public override void Update(NDArray labels, NDArray preds)
{
CheckLabelShapes(labels, preds);
var pred_label = preds.Argsort().AsNumpy().astype(NPTypeCode.Int32); //ToDo: Use numpy argpartition
var label = labels.AsNumpy().astype(NPTypeCode.Int32);
var num_samples = pred_label.shape[0];
var num_dims = pred_label.shape.Length;
if (num_dims == 1)
{
sum_metric += (pred_label.flat == label.flat).sum();
}
else if (num_dims == 2)
{
var num_classes = pred_label.shape[1];
TopK = Math.Min(num_classes, TopK);
for (var j = 0; j < TopK; j++)
{
float num_correct = (pred_label[":", num_classes - 1 - j].flat == label.flat).sum();
sum_metric += num_correct;
global_sum_metric += num_correct;
}
}
num_inst += num_samples;
global_num_inst += num_samples;
}
public override void Update(NDArray labels, NDArray preds)
{
CheckLabelShapes(labels, preds);
var pred_label = preds.Argsort().AsType(DType.Int32); //ToDo: Use numpy argpartition
var label = labels.AsType(DType.Int32);
var num_samples = pred_label.Shape[0];
var num_dims = pred_label.Shape.Dimension;
if (num_dims == 1)
{
sum_metric += nd.Equal(pred_label.Ravel(), label.Ravel()).Sum();
}
else if (num_dims == 2)
{
var num_classes = pred_label.Shape[1];
TopK = Math.Min(num_classes, TopK);
for (var j = 0; j < TopK; j++)
{
float num_correct = nd.Equal(pred_label[$":,{num_classes - 1 - j}"].Ravel(), label.Ravel()).Sum();
sum_metric += num_correct;
global_sum_metric += num_correct;
}
}
num_inst += num_samples;
global_num_inst += num_samples;
}