malikmal: Example

Arduino Disco Ball Cake

Oleh Unknown

Description

This is a fun project that will surely impress anyone you make this for. If you are having a "Disco" themed party, you cannot have a boring old cake. Let me tell you, this is probably the only Arduino project that my wife has ever been willing to be a part of. She did the hard work of putting the cake together, and I, well.... I was in charge of lighting. My biggest fear was that one of the wires would come loose and ruin the event at the most critical moment... While a wire did come loose, I managed to fix it in time before the guests arrived. Ok enough of my monologue, let me show you how to make one of these things.

Parts Required:

Arduino UNO (or compatible board)
4 x WS2812-B Programmable Colorful LED Board
330 ohm resistor
4700 uF 16V Electrolytic Capacitor
Breadboard
Female to Male Jumper wires
Breadboard Jumper wires
2.1mm DC Socket with Screw Terminals
5V 4A Plugpack power supply

Note: powering this project using batteries is possible, but not recommended, and done at your own risk.

You will also need a Disco Ball Cake which you will have to make(or buy).My wife made this one. And as you will see shortly, the cake on the inside was Pink, because it was a strawberry cake.

Arduino Libraries and IDE

You can get the Arduino IDE from here: https://www.arduino.cc/en/Main/Software
I used version 1.6.4, which is probably way out of date... but works fine nonetheless.

You can get information about how to use the FastLED library here: http://fastled.io/
And you can download it from here: FastLED Library
I used version 3.0.3, which is also probably out of date.

ARDUINO CODE:

ARDUINO CODE DESCRIPTION:

FastLED Library: You need to make sure that you have downloaded and installed the FastLED library into your Arduino IDE. The library is included in this sketch otherwise the FastLED functions will not work.
The "NUM_LEDS" variable: tells the Arduino how many LEDS are in use. In this case, we have 4 LED rings, with each LED ring containing 16 LEDs, and therefore a total of 64 LEDs. If you define a lower number, for example 16, then the sketch would only illuminate the LEDs on the first LED ring.
The "DATA_PIN" variable: tells the Arduino which Digital Pin to use for data transmission to the LED ring. In this case, I am using Digital Pin 9.
Other variables: I have a couple of other variables which are used for LED randomisation and hue control. Hue is the colour of the LED. By incrementing the hue variable, you can get the LEDs to cycle in a rainbow-like pattern. The "hue" variable is a "byte", which means that it will only go up to a maximum value of 255, before it jumps back down to zero.
Initialisation Code: If you have a different LED ring to the one in this tutorial, you may have to modify the initialisation code. This LED ring has a WS2812-B chipset (according to the ICStation website), and so this line:

FastLED.addLeds(leds, NUM_LEDS); Will tell the FastLED library which chipset is being used (NEOPIXEL), the pin used for data transmission (DATA_PIN), the LED array to be controlled (leds), and the number of LEDs to be controlled (NUM_LEDS).
In the "loop()": section of the code: the "hue" variable is incremented to create a rainbow effect, and a random LED is selected using the FastLED's random8() function.
The random8(x) function: will randomly choose a number from 0 to x.
The randomSeed() function: is there to help "truely randomise" the number. This is helped by reading the randomness of a floating analogPin (A0). It doesn't have to be analogPin 0, it can be any unused analog pin.
leds[rnd].setHSV(hue,255,255): This line sets the random LED to have a hue equal to the "hue" variable, saturation equal to 255, and brightness equal to 255. Saturation equal to zero will make the LED shine white.
Brightness of zero essentially turns the LED OFF.
FastLED.show(): No physical changes will be made to the LED ring display until a message is sent from the Arduino to the Digital input pin of the LED ring. This message is transmitted when you call the FastLED.show(); function. This tells the LED rings to update their display with the information contained within the led array (leds). So if you set all LEDs to turn on, the board will not illuminate the LEDs until the FastLED.show(); function is called. This is important to know - especially when trying to design your own LED sequences.
The delay(50) line: will set the amount of time between flashes to 50 milliseconds. You can change the delay to increase or decrease the number of flashes per second.
The leds[i].fadeToBlackBy( 180 ) function: essentially fades the LEDS by 180 units. You can increase or decrease this number to achieve the desired fade speed. Be warned however, that if you forget to call this function or if you fail to fade the LEDs sufficiently, then you may end up with ALL LEDs turning on, which could potentially destroy your Arduino board - i.e. depending on the number of LED rings you have, and how you have chosen to power them.

The Cake

Slide 1 - Base Plate: It is important to create the base plate with all of the electronics fitted and in working order BEFORE you put the Cake onto it. Trying to fit wires/cables LEDs and circuits under the base plate while there is a Cake ontop is a recipe for disaster. So prepare the base plate first, and then move to the cake making part later.
Slide 2 - Bake Cake: You will need a couple of hemisphere cake pans to make the two sides of the ball. You have to make a relatively dense cake to withstand the overall weight of the cake, icing and fondant, and to maintain it's shape. Once cooled and chilled, you can place them ontop of each other to form a sphere. They are held together by a layer of icing between them.
Slide 3 - Fondant Icing: The fondant icing has to be rolled out on a special non-stick mat. We found that adding a bit of flour helped to reduce the stickiness. There are special rollers which ensure that the thickness of the fondant is consistent throughout. You then have to cut them into square pieces (about 1 cm squares worked well for us). The squares are then painted Silver with a special/edible silver fondant glaze. You may need to use a few coats, and allowing it to dry between coats.
Slide 4 - Iced Cake on Base: The cake can either be iced on or off the base plate... probably better to do it off the base plate. But if you decide to do it on the base plate, you will need to protect the LEDs from stray icing that may fall from the cake (in the process). Once the cake has been fully iced (with icing/frosting), you will need to place the cake into the central position on the board. There may be a chance that the cake may slide from the base... so do what you need to do to make it stay put.
Slides 5-7 - Place Fondant Squares: While the icing is still soft, you will then need to quickly, methodically and tirelessly place the fondant squares in a horizontal linear pattern around the cake. Work your way towards the north and south poles of the cake doing one row at a time. You can cut a fondant circle for the north pole of the cake. In slide 7, you will see a hole at the top of the cake. This was made to cold a plastic canister inside, which would be used later the hold the decorations in place at the top of the cake. Do this before placing the fondant circle at the top of the cake.
Slide 8 - Add Glitter: After placing all of the fondant squares onto the cake, it is very possible that some of the Silver glaze may have been wiped off some of the squares. This is where you go over it again with a few more coats of silver glaze, and on the last coat, before it dries, you can sprinkle some edible glitter all around the cake to give it that extra shine.
Slide 9 - The end product: The final step is to add some wire sparklers and some other decorations to the top of the cake. Push the wires through the fondant cap at the north pole into the canister within. This will hold the wires in place without ruining all of your hard work.

LED Ring pins

WS2812-B chipset: This LED ring uses the WS2812-B chipset, and has 4 break-out pins
(GND, 5V, Din, Dout)
Power: To power this module, you need to provide 5V and up to 1A of current
Signals: To control the LED ring, you need to send signals to it via the Digital Input pin (Din).
You can connect another LED ring to this one by utilising the Digital Output pin (Dout)

Power Usage Guide

General Rule: Each individual LED on the ring can transmit Red, Green and Blue light.The combinations of these colours can make up any other colour. White light is made up of all three of these colours at the same time. Each individual colour will draw approximately 20mA of current when showing that colour at maximum brightness. When shining white at maximum brightness, the single LED will draw approximately 60mA.
Power multiplier: If each LED can draw up to 60mA and there are 16 LEDs on a single LED ring, then 16x60mA = 960mA per LED ring. To be safe, and to make the maths easier, you need to make sure that you provide enough current to accommodate 1A per LED ring. So 4 LED rings will need a 5V 4A power supply if you want to get full functionality out of the modules.

Fritzing diagram

Connecting ONE LED Ring to the Arduino- (Click to enlarge)

3 wires: You only need 3 wires to connect to the LED ring. If you only plan to light up a couple of LEDs at any one time this should be ok.
The SAFE WAY: A safer way to do this is to use an external power supply to power both the Arduino and the LED ring.
Electrolytic capacitor: By connecting a large 4700 uF 16V Electrolytic capacitor between the positive and negative terminals of power supply leads, with the negative leg of the capacitor attached to the negative terminal of the power supply, you will protect your LED rings from any initial onrush of current.

Protecting Resistor: It is also advisable to place a 300-400 ohm resistor between the Arduino's Digital Pin 9 (D9) and the LED Ring's Digital Input pin (Din). This protects the first LED from potential voltage spikes
Suitable wires: If you plan to chain a few of these LED rings together (see below), then you will probably want to keep the wires as short as possible and use a decent guage wire that can handle the current being drawn through them.

Connecting TWO LED Rings to the Arduino- (Click to enlarge)

Three extra wires:You only need 3 extra wires to connect an additional LED ring. A wire needs to connect the Digital output (Dout) of the first LED ring to the Digital Input (Din) of the 2nd LED ring.
Stay safe: Once again, a safer way to do this is to use an external power supply, a large electrolytic capacitor at the terminals, and a 300-400 ohm resistor between the Arduino and the first LED ring's digital input pin.

Connecting FOUR LED Ring to the Arduino- (Click to enlarge)

Sixty Four LEDs:You need 3 extra wires for each additional LED ring. 4 LED rings provides a total of 64 LEDs.
Watch the AMPS:At full brightness, this setup could potentially draw up to 4amps (or roughly 1 amp per LED ring)
External Supply essential: It is essential to use an external power supply to power these LEDs when there are so many of them. If you don't use an external power supply and you accidentally illuminate ALL of the LEDs, then you are likely to damage the microcontroller from excessive current draw.

Connection Tables

How to connect ONE LED Ring to the Arduino- (Click to enlarge)

How to connect TWO LED Rings to the Arduino- (Click to enlarge)

Concluding comments

In this tutorial I showed you how to go about decorating a Disco Ball cake and also showed you how to use the RGB LED rings from ICStation. If you look at the video you will see just how versatile these LED rings are. I would like to thank my wife for providing such an exciting project to work on, and ICStation for their collaborative efforts. Please make sure to share this project with all of your friends and family.

If you like this page, please do me a favour and show your appreciation :

Visit my ArduinoBasics Google + page.
Follow me on Twitter by looking for ScottC @ArduinoBasics.
I can also be found on Pinterest and Instagram.
Have a look at my videos on my YouTube channel.

Description: Arduino Disco Ball Cake Rating: 3.5 Reviewer: Unknown ItemReviewed: Arduino Disco Ball Cake

Neural Network (Part 7) : Cut and Paste Code

Oleh Unknown

Ok - so you don't like tutorials, and would rather just cut and paste some code.
This is for you.
http://www.openprocessing.org/visuals/?visualID=33991

Make sure to select "Source code" when you get there, otherwise it will be quite boring.
Here is an animated gif which shows the program in action.

See BELOW for the WHOLE screenshot so that you don't have to speed read.

If you want to know how it works, then you will have to go back and read part 1 to 6.

But as you can see from the example above:
Before the neural network is trained, the outputs are not even close to the expected outputs. After training, the neural network produces the desired results (or very close to it).

Please note, this neural network also allows more than one output neuron, so you are not limited to single yes no decisions. You can use this neural network to make classifications. You will soon see this with my LED colour sensor.

Feel free to use this Neural Network in your own projects, but please let me know if you do, just for curiosity sake.

Update: See below for the Processing Sketch (much easier than going to the open processing site). It is a bit long - but saves you from having to navigate to another site.

Processing sketch

/*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);
    }  
  }
}

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