Example usage for java.util Map forEach

Introduction

In this page you can find the example usage for java.util Map forEach.

Prototype

default void forEach(BiConsumer<? super K, ? super V> action) 

Source Link

Document

Performs the given action for each entry in this map until all entries have been processed or the action throws an exception.

Usage

From source file:Main.java

public static void main(String[] args) throws Exception {
    List<Person> persons = Arrays.asList(new Person("Max", 18), new Person("Peter", 23),
            new Person("Pamela", 23), new Person("David", 12));

    Map<Integer, List<Person>> personsByAge = persons.stream().collect(Collectors.groupingBy(p -> p.age));

    personsByAge.forEach((age, p) -> System.out.format("age %s: %s\n", age, p));
}

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From source file:Main.java

public static void main(String[] args) {
    Map<Integer, String> map = new HashMap<>();

    for (int i = 0; i < 10; i++) {
        map.putIfAbsent(i, "val" + i);
    }// w  ww . ja v a 2  s  . c om

    map.forEach((id, val) -> System.out.println(val));
}

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From source file:Main.java

public static void main(String[] args) {
    Map<Integer, String> map = new HashMap<>();

    for (int i = 0; i < 10; i++) {
        map.putIfAbsent(i, "val" + i);
    }//w  w w .j  av a2  s.c  o m

    map.forEach((id, val) -> System.out.println(val));

    System.out.println(map.getOrDefault(42, "not found")); // not found
}

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From source file:Main.java

public static void main(String[] args) {
    Map<Integer, String> map = new HashMap<>();

    for (int i = 0; i < 10; i++) {
        map.putIfAbsent(i, "val" + i);
    }//  w  w  w .j a  v a2  s  .com

    map.forEach((id, val) -> System.out.println(val));

    map.remove(3, "val3");
    System.out.println(map.get(3)); // val33

    map.remove(3, "val33");
    System.out.println(map.get(3)); // null
}

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From source file:Main.java

public static void main(String[] args) {
    Map<Integer, String> map = new HashMap<>();

    for (int i = 0; i < 10; i++) {
        map.putIfAbsent(i, "val" + i);
    }/*from www .j  a v  a  2 s.  c om*/

    map.forEach((id, val) -> System.out.println(val));

    map.merge(9, "val9", (value, newValue) -> value.concat(newValue));
    System.out.println(map.get(9)); // val9

    map.merge(9, "concat", (value, newValue) -> value.concat(newValue));
    System.out.println(map.get(9)); // val9concat
}

---

From source file:Main.java

public static void main(String[] args) {
    Map<Integer, String> map = new HashMap<>();

    for (int i = 0; i < 10; i++) {
        map.putIfAbsent(i, "val" + i);
    }//  w w w  .ja  v  a  2s  .  c o m

    map.forEach((id, val) -> System.out.println(val));

    map.computeIfPresent(3, (num, val) -> val + num);
    System.out.println(map.get(3)); // val33

    map.computeIfPresent(9, (num, val) -> null);
    System.out.println(map.containsKey(9)); // false

    map.computeIfAbsent(23, num -> "val" + num);
    System.out.println(map.containsKey(23)); // true

    map.computeIfAbsent(3, num -> "bam");
    System.out.println(map.get(3)); // val33
}

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From source file:com.ryft.spark.connector.examples.SimplePairRDDExampleJ.java

public static void main(String[] args) {
    final SparkConf sparkConf = new SparkConf().setAppName("SimplePairRDDExampleJ").setMaster("local[2]");

    final SparkContext sc = new SparkContext(sparkConf);
    final SparkContextJavaFunctions javaFunctions = RyftJavaUtil.javaFunctions(sc);
    final byte fuzziness = 0;
    final int surrounding = 10;
    final List queries = toScalaList(RyftQueryUtil.toSimpleQueries("Jones", "Thomas"));

    final RyftPairJavaRDD rdd = javaFunctions.ryftPairJavaRDD(queries,
            RyftQueryOptions.apply("passengers.txt", surrounding, fuzziness), RyftJavaUtil.ryftQueryToEmptyList,
            RyftJavaUtil.stringToEmptySet);

    final Map<String, Long> countByKey = rdd.countByKey();
    final StrBuilder sb = new StrBuilder();
    countByKey.forEach((key, value) -> sb.append(key + " -> " + value + "\n"));
    logger.info("RDD count by key: \n{}", sb.toString());
}

---

From source file:Main.java

public static void main(String[] args) {
    Map<String, String> map = new HashMap<>();
    map.put("CSS", "style");
    map.put("HTML", "mark up");
    map.put("Oracle", "database");
    map.put("XML", "data");

    map.forEach((String key, String value) -> {
        System.out.println("key=" + key + ",  value=" + value);
    });//  w w w. j  av  a 2  s  .co  m

}

---

From source file:pl.prutkowski.java.playground.java8.TestCollectors.java

/**
 * @param args the command line arguments
 *///from w  ww .ja va 2  s.  c  o  m
public static void main(String[] args) {
    Map<String, Integer> monthByLen = months.stream()
            .collect(Collectors.toMap(String::toUpperCase, m -> StringUtils.countMatches(m, "e")));

    monthByLen.forEach((month, eCount) -> System.out.println(month + " -> " + eCount));

    System.out.println("---------------------------------");

    Map<Object, List<String>> monthByLen2 = months.stream()
            .collect(Collectors.groupingBy(m -> StringUtils.countMatches(m, "e")));

    monthByLen2.forEach((count, groupedMonths) -> System.out.println(count + " -> " + groupedMonths));

    System.out.println("---------------------------------");

    Double averageLength = months.stream().collect(Collectors.averagingDouble(String::length));
    System.out.println("Average length: " + averageLength);
    System.out.println("---------------------------------");

    Double max = months.stream().collect(Collectors.summarizingDouble(String::length)).getMax();
    System.out.println("Max length: " + max);
    System.out.println("---------------------------------");

    String reduced = months.stream().collect(Collectors.reducing((m1, m2) -> (m1 + ", " + m2))).get();
    System.out.println("Reduced: " + reduced);
    System.out.println("---------------------------------");
    System.out.println(String.join(", ", months));
    System.out.println("---------------------------------");

    List<String> monthsWithZ = months.stream().filter(m -> m.contains("z")).collect(new ListCollector<>());
    System.out.println(monthsWithZ);

}

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From source file:examples.cnn.ImagesClassification.java

public static void main(String[] args) {

    SparkConf conf = new SparkConf();
    conf.setAppName("Images CNN Classification");
    conf.setMaster(String.format("local[%d]", NUM_CORES));
    conf.set(SparkDl4jMultiLayer.AVERAGE_EACH_ITERATION, String.valueOf(true));

    try (JavaSparkContext sc = new JavaSparkContext(conf)) {

        JavaRDD<String> raw = sc.textFile("data/images-data-rgb.csv");
        String first = raw.first();

        JavaPairRDD<String, String> labelData = raw.filter(f -> f.equals(first) == false).mapToPair(r -> {
            String[] tab = r.split(";");
            return new Tuple2<>(tab[0], tab[1]);
        });/*from  w w  w.ja v  a2  s.c o  m*/

        Map<String, Long> labels = labelData.map(t -> t._1).distinct().zipWithIndex()
                .mapToPair(t -> new Tuple2<>(t._1, t._2)).collectAsMap();

        log.info("Number of labels {}", labels.size());
        labels.forEach((a, b) -> log.info("{}: {}", a, b));

        NetworkTrainer trainer = new NetworkTrainer.Builder().model(ModelLibrary.net1)
                .networkToSparkNetwork(net -> new SparkDl4jMultiLayer(sc, net)).numLabels(labels.size())
                .cores(NUM_CORES).build();

        JavaRDD<Tuple2<INDArray, double[]>> labelsWithData = labelData.map(t -> {
            INDArray label = FeatureUtil.toOutcomeVector(labels.get(t._1).intValue(), labels.size());
            double[] arr = Arrays.stream(t._2.split(" ")).map(normalize1).mapToDouble(Double::doubleValue)
                    .toArray();
            return new Tuple2<>(label, arr);
        });

        JavaRDD<Tuple2<INDArray, double[]>>[] splited = labelsWithData.randomSplit(new double[] { .8, .2 },
                seed);

        JavaRDD<DataSet> testDataset = splited[1].map(t -> {
            INDArray features = Nd4j.create(t._2, new int[] { 1, t._2.length });
            return new DataSet(features, t._1);
        }).cache();
        log.info("Number of test images {}", testDataset.count());

        JavaRDD<DataSet> plain = splited[0].map(t -> {
            INDArray features = Nd4j.create(t._2, new int[] { 1, t._2.length });
            return new DataSet(features, t._1);
        });

        /*
         * JavaRDD<DataSet> flipped = splited[0].randomSplit(new double[] { .5, .5 }, seed)[0].
         */
        JavaRDD<DataSet> flipped = splited[0].map(t -> {
            double[] arr = t._2;
            int idx = 0;
            double[] farr = new double[arr.length];
            for (int i = 0; i < arr.length; i += trainer.width) {
                double[] temp = Arrays.copyOfRange(arr, i, i + trainer.width);
                ArrayUtils.reverse(temp);
                for (int j = 0; j < trainer.height; ++j) {
                    farr[idx++] = temp[j];
                }
            }
            INDArray features = Nd4j.create(farr, new int[] { 1, farr.length });
            return new DataSet(features, t._1);
        });

        JavaRDD<DataSet> trainDataset = plain.union(flipped).cache();
        log.info("Number of train images {}", trainDataset.count());

        trainer.train(trainDataset, testDataset);
    }
}

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