private void TrainModel(System.IntPtr model, double[,] values)
{
var nbInputs = values.GetUpperBound(0) - 1;
for (int y = 0; y < values.GetUpperBound(1); y++)
{
double[] inputs = new double[nbInputs];
int expected = 0;
for (int x = 0; x < values.GetUpperBound(0); x++)
{
var value = values[x, y];
if (x == nbInputs)
{
expected = Convert.ToInt32(value);
}
else
{
inputs[x] = value;
}
}
PanebWrapper.classification_train(model, inputs.Length, inputs, expected);
}
}
private void TrainModel(System.IntPtr weights)
{
for (int i = 0; i < 300; ++i)
{
foreach (var sphere in spheres)
{
var transform = sphere.GetComponent <Transform>();
var x = transform.position.x;
var z = transform.position.z;
var expected = (int)transform.position.y;
PanebWrapper.classification_train(weights, 2, new double[] { x, z }, expected);
}
}
}