MinMaxDenseBatch.java

/* file: MinMaxDenseBatch.java */
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/*
 //  Content:
 //     Java example of Min-max normalization algorithm
 */

package com.intel.daal.examples.normalization;

import com.intel.daal.algorithms.normalization.minmax.*;
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 MinMaxDenseBatch {
    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 lower and upper bounds for the algorithm */
        algorithm.parameter.setLowerBound(-1.0);
        algorithm.parameter.setUpperBound( 1.0);

        /* Set an input object for the algorithm */
        algorithm.input.set(InputId.data, input);

        /* Compute Min-max 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 Min-max normalization result:", result.get(ResultId.normalizedData), 10);

        context.dispose();
    }
}
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