LinearTraining2 createTrainingConfiguration2()
{
LinearTraining2 c = new LinearTraining2();
c.NbElements = targetTransforms.Length;
c.NbInputs = 2;
c.TrainingExemples = new double[c.NbElements * c.NbInputs];
c.ExpectedResults = new double[c.NbElements];
for (int i = 0; i < targetMeshRenderers.Length; ++i)
{
MeshRenderer mr = targetMeshRenderers[i].GetComponent <MeshRenderer>();
if (mr.material.color == redMat.color)
{
Transform t = targetTransforms[i].GetComponent <Transform>();
c.TrainingExemples[i * c.NbInputs] = t.position.x;
c.TrainingExemples[i * c.NbInputs + 1] = t.position.z;
c.ExpectedResults[i] = 1;
}
if (mr.material.color == blueMat.color)
{
Transform t = targetTransforms[i].GetComponent <Transform>();
c.TrainingExemples[i * c.NbInputs] = t.position.x;
c.TrainingExemples[i * c.NbInputs + 1] = t.position.z;
c.ExpectedResults[i] = -1;
}
}
c.MaxIteration = 10000;
return(c);
}
void linearScenarioRegression(LinearTraining2 c, System.IntPtr model)
{
perceptron_regression(model, c.TrainingExemples, c.NbElements, c.NbInputs, c.ExpectedResults);
adjustPlan2(model);
removeModel(model);
}
