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

PCACorCSRDistr.java

/* file: PCACorCSRDistr.java */
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/*
// Content:
// Java example of principal component analysis (PCA) using the correlation
// method in the distributed processing mode for sparse data
*/
package com.intel.daal.examples.pca;
import com.intel.daal.algorithms.PartialResult;
import com.intel.daal.algorithms.pca.DistributedStep1Local;
import com.intel.daal.algorithms.pca.DistributedStep2Master;
import com.intel.daal.algorithms.pca.PartialCorrelationResult;
import com.intel.daal.algorithms.pca.PartialCorrelationResultID;
import com.intel.daal.algorithms.pca.InputId;
import com.intel.daal.algorithms.pca.MasterInputId;
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.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 PCACorCSRDistr {
/* Input data set parameters */
private static final String datasetFileNames[] = new String[] { "../data/distributed/covcormoments_csr_1.csv",
"../data/distributed/covcormoments_csr_2.csv",
"../data/distributed/covcormoments_csr_3.csv",
"../data/distributed/covcormoments_csr_4.csv"
};
private static final int nNodes = 4;
private static PartialResult[] pres = new PartialResult[nNodes];
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
for (int i = 0; i < nNodes; i++) {
DaalContext localContext = new DaalContext();
/* Read the input data from a file */
CSRNumericTable data = Service.createSparseTable(localContext, datasetFileNames[i]);
/* Create an algorithm to compute PCA decomposition using the correlation method on local nodes */
DistributedStep1Local pcaLocal = new DistributedStep1Local(localContext, Float.class,
Method.correlationDense);
com.intel.daal.algorithms.covariance.DistributedStep1Local covarianceSparse
= new com.intel.daal.algorithms.covariance.DistributedStep1Local(localContext, Float.class,
com.intel.daal.algorithms.covariance.Method.fastCSR);
pcaLocal.parameter.setCovariance(covarianceSparse);
/* Set the input data on local nodes */
pcaLocal.input.set(InputId.data, data);
/* Compute PCA decomposition on local nodes */
pres[i] = pcaLocal.compute();
pres[i].pack();
localContext.dispose();
}
/* Create an algorithm to compute PCA decomposition using the correlation method on the master node */
DistributedStep2Master pcaMaster = new DistributedStep2Master(context, Float.class, Method.correlationDense);
com.intel.daal.algorithms.covariance.DistributedStep2Master covarianceSparse
= new com.intel.daal.algorithms.covariance.DistributedStep2Master(context, Float.class,
com.intel.daal.algorithms.covariance.Method.fastCSR);
pcaMaster.parameter.setCovariance(covarianceSparse);
/* Add partial results computed on local nodes to the algorithm on the master node */
for (int i = 0; i < nNodes; i++) {
pres[i].unpack(context);
pcaMaster.input.add(MasterInputId.partialResults, pres[i]);
}
/* Compute PCA decomposition on the master node */
pcaMaster.compute();
/* Finalize computations and retrieve the results */
Result res = pcaMaster.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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