com.github.mrdimosthenis.synapses

Class Stats

java.lang.Object

com.github.mrdimosthenis.synapses.Stats

public class Stats
extends java.lang.Object

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

 
 Supplier<Stream<double[][]>> expAndPredVals = () -> Arrays.stream(
         new double[][][]{
                 {{0.0, 0.0, 1.0}, {0.0, 0.1, 0.9}},
                 {{0.0, 1.0, 0.0}, {0.8, 0.2, 0.0}},
                 {{1.0, 0.0, 0.0}, {0.7, 0.1, 0.2}},
                 {{1.0, 0.0, 0.0}, {0.3, 0.3, 0.4}},
                 {{0.0, 0.0, 1.0}, {0.2, 0.2, 0.6}}
         }
 );
 
 

Calculate the root mean square error:

 
  Stats.rmse(expAndPredVals.get());
 
 

Calculate the score of the classification accuracy:

 
  Stats.score(expAndPredVals.get());
 
 

Constructor Summary

Constructors

Constructor and Description
Stats()

Method Summary

Modifier and Type	Method and Description
static double	rmse(java.util.stream.Stream<double[][]> outputPairs) Root Mean Square Error.
static double	score(java.util.stream.Stream<double[][]> outputPairs) Classification Accuracy.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Stats

public Stats()

Method Detail

rmse

public static double rmse(java.util.stream.Stream<double[][]> outputPairs)

Root Mean Square Error.

RMSE is the standard deviation of the prediction errors.

Parameters:

outputPairs - A stream of array-pairs that contain the expected and predicted values.

Returns:

The value of the RMSE metric.

score

public static double score(java.util.stream.Stream<double[][]> outputPairs)

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.

Parameters:

outputPairs - A stream of array-pairs that contain the expected and predicted values.

Returns:

The score of the classification accuracy.