Examples with ToDoubleFunction java.util.function.ToDoubleFunction used on opensource projects

Example 6 with ToDoubleFunction

use of java.util.function.ToDoubleFunction in project graphhopper by graphhopper.

the class IsochroneResource method doGet.

@GET
@Produces(MediaType.APPLICATION_JSON)
public Response doGet(@Context UriInfo uriInfo, @QueryParam("profile") String profileName, @QueryParam("buckets") @Range(min = 1, max = 20) @DefaultValue("1") IntParam nBuckets, @QueryParam("reverse_flow") @DefaultValue("false") boolean reverseFlow, @QueryParam("point") @NotNull GHPointParam point, @QueryParam("time_limit") @DefaultValue("600") LongParam timeLimitInSeconds, @QueryParam("distance_limit") @DefaultValue("-1") LongParam distanceLimitInMeter, @QueryParam("weight_limit") @DefaultValue("-1") LongParam weightLimit, @QueryParam("type") @DefaultValue("json") ResponseType respType, @QueryParam("tolerance") @DefaultValue("0") double toleranceInMeter, @QueryParam("full_geometry") @DefaultValue("false") boolean fullGeometry) {
    StopWatch sw = new StopWatch().start();
    PMap hintsMap = new PMap();
    RouteResource.initHints(hintsMap, uriInfo.getQueryParameters());
    hintsMap.putObject(Parameters.CH.DISABLE, true);
    hintsMap.putObject(Parameters.Landmark.DISABLE, true);
    if (Helper.isEmpty(profileName)) {
        profileName = profileResolver.resolveProfile(hintsMap).getName();
        removeLegacyParameters(hintsMap);
    }
    errorIfLegacyParameters(hintsMap);
    Profile profile = graphHopper.getProfile(profileName);
    if (profile == null)
        throw new IllegalArgumentException("The requested profile '" + profileName + "' does not exist");
    LocationIndex locationIndex = graphHopper.getLocationIndex();
    Graph graph = graphHopper.getGraphHopperStorage();
    Weighting weighting = graphHopper.createWeighting(profile, hintsMap);
    BooleanEncodedValue inSubnetworkEnc = graphHopper.getEncodingManager().getBooleanEncodedValue(Subnetwork.key(profileName));
    if (hintsMap.has(Parameters.Routing.BLOCK_AREA)) {
        GraphEdgeIdFinder.BlockArea blockArea = GraphEdgeIdFinder.createBlockArea(graph, locationIndex, Collections.singletonList(point.get()), hintsMap, new FiniteWeightFilter(weighting));
        weighting = new BlockAreaWeighting(weighting, blockArea);
    }
    Snap snap = locationIndex.findClosest(point.get().lat, point.get().lon, new DefaultSnapFilter(weighting, inSubnetworkEnc));
    if (!snap.isValid())
        throw new IllegalArgumentException("Point not found:" + point);
    QueryGraph queryGraph = QueryGraph.create(graph, snap);
    TraversalMode traversalMode = profile.isTurnCosts() ? EDGE_BASED : NODE_BASED;
    ShortestPathTree shortestPathTree = new ShortestPathTree(queryGraph, queryGraph.wrapWeighting(weighting), reverseFlow, traversalMode);
    double limit;
    if (weightLimit.get() > 0) {
        limit = weightLimit.get();
        shortestPathTree.setWeightLimit(limit + Math.max(limit * 0.14, 2_000));
    } else if (distanceLimitInMeter.get() > 0) {
        limit = distanceLimitInMeter.get();
        shortestPathTree.setDistanceLimit(limit + Math.max(limit * 0.14, 2_000));
    } else {
        limit = timeLimitInSeconds.get() * 1000;
        shortestPathTree.setTimeLimit(limit + Math.max(limit * 0.14, 200_000));
    }
    ArrayList<Double> zs = new ArrayList<>();
    double delta = limit / nBuckets.get();
    for (int i = 0; i < nBuckets.get(); i++) {
        zs.add((i + 1) * delta);
    }
    ToDoubleFunction<ShortestPathTree.IsoLabel> fz;
    if (weightLimit.get() > 0) {
        fz = l -> l.weight;
    } else if (distanceLimitInMeter.get() > 0) {
        fz = l -> l.distance;
    } else {
        fz = l -> l.time;
    }
    Triangulator.Result result = triangulator.triangulate(snap, queryGraph, shortestPathTree, fz, degreesFromMeters(toleranceInMeter));
    ContourBuilder contourBuilder = new ContourBuilder(result.triangulation);
    ArrayList<Geometry> isochrones = new ArrayList<>();
    for (Double z : zs) {
        logger.info("Building contour z={}", z);
        MultiPolygon isochrone = contourBuilder.computeIsoline(z, result.seedEdges);
        if (fullGeometry) {
            isochrones.add(isochrone);
        } else {
            Polygon maxPolygon = heuristicallyFindMainConnectedComponent(isochrone, isochrone.getFactory().createPoint(new Coordinate(point.get().lon, point.get().lat)));
            isochrones.add(isochrone.getFactory().createPolygon(((LinearRing) maxPolygon.getExteriorRing())));
        }
    }
    ArrayList<JsonFeature> features = new ArrayList<>();
    for (Geometry isochrone : isochrones) {
        JsonFeature feature = new JsonFeature();
        HashMap<String, Object> properties = new HashMap<>();
        properties.put("bucket", features.size());
        if (respType == geojson) {
            properties.put("copyrights", ResponsePathSerializer.COPYRIGHTS);
        }
        feature.setProperties(properties);
        feature.setGeometry(isochrone);
        features.add(feature);
    }
    ObjectNode json = JsonNodeFactory.instance.objectNode();
    sw.stop();
    ObjectNode finalJson = null;
    if (respType == geojson) {
        json.put("type", "FeatureCollection");
        json.putPOJO("features", features);
        finalJson = json;
    } else {
        json.putPOJO("polygons", features);
        final ObjectNode info = json.putObject("info");
        info.putPOJO("copyrights", ResponsePathSerializer.COPYRIGHTS);
        info.put("took", Math.round((float) sw.getMillis()));
        finalJson = json;
    }
    logger.info("took: " + sw.getSeconds() + ", visited nodes:" + shortestPathTree.getVisitedNodes());
    return Response.ok(finalJson).header("X-GH-Took", "" + sw.getSeconds() * 1000).build();
}

Example 7 with ToDoubleFunction

use of java.util.function.ToDoubleFunction in project CoreNLP by stanfordnlp.

the class SVMLightClassifierFactory method crossValidateSetC.

/**
 * This method will cross validate on the given data and number of folds
 * to find the optimal C.  The scorer is how you determine what to
 * optimize for (F-score, accuracy, etc).  The C is then saved, so that
 * if you train a classifier after calling this method, that C will be used.
 */
public void crossValidateSetC(GeneralDataset<L, F> dataset, int numFolds, final Scorer<L> scorer, LineSearcher minimizer) {
    System.out.println("in Cross Validate");
    useAlphaFile = true;
    boolean oldUseSigmoid = useSigmoid;
    useSigmoid = false;
    final CrossValidator<L, F> crossValidator = new CrossValidator<>(dataset, numFolds);
    final ToDoubleFunction<Triple<GeneralDataset<L, F>, GeneralDataset<L, F>, CrossValidator.SavedState>> score = fold -> {
        GeneralDataset<L, F> trainSet = fold.first();
        GeneralDataset<L, F> devSet = fold.second();
        alphaFile = (File) fold.third().state;
        // train(trainSet,true,true);
        SVMLightClassifier<L, F> classifier = trainClassifierBasic(trainSet);
        fold.third().state = alphaFile;
        return scorer.score(classifier, devSet);
    };
    DoubleUnaryOperator negativeScorer = cToTry -> {
        C = cToTry;
        if (verbose) {
            System.out.print("C = " + cToTry + " ");
        }
        Double averageScore = crossValidator.computeAverage(score);
        if (verbose) {
            System.out.println(" -> average Score: " + averageScore);
        }
        return -averageScore;
    };
    C = minimizer.minimize(negativeScorer);
    useAlphaFile = false;
    useSigmoid = oldUseSigmoid;
}

Example 8 with ToDoubleFunction

use of java.util.function.ToDoubleFunction in project CoreNLP by stanfordnlp.

the class LinearClassifierFactory method crossValidateSetSigma.

/**
 * Sets the sigma parameter to a value that optimizes the cross-validation score given by {@code scorer}.  Search for an optimal value
 * is carried out by {@code minimizer}.
 *
 * @param dataset the data set to optimize sigma on.
 */
public void crossValidateSetSigma(GeneralDataset<L, F> dataset, int kfold, final Scorer<L> scorer, LineSearcher minimizer) {
    logger.info("##in Cross Validate, folds = " + kfold);
    logger.info("##Scorer is " + scorer);
    featureIndex = dataset.featureIndex;
    labelIndex = dataset.labelIndex;
    final CrossValidator<L, F> crossValidator = new CrossValidator<>(dataset, kfold);
    final ToDoubleFunction<Triple<GeneralDataset<L, F>, GeneralDataset<L, F>, CrossValidator.SavedState>> scoreFn = fold -> {
        GeneralDataset<L, F> trainSet = fold.first();
        GeneralDataset<L, F> devSet = fold.second();
        double[] weights = (double[]) fold.third().state;
        double[][] weights2D;
        // must of course bypass sigma tuning here.
        weights2D = trainWeights(trainSet, weights, true);
        fold.third().state = ArrayUtils.flatten(weights2D);
        LinearClassifier<L, F> classifier = new LinearClassifier<>(weights2D, trainSet.featureIndex, trainSet.labelIndex);
        double score = scorer.score(classifier, devSet);
        // System.out.println("score: "+score);
        System.out.print(".");
        return score;
    };
    DoubleUnaryOperator negativeScorer = sigmaToTry -> {
        // sigma = sigmaToTry;
        setSigma(sigmaToTry);
        Double averageScore = crossValidator.computeAverage(scoreFn);
        logger.info("##sigma = " + getSigma() + " -> average Score: " + averageScore);
        return -averageScore;
    };
    double bestSigma = minimizer.minimize(negativeScorer);
    logger.info("##best sigma: " + bestSigma);
    setSigma(bestSigma);
}

Example 9 with ToDoubleFunction

use of java.util.function.ToDoubleFunction in project RecurrentComplex by Ivorforce.

the class CommandSearchStructure method execute.

@Override
public void execute(MinecraftServer server, ICommandSender sender, String[] args) throws CommandException {
    Parameters parameters = Parameters.of(args, expect()::declare);
    List<ToDoubleFunction<String>> ranks = new ArrayList<>();
    consider(ranks, parameters.get(0), Parameter::varargsList, (s, t) -> StructureSearch.searchRank(t, StructureSearch.keywords(StructureRegistry.INSTANCE.id(s), s)));
    consider(ranks, parameters.get("containing"), e -> RCP.expression(e, new BlockExpression(RecurrentComplex.specialRegistry)), StructureSearch::containedBlocks);
    consider(ranks, parameters.get("biome"), MCP::biome, StructureSearch::biome);
    consider(ranks, parameters.get("dimension"), MCP.dimension(server, sender), StructureSearch::dimension);
    consider(ranks, parameters.get("maze"), p -> p, StructureSearch::maze);
    consider(ranks, parameters.get("list"), p -> p, StructureSearch::list);
    consider(ranks, parameters.get("author"), p -> p, StructureSearch::author);
    boolean all = parameters.has("all");
    if (ranks.stream().noneMatch(Objects::nonNull))
        throw new WrongUsageException(getUsage(sender));
    postResultMessage("Results: ", sender, RCTextStyle::structure, search(all ? StructureRegistry.INSTANCE.ids() : StructureRegistry.INSTANCE.activeIDs(), name -> ranks.stream().filter(Objects::nonNull).mapToDouble(f -> f.applyAsDouble(name)).reduce(1, (a, b) -> a * b)));
}