Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

OutDetectMultDenseBatch.java

/* 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,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
/* Retrieve the data from the input file */
dataSource.loadDataBlock();
/* 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);
context.dispose();
}
}

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