Examples with DoubleStream java.util.stream.DoubleStream used on opensource projects

Example 21 with DoubleStream

use of java.util.stream.DoubleStream in project ignite by apache.

the class ColumnDecisionTreeTrainerTest method testByGen.

/**
 */
private <D extends ContinuousRegionInfo> void testByGen(int totalPts, HashMap<Integer, Integer> catsInfo, SplitDataGenerator<DenseLocalOnHeapVector> gen, IgniteFunction<ColumnDecisionTreeTrainerInput, ? extends ContinuousSplitCalculator<D>> calc, IgniteFunction<ColumnDecisionTreeTrainerInput, IgniteFunction<DoubleStream, Double>> catImpCalc, IgniteFunction<DoubleStream, Double> regCalc, Random rnd) {
    List<IgniteBiTuple<Integer, DenseLocalOnHeapVector>> lst = gen.points(totalPts, (i, rn) -> i).collect(Collectors.toList());
    int featCnt = gen.featuresCnt();
    Collections.shuffle(lst, rnd);
    SparseDistributedMatrix m = new SparseDistributedMatrix(totalPts, featCnt + 1, StorageConstants.COLUMN_STORAGE_MODE, StorageConstants.RANDOM_ACCESS_MODE);
    Map<Integer, List<LabeledVectorDouble>> byRegion = new HashMap<>();
    int i = 0;
    for (IgniteBiTuple<Integer, DenseLocalOnHeapVector> bt : lst) {
        byRegion.putIfAbsent(bt.get1(), new LinkedList<>());
        byRegion.get(bt.get1()).add(asLabeledVector(bt.get2().getStorage().data()));
        m.setRow(i, bt.get2().getStorage().data());
        i++;
    }
    ColumnDecisionTreeTrainer<D> trainer = new ColumnDecisionTreeTrainer<>(3, calc, catImpCalc, regCalc, ignite);
    DecisionTreeModel mdl = trainer.train(new MatrixColumnDecisionTreeTrainerInput(m, catsInfo));
    byRegion.keySet().forEach(k -> {
        LabeledVectorDouble sp = byRegion.get(k).get(0);
        Tracer.showAscii(sp.features());
        X.println("Actual and predicted vectors [act=" + sp.label() + " " + ", pred=" + mdl.apply(sp.features()) + "]");
        assert mdl.apply(sp.features()) == sp.doubleLabel();
    });
}

Example 22 with DoubleStream

use of java.util.stream.DoubleStream in project ignite by apache.

the class ColumnDecisionTreeTrainerBenchmark method tstF1.

/**
 * Test decision tree regression.
 * To run this test rename this method so it starts from 'test'.
 */
public void tstF1() {
    IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
    int ptsCnt = 10000;
    Map<Integer, double[]> ranges = new HashMap<>();
    ranges.put(0, new double[] { -100.0, 100.0 });
    ranges.put(1, new double[] { -100.0, 100.0 });
    ranges.put(2, new double[] { -100.0, 100.0 });
    int featCnt = 100;
    double[] defRng = { -1.0, 1.0 };
    Vector[] trainVectors = vecsFromRanges(ranges, featCnt, defRng, new Random(123L), ptsCnt, f1);
    SparseDistributedMatrix m = new SparseDistributedMatrix(ptsCnt, featCnt + 1, StorageConstants.COLUMN_STORAGE_MODE, StorageConstants.RANDOM_ACCESS_MODE);
    SparseDistributedMatrixStorage sto = (SparseDistributedMatrixStorage) m.getStorage();
    loadVectorsIntoSparseDistributedMatrixCache(sto.cache().getName(), sto.getUUID(), Arrays.stream(trainVectors).iterator(), featCnt + 1);
    IgniteFunction<DoubleStream, Double> regCalc = s -> s.average().orElse(0.0);
    ColumnDecisionTreeTrainer<VarianceSplitCalculator.VarianceData> trainer = new ColumnDecisionTreeTrainer<>(10, ContinuousSplitCalculators.VARIANCE, RegionCalculators.VARIANCE, regCalc, ignite);
    X.println("Training started.");
    long before = System.currentTimeMillis();
    DecisionTreeModel mdl = trainer.train(new MatrixColumnDecisionTreeTrainerInput(m, new HashMap<>()));
    X.println("Training finished in: " + (System.currentTimeMillis() - before) + " ms.");
    Vector[] testVectors = vecsFromRanges(ranges, featCnt, defRng, new Random(123L), 20, f1);
    IgniteTriFunction<Model<Vector, Double>, Stream<IgniteBiTuple<Vector, Double>>, Function<Double, Double>, Double> mse = Estimators.MSE();
    Double accuracy = mse.apply(mdl, Arrays.stream(testVectors).map(v -> new IgniteBiTuple<>(v.viewPart(0, featCnt), v.getX(featCnt))), Function.identity());
    X.println("MSE: " + accuracy);
}

Example 23 with DoubleStream

use of java.util.stream.DoubleStream in project MindsEye by SimiaCryptus.

the class RecursiveSubspace method buildSubspace.

/**
 * Build subspace nn layer.
 *
 * @param subject     the subject
 * @param measurement the measurement
 * @param monitor     the monitor
 * @return the nn layer
 */
@Nullable
public Layer buildSubspace(@Nonnull Trainable subject, @Nonnull PointSample measurement, @Nonnull TrainingMonitor monitor) {
    @Nonnull PointSample origin = measurement.copyFull().backup();
    @Nonnull final DeltaSet<Layer> direction = measurement.delta.scale(-1);
    final double magnitude = direction.getMagnitude();
    if (Math.abs(magnitude) < 1e-10) {
        monitor.log(String.format("Zero gradient: %s", magnitude));
    } else if (Math.abs(magnitude) < 1e-5) {
        monitor.log(String.format("Low gradient: %s", magnitude));
    }
    boolean hasPlaceholders = direction.getMap().entrySet().stream().filter(x -> x.getKey() instanceof PlaceholderLayer).findAny().isPresent();
    List<Layer> deltaLayers = direction.getMap().entrySet().stream().map(x -> x.getKey()).filter(x -> !(x instanceof PlaceholderLayer)).collect(Collectors.toList());
    int size = deltaLayers.size() + (hasPlaceholders ? 1 : 0);
    if (null == weights || weights.length != size)
        weights = new double[size];
    return new LayerBase() {

        @Nonnull
        Layer self = this;

        @Nonnull
        @Override
        public Result eval(Result... array) {
            assertAlive();
            origin.restore();
            IntStream.range(0, deltaLayers.size()).forEach(i -> {
                direction.getMap().get(deltaLayers.get(i)).accumulate(weights[hasPlaceholders ? (i + 1) : i]);
            });
            if (hasPlaceholders) {
                direction.getMap().entrySet().stream().filter(x -> x.getKey() instanceof PlaceholderLayer).distinct().forEach(entry -> entry.getValue().accumulate(weights[0]));
            }
            PointSample measure = subject.measure(monitor);
            double mean = measure.getMean();
            monitor.log(String.format("RecursiveSubspace: %s <- %s", mean, Arrays.toString(weights)));
            direction.addRef();
            return new Result(TensorArray.wrap(new Tensor(mean)), (DeltaSet<Layer> buffer, TensorList data) -> {
                DoubleStream deltaStream = deltaLayers.stream().mapToDouble(layer -> {
                    Delta<Layer> a = direction.getMap().get(layer);
                    Delta<Layer> b = measure.delta.getMap().get(layer);
                    return b.dot(a) / Math.max(Math.sqrt(a.dot(a)), 1e-8);
                });
                if (hasPlaceholders) {
                    deltaStream = DoubleStream.concat(DoubleStream.of(direction.getMap().keySet().stream().filter(x -> x instanceof PlaceholderLayer).distinct().mapToDouble(layer -> {
                        Delta<Layer> a = direction.getMap().get(layer);
                        Delta<Layer> b = measure.delta.getMap().get(layer);
                        return b.dot(a) / Math.max(Math.sqrt(a.dot(a)), 1e-8);
                    }).sum()), deltaStream);
                }
                buffer.get(self, weights).addInPlace(deltaStream.toArray()).freeRef();
            }) {

                @Override
                protected void _free() {
                    measure.freeRef();
                    direction.freeRef();
                }

                @Override
                public boolean isAlive() {
                    return true;
                }
            };
        }

        @Override
        protected void _free() {
            direction.freeRef();
            origin.freeRef();
            super._free();
        }

        @Nonnull
        @Override
        public JsonObject getJson(Map<CharSequence, byte[]> resources, DataSerializer dataSerializer) {
            throw new IllegalStateException();
        }

        @Nullable
        @Override
        public List<double[]> state() {
            return null;
        }
    };
}

Example 24 with DoubleStream

use of java.util.stream.DoubleStream in project guava by google.

the class Streams method concat.

/**
 * Returns a {@link DoubleStream} containing the elements of the first stream, followed by the
 * elements of the second stream, and so on.
 *
 * <p>This is equivalent to {@code Stream.of(streams).flatMapToDouble(stream -> stream)}, but the
 * returned stream may perform better.
 *
 * @see DoubleStream#concat(DoubleStream, DoubleStream)
 */
public static DoubleStream concat(DoubleStream... streams) {
    boolean isParallel = false;
    int characteristics = Spliterator.ORDERED | Spliterator.SIZED | Spliterator.NONNULL;
    long estimatedSize = 0L;
    ImmutableList.Builder<Spliterator.OfDouble> splitrsBuilder = new ImmutableList.Builder<>(streams.length);
    for (DoubleStream stream : streams) {
        isParallel |= stream.isParallel();
        Spliterator.OfDouble splitr = stream.spliterator();
        splitrsBuilder.add(splitr);
        characteristics &= splitr.characteristics();
        estimatedSize = LongMath.saturatedAdd(estimatedSize, splitr.estimateSize());
    }
    return StreamSupport.doubleStream(CollectSpliterators.flatMapToDouble(splitrsBuilder.build().spliterator(), splitr -> splitr, characteristics, estimatedSize), isParallel).onClose(() -> closeAll(streams));
}

Example 25 with DoubleStream

use of java.util.stream.DoubleStream in project flow by vaadin.

the class JsonUtilsTest method testNumberStream.

@Test
public void testNumberStream() {
    double[] values = new double[] { 3.14, 42, Double.MAX_VALUE };
    JsonArray array = DoubleStream.of(values).mapToObj(Json::create).collect(JsonUtils.asArray());
    DoubleStream numberStream = JsonUtils.numberStream(array);
    Assert.assertArrayEquals(values, numberStream.toArray(), 0);
}