Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

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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