Stats

object Stats

Measure the difference between the values predicted by a neural network and the observed values.

Calculate the root mean square error:

Stats.rmse(
  Iterator(
    (List(0.0, 0.0, 1.0), List(0.0, 0.0, 1.0)),
    (List(0.0, 0.0, 1.0), List(0.0, 1.0, 1.0))
  )
)

Calculate the score of the classification accuracy:

Stats.score(
  Iterator(
    (List(0.0, 0.0, 1.0), List(0.0, 0.1, 0.9)),
    (List(0.0, 1.0, 0.0), List(0.8, 0.2, 0.0)),
    (List(1.0, 0.0, 0.0), List(0.7, 0.1, 0.2)),
    (List(1.0, 0.0, 0.0), List(0.3, 0.3, 0.4)),
    (List(0.0, 0.0, 1.0), List(0.2, 0.2, 0.6))
    (List(0.0, 0.0, 1.0), List(0.0, 0.1, 0.9)),
    (List(0.0, 1.0, 0.0), List(0.8, 0.2, 0.0)),
    (List(1.0, 0.0, 0.0), List(0.7, 0.1, 0.2)),
    (List(1.0, 0.0, 0.0), List(0.3, 0.3, 0.4)),
    (List(0.0, 0.0, 1.0), List(0.2, 0.2, 0.6))
  )
)

class Object

trait Matchable

class Any

Value members

Concrete methods

Root Mean Square Error.

RMSE is the standard deviation of the prediction errors.

Value Params

outputPairs: An iterator of pairs that contain the expected and predicted values.

Returns

The value of the RMSE metric.

Classification Accuracy.

The ratio of number of correct predictions to the total number of provided pairs. For a prediction to be considered as correct, the index of its maximum expected value needs to be the same with the index of its maximum predicted value.

Value Params

outputPairs: An iterator of pairs that contain the expected and predicted values.

Returns

The score of the classification accuracy.