private bool[] What_should_I_doJ()
{
#region input order
/* 1. speed
* 2.distance
* 3.width
* 4.can jump
*/
#endregion
inputs[0] = Tools.pm.getEngine(this.ID).velocity.X;
List <float> ins = GetAsentialData.findDif(this, Global.bg.obstacleList);
if (ins.Count != 0)
{
inputs[1] = ins[0] / 10; //distamce between obstacle and jump
inputs[2] = ins[1] / 10; //obstacle width
}
else
{
bool[] answers1 = new bool[outputNum];
answers1[0] = false;;
return(answers1);
}
if (GetAsentialData.CanJump(this))
{
inputs[3] = 1;
}
else
{
inputs[3] = -1;
}
//activating neuralNet
float[] output = nn.calculate(inputs);
//maping the outputs
bool[] answers = new bool[outputNum];
for (int i = 0; i < answers.Length; i++)
{
if (output[i] > 0)
{
answers[i] = true;
}
else
{
answers[i] = false;
}
}
//returning answers
return(answers);
}
private bool[] What_should_I_doJR()
{
#region input order
/* 1. speed
* 2.distance
* 3.width
* 4.can jump
* 5. distance to second group
*/
#endregion
//// setting input
inputs[0] = Tools.pm.getEngine(this.ID).velocity.X; // speed
List <float> ins = GetAsentialData.findDif(this, Global.bg.obstacleList); // find data for jump
inputs[1] = ins[0] / 100; //distamce between obstacle and jump
inputs[2] = ins[1] / 10; //obstacle width
//if can jump 1 else -1
if (GetAsentialData.CanJump(this))
{
inputs[3] = 1;
}
else
{
inputs[3] = -1;
}
//distance to next Obstacle
inputs[4] = GetAsentialData.findDifNext(this, Global.bg.obstacleList) / 100;
//activating neuralNet
float[] output = nn.calculate(inputs);
//maping the outputs
bool[] answers = new bool[outputNum];
for (int i = 0; i < answers.Length; i++)
{
if (output[i] > 0)
{
answers[i] = true;
}
else
{
answers[i] = false;
}
}
//returning answers
return(answers);
}
private bool[] What_should_I_do()
{
#region input order
/* 1. my real poisition.X
* 2. my width
* 3. my X speed
* 4. can I Jump
* 5. obstacle Height
* 6. first obstacle posX
* 7. second obstacle posX
* 8. third obstacle posX
* 9. fourth obstacle posX
* 10. obstacle Width
* 11. my scale
* 12. an obstacle scale
* 13. bullet Left pos
*/
#endregion
//// setting input
//float[] inputs = new float[inputNum];
inputs[0] = (this.Position - this.Origin).X;
inputs[1] = this.CurrentFrame.Width;
inputs[2] = pm.getEngine(ID).velocity.X;
//if can jump 1 else 0
if (GetAsentialData.CanJump(this))
{
inputs[3] = 1;
}
else
{
inputs[3] = 0;
}
inputs[4] = Global.bg.obstacleList[0].CurrentFrame.Height;
inputs[5] = GetAsentialData.GetFirstObstaclePosX(Global.bg.obstacleList, this);
inputs[6] = GetAsentialData.GetSecondObstaclePosX(Global.bg.obstacleList, this);
inputs[7] = GetAsentialData.GetThirdObstaclePosX(Global.bg.obstacleList, this);
inputs[8] = GetAsentialData.GetFourthObstaclePosX(Global.bg.obstacleList, this);
inputs[9] = Global.bg.obstacleList[0].CurrentFrame.Width;
inputs[10] = this.scale.X;
inputs[11] = Global.bg.obstacleList[0].scale.X;
inputs[12] = GetAsentialData.GetBulletLeftPosX();
//normalizing all inputs
for (int i = 0; i < inputs.Length; i++)
{
inputs[i] = GetAsentialData.NoramlizeValue(inputs[i]);
}
//activating neuralNet
float[] output = nn.calculate(inputs);
//maping the outputs
bool[] answers = new bool[outputNum];
for (int i = 0; i < answers.Length; i++)
{
if (output[i] > 0)
{
answers[i] = true;
}
else
{
answers[i] = false;
}
}
//returning answers
return(answers);
}
