Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

PCACorCSRBatch.java

/* file: PCACorCSRBatch.java */
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/*
// Content:
// Java example of principal component analysis (PCA) using the correlation
// method in the batch processing mode for sparse data
*/
package com.intel.daal.examples.pca;
import com.intel.daal.algorithms.pca.Batch;
import com.intel.daal.algorithms.pca.InputId;
import com.intel.daal.algorithms.pca.Method;
import com.intel.daal.algorithms.pca.Result;
import com.intel.daal.algorithms.pca.ResultId;
import com.intel.daal.algorithms.pca.ResultsToComputeId;
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 PCACorCSRBatch {
/* Input data set parameters */
private static DaalContext context = new DaalContext();
private static final String datasetFileName = "../data/batch/covcormoments_csr.csv";
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Read a data set from a file and create a numeric table for storing the input data */
CSRNumericTable data = Service.createSparseTable(context, datasetFileName);
/* Create an algorithm to compute PCA decomposition using the correlation method */
Batch pcaAlgorithm = new Batch(context, Float.class, Method.correlationDense);
com.intel.daal.algorithms.covariance.Batch covarianceSparse
= new com.intel.daal.algorithms.covariance.Batch(context, Float.class, com.intel.daal.algorithms.covariance.Method.fastCSR);
pcaAlgorithm.parameter.setCovariance(covarianceSparse);
pcaAlgorithm.parameter.setResultsToCompute(ResultsToComputeId.mean | ResultsToComputeId.variance | ResultsToComputeId.eigenvalue);
pcaAlgorithm.parameter.setIsDeterministic(true);
/* Set the input data */
pcaAlgorithm.input.set(InputId.data, data);
/* Compute PCA decomposition */
Result res = pcaAlgorithm.compute();
NumericTable eigenValues = res.get(ResultId.eigenValues);
NumericTable eigenVectors = res.get(ResultId.eigenVectors);
NumericTable means = res.get(ResultId.means);
NumericTable variances = res.get(ResultId.variances);
Service.printNumericTable("Eigenvalues:", eigenValues);
Service.printNumericTable("Eigenvectors:", eigenVectors);
Service.printNumericTable("Means:", means);
Service.printNumericTable("Variances:", variances);
context.dispose();
}
}

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