Package hivemall.io

Examples of hivemall.io.DenseModel

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                        + " initial dimensions" + (useCovar ? " w/ covariances" : ""));
                model = new SpaceEfficientDenseModel(model_dims, useCovar);
            } else {
                logger.info("Build a dense model with initial with " + model_dims
                        + " initial dimensions" + (useCovar ? " w/ covariances" : ""));
                model = new DenseModel(model_dims, useCovar);
            }
        } else {
            int initModelSize = getInitialModelSize();
            logger.info("Build a sparse model with initial with " + initModelSize
                    + " initial dimensions");
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        ExecutorService serverExec = Executors.newSingleThreadExecutor();
        serverExec.submit(server);

        waitForState(server, ServerState.RUNNING);

        PredictionModel model = new DenseModel(16777216, false);
        model.configureClock();
        MixClient client = null;
        try {
            client = new MixClient(MixEventName.average, "testSSL", "localhost:" + port, true, 2, model);
            model.setUpdateHandler(client);

            final Random rand = new Random(43);
            for(int i = 0; i < 100000; i++) {
                Integer feature = Integer.valueOf(rand.nextInt(100));
                float weight = (float) rand.nextGaussian();
                model.set(feature, new WeightValue(weight));
            }

            waitForMixed(model, 48000, 10000L);

            int numMixed = model.getNumMixed();
            //System.out.println("number of mix events: " + numMixed);
            Assert.assertTrue("number of mix events: " + numMixed, numMixed > 0);

            serverExec.shutdown();
        } finally {
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        clientsExec.shutdown();
        serverExec.shutdown();
    }

    private static void invokeClient(String groupId, int serverPort) throws InterruptedException {
        PredictionModel model = new DenseModel(16777216, false);
        model.configureClock();
        MixClient client = null;
        try {
            client = new MixClient(MixEventName.average, groupId, "localhost:" + serverPort, false, 2, model);
            model.setUpdateHandler(client);

            final Random rand = new Random(43);
            for(int i = 0; i < 100000; i++) {
                Integer feature = Integer.valueOf(rand.nextInt(100));
                float weight = (float) rand.nextGaussian();
                model.set(feature, new WeightValue(weight));
            }

            waitForMixed(model, 48000, 10000L);

            int numMixed = model.getNumMixed();
            //System.out.println("number of mix events: " + numMixed);
            Assert.assertTrue("number of mix events: " + numMixed, numMixed > 0);
        } finally {
            IOUtils.closeQuietly(client);
        }
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        serverExec.shutdown();
    }

    private static void invokeClient01(String groupId, int serverPort, boolean denseModel)
            throws InterruptedException {
        PredictionModel model = denseModel ? new DenseModel(100, false)
                : new SparseModel(100, false);
        model.configureClock();
        MixClient client = null;
        try {
            client = new MixClient(MixEventName.average, groupId, "localhost:" + serverPort, false, 3, model);
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        ExecutorService serverExec = Executors.newSingleThreadExecutor();
        serverExec.submit(server);

        waitForState(server, ServerState.RUNNING);

        PredictionModel model = new DenseModel(16777216, false);
        model.configureClock();
        MixClient client = null;
        try {
            client = new MixClient(MixEventName.average, "testSimpleScenario", "localhost:" + port, false, 2, model);
            model.setUpdateHandler(client);

            final Random rand = new Random(43);
            for(int i = 0; i < 100000; i++) {
                Integer feature = Integer.valueOf(rand.nextInt(100));
                float weight = (float) rand.nextGaussian();
                model.set(feature, new WeightValue(weight));
            }

            waitForMixed(model, 48000, 10000L);

            int numMixed = model.getNumMixed();
            //System.out.println("number of mix events: " + numMixed);
            Assert.assertTrue("number of mix events: " + numMixed, numMixed > 0);

            serverExec.shutdown();
        } finally {
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Related Classes of hivemall.io.DenseModel

  • hivemall.io.WeightValue.WeightValueParamsF1
  • hivemall.io.WeightValue.WeightValueParamsF2
  • hivemall.io.WeightValue.WeightValueWithCovar
  • hivemall.LearnerBaseUDTF
  • hivemall.mix.server.MixServerTest
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