Examples with Tuple2 com.simiacryptus.util.lang.Tuple2 used on opensource projects

Example 6 with Tuple2

use of com.simiacryptus.util.lang.Tuple2 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 Tuple2

use of com.simiacryptus.util.lang.Tuple2 in project MindsEye by SimiaCryptus.

the class MaxPoolingLayer method calcRegions.

private static List<Tuple2<Integer, int[]>> calcRegions(@Nonnull final MaxPoolingLayer.CalcRegionsParameter p) {
    @Nonnull final Tensor input = new Tensor(p.inputDims);
    final int[] newDims = IntStream.range(0, p.inputDims.length).map(i -> {
        // assert 0 == p.inputDims[i] % p.kernelDims[i];
        return (int) Math.ceil(p.inputDims[i] * 1.0 / p.kernelDims[i]);
    }).toArray();
    @Nonnull final Tensor output = new Tensor(newDims);
    List<Tuple2<Integer, int[]>> tuple2s = output.coordStream(true).map(o -> {
        Tensor tensor = new Tensor(p.kernelDims);
        final int[] inCoords = tensor.coordStream(true).mapToInt(kernelCoord -> {
            @Nonnull final int[] result = new int[o.getCoords().length];
            for (int index = 0; index < o.getCoords().length; index++) {
                final int outputCoordinate = o.getCoords()[index];
                final int kernelSize = p.kernelDims[index];
                final int baseCoordinate = Math.min(outputCoordinate * kernelSize, p.inputDims[index] - kernelSize);
                final int kernelCoordinate = kernelCoord.getCoords()[index];
                result[index] = baseCoordinate + kernelCoordinate;
            }
            return input.index(result);
        }).toArray();
        tensor.freeRef();
        return new Tuple2<>(o.getIndex(), inCoords);
    }).collect(Collectors.toList());
    input.freeRef();
    output.freeRef();
    return tuple2s;
}