Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

ZScoreDenseBatch.java

/* file: ZScoreDenseBatch.java */
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/*
// Content:
// Java example of Z-score normalization algorithm
*/
package com.intel.daal.examples.normalization;
import com.intel.daal.algorithms.normalization.zscore.*;
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 ZScoreDenseBatch {
private static final String dataset = "../data/batch/normalization.csv";
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Retrieve the input data */
FileDataSource dataSource = new FileDataSource(context, dataset,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
dataSource.loadDataBlock();
NumericTable input = dataSource.getNumericTable();
/* Create an algorithm */
Batch algorithm = new Batch(context, Float.class, Method.defaultDense);
/* Set an input object for the algorithm */
algorithm.input.set(InputId.data, input);
algorithm.parameter.setResultsToCompute(ResultsToComputeId.mean);// | ResultsToComputeId.variance);
/* Compute Z-score normalization function */
Result result = algorithm.compute();
/* Print the results of stage */
Service.printNumericTable("First 10 rows of the input data:", input, 10);
Service.printNumericTable("First 10 rows of the z-score normalization result:", result.get(ResultId.normalizedData), 10);
//Service.printNumericTable("Means:", result.get(ResultId.means));
//Service.printNumericTable("Variances:", result.get(ResultId.variances));
context.dispose();
}
}

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