src/core/metrics/ClassificationMetric.java
private void updateConfusion(Sequence predicted, Sequence actual) throws MatrixException, NeuralNetworkException {
        if (actual.sampleSize() == 0) throw new NeuralNetworkException("Nothing to classify");
        predicted = getClassification(predicted);
        update(predicted, actual);
    }
/**
     * Updates confusion and classification statistics by including multiple new predicted / actual (true) sample pairs.<br>
     *
     * @param predicted predicted samples.
     * @param actual actual (true) samples.
     * @throws MatrixException throws exception if matrix operation fails.
     * @throws NeuralNetworkException throws exception if classification statistics update fails.
     */
Updates confusion and classification statistics by including multiple new predicted / actual (true) sample pairs.

@param predicted predicted samples.
@param actual actual (true) samples.
@throws MatrixException throws exception if matrix operation fails.
@throws NeuralNetworkException throws exception if classification statistics update fails.
Updates confusion and classification statistics by including multiple new predicted / actual ( true ) sample pairs . @param predicted predicted samples . @param actual actual ( true ) samples . @throws MatrixException throws exception if matrix operation fails . @throws NeuralNetworkException throws exception if classification statistics update fails .
private void updateConfusion(Sequence predicted, Sequence actual) throws MatrixException, NeuralNetworkException {
        if (actual.sampleSize() == 0) throw new NeuralNetworkException("Nothing to classify");
        predicted = getClassification(predicted);
        update(predicted, actual);
    }
private void updateConfusion ( Sequence predicted , Sequence actual ) throws MatrixException , NeuralNetworkException { if ( actual . sampleSize ( ) == 0 ) throw new NeuralNetworkException ( "Nothing to classify" ) ; predicted = getClassification ( predicted ) ; update ( predicted , actual ) ; }
Updates confusion and classification statistics by including multiple new predicted / actual (true) sample pairs.<br>
Updates confusion and classification statistics by including multiple new predicted / actual ( true ) sample pairs . <br >