Java/benchmark/src/main/java/com/amazon/randomcutforest/StateMapperBenchmark.java [50:77]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    public static final int NUM_TRAIN_SAMPLES = 2048;
    public static final int NUM_TEST_SAMPLES = 50;

    @State(Scope.Thread)
    public static class BenchmarkState {
        @Param({ "10" })
        int dimensions;

        @Param({ "50" })
        int numberOfTrees;

        @Param({ "256" })
        int sampleSize;

        @Param({ "false", "true" })
        boolean saveTreeState;

        @Param({ "FLOAT_32", "FLOAT_64" })
        Precision precision;

        double[][] trainingData;
        double[][] testData;
        RandomCutForestState forestState;
        String json;
        byte[] protostuff;

        @Setup(Level.Trial)
        public void setUpData() {
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



Java/benchmark/src/main/java/com/amazon/randomcutforest/StateMapperShingledBenchmark.java [53:80]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    public static final int NUM_TRAIN_SAMPLES = 2048;
    public static final int NUM_TEST_SAMPLES = 50;

    @State(Scope.Thread)
    public static class BenchmarkState {
        @Param({ "10" })
        int dimensions;

        @Param({ "50" })
        int numberOfTrees;

        @Param({ "256" })
        int sampleSize;

        @Param({ "false", "true" })
        boolean saveTreeState;

        @Param({ "FLOAT_32", "FLOAT_64" })
        Precision precision;

        double[][] trainingData;
        double[][] testData;
        RandomCutForestState forestState;
        String json;
        byte[] protostuff;

        @Setup(Level.Trial)
        public void setUpData() {
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -