public unsafe void TrainAndTest()
{
Debug.Log("Training and Testing");
int count = 0;
int blue_count = 0;
int red_count = 0;
// Créer dataset_inputs
// Créer dataest_expected_outputs
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 > 0 ? 1 : -1;
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_classification(model, linear_inputs, trainSpheresTransforms.Length, 2, Y, Y.Length, 1000000, 0.01f);
//double* model = {-0.4, 0.4, 0.2};
//double[] model = new double [] {-0.4, 0.4, 0.2};
// 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 };
//double y = (float)(-model[1] / model[2] * testSpheresTransform.position.x - model[0] / model[2]);
float y = (float)VisualStudioLibWrapper.linear_model_predict_classification(model, input, 2);
testSpheres.position = new Vector3(
testSpheres.position.x,
y,
testSpheres.position.z
);
}
// Delete Model
VisualStudioLibWrapper.clearArray(model);
}
public unsafe void TrainAndTest()
{
Debug.Log("Training and Testing");
var Y = new double[trainSpheresTransforms.Length];
var inputs = new double[trainSpheresTransforms.Length * 2];
for (int i = 0; i < trainSpheresTransforms.Length; i++)
{
/*if(trainSpheresTransforms[i].position.y == classSphere[2]) {
* Y[i] = classSphere[2];
* } else if (trainSpheresTransforms[i].position.y == classSphere[1] ) {
* Y[i] = classSphere[1];
* } else if (trainSpheresTransforms[i].position.y == classSphere[0] ) {
* Y[i] = classSphere[0];
* }*/
Y[i] = trainSpheresTransforms[i].position.y >= 0 ? 1 : -1;
inputs[i * 2] = trainSpheresTransforms[i].position.x;
inputs[(i * 2) + 1] = trainSpheresTransforms[i].position.z;
}
//int[] npl = {2, 1};
IntPtr model = VisualStudioLibWrapper.create_mlp_model(npl, npl.Length);
VisualStudioLibWrapper.mlp_model_train_classification(model, inputs, trainSpheresTransforms.Length, 2, Y, Y.Length, 1000000, 0.001f);
// For each testSphere : Predict
double max = -2;
int idmax = 1;
foreach (var testSpheres in testSpheresTransforms)
{
double[] input = { testSpheres.position.x, testSpheres.position.z };
IntPtr y = VisualStudioLibWrapper.mlp_model_predict_classification(model, input, false);
/*double[] res = new double[npl[npl.Length - 1]];
* Marshal.Copy(res, 0, y, res.Length);*/
double *r = (double *)y.ToPointer();
// Debug.Log("size = " + npl[npl.Length - 1]);
//Debug.Log(.GetLength(0));
max = r[1];
for (int i = 1; i < classSphere.Length + 1; i++)
{
//Debug.Log("r =" + r[i]);
if (r[i] > max)
{
max = r[i];
idmax = i;
}
}
if (classSphere.Length == 2)
{
testSpheres.position = new Vector3(
testSpheres.position.x,
(float)r[1],
testSpheres.position.z
);
}
else
{
testSpheres.position = new Vector3(
testSpheres.position.x,
(float)classSphere[idmax - 1],
testSpheres.position.z
);
}
}
VisualStudioLibWrapper.clearArray(model);
}
