public StudentSnake(Color headColor, Color bodyColor,
int brainHeight, int brainWidth,
int initY, int initX, uint initSize) :
base(headColor, bodyColor, initY, initX, initSize)
{
Studying = true;
Brain = new BrainBase(brainHeight, brainWidth);
}
public void ChangeTemplateBrainType(BrainType newBrainType)
{
if(brainType != newBrainType) { // only do something if the brainType has changed
brainType = newBrainType;
templateBrain = null;
templateBrain = new BrainBase();
// Test Type (manual coded brain)
if(brainType == BrainType.Test) {
templateBrain = new BrainTest();
}
// ANN Feed-Forward Layered Static All-to-All:
if(brainType == BrainType.ANN_FF_Layered_AllToAll) {
templateBrain = new BrainANN_FF_Layers_A2A();
}
}
}
public void TimeoutChangesStateToFollower()
{
var brain = new BrainBase(new RobotBase(new DefaultEnvironment(new Map(10, 10))));
var decoratedState = new IdleState();
var followerState = new IdleState();
var state = new TimeoutStateDecorator(decoratedState, 3, followerState);
brain.CurrentState = state;
Assert.Equal(state, brain.CurrentState);
state.Tick();
Assert.Equal(state, brain.CurrentState);
state.Tick();
Assert.Equal(state, brain.CurrentState);
state.Tick();
Assert.Equal(followerState, brain.CurrentState);
state.Tick();
}
public override void Update(Field gameField, IEnumerable <SnakeBase> otherSnakes)
{
--CurrentEnergy;
base.Update(gameField, otherSnakes);
if (CurrentEnergy <= EnergyToDie)
{
base.Die();
return;
}
if (CurrentEnergy >= EnergyToCreate)
{
int size = Body.Count / 2;
List <Point> newBodyPositions = Body.Skip(size).Select(bs => bs.Position).ToList();
Body.RemoveRange(size, Body.Count - size);
BrainBase newBrain = new BrainBase(Brain);
Random random = new Random();
int mutations = random.Next(1, 5);
for (int i = 0; i < mutations; ++i)
{
newBrain.RandomMutation();
}
newBodyPositions.Reverse();
DividingStudentSnake newSnake = new DividingStudentSnake(
HeadColor, newBodyPositions.First(), newBodyPositions.Skip(1), newBrain,
CurrentEnergy / 2, EnergyToCreate, EnergyToDie, EnergyPerFood
);
++TotalCreated;
CurrentEnergy /= 2;
OnDevide?.Invoke(this, new SnakeCreatedEventArgs(newSnake));
}
}
public virtual void CopyBrainSettingsFrom(BrainBase sourceBrain)
{
}
public Result(BrainBase brain, int score, int produced)
{
Brain = brain;
Score = score;
Produced = produced;
}
/*
// Run the network forwards to produce output values
public void Run(ref float[] input, out float[] output)
{
// Make sure we have enough data
if (input.Length != inputSize)
throw new ArgumentException("Input data is not of the correct dimension.");
//Dimension
output = new float[layerSize[layerCount - 1]]; // set output size to that of last layer (output)
// Run the network!
for (int l = 0; l < layerCount; l++)
{
for (int j = 0; j < layerSize[l]; j++) // for each node in the current layer l,
{
float sum = 0.0f;
for (int i = 0; i < (l == 0 ? inputSize : layerSize[l - 1]); i++) // for each node in the previous layer l-1,
sum += weight[l][i][j] * (l == 0 ? input[i] : layerOutput[l - 1][i]); // sum for that node is the sum of all inputs multiplied by weights from previous layer
sum += bias[l][j]; // add bias value to this node
layerInput[l][j] = sum; // store layerInput values for this layer
layerOutput[l][j] = TransferFunctions.Evaluate(transferFunctions[l][j], sum); // pass the sum into the transferFunction for that layer
}
}
//Copy the output to the output array
for (int i = 0; i < layerSize[layerCount - 1]; i++)
output[i] = layerOutput[layerCount - 1][i];
}
*/
public override void CopyBrainSettingsFrom(BrainBase sourceBrain)
{
}
/*
* // Run the network forwards to produce output values
* public void Run(ref float[] input, out float[] output)
* {
* // Make sure we have enough data
* if (input.Length != inputSize)
* throw new ArgumentException("Input data is not of the correct dimension.");
*
* //Dimension
* output = new float[layerSize[layerCount - 1]]; // set output size to that of last layer (output)
*
* // Run the network!
* for (int l = 0; l < layerCount; l++)
* {
* for (int j = 0; j < layerSize[l]; j++) // for each node in the current layer l,
* {
* float sum = 0.0f;
* for (int i = 0; i < (l == 0 ? inputSize : layerSize[l - 1]); i++) // for each node in the previous layer l-1,
* sum += weight[l][i][j] * (l == 0 ? input[i] : layerOutput[l - 1][i]); // sum for that node is the sum of all inputs multiplied by weights from previous layer
*
* sum += bias[l][j]; // add bias value to this node
* layerInput[l][j] = sum; // store layerInput values for this layer
*
* layerOutput[l][j] = TransferFunctions.Evaluate(transferFunctions[l][j], sum); // pass the sum into the transferFunction for that layer
* }
* }
*
* //Copy the output to the output array
* for (int i = 0; i < layerSize[layerCount - 1]; i++)
* output[i] = layerOutput[layerCount - 1][i];
* }
*/
public override void CopyBrainSettingsFrom(BrainBase sourceBrain)
{
}
public StudentSnake(Color headColor, Color bodyColor,
Point head, IEnumerable <Point> body,
BrainBase brain) : base(headColor, bodyColor, head, body) => Brain = brain;
private DividingStudentSnake(Color headColor, Point head, IEnumerable <Point> body, BrainBase brain, int initEnegry, int energyToCreate,
int energyToDie, int energyPerFood) : base(headColor, GetRandomColor(), head, body, brain)
{
CurrentEnergy = initEnegry;
EnergyToCreate = energyToCreate;
EnergyToDie = energyToDie;
EnergyPerFood = energyPerFood;
OnEat += DividingStudentSnake_OnEat;
}
private void Start()
{
m_brain = GetComponent <BrainBase>();
m_body = GetComponent <BodyAISimple>();
m_agent = GetComponent <NavMeshAgent>();
}
protected void Init()
{
m_brain = GetComponent <BrainBase>();
m_job = GetComponent <JobBase>();
m_homePos = base.transform.position;
}
public virtual void CopyBrainSettingsFrom(BrainBase sourceBrain)
{
}
protected void Init()
{
m_brain = GetComponent <BrainBase>();
m_body = GetComponent <BodyBase>();
}
