C++ API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

kmeans_csr_batch.cpp

/* file: kmeans_csr_batch.cpp */
/*******************************************************************************
* Copyright 2014-2019 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:
! C++ example of sparse K-Means clustering in the batch processing mode
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms;
typedef float algorithmFPType; /* Algorithm floating-point type */
/* Input data set parameters */
string datasetFileName = "../data/batch/kmeans_csr.csv";
/* K-Means algorithm parameters */
const size_t nClusters = 20;
const size_t nIterations = 5;
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 1, &datasetFileName);
/* Retrieve the data from the input file */
CSRNumericTablePtr dataTable(createSparseTable<float>(datasetFileName));
/* Get initial clusters for the K-Means algorithm */
kmeans::init::Batch<algorithmFPType, kmeans::init::randomCSR> init(nClusters);
init.input.set(kmeans::init::data, dataTable);
init.compute();
NumericTablePtr centroids = init.getResult()->get(kmeans::init::centroids);
/* Create an algorithm object for the K-Means algorithm */
kmeans::Batch<algorithmFPType, kmeans::lloydCSR> algorithm(nClusters, nIterations);
algorithm.input.set(kmeans::data, dataTable);
algorithm.input.set(kmeans::inputCentroids, centroids);
algorithm.compute();
/* Print the clusterization results */
printNumericTable(algorithm.getResult()->get(kmeans::assignments), "First 10 cluster assignments:", 10);
printNumericTable(algorithm.getResult()->get(kmeans::centroids ), "First 10 dimensions of centroids:", 20, 10);
printNumericTable(algorithm.getResult()->get(kmeans::objectiveFunction), "Objective function value:");
return 0;
}

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