputs(myInputDataC1);
  float[] myOutputDataC1={};
  myOutputDataC1=NN.getOutputs();
  println("Feed Forward:  INPUT = 1,0; OUTPUT=" + myOutputDataC1[0]);
   
  float[] myInputDataD1={1,1};
  NN.processInputsToOutputs(myInputDataD1);
  float[] myOutputDataD1={};
  myOutputDataD1=NN.getOutputs();
  println("Feed Forward:  INPUT = 1,1; OUTPUT=" + myOutputDataD1[0]);
 
  println("");
  println("--------------------------------------------");
   
  println("Begin Training");
  NN.autoTrainNetwork(myTrainingInputs,myTrainingOutputs,0.0001,500000);
  println("");
  println("End Training");
  println("");
  println("--------------------------------------------");
  println("Test the neural network");
  float[] myInputDataA2={0,0};
  NN.processInputsToOutputs(myInputDataA2);
  float[] myOutputDataA2={};
  myOutputDataA2=NN.getOutputs();
  println("Feed Forward:  INPUT = 0,0; OUTPUT=" + myOutputDataA2[0]);
   
  float[] myInputDataB2={0,1};
  NN.processInputsToOutputs(myInputDataB2);
  float[] myOutputDataB2={};
  myOutputDataB2=NN.getOutputs();
  println("Feed Forward:  INPUT = 0,1; OUTPUT=" + myOutputDataB2[0]);
   
  float[] myInputDataC2={1,0};
  NN.processInputsToOutputs(myInputDataC2);
  float[] myOutputDataC2={};
  myOutputDataC2=NN.getOutputs();
  println("Feed Forward:  INPUT = 1,0; OUTPUT=" + myOutputDataC2[0]);
   
  float[] myInputDataD2={1,1};
  NN.processInputsToOutputs(myInputDataD2);
  float[] myOutputDataD2={};
  myOutputDataD2=NN.getOutputs();
  println("Feed Forward:  INPUT = 1,1; OUTPUT=" + myOutputDataD2[0]);
  
   
}
 
 
/* ---------------------------------------------------------------------
A connection determines how much of a signal is passed through to the neuron.
--------------------------------------------------------------------  */
 
class Connection{
  float connEntry;
  float weight;
  float connExit;
   
  //This is the default constructor for an Connection
  Connection(){
    randomiseWeight();
  }
   
  //A custom weight for this Connection constructor
  Connection(float tempWeight){
    setWeight(tempWeight);
  }
   
  //Function to set the weight of this connection
  void setWeight(float tempWeight){
    weight=tempWeight;
  }
   
  //Function to randomise the weight of this connection
  void randomiseWeight(){
    setWeight(random(2)-1);
  }
   
  //Function to calculate and store the output of this Connection
  float calcConnExit(float tempInput){
    connEntry = tempInput;
    connExit = connEntry * weight;
    return connExit;
  }
}
 
 
/* -----------------------------------------------------------------
   A neuron does all the processing and calculation to convert an input into an output
--------------------------------------------------------------------- */
 
class Neuron{
  Connection[] connections={};
  float bias;
  float neuronInputValue;
  float neuronOutputValue;
  float deltaError;
   
  //The default constructor for a Neuron
  Neuron(){
  }
   
  /*The typical constructor of a Neuron - with random Bias and Connection weights */
  Neuron(int numOfConnections){
    randomiseBias();
    for(int i=0; i<numOfConnections; i++){
      Connection conn = new Connection();
      addConnection(conn);
    }
  }
   
  //Function to add a Connection to this neuron
  void addConnection(Connection conn){
      connections = (Connection[]) append(connections, conn);
  }
   
  /* Function to return the number of connections associated with this neuron.*/
  int getConnectionCount(){
      return connections.length;
  }
   
  //Function to set the bias of this Neron
  void setBias(float tempBias){
    bias = tempBias;
  }
   
  //Function to randomise the bias of this Neuron
  void randomiseBias(){
    setBias(random(1));
  }
   
  /*Function to convert the inputValue to an outputValue
  Make sure that the number of connEntryValues matches the number of connections */
   
  float getNeuronOutput(float[] connEntryValues){
    if(connEntryValues.length!=getConnectionCount()){
      println("Neuron Error: getNeuronOutput() : Wrong number of connEntryValues");
      exit();
    }
     
    neuronInputValue=0;
     
    /* First SUM all of the weighted connection values (connExit) attached to this neuron. This becomes the neuronInputValue. */
    for(int i=0; i<getConnectionCount(); i++){
      neuronInputValue+=connections[i].calcConnExit(connEntryValues[i]);
    }
     
    //Add the bias to the Neuron's inputValue
    neuronInputValue+=bias;
     
    /* Send the inputValue through the activation function to produce the Neuron's outputValue */
    neuronOutputValue=Activation(neuronInputValue);
     
    //Return the outputValue
    return neuronOutputValue;
  }
   
  //Activation function
  float Activation(float x){
    float activatedValue = 1 / (1 + exp(-1 * x));
    return activatedValue;
  }
   
}
 
class Layer{
  Neuron[] neurons = {};
  float[] layerINPUTs={};
  float[] actualOUTPUTs={};
  float[] expectedOUTPUTs={};
  float layerError;
  float learningRate;
   
   
  /* This is the default constructor for the Layer */
  Layer(int numberConnections, int numberNeurons){
    /* Add all the neurons and actualOUTPUTs to the layer */
    for(int i=0; i<numberNeurons; i++){
      Neuron tempNeuron = new Neuron(numberConnections);
      addNeuron(tempNeuron);
      addActualOUTPUT();
    }
  }
     
   
  /* Function to add an input or output Neuron to this Layer */
  void addNeuron(Neuron xNeuron){
        neurons = (Neuron[]) append(neurons, xNeuron);
  }
   
   
  /* Function to get the number of neurons in this layer */
  int getNeuronCount(){
    return neurons.length;
  }
   
   
  /* Function to increment the size of the actualOUTPUTs array by one. */
  void addActualOUTPUT(){
      actualOUTPUTs = (float[]) expand(actualOUTPUTs,(actualOUTPUTs.length+1));
  }
   
   
  /* Function to set the ENTIRE expectedOUTPUTs array in one go. */
  void setExpectedOUTPUTs(float[] tempExpectedOUTPUTs){
    expectedOUTPUTs=tempExpectedOUTPUTs;
  }
   
   
  /* Function to clear ALL values from the expectedOUTPUTs array */
  void clearExpectedOUTPUT(){
       expectedOUTPUTs = (float[]) expand(expectedOUTPUTs, 0);
  }
   
   
  /* Function to set the learning rate of the layer */
  void setLearningRate(float tempLearningRate){
    learningRate=tempLearningRate;
  }
   
   
  /* Function to set the inputs of this layer */
  void setInputs(float[] tempInputs){
    layerINPUTs=tempInputs;
  }
   
   
   
  /* Function to convert ALL the Neuron input values into Neuron output values in this layer, through a special activation function. */
  void processInputsToOutputs(){
     
    /* neuronCount is used a couple of times in this function. */
    int neuronCount = getNeuronCount();
     
    /* Check to make sure that there are neurons in this layer to process the inputs */
    if(neuronCount>0) {
      /* Check to make sure that the number of inputs matches the number of Neuron Connections. */
      if(layerINPUTs.length!=neurons[0].getConnectionCount()){
        println("Error in Layer: processInputsToOutputs: The number of inputs do NOT match the number of Neuron connections in this layer");
        exit();
      } else {
        /* The number of inputs are fine : continue
           Calculate the actualOUTPUT of each neuron in this layer,
           based on their layerINPUTs (which were previously calculated).
           Add the value to the layer's actualOUTPUTs array. */
        for(int i=0; i<neuronCount;i++){
          actualOUTPUTs[i]=neurons[i].getNeuronOutput(layerINPUTs);
        }
      }
    }else{
      println("Error in Layer: processInputsToOutputs: There are no Neurons in this layer");
      exit();
    }
  }
   
   
  /* Function to get the error of this layer */
  float getLayerError(){
    return layerError;
  }
   
   
  /* Function to set the error of this layer */
  void setLayerError(float tempLayerError){
    layerError=tempLayerError;
  }
   
   
  /* Function to increase the layerError by a certain amount */
  void increaseLayerErrorBy(float tempLayerError){
    layerError+=tempLayerError;
  }
   
   
  /* Function to calculate and set the deltaError of each neuron in the layer */
  void setDeltaError(float[] expectedOutputData){
    setExpectedOUTPUTs(expectedOutputData);
    int neuronCount = getNeuronCount();
    /* Reset the layer error to 0 before cycling through each neuron */
    setLayerError(0);
     for(int i=0; i<neuronCount;i++){
       neurons[i].deltaError = actualOUTPUTs[i]*(1-actualOUTPUTs[i])*(expectedOUTPUTs[i]-actualOUTPUTs[i]);
        
       /* Increase the layer Error by the absolute difference between the calculated value (actualOUTPUT) and the expected value (expectedOUTPUT). */
       increaseLayerErrorBy(abs(expectedOUTPUTs[i]-actualOUTPUTs[i]));
     }
  }
   
   
  /* Function to train the layer : which uses a training set to adjust the connection weights and biases of the neurons in this layer */
  void trainLayer(float tempLearningRate){
    setLearningRate(tempLearningRate);
     
    int neuronCount = getNeuronCount();
     
    for(int i=0; i<neuronCount;i++){
      /* update the bias for neuron[i] */
      neurons[i].bias += (learningRate * 1 * neurons[i].deltaError);
       
      /* update the weight of each connection for this neuron[i] */
      for(int j=0; j<neurons[i].getConnectionCount(); j++){
          neurons[i].connections[j].weight += (learningRate * neurons[i].connections[j].connEntry * neurons[i].deltaError);
      }  
    }
  }
}
 
/* -------------------------------------------------------------
   The Neural Network class is a container to hold and manage all the layers
   ---------------------------------------------------------------- */
 
class NeuralNetwork{
  Layer[] layers = {};
  float[] arrayOfInputs={};
  float[] arrayOfOutputs={};
  float learningRate;
  float networkError;
  float trainingError;
  int retrainChances=0;
   
  NeuralNetwork(){
    /* the default learning rate of a neural network is set to 0.1, which can changed by the setLearningRate(lR) function. */
    learningRate=0.1;
  }
   
   
   
  /* Function to add a Layer to the Neural Network */
  void addLayer(int numConnections, int numNeurons){
    layers = (Layer[]) append(layers, new Layer(numConnections,numNeurons));
  }
 
 
 
  /* Function to return the number of layers in the neural network */
  int getLayerCount(){
      return layers.length;
  }
   
   
   
  /* Function to set the learningRate of the Neural Network */
  void setLearningRate(float tempLearningRate){
    learningRate=tempLearningRate;
  }
   
   
   
  /* Function to set the inputs of the neural network */
  void setInputs(float[] tempInputs){
    arrayOfInputs=tempInputs;
  }
   
   
   
  /* Function to set the inputs of a specified layer */
  void setLayerInputs(float[] tempInputs, int layerIndex){
    if(layerIndex>getLayerCount()-1){
      println("NN Error: setLayerInputs: layerIndex=" + layerIndex + " exceeded limits= " + (getLayerCount()-1));
    } else {
      layers[layerIndex].setInputs(tempInputs);
    }
  }
   
   
   
  /* Function to set the outputs of the neural network */
  void setOutputs(float[] tempOutputs){
    arrayOfOutputs=tempOutputs;
  }
   
   
   
  /* Function to return the outputs of the Neural Network */
  float[] getOutputs(){
    return arrayOfOutputs;
  }
   
   
   
  /* Function to process the Neural Network's input values and convert them to an output pattern using ALL layers in the network */
  void processInputsToOutputs(float[] tempInputs){
    setInputs(tempInputs);
     
    /* Check to make sure that the number of NeuralNetwork inputs matches the Neuron Connection Count in the first layer. */
    if(getLayerCount()>0){
      if(arrayOfInputs.length!=layers[0].neurons[0].getConnectionCount()){
        println("NN Error: processInputsToOutputs: The number of inputs do NOT match the NN");
        exit();
      } else {
        /* The number of inputs are fine : continue */
        for(int i=0; i<getLayerCount(); i++){
           
          /*Set the INPUTs for each layer: The first layer gets it's input data from the NN, whereas the 2nd and subsequent layers get their input data from the previous layer's actual output. */
          if(i==0){
            setLayerInputs(arrayOfInputs,i);
          } else {
            setLayerInputs(layers[i-1].actualOUTPUTs, i);
          }
           
          /* Now that the layer has had it's input values set, it can now process this data, and convert them into an output using the layer's neurons. The outputs will be used as inputs in the next layer (if available). */
          layers[i].processInputsToOutputs();
        }
        /* Once all the data has filtered through to the end of network, we can grab the actualOUTPUTs of the LAST layer
           These values become or will be set to the NN output values (arrayOfOutputs), through the setOutputs function call. */
        setOutputs(layers[getLayerCount()-1].actualOUTPUTs);
      }
    }else{
      println("Error: There are no layers in this Neural Network");
      exit();
    }
  }
   
   
   
   
  /* Function to train the entire network using an array. */
  void trainNetwork(float[] inputData, float[] expectedOutputData){
    /* Populate the ENTIRE network by processing the inputData. */
    processInputsToOutputs(inputData);
     
    /* train each layer - from back to front (back propagation) */
    for(int i=getLayerCount()-1; i>-1; i--){
      if(i==getLayerCount()-1){
        layers[i].setDeltaError(expectedOutputData);
        layers[i].trainLayer(learningRate);
        networkError=layers[i].getLayerError();
      } else {
        /* Calculate the expected value for each neuron in this layer (eg. HIDDEN LAYER) */
        for(int j=0; j<layers[i].getNeuronCount(); j++){
          /* Reset the delta error of this neuron to zero. */
          layers[i].neurons[j].deltaError=0;
          /* The delta error of a hidden layer neuron is equal to the SUM of [the PRODUCT of the connection.weight and error of the neurons in the next layer(eg OUTPUT Layer)]. */
          /* Connection#1 of each neuron in the output layer connect with Neuron#1 in the hidden layer */
          for(int k=0; k<layers[i+1].getNeuronCount(); k++){
            layers[i].neurons[j].deltaError += (layers[i+1].neurons[k].connections[j].weight * layers[i+1].neurons[k].deltaError);
          }
          /* Now that we have the sum of Errors x weights attached to this neuron. We must multiply it by the derivative of the activation function. */
          layers[i].neurons[j].deltaError *= (layers[i].neurons[j].neuronOutputValue * (1-layers[i].neurons[j].neuronOutputValue));
        }
        /* Now that you have all the necessary fields populated, you can now Train this hidden layer and then clear the Expected outputs, ready for the next round. */
        layers[i].trainLayer(learningRate);
        layers[i].clearExpectedOUTPUT();
      }
    }
  }
   
   
   
   
   
  /* Function to train the entire network, using an array of input and expected data within an ArrayList */
  void trainingCycle(ArrayList trainingInputData, ArrayList trainingExpectedData, Boolean trainRandomly){
      int dataIndex;
       
      /* re-initialise the training Error with every cycle */
      trainingError=0;
       
      /* Cycle through the training data either randomly or sequentially */
      for(int i=0; i<trainingInputData.size(); i++){
        if(trainRandomly){
          dataIndex=(int) (random(trainingInputData.size()));
        } else {
          dataIndex=i;
        }
  
        trainNetwork((float[]) trainingInputData.get(dataIndex),(float[]) trainingExpectedData.get(dataIndex));
         
        /* Use the networkError variable which is calculated at the end of each individual training session to calculate the entire trainingError. */
        trainingError+=abs(networkError);
      }
  }
   
   
   
   
   
  /* Function to train the network until the Error is below a specific threshold */
  void autoTrainNetwork(ArrayList trainingInputData, ArrayList trainingExpectedData, float trainingErrorTarget, int cycleLimit){
    trainingError=9999;
    int trainingCounter=0;
     
     
    /* cycle through the training data until the trainingError gets below trainingErrorTarget (eg. 0.0005) or the training cycles have exceeded the cycleLimit
 
variable (eg. 10000). */
    while(trainingError>trainingErrorTarget && trainingCounter<cycleLimit){
       
      /* re-initialise the training Error with every cycle */
      trainingError=0;
       
      /* Cycle through the training data randomly */
      trainingCycle(trainingInputData, trainingExpectedData, true);
       
      /* increment the training counter to prevent endless loop */
      trainingCounter++;
    }
     
    /* Due to the random nature in which this neural network is trained. There may be occasions when the training error may drop below the threshold
       To check if this is the case, we will go through one more cycle (but sequentially this time), and check the trainingError for that cycle
       If the training error is still below the trainingErrorTarget, then we will end the training session.
       If the training error is above the trainingErrorTarget, we will continue to train. It will do this check a  Maximum of 9 times. */
    if(trainingCounter<cycleLimit){
       trainingCycle(trainingInputData, trainingExpectedData, false);
       trainingCounter++;
       
       if(trainingError>trainingErrorTarget){
         if (retrainChances<10){
           retrainChances++;
           autoTrainNetwork(trainingInputData, trainingExpectedData,trainingErrorTarget, cycleLimit);
         }
       }
        
    } else {
      println("CycleLimit has been reached. Has been retrained " + retrainChances + " times.  Error is = " + trainingError);
    }  
  }
}