public unsafe void TrainAndTest()
{
Debug.Log("Training and Testing");
var Y = new double[trainSpheresTransforms.Length];
IntPtr model;
var linear_inputs = new double[trainSpheresTransforms.Length * 2];
for (int i = 0; i < trainSpheresTransforms.Length; i++)
{
var position = new Vector3();
if (transfo != -1)
{
position = AppliTransfo(trainSpheresTransforms[i].position);
}
else
{
position = trainSpheresTransforms[i].position;
}
Y[i] = position.y;
linear_inputs[i * 2] = position.x;
linear_inputs[(i * 2) + 1] = position.z;
}
// Create Model
model = VisualStudioLibWrapper.linear_model_create(2);
// Train Model
VisualStudioLibWrapper.linear_model_train_regression(model, linear_inputs, trainSpheresTransforms.Length, 2, Y, Y.Length, 1000000, 0.01f);
// For each testSphere : Predict
foreach (var testSpheres in testSpheresTransforms)
{
var position = new Vector3();
if (transfo != -1)
{
position = AppliTransfo(testSpheres.position);
}
else
{
position = testSpheres.position;
}
double[] input = { position.x, position.z };
float y = (float)VisualStudioLibWrapper.linear_model_predict_regression(model, input, 2);
testSpheres.position = new Vector3(
position.x,
y,
position.z
);
}
}
void RegressionTrain(IntPtr model)
{
Debug.Log("Train Model");
List <double> inputs = new List <double>();
List <double> expecteds = new List <double>();
foreach (var trainSpheresTransform in trainSpheresTransforms)
{
inputs.Add(trainSpheresTransform.position.x);
inputs.Add(trainSpheresTransform.position.z);
expecteds.Add(trainSpheresTransform.position.y);
}
VisualStudioLibWrapper.linear_model_train_regression(model, inputs.ToArray(), inputs.Count() / 2, 2, expecteds.ToArray());
}