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);
}
