void RegressionPredict(IntPtr model)
{
Debug.Log("Predict Model");
foreach (var testSpheresTransform in testSpheresTransforms)
{
double [] inputs = new double[2];
inputs[0] = testSpheresTransform.position.x;
inputs[1] = testSpheresTransform.position.z;
double result = VisualStudioLibWrapper.linear_model_predict_regression(model, inputs, inputs.Length);
testSpheresTransform.position = new Vector3(
testSpheresTransform.position.x,
(float)result,
testSpheresTransform.position.z
);
Debug.Log("Result Predict Model = " + result);
}
//double[] inputs = input_array.ToArray();
// Debug.Log("Taille tableau d'inputs " + inputs.Length);
//double result = VisualStudioLibWrapper.linear_model_predict_regression(model, inputs, inputs.Length);
//Debug.Log("Result Predict Model = " + result);
}
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
);
}
}
