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); } } }