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

PCASVDDenseDistr.java

/* file: PCASVDDenseDistr.java */
/*******************************************************************************
* Copyright 2014-2018 Intel Corporation.
*
* This software and the related documents are Intel copyrighted materials, and
* your use of them is governed by the express license under which they were
* provided to you (License). Unless the License provides otherwise, you may not
* use, modify, copy, publish, distribute, disclose or transmit this software or
* the related documents without Intel's prior written permission.
*
* This software and the related documents are provided as is, with no express
* or implied warranties, other than those that are expressly stated in the
* License.
*******************************************************************************/
/*
// Content:
// Java example of principal component analysis (PCA) using the singular
// value decomposition (SVD) method in the distributed processing mode
*/
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.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_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 PCASVDDenseDistr {
/* Input data set parameters */
private static final String[] dataset = { "../data/distributed/pca_normalized_1.csv",
"../data/distributed/pca_normalized_2.csv", "../data/distributed/pca_normalized_3.csv",
"../data/distributed/pca_normalized_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++) {
/* Initialize FileDataSource to retrieve the input data from a .csv file */
FileDataSource dataSource = new FileDataSource(context, dataset[i],
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
/* Retrieve the data from the input file */
dataSource.loadDataBlock();
/* Create an algorithm to compute PCA decomposition using the SVD method on local nodes*/
DistributedStep1Local pcaLocal = new DistributedStep1Local(context, Float.class, Method.svdDense);
/* Set the input data on local nodes */
NumericTable data = dataSource.getNumericTable();
pcaLocal.input.set(InputId.data, data);
/* Compute PCA on local nodes */
pres[i] = pcaLocal.compute();
}
/* Create an algorithm to compute PCA decomposition using the SVD method on the master node */
DistributedStep2Master pcaMaster = new DistributedStep2Master(context, Float.class, Method.svdDense);
/* Add partial results computed on local nodes to the algorithm on the master node */
for (int i = 0; i < nNodes; i++) {
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();
}
}

For more complete information about compiler optimizations, see our Optimization Notice.