sampleWeights in module ml
Hi,
In trainData a weight per sample can be defined in create method. Sample Weights are used in backprop, rprop method.
When you call calcError sample weigths are not used.
I think that calcError must use sample weights to give a good error value. Am I wrong?
arent those weights only used for the training ? e.g. to mend an "uneven" class distribution, where you don't have enough samples for a certain class ?
with a real world prediction, you also would not know the label beforehand, so you could not know, which weight to apply.
No my problem is that I use simulated annealing to optimize node weight and bias, and I need a global error : calcError. Rprop and backprop use sample weights. Hence if I use calcError and I will not optimize same neural network than in rprop because traindata weights are not the same.