PCACorDenseOnline.java

/* file: PCACorDenseOnline.java */
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/*
 //  Content:
 //     Java example of principal component analysis (PCA) using the correlation
 //     method in the online processing mode
 */

package com.intel.daal.examples.pca;

import com.intel.daal.algorithms.pca.*;
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 PCACorDenseOnline {
    /* Input data set parameters */
    private static final String dataset         = "../data/online/pca_normalized.csv";
    private static final int    nVectorsInBlock = 250;

    private static DaalContext context = new DaalContext();

    public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
        /* Initialize FileDataSource to retrieve the input data from a .csv file */
        FileDataSource dataSource = new FileDataSource(context, dataset,
                                                       DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
                                                       DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);

        /* Create an algorithm to compute PCA decomposition using the correlation method */
        Online pcaAlgorithm = new Online(context, Float.class, Method.correlationDense);

        /* Set the input data */
        NumericTable data = dataSource.getNumericTable();
        pcaAlgorithm.input.set(InputId.data, data);

        while (dataSource.loadDataBlock(nVectorsInBlock) == nVectorsInBlock) {
            /* Update PCA decomposition */
            pcaAlgorithm.compute();
        }

        /* Finalize computations and retrieve the results */
        Result res = pcaAlgorithm.finalizeCompute();

        NumericTable eigenValues = res.get(ResultId.eigenValues);
        NumericTable eigenVectors = res.get(ResultId.eigenVectors);
        Service.printNumericTable("Eigenvalues:", eigenValues);
        Service.printNumericTable("Eigenvectors:", eigenVectors);

        context.dispose();
    }
}
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