public void radarGegevens()
{
AfstandRadar Radar = GetComponentInChildren <AfstandRadar>();
inputValues[0] = Radar.TargetDistanceLeft;
inputValues[1] = Radar.TargetDistanceLeft45;
inputValues[2] = Radar.TargetDistanceFWD;
inputValues[3] = Radar.TargetDistanceRight45;
inputValues[4] = Radar.TargetDistanceRight;
}
// Start is called before the first frame update
private void Start()
{
moveSpeed = 0;
AfstandRadar Radar = GetComponentInChildren <AfstandRadar>();
// De beginwaardes worden opgeslagen.
(defRot, defPos, defScale) = (transform.rotation, transform.position, transform.localScale);
lastPosition = transform.position;
if (NeuralNetwork.multiHidden == false)
{
(biasInput, biasHidden, weightsInput, weightsHidden) = NeuralNetwork.NieuwNetwerk(inputCount, hiddenCount, outputCount);
}
else
{
(biasInput, biasHidden, biasSecondHidden, weightsInput, weightsHidden, weightsSecondHidden) = NeuralNetwork.NieuwDiepNetwerk(inputCount, hiddenCount, secondHiddenCount, outputCount);
}
}
// Update is called once per frame
private void FixedUpdate()
{
// Afstand
distanceTraveled += Vector3.Distance(transform.position, lastPosition);
lastPosition = transform.position;
// Versnelling van de auto
if (moveSpeed < NeuralNetwork.moveSpeed && gebotst == false)
{
moveSpeed += NeuralNetwork.accelerationSpeed;
}
// Sturen van de auto
rotateSpeed = 15f * moveSpeed;
// Vraagt de gegevens op bij de radar.
AfstandRadar Radar = GetComponentInChildren <AfstandRadar>();
// Input
inputValues[0] = Radar.TargetDistanceLeft;
inputValues[1] = Radar.TargetDistanceLeft45;
inputValues[2] = Radar.TargetDistanceFWD;
inputValues[3] = Radar.TargetDistanceRight45;
inputValues[4] = Radar.TargetDistanceRight;
// moveSpeed wordt meegegeven als parameter. Wordt gedeeld door maximale moveSpeed om te normaliseren.
//inputValues[5] = moveSpeed;
// Er wordt een nieuwe array aangemaakt waar de output in komt te staan.
float[] outputValues = new float[NeuralNetwork.outputCount];
if (NeuralNetwork.multiHidden == false)
{
float[] hiddenValues = NeuralNetwork.calculateOutcome(weightsInput, biasInput, inputValues);
outputValues = NeuralNetwork.calculateOutcome(weightsHidden, biasHidden, hiddenValues);
}
else
{
float[] hiddenValues = NeuralNetwork.calculateOutcome(weightsInput, biasInput, inputValues);
float[] secondHiddenValues = NeuralNetwork.calculateOutcome(weightsHidden, biasHidden, hiddenValues);
outputValues = NeuralNetwork.calculateOutcome(weightsSecondHidden, biasSecondHidden, secondHiddenValues);
}
// Verwerking van de output.
if (NeuralNetwork.enkeleOutput == true)
{
//if (moveSpeed < maxsnelheid && moveSpeed > 1.0 && gebotst == false)
//{
// moveSpeed += (float)outputValues[1];
//}
// Snelheid
transform.Translate(moveSpeed * Time.deltaTime, 0f, 0f);
// Draaisnelheid
rotateSpeed = moveSpeed * 15f;
// Draaien
transform.Rotate(Vector3.up * rotateSpeed * (float)outputValues[0] * Time.deltaTime);
}
else
{
if (outputValues[0] == outputValues.Max())
{
transform.Rotate(Vector3.up * rotateSpeed * 0 * Time.deltaTime);
}
else if (outputValues[1] == outputValues.Max())
{
transform.Rotate(Vector3.up * rotateSpeed * 1 * Time.deltaTime);
}
else
{
transform.Rotate(Vector3.up * rotateSpeed * -1 * Time.deltaTime);
}
transform.Translate(moveSpeed * 1f * Time.deltaTime, 0f, 0f);
}
}
