use of edu.iu.dsc.tws.task.window.constant.WindowType in project twister2 by DSC-SPIDAL.
the class SvmSgdOnlineRunner method getWindowSinkInstance.
private BaseWindowedSink getWindowSinkInstance() {
BaseWindowedSink baseWindowedSink = new IterativeStreamingWindowedCompute(new ProcessWindowFunctionImpl(), OperationMode.STREAMING, this.svmJobParameters, this.binaryBatchModel, "online-training-graph");
WindowArguments windowArguments = this.svmJobParameters.getWindowArguments();
TimeUnit timeUnit = TimeUnit.MICROSECONDS;
if (windowArguments != null) {
WindowType windowType = windowArguments.getWindowType();
if (windowArguments.isDuration()) {
if (windowType.equals(WindowType.TUMBLING)) {
baseWindowedSink.withTumblingDurationWindow(windowArguments.getWindowLength(), timeUnit);
}
if (windowType.equals(WindowType.SLIDING)) {
baseWindowedSink.withSlidingDurationWindow(windowArguments.getWindowLength(), timeUnit, windowArguments.getSlidingLength(), timeUnit);
}
} else {
if (windowType.equals(WindowType.TUMBLING)) {
baseWindowedSink.withTumblingCountWindow(windowArguments.getWindowLength());
}
if (windowType.equals(WindowType.SLIDING)) {
baseWindowedSink.withSlidingCountWindow(windowArguments.getWindowLength(), windowArguments.getSlidingLength());
}
}
}
return baseWindowedSink;
}
use of edu.iu.dsc.tws.task.window.constant.WindowType in project twister2 by DSC-SPIDAL.
the class SVMJobParameters method build.
/**
* Builds the Job Parameters relevant SVM Algorithm
*
* @param cfg : this must be initialized where Job is initialized.
*/
public static SVMJobParameters build(Config cfg) {
SVMJobParameters svmJobParameters = new SVMJobParameters();
svmJobParameters.features = cfg.getIntegerValue(MLDataObjectConstants.SgdSvmDataObjectConstants.FEATURES, 10);
svmJobParameters.samples = cfg.getIntegerValue(MLDataObjectConstants.SgdSvmDataObjectConstants.SAMPLES, 1000);
svmJobParameters.testingSamples = cfg.getIntegerValue(MLDataObjectConstants.SgdSvmDataObjectConstants.TESTING_SAMPLES, 1000);
svmJobParameters.isStreaming = cfg.getBooleanValue(MLDataObjectConstants.STREAMING, false);
svmJobParameters.isSplit = cfg.getBooleanValue(MLDataObjectConstants.SPLIT, false);
svmJobParameters.trainingDataDir = cfg.getStringValue(MLDataObjectConstants.TRAINING_DATA_DIR);
svmJobParameters.testingDataDir = cfg.getStringValue(MLDataObjectConstants.TESTING_DATA_DIR);
svmJobParameters.weightVectorDataDir = cfg.getStringValue(MLDataObjectConstants.WEIGHT_VECTOR_DATA_DIR);
svmJobParameters.crossValidationDataDir = cfg.getStringValue(MLDataObjectConstants.CROSS_VALIDATION_DATA_DIR);
svmJobParameters.modelSaveDir = cfg.getStringValue(MLDataObjectConstants.MODEL_SAVE_PATH);
svmJobParameters.iterations = cfg.getIntegerValue(MLDataObjectConstants.SgdSvmDataObjectConstants.ITERATIONS, 100);
svmJobParameters.c = cfg.getDoubleValue(MLDataObjectConstants.SgdSvmDataObjectConstants.C, 1.0);
svmJobParameters.alpha = cfg.getDoubleValue(MLDataObjectConstants.SgdSvmDataObjectConstants.ALPHA, 0.001);
svmJobParameters.isDummy = cfg.getBooleanValue(MLDataObjectConstants.DUMMY, true);
svmJobParameters.parallelism = cfg.getIntegerValue(WorkerConstants.PARALLELISM, 4);
svmJobParameters.experimentName = cfg.getStringValue(MLDataObjectConstants.SgdSvmDataObjectConstants.EXP_NAME);
// set up window params
WindowType windowType = cfg.getStringValue(WindowingConstants.WINDOW_TYPE).equalsIgnoreCase("tumbling") ? WindowType.TUMBLING : WindowType.SLIDING;
long windowLength = Long.parseLong(cfg.getStringValue(WindowingConstants.WINDOW_LENGTH));
long slidingLength = 0;
if (cfg.getStringValue(WindowingConstants.SLIDING_WINDOW_LENGTH) != null) {
slidingLength = Long.parseLong(cfg.getStringValue(WindowingConstants.SLIDING_WINDOW_LENGTH));
}
WindowArguments windowArguments = new WindowArguments(windowType, windowLength, slidingLength, cfg.getBooleanValue(WindowingConstants.WINDOW_CAPACITY_TYPE));
svmJobParameters.windowArguments = windowArguments;
return svmJobParameters;
}
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