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);
}
IntPtr CreateModel(int size)
{
Debug.Log("Create Model");
var modelPtr = VisualStudioLibWrapper.linear_model_create(size);
/*double[] model = new double[size+1];
* Marshal.Copy(modelPtr, model, 0, size + 1);
* Debug.Log(model[size]);*/
return(modelPtr);
}
// Start is called before the first frame update
void Start()
{
Debug.Log("With CLion DLL : ");
Debug.Log(CLionLibWrapper.my_add(42, 51));
Debug.Log(CLionLibWrapper.my_mul(2, 3));
Debug.Log(CLionLibWrapper.my_test(10, 100, 100));
Debug.Log("With Visual Studio DLL : ");
Debug.Log(VisualStudioLibWrapper.my_add(42, 51));
Debug.Log(VisualStudioLibWrapper.my_mul(2, 3));
}
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
);
}
}
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++)
{
Y[i] = trainSpheresTransforms[i].position.y > 0 ? 1 : -1;
linear_inputs[i * 2] = trainSpheresTransforms[i].position.x;
linear_inputs[(i * 2) + 1] = trainSpheresTransforms[i].position.z;
}
var matrix_inputs = RBFEntriesD(0.5, Y);
// Create Model
model = VisualStudioLibWrapper.linear_model_create(trainSpheresTransforms.Length);
// Train Model
VisualStudioLibWrapper.linear_model_train_classification(model, matrix_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)
{
double[] input = { testSpheres.position.x, testSpheres.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);
}
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());
}
void ClassificationTrainForXOR(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 * trainSpheresTransform.position.z);
inputs.Add(trainSpheresTransform.position.z * trainSpheresTransform.position.x);
expecteds.Add(trainSpheresTransform.position.y >= 0 ? 1.0 : -1.0);
}
VisualStudioLibWrapper.linear_model_train_classification(model, inputs.ToArray(), inputs.Count() / 2, 2, expecteds.ToArray(), iterationCount, alpha);
}
void ClassificationPredict(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_classification(model, inputs, inputs.Length);
testSpheresTransform.position = new Vector3(
testSpheresTransform.position.x,
(float)result,
testSpheresTransform.position.z
);
Debug.Log("Result Predict Model = " + result);
}
}
public unsafe void RBFEntries()
{
double epsilon = 0.8;
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++)
{
Y[i] = trainSpheresTransforms[i].position.y > 0 ? 1 : -1;
linear_inputs[i * 2] = trainSpheresTransforms[i].position.x;
linear_inputs[(i * 2) + 1] = trainSpheresTransforms[i].position.z;
}
var Ox = new double[trainSpheresTransforms.Length, trainSpheresTransforms.Length];
//var Oz = new double[trainSpheresTransforms.Length,trainSpheresTransforms.Length];
var multiY = new double[Y.Length, 1];
for (int i = 0; i < trainSpheresTransforms.Length; i++)
{
for (int j = 0; j < trainSpheresTransforms.Length; j++)
{
Ox[i, j] = getRBFValue(epsilon, trainSpheresTransforms[i].position, trainSpheresTransforms[j].position);
//Oz[i,j] = getRBFValue(epsilon, trainSpheresTransforms[i].position.z, trainSpheresTransforms[j].position.z);
}
multiY[i, 0] = Y[i];
}
var l = trainSpheresTransforms.Length;
Matrix <double> OxMatrix = DenseMatrix.OfArray(Ox);
for (int i = 0; i < l; i++)
{
string str = "";
for (int j = 0; j < l; j++)
{
// Debug.Log(i + " " + j + ": " + Ox[i, j]);
str += OxMatrix[i, j] + "/ ";
}
Debug.Log(str);
}
//Matrix<double> OzMatrix = DenseMatrix.OfArray(Oz);
//Matrix<double> YMatrix = DenseMatrix.OfArray(Y);
// Debug.Log(OxMatrix.RowCount);
// Debug.Log(OxMatrix.ColumnCount);
Matrix <double> OxInv = OxMatrix.Inverse();
Matrix <double> mY = DenseMatrix.OfArray(multiY);
Matrix <double> resMatrix = OxInv.Multiply(mY);
Debug.Log("row = " + resMatrix.RowCount);
Debug.Log("col = " + resMatrix.ColumnCount);
var inputs = new double[resMatrix.RowCount];
for (int i = 0; i < resMatrix.RowCount; i++)
{
inputs[i] = resMatrix[i, 0];
}
model = VisualStudioLibWrapper.linear_model_create(2);
// Train Model
VisualStudioLibWrapper.linear_model_train_classification(model, inputs, 3, 1, Y, 3, 100000, 0.01f);
double *mdl = (double *)model.ToPointer();
for (int i = 0; i < 3; i++)
{
Debug.Log("model = " + mdl[i]);
}
var c1 = new Color(1, 2, 3);
var c2 = new Color(3, 2, 1);
foreach (var testSpheres in testSpheresTransforms)
{
double[] input = { testSpheres.position.x, testSpheres.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
// );*
testSpheres.GetComponent <MeshRenderer>().material.color = y >= 0 ? c1 : c2;
}
// IntPtr model = VisualStudioLibWrapper.linear_model_create(2);
// VisualStudioLibWrapper.linear_model_train_classification(model, inputs, 3, 1, Y, 3, 100000, 0.01);
}
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);
}
void Delete(IntPtr model)
{
Debug.Log("Delete Model");
VisualStudioLibWrapper.linear_model_delete(model);
}
