List of usage examples for com.google.common.collect Maps newHashMap
public static <K, V> HashMap<K, V> newHashMap()
From source file:org.sbs.util.MapUtils.java
public static void main(String[] args) { System.out.println((null instanceof Object)); HashMap<String, Object> m1 = Maps.newHashMap(); m1.put("a", "1"); m1.put("b", "x"); m1.put("d", Lists.newArrayList(1, 2, 34)); HashMap<String, Object> m2 = Maps.newHashMap(); m2.put("a", "x"); m2.put("b", "y"); m2.put("c", "3"); m1.put("d", Lists.newArrayList(1, 2, 34, 5)); m1.put("e", Maps.newHashMap()); System.out.println(mager(m1, m2)); System.out.println(m1);/*from ww w .ja v a2s.com*/ }
From source file:org.lisapark.octopus.util.xml.ConstraintUtils.java
public static void main(String[] args) { List<Map<String, Object>> data = getTestData(); Map<String, List<String>> prodVarMap = createProdMap(); Map<String, List<String>> machineVarMap = createMachineMap(); // Make a map to collect profit and unit values Map<String, Integer> profitMap = Maps.newHashMap(); Map<String, Integer> unitValueMap = Maps.newHashMap(); Map<String, IntVar> intVarMap = Maps.newHashMap(); // Create constrainer Constrainer model = new Constrainer("SIMPP"); // Collect variables for products and machines // for(Map<String, Object> map : data){ // // Use only those data that presented in Product and Machine Lists // if (prodVarMap.containsKey((String)map.get(PROD)) // && machineVarMap.containsKey((String)map.get(MACHINE))) { // String name = getName(map); // profitMap.put(name, (Integer) map.get(PROFIT)); // unitValueMap.put(name, (Integer) map.get(UNIT_VALUE)); // // // Create map of all Solution IntegerVariables // IntVar intvar = (IntVar) model.addIntVar(LOW, HIGH, name); // intVarMap.put(name, intvar); ///* ww w . ja v a2s. co m*/ // prodVarMap.get((String) map.get(PROD)).add(name); // machineVarMap.get((String) map.get(MACHINE)).add(name); // } // } // Converting collections to arrays and IntegerVariables //====================================================================== // Creating Cost criteria variable IntVar cost = (IntVar) model.addIntVar(1, HIGH_COST, COST); // // Machines // Map<String, Pair<int[], IntVar[]>> machineVarMapArray = // createVarMapArray(machineVarMap, unitValueMap, intVarMap); // // // Products // Map<String, Pair<int[], IntVar[]>> prodVarMapArray = // createVarMapArray(prodVarMap, unitValueMap, intVarMap); // // // create profits and unitValues arrays // IntExpArray varMapArray = // mergeVarMapArray(profitMap, intVarMap); // Now we are ready to make a model //====================================================================== // Add products constraints to the model // for(Entry entry : prodVarMapArray.entrySet()){ // String name = (String) entry.getKey(); // int[] values = (int[]) ((Pair)entry.getValue()).getFirst(); // IntVar[] intVars = (IntVar[]) ((Pair)entry.getValue()).getSecond(); // IntExpArray intexps = new IntExpArray(model, intVars); // int lowB = (int) ((int)getProdMap().get(name) * .8); // int uppB = (int) ((int)getProdMap().get(name) * 1.2); // IntVar intVar = (IntVar) model.addIntVar(lowB, uppB, name); //// model.addConstraint(Choco.eq(Choco.scalar(values, intVars), intVar)); //// int upRange = (int)getProdMap().get(name); //// model.postConstraint(intVar.eq(intVars., values)); // } // // // Add machines constraints to the model // for(Entry entry : machineVarMapArray.entrySet()){ // String name = (String) entry.getKey(); // int[] values = (int[]) ((Pair)entry.getValue()).getFirst(); // IntVar[] intVars = (IntVar[]) ((Pair)entry.getValue()).getSecond(); // model.addConstraint(Choco.leq(Choco.scalar(values, intVars), (int)getMachineMap().get(name))); // } // // Add fixed values constraints ti the model // To Be Implemented (TBI) // Add cost criteria constrains // Plan should maximize profit // int[] values = varMapArray.getFirst(); // IntVar[] intVars = varMapArray.getSecond(); // model.addConstraint(Choco.geq(Choco.scalar(values, intVars), cost)); // // Solver solver = new CPSolver(); // solver.read(model); //// solver.setValIntIterator(new DecreasingDomain()); // solver.maximize(solver.getVar(cost), false); }
From source file:ShuffleBlamedForParser.java
public static void main(String[] args) throws Exception { File inputFile = new File(args[0]); Preconditions.checkArgument(inputFile.exists(), "Please provide valid file; currentFile = " + inputFile); Pattern BLAMED_FOR = Pattern.compile("TaskAttemptImpl:(.*) blamed for read error from (.*) at"); //HDP 2.3.4 (as the log file format changed) //BLAMED_FOR = Pattern.compile("TaskAttemptImpl\\|:(.*) blamed for read error from (.*) at"); Pattern HOST_PATTERN = Pattern.compile("task=(.*), containerHost=(.*), localityMatchType"); Map<String, String> hostMap = Maps.newHashMap(); Map<String, Integer> fetcherFailure = Maps.newHashMap(); try (BufferedReader reader = new BufferedReader(new FileReader(inputFile))) { while (reader.ready()) { String line = reader.readLine(); if (line.contains("task") && line.contains("containerHost")) { Matcher matcher = HOST_PATTERN.matcher(line); while (matcher.find()) { String attempt = matcher.group(1).trim(); String host = matcher.group(2).trim(); fetcherFailure.put(attempt, 0); //Just initializing hostMap.put(attempt, host); }//from ww w .j a va 2 s .c o m } } } Set<String> hosts = new HashSet(hostMap.values()); System.out.println("Unique hosts : " + hosts.size()); Set<String> srcMachines = new HashSet<String>(); Set<String> fetcherMachines = new HashSet<String>(); try (BufferedReader reader = new BufferedReader(new FileReader(inputFile))) { try (FileWriter writer = new FileWriter(new File(".", "output.txt"))) { while (reader.ready()) { String line = reader.readLine(); if (line.contains("blamed for read error")) { Matcher matcher = BLAMED_FOR.matcher(line); while (matcher.find()) { String srcAttempt = matcher.group(1).trim(); String fetcherAttempt = matcher.group(2).trim(); fetcherFailure.put(fetcherAttempt, fetcherFailure.get(fetcherAttempt) + 1); if (hostMap.get(srcAttempt) == null) { System.out.println("ISSUE"); } String s = "src=" + srcAttempt + ", srcMachine=" + hostMap.get(srcAttempt.trim()) + ", fetcher=" + fetcherAttempt + ", fetcherMachine=" + hostMap.get(fetcherAttempt.trim()) //+ ", size=" + hostMap.size() + ", failure=" + fetcherFailure.get(fetcherAttempt); srcMachines.add(hostMap.get(srcAttempt.trim())); fetcherMachines.add(hostMap.get(fetcherAttempt.trim())); System.out.println(s); writer.write(s + "\n"); } } } } } //Summary System.out.println(); System.out.println(); System.out.println("Source Machines being blamed for "); for (String src : srcMachines) { System.out.println("\t" + src); } System.out.println(); System.out.println(); System.out.println("Fetcher Machines"); for (String fetcher : fetcherMachines) { System.out.println("\t" + fetcher); } }
From source file:org.apache.curator.example.DiscoveryExample.java
public static void main(String[] args) throws Exception { // This method is scaffolding to get the example up and running TestingServer server = new TestingServer(); CuratorFramework client = null;//from ww w .ja va2s . c o m ServiceDiscovery<InstanceDetails> serviceDiscovery = null; Map<String, ServiceProvider<InstanceDetails>> providers = Maps.newHashMap(); try { client = CuratorFrameworkFactory.newClient(server.getConnectString(), new ExponentialBackoffRetry(1000, 3)); client.start(); JsonInstanceSerializer<InstanceDetails> serializer = new JsonInstanceSerializer<InstanceDetails>( InstanceDetails.class); serviceDiscovery = ServiceDiscoveryBuilder.builder(InstanceDetails.class).client(client).basePath(PATH) .serializer(serializer).build(); serviceDiscovery.start(); processCommands(serviceDiscovery, providers, client); } finally { for (ServiceProvider<InstanceDetails> cache : providers.values()) { CloseableUtils.closeQuietly(cache); } CloseableUtils.closeQuietly(serviceDiscovery); CloseableUtils.closeQuietly(client); CloseableUtils.closeQuietly(server); } }
From source file:org.apache.streams.sysomos.example.SysomosMongo.java
public static void main(String[] args) { LOGGER.info(StreamsConfigurator.config.toString()); Config sysomos = StreamsConfigurator.config.getConfig("sysomos"); Config mongo = StreamsConfigurator.config.getConfig("mongo"); SysomosConfiguration config = new SysomosConfiguration(); config.setHeartbeatIds(sysomos.getStringList("heartbeatIds")); config.setApiBatchSize(sysomos.getLong("apiBatchSize")); config.setApiKey(sysomos.getString("apiKey")); config.setMinDelayMs(sysomos.getLong("minDelayMs")); config.setScheduledDelayMs(sysomos.getLong("scheduledDelayMs")); config.setMaxBatchSize(sysomos.getLong("maxBatchSize")); SysomosProvider provider = new SysomosProvider(config); MongoPersistWriter writer = new MongoPersistWriter(); Map<String, Object> streamConfig = Maps.newHashMap(); streamConfig.put(LocalStreamBuilder.TIMEOUT_KEY, 20 * 60 * 1000); StreamBuilder builder = new LocalStreamBuilder(1000, streamConfig); builder.newPerpetualStream("SysomosProvider", provider); builder.addStreamsProcessor("SysomosActivityConverter", new SysomosTypeConverter(), 10, "SysomosProvider"); builder.addStreamsPersistWriter("mongo", writer, 1, "SysomosActivityConverter"); builder.start();//w w w . j a v a 2 s . c om }
From source file:org.carrot2.examples.core.LoadingAttributeValuesFromXml.java
public static void main(String[] args) throws Exception { InputStream xmlStream = null; try {//from w w w.j av a 2 s.c o m xmlStream = LoadingAttributeValuesFromXml.class.getResourceAsStream("algorithm-lingo-attributes.xml"); // Load attribute value sets from the XML stream final AttributeValueSets attributeValueSets = AttributeValueSets.deserialize(xmlStream); // Get the desired set of attribute values for use with further processing final Map<String, Object> defaultAttributes = attributeValueSets.getDefaultAttributeValueSet() .getAttributeValues(); final Map<String, Object> fasterClusteringAttributes = attributeValueSets .getAttributeValueSet("faster-clustering").getAttributeValues(); // Perform processing using the attribute values final Controller controller = ControllerFactory.createSimple(); // Initialize the controller with one attribute set controller.init(fasterClusteringAttributes); // Perform clustering using the attribute set provided at initialization time Map<String, Object> requestAttributes = Maps.newHashMap(); CommonAttributesDescriptor.attributeBuilder(requestAttributes) .documents(Lists.newArrayList(SampleDocumentData.DOCUMENTS_DATA_MINING)).query("data mining"); ProcessingResult results = controller.process(requestAttributes, LingoClusteringAlgorithm.class); ConsoleFormatter.displayClusters(results.getClusters()); // Perform clustering using some other attribute set, in this case the // one that is the default in the XML file. requestAttributes = CommonAttributesDescriptor.attributeBuilder(Maps.newHashMap(defaultAttributes)) .documents(Lists.newArrayList(SampleDocumentData.DOCUMENTS_DATA_MINING)) .query("data mining").map; results = controller.process(requestAttributes, LingoClusteringAlgorithm.class); ConsoleFormatter.displayClusters(results.getClusters()); } finally { CloseableUtils.close(xmlStream); } }
From source file:org.apache.streams.datasift.example.DatasiftInstagramElasticsearch.java
public static void main(String[] args) { LOGGER.info(StreamsConfigurator.config.toString()); Config datasift = StreamsConfigurator.config.getConfig("datasift"); DatasiftConfiguration datasiftConfiguration = DatasiftStreamConfigurator.detectConfiguration(datasift); Config elasticsearch = StreamsConfigurator.config.getConfig("elasticsearch"); ElasticsearchWriterConfiguration elasticsearchWriterConfiguration = ElasticsearchConfigurator .detectWriterConfiguration(elasticsearch); Map<String, Object> streamConfig = Maps.newHashMap(); streamConfig.put(LocalStreamBuilder.TIMEOUT_KEY, 20 * 60 * 1000 * 1000); StreamBuilder builder = new LocalStreamBuilder(100, streamConfig); DatasiftStreamProvider stream = new DatasiftStreamProvider(new DatasiftStreamProvider.DeleteHandler(), datasiftConfiguration);/*from w w w. j a va2s . c o m*/ DatasiftTypeConverterProcessor datasiftTypeConverter = new DatasiftTypeConverterProcessor(Activity.class); RegexMentionsExtractor regexMentionsExtractor = new RegexMentionsExtractor(); ElasticsearchPersistWriter writer = new ElasticsearchPersistWriter(elasticsearchWriterConfiguration); builder.newPerpetualStream("stream", stream); builder.addStreamsProcessor("converter", datasiftTypeConverter, 2, "stream"); builder.addStreamsProcessor("RegexMentionsExtractor", regexMentionsExtractor, 2, "CleanAdditionalProperties"); builder.addStreamsPersistWriter(ElasticsearchPersistWriter.STREAMS_ID, writer, 1, "RegexMentionsExtractor"); builder.start(); }
From source file:com.sfxie.extension.zookeeper.curator.discover.DiscoveryExample.java
public static void main(String[] args) throws Exception { // This method is scaffolding to get the example up and running TestingServer server = new TestingServer("192.168.23.4", 2181); CuratorFramework client = null;//from w w w. j a va 2 s . c om ServiceDiscovery<InstanceDetails> serviceDiscovery = null; Map<String, ServiceProvider<InstanceDetails>> providers = Maps.newHashMap(); try { client = CuratorFrameworkFactory.newClient(server.getConnectString(), new ExponentialBackoffRetry(1000, 3)); client.start(); JsonInstanceSerializer<InstanceDetails> serializer = new JsonInstanceSerializer<InstanceDetails>( InstanceDetails.class); serviceDiscovery = ServiceDiscoveryBuilder.builder(InstanceDetails.class).client(client).basePath(PATH) .serializer(serializer).build(); serviceDiscovery.start(); processCommands(serviceDiscovery, providers, client); } finally { for (ServiceProvider<InstanceDetails> cache : providers.values()) { CloseableUtils.closeQuietly(cache); } CloseableUtils.closeQuietly(serviceDiscovery); CloseableUtils.closeQuietly(client); CloseableUtils.closeQuietly(server); } }
From source file:org.apache.flink.streaming.connectors.elasticsearch.examples.ElasticsearchExample.java
public static void main(String[] args) throws Exception { StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); DataStreamSource<String> source = env.addSource(new SourceFunction<String>() { private static final long serialVersionUID = 1L; private volatile boolean running = true; @Override//from w ww .j av a 2s. co m public void run(SourceContext<String> ctx) throws Exception { for (int i = 0; i < 20 && running; i++) { ctx.collect("message #" + i); } } @Override public void cancel() { running = false; } }); Map<String, String> config = Maps.newHashMap(); // This instructs the sink to emit after every element, otherwise they would be buffered config.put(ElasticsearchSink.CONFIG_KEY_BULK_FLUSH_MAX_ACTIONS, "1"); source.addSink(new ElasticsearchSink<>(config, new IndexRequestBuilder<String>() { @Override public IndexRequest createIndexRequest(String element, RuntimeContext ctx) { Map<String, Object> json = new HashMap<>(); json.put("data", element); return Requests.indexRequest().index("my-index").type("my-type").source(json); } })); env.execute("Elasticsearch Example"); }
From source file:org.carrot2.examples.clustering.MoreConfigurationsOfOneAlgorithmInCachingController.java
@SuppressWarnings({ "unchecked" })
public static void main(String[] args) {
/*/*www .j ava 2s . c om*/
* Create a controller that caches all documents.
*/
final Controller controller = ControllerFactory.createCachingPooling(IDocumentSource.class);
/*
* You can define global values for some attributes. These will apply to all
* configurations we will define below, unless the specific configuration
* overrides the global attributes.
*/
final Map<String, Object> globalAttributes = new HashMap<String, Object>();
CompletePreprocessingPipelineDescriptor.attributeBuilder(globalAttributes).documentAssigner()
.exactPhraseAssignment(false);
/*
* Now we will define two different configurations of the Lingo algorithm. One
* will be optimized for speed of clustering, while the other will optimize the
* quality of clusters.
*/
final Map<String, Object> fastAttributes = Maps.newHashMap();
LingoClusteringAlgorithmDescriptor.attributeBuilder(fastAttributes).desiredClusterCountBase(20)
.matrixReducer().factorizationQuality(FactorizationQuality.LOW);
CompletePreprocessingPipelineDescriptor.attributeBuilder(fastAttributes).caseNormalizer().dfThreshold(2);
final Map<String, Object> accurateAttributes = Maps.newHashMap();
LingoClusteringAlgorithmDescriptor.attributeBuilder(accurateAttributes).desiredClusterCountBase(40)
.matrixReducer().factorizationQuality(FactorizationQuality.HIGH);
CompletePreprocessingPipelineDescriptor.attributeBuilder(accurateAttributes).documentAssigner()
.exactPhraseAssignment(true);
CompletePreprocessingPipelineDescriptor.attributeBuilder(fastAttributes).caseNormalizer().dfThreshold(1);
/*
* We initialize the controller passing the global attributes and the two
* configurations. Notice that a configuration consists of the component
* class (can be a document source as well as a clustering algorithm), its
* string identifier and attributes.
*/
controller.init(globalAttributes,
new ProcessingComponentConfiguration(LingoClusteringAlgorithm.class, "lingo-fast", fastAttributes),
new ProcessingComponentConfiguration(LingoClusteringAlgorithm.class, "lingo-accurate",
accurateAttributes));
/*
* Now we can call the two different clustering configurations. Notice that
* because we now use string identifiers instead of classes, we pass the document
* source class name rather than the class itself.
*/
final Map<String, Object> attributes = new HashMap<String, Object>();
CommonAttributesDescriptor.attributeBuilder(attributes).query("data mining");
final ProcessingResult fastResult = controller.process(attributes, Bing3WebDocumentSource.class.getName(),
"lingo-fast");
ConsoleFormatter.displayClusters(fastResult.getClusters());
final ProcessingResult accurateResult = controller.process(attributes,
Bing3WebDocumentSource.class.getName(), "lingo-accurate");
ConsoleFormatter.displayClusters(accurateResult.getClusters());
}