OutDetectUniDenseBatch.java

/* file: OutDetectUniDenseBatch.java */
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/*
 //  Content:
 //     Java example of univariate outlier detection
 */

package com.intel.daal.examples.outlier_detection;

import com.intel.daal.algorithms.univariate_outlier_detection.*;
import com.intel.daal.data_management.data.NumericTable;
import com.intel.daal.data_management.data_source.DataSource;
import com.intel.daal.data_management.data_source.FileDataSource;
import com.intel.daal.examples.utils.Service;

import com.intel.daal.services.DaalContext;

class OutDetectUniDenseBatch {

    /* Input data set parameters */
    private static final String datasetFileName = "../data/batch/outlierdetection.csv";

    private static DaalContext context = new DaalContext();

    public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
        FileDataSource dataSource = new FileDataSource(context, datasetFileName,
                DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
                DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);

        /* Retrieve the data from the input file */
        dataSource.loadDataBlock();
        NumericTable data = dataSource.getNumericTable();

        /* Create an algorithm to detect outliers using the default method */
        Batch alg = new Batch(context, Float.class, Method.defaultDense);

        /* Set input objects for the algorithm */
        alg.input.set(InputId.data, data);

        /* Detect outliers */
        Result result = alg.compute();

        NumericTable weights = result.get(ResultId.weights);

        Service.printNumericTable("Input data", data);
        Service.printNumericTable("Univariate outlier detection result:", weights);

        context.dispose();
    }
}
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