/* file: OutDetectMultDenseBatch.java */
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 //  Content:
 //     Java example of multivariate outlier detection

package com.intel.daal.examples.outlier_detection;

import com.intel.daal.algorithms.multivariate_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 OutDetectMultDenseBatch {

    /* 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,

        /* Retrieve the data from the input file */

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

        NumericTable data = dataSource.getNumericTable();
        alg.input.set(InputId.data, data);

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

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

        Service.printNumericTables(data,weights,"Input data","Weights","Outlier detection result (Default method)",0);
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