Example usage for org.apache.commons.lang3 ObjectUtils mode

Introduction

In this page you can find the example usage for org.apache.commons.lang3 ObjectUtils mode.

Prototype

public static <T> T mode(final T... items) 

Source Link

Document

Find the most frequently occurring item.

Usage

From source file:org.wso2.carbon.ml.siddhi.extension.PredictStreamProcessor.java

@Override
protected void process(ComplexEventChunk<StreamEvent> streamEventChunk, Processor nextProcessor,
        StreamEventCloner streamEventCloner, ComplexEventPopulater complexEventPopulater) {

    while (streamEventChunk.hasNext()) {

        StreamEvent event = streamEventChunk.next();
        String[] featureValues = new String[attributeIndexMap.size()];

        for (Map.Entry<Integer, int[]> entry : attributeIndexMap.entrySet()) {
            int featureIndex = entry.getKey();
            int[] attributeIndexArray = entry.getValue();
            Object dataValue = null;
            switch (attributeIndexArray[2]) {
            case 0:
                dataValue = event.getBeforeWindowData()[attributeIndexArray[3]];
                break;
            case 2:
                dataValue = event.getOutputData()[attributeIndexArray[3]];
                break;
            }//from   w  ww  . j a v a2s.com
            featureValues[featureIndex] = String.valueOf(dataValue);
        }

        if (featureValues != null) {
            try {
                Object[] predictionResults = new Object[modelHandlers.length];
                Object predictionResult = null;

                if (AlgorithmType.CLASSIFICATION.getValue().equals(algorithmClass)) {
                    for (int i = 0; i < modelHandlers.length; i++) {
                        predictionResults[i] = modelHandlers[i].predict(featureValues, outputType);
                    }
                    // Gets the majority vote
                    predictionResult = ObjectUtils.mode(predictionResults);
                } else if (AlgorithmType.NUMERICAL_PREDICTION.getValue().equals(algorithmClass)) {
                    double sum = 0;
                    for (int i = 0; i < modelHandlers.length; i++) {
                        sum += Double
                                .parseDouble(modelHandlers[i].predict(featureValues, outputType).toString());
                    }
                    // Gets the average value of predictions
                    predictionResult = sum / modelHandlers.length;
                } else if (AlgorithmType.ANOMALY_DETECTION.getValue().equals(algorithmClass)) {
                    for (int i = 0; i < modelHandlers.length; i++) {
                        predictionResults[i] = modelHandlers[i].predict(featureValues, outputType,
                                percentileValue);
                    }
                    // Gets the majority vote
                    predictionResult = ObjectUtils.mode(predictionResults);

                } else if (AlgorithmType.DEEPLEARNING.getValue().equals(algorithmClass)) {
                    // if H2O cluster is not available
                    if (deeplearningWithoutH2O) {
                        for (int i = 0; i < modelHandlers.length; i++) {
                            predictionResults[i] = modelHandlers[i].predict(featureValues, outputType,
                                    pojoPredictor[i]);
                        }
                        // Gets the majority vote
                        predictionResult = ObjectUtils.mode(predictionResults);
                    } else {
                        for (int i = 0; i < modelHandlers.length; i++) {
                            predictionResults[i] = modelHandlers[i].predict(featureValues, outputType);
                        }
                        // Gets the majority vote
                        predictionResult = ObjectUtils.mode(predictionResults);
                    }
                } else {
                    String msg = String.format(
                            "Error while predicting. Prediction is not supported for the algorithm class %s. ",
                            algorithmClass);
                    throw new ExecutionPlanRuntimeException(msg);
                }

                Object[] output = new Object[] { predictionResult };
                complexEventPopulater.populateComplexEvent(event, output);
            } catch (Exception e) {
                log.error("Error while predicting", e);
                throw new ExecutionPlanRuntimeException("Error while predicting", e);
            }
        }
    }
    nextProcessor.process(streamEventChunk);
}

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