void predictMLPMulticlass()
{
int bestModel = 0;
double bestValue = 0.0f;
IntPtr pointerToValues = MlDllWrapper.PredictMLPModel(MyModel, requestInput, _inputSize,
numberLayer, npl, isClassification);
string[] arrayValuesasString = Marshal.PtrToStringAnsi(pointerToValues).Split(';');
for (int i = 0; i < _outputSize; i++)
{
double currentValue = Convert.ToDouble(arrayValuesasString[i].Replace('.', ','));
print($"{currentValue} and model {i}");
//print($"{currentValue} and model {i}");
if (currentValue > bestValue)
{
bestModel = i;
bestValue = currentValue;
}
}
print($"The best value is {bestValue} with the model {bestModel}");
//Regression Lineaire
/*if(pointerToValues != IntPtr.Zero)
* MlDllWrapper.DeleteLinearModel(pointerToValues);*/
}
void predictMLPMulticlassRegression()
{
for (int j = 0; j < testDataSet.Count; j++)
{
_multiclassResult.Clear();
int bestModel = 0;
double bestValue = 0.0f;
IntPtr pointerToValues = MlDllWrapper.PredictMLPModel(MyModel, testDataSet[j], _inputSize,
numberLayer, npl, isClassification);
string[] arrayValuesasString = Marshal.PtrToStringAnsi(pointerToValues).Split(';');
for (int i = 0; i < _outputSize; i++)
{
double currentValue = Convert.ToDouble(arrayValuesasString[i].Replace('.', ','));
print($"{currentValue} and model {i}");
if (currentValue > bestValue)
{
bestModel = i;
bestValue = currentValue;
}
}
print(arrayValuesasString[0]);
//Regression Lineaire
spheres[j].changeZ((float)Convert.ToDouble(arrayValuesasString[0].Replace('.', ',')));
/*if(pointerToValues != IntPtr.Zero)
* MlDllWrapper.DeleteLinearModel(pointerToValues);*/
}
}
void predictMLPMulticlass()
{
for (int j = 0; j < testDataSet.Count; j++)
{
_multiclassResult.Clear();
int bestModel = 0;
double bestValue = 0.0f;
IntPtr pointerToValues = MlDllWrapper.PredictMLPModel(MyModel, testDataSet[j], _inputSize,
numberLayer, npl, isClassification);
string[] arrayValuesasString = Marshal.PtrToStringAnsi(pointerToValues).Split(';');
for (int i = 0; i < _outputSize; i++)
{
double currentValue = Convert.ToDouble(arrayValuesasString[i].Replace('.', ','));
if (currentValue > 0)
{
spheres[j].ChangeMaterial(blueMat);
}
else
{
spheres[j].ChangeMaterial(redMat);
}
}
/*if(pointerToValues != IntPtr.Zero)
* MlDllWrapper.DeleteLinearModel(pointerToValues);*/
}
}
int predictDatasetSample(int k)
{
int bestModel = 0;
double bestValue = 0.0f;
List <double> tmp = new List <double>();
for (int i = k * npl[0]; i < npl[0] * (k + 1); i++)
{
tmp.Add(trainningInput[i]);
}
double[] tmp2 = tmp.ToArray();
IntPtr pointerToValues = MlDllWrapper.PredictMLPModel(MyModel, tmp2, _inputSize,
numberLayer, npl, isClassification);
string[] arrayValuesasString = Marshal.PtrToStringAnsi(pointerToValues).Split(';');
for (int i = 0; i < _outputSize; i++)
{
double currentValue = Convert.ToDouble(arrayValuesasString[i].Replace('.', ','));
// print($"{currentValue} and model {i}");
//print($"{currentValue} and model {i}");
if (currentValue > bestValue)
{
bestModel = i;
bestValue = currentValue;
}
}
return(bestModel);
}