Examples with DoubleStatistics com.simiacryptus.util.data.DoubleStatistics used on opensource projects

Example 6 with DoubleStatistics

use of com.simiacryptus.util.data.DoubleStatistics in project MindsEye by SimiaCryptus.

the class PerformanceTester method test.

/**
 * Test.
 *
 * @param component      the component
 * @param inputPrototype the input prototype
 */
public void test(@Nonnull final Layer component, @Nonnull final Tensor[] inputPrototype) {
    log.info(String.format("%s batch length, %s trials", batches, samples));
    log.info("Input Dimensions:");
    Arrays.stream(inputPrototype).map(t -> "\t" + Arrays.toString(t.getDimensions())).forEach(System.out::println);
    log.info("Performance:");
    List<Tuple2<Double, Double>> performance = IntStream.range(0, samples).mapToObj(i -> {
        return testPerformance(component, inputPrototype);
    }).collect(Collectors.toList());
    if (isTestEvaluation()) {
        @Nonnull final DoubleStatistics statistics = new DoubleStatistics().accept(performance.stream().mapToDouble(x -> x._1).toArray());
        log.info(String.format("\tEvaluation performance: %.6fs +- %.6fs [%.6fs - %.6fs]", statistics.getAverage(), statistics.getStandardDeviation(), statistics.getMin(), statistics.getMax()));
    }
    if (isTestLearning()) {
        @Nonnull final DoubleStatistics statistics = new DoubleStatistics().accept(performance.stream().mapToDouble(x -> x._2).toArray());
        if (null != statistics) {
            log.info(String.format("\tLearning performance: %.6fs +- %.6fs [%.6fs - %.6fs]", statistics.getAverage(), statistics.getStandardDeviation(), statistics.getMin(), statistics.getMax()));
        }
    }
}

Example 7 with DoubleStatistics

use of com.simiacryptus.util.data.DoubleStatistics in project MindsEye by SimiaCryptus.

the class ReferenceIO method test.

@Nullable
@Override
public ToleranceStatistics test(@Nonnull final NotebookOutput log, @Nonnull final Layer layer, @Nonnull final Tensor... inputPrototype) {
    if (!referenceIO.isEmpty()) {
        log.h1("Reference Input/Output Pairs");
        log.p("Display pre-setBytes input/output example pairs:");
        referenceIO.forEach((input, output) -> {
            log.code(() -> {
                @Nonnull final SimpleEval eval = SimpleEval.run(layer, input);
                Tensor add = output.scale(-1).addAndFree(eval.getOutput());
                @Nonnull final DoubleStatistics error = new DoubleStatistics().accept(add.getData());
                add.freeRef();
                String format = String.format("--------------------\nInput: \n[%s]\n--------------------\nOutput: \n%s\nError: %s\n--------------------\nDerivative: \n%s", Arrays.stream(input).map(t -> t.prettyPrint()).reduce((a, b) -> a + ",\n" + b).get(), eval.getOutput().prettyPrint(), error, Arrays.stream(eval.getDerivative()).map(t -> t.prettyPrint()).reduce((a, b) -> a + ",\n" + b).get());
                eval.freeRef();
                return format;
            });
        });
    } else {
        log.h1("Example Input/Output Pair");
        log.p("Display input/output pairs from random executions:");
        log.code(() -> {
            @Nonnull final SimpleEval eval = SimpleEval.run(layer, inputPrototype);
            String format = String.format("--------------------\nInput: \n[%s]\n--------------------\nOutput: \n%s\n--------------------\nDerivative: \n%s", Arrays.stream(inputPrototype).map(t -> t.prettyPrint()).reduce((a, b) -> a + ",\n" + b).orElse(""), eval.getOutput().prettyPrint(), Arrays.stream(eval.getDerivative()).map(t -> t.prettyPrint()).reduce((a, b) -> a + ",\n" + b).orElse(""));
            eval.freeRef();
            return format;
        });
    }
    return null;
}

Example 8 with DoubleStatistics

use of com.simiacryptus.util.data.DoubleStatistics in project MindsEye by SimiaCryptus.

the class PCAUtil method getCovariance.

/**
 * Forked from Apache Commons Math
 *
 * @param stream the stream
 * @return covariance covariance
 */
@Nonnull
public static RealMatrix getCovariance(@Nonnull final Supplier<Stream<double[]>> stream) {
    final int dimension = stream.get().findAny().get().length;
    final List<DoubleStatistics> statList = IntStream.range(0, dimension * dimension).mapToObj(i -> new DoubleStatistics()).collect(Collectors.toList());
    stream.get().forEach(array -> {
        for (int i = 0; i < dimension; i++) {
            for (int j = 0; j <= i; j++) {
                statList.get(i * dimension + j).accept(array[i] * array[j]);
            }
        }
        RecycleBin.DOUBLES.recycle(array, array.length);
    });
    @Nonnull final RealMatrix covariance = new BlockRealMatrix(dimension, dimension);
    for (int i = 0; i < dimension; i++) {
        for (int j = 0; j <= i; j++) {
            final double v = statList.get(i + dimension * j).getAverage();
            covariance.setEntry(i, j, v);
            covariance.setEntry(j, i, v);
        }
    }
    return covariance;
}