//LEGACY TESTS:
//A simple 1-layer back-propigation test. (Does it improve? (everything approches 1?))
void Simple_Back()
{
Matrix v1 = new Matrix(1, 1, new double[, ] {
{ 1 }
});
Matrix r1 = new Matrix(1, 3, new double[, ] {
{ 1, 1, 1 }
});
//Matrix fun = new Matrix(3,3, new double[,]{{1,1,1},{2,3,4},{5,6,7}});
//Debug.Log(fun);
//r1 = Matrix.Transpose(r1);
l1 = new Layer(1, 3, Misc.sigmoidNF());
Matrix a = l1.Forward(v1);
Debug.Log(a.ToString());
l1.Backwards(r1);
//Debug.Log(r1.ToString());
a = l1.Forward(v1);
Debug.Log(a.ToString());
}
void not_testcase(bool prnt = false)
{
//Matrix TestData = new Matrix(2,2, new double[,] { {1,0}{0,1}});
Matrix v1 = new Matrix(1, 1, new double[, ] {
{ 1 }
});
Matrix v0 = new Matrix(1, 1, new double[, ] {
{ 0 }
});
Matrix a = l2.Forward(l1.Forward(v1)); //Forward
if (prnt)
{
Debug.Log("1: forward." + a.ToString());
}
l1.Backwards(l2.Backwards(v0, true));
//Debug.Log("ffs: "+a.ToString());
//l1.Backwards(a,false);
a = l2.Forward(l1.Forward(v0)); //Forward
if (prnt)
{
Debug.Log("0: forward." + a.ToString());
}
l1.Backwards(l2.Backwards(v1, true));
//}
}
void network_train1(bool prnt = false)
{
for (int i = 0; i < ndset.Rows; i++)
{
Matrix a = new Matrix(1, 4, new double[, ] {
{ ndset.Elements[i, 0], ndset.Elements[i, 1], ndset.Elements[i, 2], ndset.Elements[i, 3] }
}); //Gets the arguments.
Matrix b = net1.Forward(a);
if (prnt)
{
Debug.Log("Training...: Input " + a.ToString() + " Target: " + ndset.Elements[i, 4] + " Output: " + b.ToString());
}
net1.Backward(new Matrix(1, 1, new double[, ] {
{ ndset.Elements[i, 4] }
}));
}
}
void train(Matrix dataset, Layer layer, bool prnt = false)
{
if (prnt)
{
Debug.Log("Training sofar:");
}
for (int i = 0; i < dataset.Rows; i++) //For each row.
{
Matrix a = null;
if (dataset.Columns == 3)
{
a = new Matrix(1, 2, new double[, ] {
{ dataset.Elements[i, 0], dataset.Elements[i, 1] }
}); //Gets the arguments.
}
if (dataset.Columns == 2)
{
a = new Matrix(1, 2, new double[, ] {
{ dataset.Elements[i, 0], dataset.Elements[i, 1] }
}); //Gets the arguments.
}
Matrix b = layer.Forward(a);
if (prnt)
{
Debug.Log("Training...: Input " + a.ToString() + " Target: " + dataset.Elements[i, 2] + " Output: " + b.ToString());
}
layer.Backwards(new Matrix(1, 1, new double[, ] {
{ dataset.Elements[i, 2] }
}));
}
}
//this one is a prototype
void brain()
{
NeuralNet.Matrix input = eye.gameObject.GetComponent <Eye_script> ().look(); //Dirty hack to get the input from the eye... isn't great..
//NeuralNet.Matrix input2 = NeuralNet.Matrix.AddBias (NeuralNet.Matrix.AddBias (input));
/*
* NeuralNet.Matrix input2 = new NeuralNet.Matrix(1,input.Columns+4);
* for (int i = 0; i < input.Columns; i++) { //Only 1 row.
* input2.Elements [0,i] = input.Elements[0,i];
* }*/
//input2 [0,0] = 0;
//input2 [0,1] = 1;
NeuralNet.Matrix brainRet = mind.Forward(input);
if (getLastBrainStr)
{
lastBrainStr = brainRet.ToString();
}
//Hardcoding ret to test:
//ret[0,0] = input[0,0] / Mathf.PI + 0.5 ;
//Abstract the values out of the NN.
double rotate_value = ((brainRet[0, 0] - 0.5) * 180.0) / 10.0;
double accel_value = (brainRet[0, 1] - 0.5) * 1;
//ROTATION//
transform.rotation = Quaternion.AngleAxis((float)rotate_value, Vector3.up) * transform.rotation;
//VELOCITY//
body.velocity = body.velocity * 0.9f; //Dampen vel
body.velocity += ( float)accel_value * body.transform.forward;
//Velocity = (brainoutput 1*45)*Velocity + accel
//Rotates the velocity and applies the body/etc
//body.velocity = Quaternion.AngleAxis((float)((ret[0,0]-0.5)*180.0/100), Vector3.up)*body.velocity + (body.transform.forward * (float)ret[0,1] * 0); //The rotation is based off the result.
//food -= (float)(ret[0,1]); //Accel costs.
//Rotate
// transform.rotation = Quaternion.AngleAxis((float)((ret[0,1]-ret[0,2])*10), Vector3.up)*transform.rotation ;
//transform.rotation = Quaternion.AngleAxis((float)((input[0,0])), Vector3.up)*transform.rotation ; //TRY IT RAW!
//transform.rotation = new Quaternion (((float)(ret [0, 1]+ret [0, 2]-ret [0, 3]-ret [0, 4])*2) , Vector3.up[0],Vector3.up[1],Vector3.up[2]);
//transform.rotation = new Quaternion (((float)(ret [0, 1]-ret [0, 2])) , Vector3.up[0],Vector3.up[1],Vector3.up[2]);
//Move
// if(ret[0,0] > 0)
// body.velocity = (float)(ret[0,0]-0.3)*5f*body.transform.forward;
//if (body.velocity.magnitude < 0.1) {
//food+=2;
// train_bad();
//}
//food -= (float)(ret[0,0]);
/*
* if (Random.Range(0, 100) < 10)
* {
* body.velocity = Quaternion.AngleAxis(Random.Range(-45,45), Vector3.up)*body.velocity;
* // body.velocity = new Vector3(0,0,10);
* }
* //Face velocity dir.
* if (body.velocity != new Vector3 (0, 0, 0)) {
* transform.rotation = Quaternion.LookRotation (body.velocity);
* }//*/
}
