public static float[] ToVector(R2Tensor Tensor)
{
if (Tensor.row != 1 & Tensor.col != 1)
{
throw new MismatchedDimensionException("The matrix must be a row or column matrix");
}
if (Tensor.row == 1)
{
float[] vector_out = new float[Tensor.col];
for (int i = 0; i < Tensor.col; i++)
{
vector_out[i] = Tensor.tensor[0, i];
}
return(vector_out);
}
if (Tensor.col == 1)
{
float[] vector_out = new float[Tensor.row];
for (int i = 0; i < Tensor.row; i++)
{
vector_out[i] = Tensor.tensor[i, 0];
}
return(vector_out);
}
else
{
return(null);
}
}
private void Start()
{
Manager2 manager = FindObjectOfType <Manager2>();
max_v = manager.max_v;
acceleration = manager.acceleration;
dampspeed = manager.dampspeed;
maxhealth = manager.maxhealth;
inman = FindObjectOfType <InputManager>();
currhealth = maxhealth;
//Load brain
int inputNeuron = nn.Input;
int hiddenNeuron = nn.Hidden;
int outputNeuron = nn.Output;
R2Tensor ihWeight = new R2Tensor(hiddenNeuron, inputNeuron, nn.ih_weight);
R2Tensor ihBias = new R2Tensor(hiddenNeuron, 1, nn.ih_bias);
R2Tensor ohWeight = new R2Tensor(outputNeuron, hiddenNeuron, nn.ho_weight);
R2Tensor ohBias = new R2Tensor(outputNeuron, 1, nn.ho_bias);
brain = new NeuralNetwork(ihWeight, ihBias, ohWeight, ohBias, inputNeuron, hiddenNeuron, outputNeuron);
//Particles
if (particleson)
{
part = Instantiate(particlesystem, Vector3.zero, Quaternion.identity, gameObject.transform).GetComponent <ParticleSystem>();
}
}
private static R2Tensor Sign(R2Tensor Tensor)
{
float[,] tensor_out = new float[Tensor.row, Tensor.col];
for (int i = 0; i < Tensor.row; i++)
{
tensor_out[i, 0] = Mathf.Sign(Tensor.tensor[i, 0]);
}
return(new R2Tensor(tensor_out));
}
public NeuralNetwork(R2Tensor ihWeight, R2Tensor ihBias, R2Tensor hoWeight, R2Tensor hoBias, int InputLayer, int HiddenLayer, int OutputLayer)
{
input = InputLayer;
hidden = HiddenLayer;
output = OutputLayer;
ih_weight = ihWeight.Copy();
ih_bias = ihBias.Copy();
ho_weight = hoWeight.Copy();
ho_bias = hoBias.Copy();
}
public NeuralNetwork(int InputLayer, int HiddenLayer, int OutputLayer)
{
input = InputLayer;
hidden = HiddenLayer;
output = OutputLayer;
ih_weight = new R2Tensor(HiddenLayer, InputLayer);
ih_bias = new R2Tensor(HiddenLayer, 1);
ho_weight = new R2Tensor(OutputLayer, HiddenLayer);
ho_bias = new R2Tensor(OutputLayer, 1);
}
private static R2Tensor Softmax(R2Tensor Tensor)
{
float[,] tensor_out = new float[Tensor.row, Tensor.col];
float total = 0;
for (int i = 0; i < Tensor.row; i++)
{
total += Mathf.Exp(Tensor.tensor[i, 0]);
tensor_out[i, 0] = Mathf.Exp(Tensor.tensor[i, 0]);
}
return(new R2Tensor(tensor_out).Scale(1 / total));
}
public R2Tensor ForwardPropagation(R2Tensor Input, bool norm = true)
{
R2Tensor Hidden = Tanh(R2Tensor.Add(R2Tensor.MatMul(ih_weight, Input), ih_bias));
if (!norm)
{
return(Hidden);
}
R2Tensor Output = R2Tensor.Add(R2Tensor.MatMul(ho_weight, Hidden), ho_bias);
return(Sign(Output));
}
public string PrintNeuron(R2Tensor tensor)
{
string string_out = "";
for (int y = 0; y < tensor.row; y++)
{
string_out = string_out + " | ";
for (int x = 0; x < tensor.col; x++)
{
string_out = string_out + Truncate(tensor.tensor[y, x]) + " | ";
}
string_out = string_out + "\n";
}
return(string_out);
}
public static R2Tensor HadamardMul(R2Tensor A, R2Tensor B)
{
if (A.row != B.row | A.col != B.col)
{
throw new MismatchedDimensionException("Both Matrix must have the same dimension in order to be multiplied together");
}
float[,] tensor_out = new float[A.row, A.col];
for (int y = 0; y < A.row; y++)
{
for (int x = 0; x < A.col; x++)
{
tensor_out[y, x] = A.tensor[y, x] * B.tensor[y, x];
}
}
return(new R2Tensor(tensor_out));
}
public static R2Tensor MatMul(R2Tensor A, R2Tensor B)
{
if (A.col != B.row)
{
throw new MismatchedDimensionException("Number of column of the first matrix must match the number of row of the second matrix");
}
float[,] tensor_out = new float[A.row, B.col];
for (int y = 0; y < A.row; y++)
{
for (int x = 0; x < B.col; x++)
{
tensor_out[y, x] = Dot(A.GetRow(y), B.GetCol(x));
}
}
return(new R2Tensor(tensor_out));
}
private int Max(R2Tensor tensor)
{
int intout = 0;
float max = 0;
for (int i = 0; i < tensor.row; i++)
{
int currin = intout;
if (tensor.tensor[i, 0] > max)
{
currin = i;
}
intout = currin;
}
return(intout);
}
public void Mutate(R2Tensor Tensor, float mutationrate, float magnitude, float clamping)
{
for (int i = 0; i < Tensor.row; i++)
{
for (int j = 0; j < Tensor.col; j++)
{
if (mutationrate > Random.Range(0f, 1f))
{
Tensor.tensor[i, j] += Random.Range(-1f * magnitude, magnitude);
}
if (Tensor.tensor[i, j] > clamping)
{
Tensor.tensor[i, j] = clamping;
}
if (Tensor.tensor[i, j] < -1 * clamping)
{
Tensor.tensor[i, j] = -1 * clamping;
}
}
}
}
public R2Tensor ForwardPropagation(R2Tensor Input, bool norm = true)
{
R2Tensor Hidden = Tanh(R2Tensor.Add(R2Tensor.MatMul(ih_weight, Input), ih_bias));
if (!norm)
{
return(Hidden);
}
R2Tensor Output = R2Tensor.Add(R2Tensor.MatMul(ho_weight, Hidden), ho_bias);
switch (outtype)
{
case "Basisdirection":
return(Tanh(Output));
case "Direction":
int maxind = Max(Output);
float[] tensor_out = new float[9];
for (int i = 0; i < 9; i++)
{
if (i == maxind)
{
tensor_out[i] = 1; continue;
}
tensor_out[i] = 0;
}
return(R2Tensor.ToMatrix(tensor_out, "col"));
case "Keystroke":
return(Sign(Output));
case "Debug":
return(Output);
default:
return(Output);
}
}
private void Update()
{
if (networkvisual == null)
{
networkvisual = GameObject.Find("Network");
}
if (afkenable)
{
if (speedup * deathtimer >= afk & !inlas & !dead)
{
FindObjectOfType <Holocaust_2>().ImDeadLOL(prisoner_number);
Destroy(gameObject);
dead = true;
}
if (velocity.x == 0 & velocity.y == 0)
{
deathtimer += Time.deltaTime;
}
else
{
deathtimer = 0;
}
}
if (singafkenable)
{
if (speedup * deathtimer2 >= singafk & !inlas & !dead)
{
FindObjectOfType <Holocaust_2>().ImDeadLOL(prisoner_number);
Destroy(gameObject);
}
if (velocity.x == 0 & xory == 1)
{
deathtimer2 += Time.deltaTime;
}
else
{
if (velocity.y == 0 & xory == 0)
{
deathtimer2 += Time.deltaTime;
}
else
{
deathtimer2 = 0; xory = 1 - xory;
}
}
}
#region Think
float[] input_array = inman.GetInput(gameObject);
int input_array_length = input_array.Length;
input_array[input_array_length - 4] = Tanh(obj.position.x, 4f);
input_array[input_array_length - 3] = Tanh(obj.position.y, 9f / 4f);
input_array[input_array_length - 2] = Tanh(velocity.x, max_v.x / 2f);
input_array[input_array_length - 1] = Tanh(velocity.y, max_v.y / 2f);
R2Tensor finproc = brain.ForwardPropagation(R2Tensor.ToMatrix(input_array, "col"));
float[] thought = R2Tensor.ToVector(finproc);
if (networkvisual != null)
{
if (networkvisual.activeSelf & prisoner_number == best_n - 1)
{
inputactivation.text = PrintNeuron(R2Tensor.ToMatrix(input_array, "col"));
hiddenactivation.text = PrintNeuron(brain.ForwardPropagation(R2Tensor.ToMatrix(input_array, "col"), false));
outputactivation.text = PrintNeuron(finproc);
}
}
//R2Tensor.ToMatrix(thought, "row").PrintMatrix();
int vert = 0;
int hori = 0;
switch (output)
{
case "Basisdirection":
if (thought[0] > 0.5f)
{
hori = 1;
}
if (thought[0] < -0.5f)
{
hori = -1;
}
if (thought[1] > 0.5f)
{
vert = 1;
}
if (thought[1] < -0.5f)
{
vert = -1;
}
break;
case "Direction":
if (thought[0] == 1 | thought[4] == 1 | thought[5] == 1)
{
vert = 1;
}
if (thought[1] == 1 | thought[6] == 1 | thought[7] == 1)
{
vert = -1;
}
if (thought[3] == 1 | thought[5] == 1 | thought[7] == 1)
{
hori = 1;
}
if (thought[2] == 1 | thought[4] == 1 | thought[6] == 1)
{
hori = -1;
}
break;
case "Keystroke":
if (thought[0] == 1)
{
vert += 1;
}
if (thought[1] == 1)
{
vert -= 1;
}
if (thought[2] == 1)
{
hori -= 1;
}
if (thought[3] == 1)
{
hori += 1;
}
break;
}
#endregion
#region Translation
if (firstbump_h & Mathf.Abs(obj.position.x) > 8.25f)
{
velocity = new Vector2(0, velocity.y);
firstbump_h = false;
}
if (!firstbump_h & Mathf.Abs(obj.position.x) < 8.25f)
{
firstbump_h = true;
}
if (firstbump_v & Mathf.Abs(obj.position.y) > 4.37f)
{
velocity = new Vector2(velocity.x, 0);
firstbump_v = false;
}
if (!firstbump_v & Mathf.Abs(obj.position.y) < 4.37f)
{
firstbump_v = true;
}
if ((hori == -1 & obj.position.x > -8.25f) | (hori == 1 & obj.position.x < 8.25f))
{
if (hori * velocity.x < max_v.x)
{
if (hori != 0)
{
velocity += new Vector2(hori * acceleration * Time.deltaTime * speedup, 0);
}
}
}
else
{
if (Mathf.Abs(velocity.x) < 0.001)
{
velocity = new Vector2(0, velocity.y);
}
else
{
velocity -= new Vector2(dampspeed * velocity.x * Time.deltaTime * speedup, 0);
}
}
if ((vert == 1 & obj.position.y < 4.37f) | (vert == -1 & obj.position.y > -4.37f))
{
if (vert * velocity.y < max_v.y)
{
if (vert != 0)
{
velocity += new Vector2(0, vert * acceleration * Time.deltaTime * speedup);
}
}
}
else
{
if (Mathf.Abs(velocity.y) < 0.001)
{
velocity = new Vector2(velocity.x, 0);
}
else
{
velocity -= new Vector2(0, dampspeed * velocity.y * Time.deltaTime * speedup);
}
}
obj.position += new Vector3(velocity.x * speedup, velocity.y * speedup, 0);
if (obj.position.x < -8.25f)
{
obj.position = new Vector3(-8.25f, obj.position.y, 0); velocity.x = 0;
}
if (obj.position.x > 8.25f)
{
obj.position = new Vector3(8.25f, obj.position.y, 0); velocity.x = 0;
}
if (obj.position.y > 4.37f)
{
obj.position = new Vector3(obj.position.x, 4.37f, 0); velocity.y = 0;
}
if (obj.position.y < -4.37f)
{
obj.position = new Vector3(obj.position.x, -4.37f, 0); velocity.y = 0;
}
#endregion
#region Rotation
float anglef = ((Mathf.Atan2(velocity.y, velocity.x) * 180 / Mathf.PI + 270) % 360);
if (velocity.y != 0 | velocity.x != 0)
{
obj.eulerAngles = new Vector3(0, 0, anglef);
}
#endregion
}
private void Update()
{
#region Think
float[] input_array = inman.GetInput(obj, velocity);
R2Tensor finproc = brain.ForwardPropagation(R2Tensor.ToMatrix(input_array, "col"));
float[] thought = R2Tensor.ToVector(finproc);
int vert = 0;
int hori = 0;
if (thought[0] == 1)
{
vert += 1;
}
if (thought[1] == 1)
{
vert -= 1;
}
if (thought[2] == 1)
{
hori -= 1;
}
if (thought[3] == 1)
{
hori += 1;
}
#endregion
#region Translation
if (firstbump_h & Mathf.Abs(obj.position.x) > 8.25f)
{
velocity = new Vector2(0, velocity.y);
firstbump_h = false;
}
if (!firstbump_h & Mathf.Abs(obj.position.x) < 8.25f)
{
firstbump_h = true;
}
if (firstbump_v & Mathf.Abs(obj.position.y) > 4.37f)
{
velocity = new Vector2(velocity.x, 0);
firstbump_v = false;
}
if (!firstbump_v & Mathf.Abs(obj.position.y) < 4.37f)
{
firstbump_v = true;
}
if ((hori == -1 & obj.position.x > -8.25f) | (hori == 1 & obj.position.x < 8.25f))
{
if (hori * velocity.x < max_v.x)
{
if (hori != 0)
{
velocity += new Vector2(hori * acceleration * Time.deltaTime, 0);
}
}
}
else
{
if (Mathf.Abs(velocity.x) < 0.001)
{
velocity = new Vector2(0, velocity.y);
}
else
{
velocity -= new Vector2(dampspeed * velocity.x * Time.deltaTime, 0);
}
}
if ((vert == 1 & obj.position.y < 4.37f) | (vert == -1 & obj.position.y > -4.37f))
{
if (vert * velocity.y < max_v.y)
{
if (vert != 0)
{
velocity += new Vector2(0, vert * acceleration * Time.deltaTime);
}
}
}
else
{
if (Mathf.Abs(velocity.y) < 0.001)
{
velocity = new Vector2(velocity.x, 0);
}
else
{
velocity -= new Vector2(0, dampspeed * velocity.y * Time.deltaTime);
}
}
obj.position += new Vector3(velocity.x, velocity.y, 0);
if (obj.position.x < -8.25f)
{
obj.position = new Vector3(-8.25f, obj.position.y, 0); velocity.x = 0;
}
if (obj.position.x > 8.25f)
{
obj.position = new Vector3(8.25f, obj.position.y, 0); velocity.x = 0;
}
if (obj.position.y > 4.37f)
{
obj.position = new Vector3(obj.position.x, 4.37f, 0); velocity.y = 0;
}
if (obj.position.y < -4.37f)
{
obj.position = new Vector3(obj.position.x, -4.37f, 0); velocity.y = 0;
}
#endregion
#region Rotation
float anglef = ((Mathf.Atan2(velocity.y, velocity.x) * 180 / Mathf.PI + 270) % 360);
if (velocity.y != 0 | velocity.x != 0)
{
obj.eulerAngles = new Vector3(0, 0, anglef);
}
#endregion
}
private void Start()
{
speedup = parameters.SpeedUp;
hidden = parameters.HiddenLayer;
output = parameters.output.ToString();
afk = parameters.afk;
afkenable = parameters.afk_kill;
singafk = parameters.singlelineafk;
singafkenable = parameters.singlelineafkkill;
invert = parameters.Invert;
// BestPrison stuff
BestPrison camp = FindObjectOfType <BestPrison>();
networkvisual = GameObject.Find("Network");
if (networkvisual != null)
{
inputactivation = GameObject.Find("InputText").GetComponent <Text>();
hiddenactivation = GameObject.Find("HiddenText").GetComponent <Text>();
outputactivation = GameObject.Find("OutputText").GetComponent <Text>();
inputhiddenweight = GameObject.Find("InputHiddenWeight").GetComponent <Text>();
inputhiddenbias = GameObject.Find("InputHiddenBias").GetComponent <Text>();
hiddenoutputweight = GameObject.Find("HiddenOutputWeight").GetComponent <Text>();
hiddenoutputbias = GameObject.Find("HiddenOutputBias").GetComponent <Text>();
}
//load brain
if (parameters.PlayMode.ToString() == "Load")
{
NetData loadedBrain = DataManager.LoadBrain(parameters.LoadIndex);
int inputNeuron = loadedBrain.Input;
int hiddenNeuron = loadedBrain.Hidden;
int outputNeuron = loadedBrain.Output;
R2Tensor ihWeight = new R2Tensor(hiddenNeuron, inputNeuron, loadedBrain.ih_weight);
R2Tensor ihBias = new R2Tensor(hiddenNeuron, 1, loadedBrain.ih_bias);
R2Tensor ohWeight = new R2Tensor(outputNeuron, hiddenNeuron, loadedBrain.ho_weight);
R2Tensor ohBias = new R2Tensor(outputNeuron, 1, loadedBrain.ho_bias);
brain = new NeuralNetwork(ihWeight, ihBias, ohWeight, ohBias, inputNeuron, hiddenNeuron, outputNeuron, loadedBrain.outputType);
}
else
{
int inputNeuron = bestbrain.Input;
int hiddenNeuron = bestbrain.Hidden;
int outputNeuron = bestbrain.Output;
R2Tensor ihWeight = new R2Tensor(hiddenNeuron, inputNeuron, bestbrain.ih_weight);
R2Tensor ihBias = new R2Tensor(hiddenNeuron, 1, bestbrain.ih_bias);
R2Tensor ohWeight = new R2Tensor(outputNeuron, hiddenNeuron, bestbrain.ho_weight);
R2Tensor ohBias = new R2Tensor(outputNeuron, 1, bestbrain.ho_bias);
brain = new NeuralNetwork(ihWeight, ihBias, ohWeight, ohBias, inputNeuron, hiddenNeuron, outputNeuron, bestbrain.outputType);
}
if (networkvisual != null)
{
if (networkvisual.activeSelf)
{
inputhiddenweight.text = PrintNeuron(brain.ih_weight);
inputhiddenbias.text = PrintNeuron(brain.ih_bias);
hiddenoutputweight.text = PrintNeuron(brain.ho_weight);
hiddenoutputbias.text = PrintNeuron(brain.ho_bias);
}
}
Manager2 manager = FindObjectOfType <Manager2>();
max_v = manager.max_v;
acceleration = manager.acceleration;
dampspeed = manager.dampspeed;
maxhealth = parameters.maxhealth;
inman = FindObjectOfType <InputManager>();
currhealth = maxhealth;
//Particles
if (particleson)
{
part = Instantiate(particlesystem, Vector3.zero, Quaternion.identity, gameObject.transform).GetComponent <ParticleSystem>();
}
}
