Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

PCACorCSROnline.java

/* file: PCACorCSROnline.java */
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/*
// Content:
// Java example of principal component analysis (PCA) using the correlation
// method in the online processing mode for sparse data
*/
package com.intel.daal.examples.pca;
import com.intel.daal.algorithms.pca.InputId;
import com.intel.daal.algorithms.pca.Method;
import com.intel.daal.algorithms.pca.Online;
import com.intel.daal.algorithms.pca.Result;
import com.intel.daal.algorithms.pca.ResultId;
import com.intel.daal.data_management.data.NumericTable;
import com.intel.daal.data_management.data.CSRNumericTable;
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 PCACorCSROnline {
/* Input data set parameters */
private static DaalContext context = new DaalContext();
/* Input data set parameters */
private static final String datasetFileNames[] = new String[] { "../data/online/covcormoments_csr_1.csv",
"../data/online/covcormoments_csr_2.csv",
"../data/online/covcormoments_csr_3.csv",
"../data/online/covcormoments_csr_4.csv"
};
private static final int nBlocks = 4;
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Create an algorithm to compute PCA decomposition using the correlation method */
Online pcaAlgorithm = new Online(context, Float.class, Method.correlationDense);
com.intel.daal.algorithms.covariance.Online covarianceSparse
= new com.intel.daal.algorithms.covariance.Online(context, Float.class, com.intel.daal.algorithms.covariance.Method.fastCSR);
pcaAlgorithm.parameter.setCovariance(covarianceSparse);
for (int i = 0; i < nBlocks; i++) {
/* Read the input data from a file */
CSRNumericTable data = Service.createSparseTable(context, datasetFileNames[i]);
/* Set the input data */
pcaAlgorithm.input.set(InputId.data, data);
/* Compute partial estimates */
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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