C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

pca_cor_csr_online.cpp

/* file: pca_cor_csr_online.cpp */
/*******************************************************************************
* Copyright 2014-2017 Intel Corporation
* All Rights Reserved.
*
* If this software was obtained under the Intel Simplified Software License,
* the following terms apply:
*
* The source code, information and material ("Material") contained herein is
* owned by Intel Corporation or its suppliers or licensors, and title to such
* Material remains with Intel Corporation or its suppliers or licensors. The
* Material contains proprietary information of Intel or its suppliers and
* licensors. The Material is protected by worldwide copyright laws and treaty
* provisions. No part of the Material may be used, copied, reproduced,
* modified, published, uploaded, posted, transmitted, distributed or disclosed
* in any way without Intel's prior express written permission. No license under
* any patent, copyright or other intellectual property rights in the Material
* is granted to or conferred upon you, either expressly, by implication,
* inducement, estoppel or otherwise. Any license under such intellectual
* property rights must be express and approved by Intel in writing.
*
* Unless otherwise agreed by Intel in writing, you may not remove or alter this
* notice or any other notice embedded in Materials by Intel or Intel's
* suppliers or licensors in any way.
*
*
* If this software was obtained under the Apache License, Version 2.0 (the
* "License"), the following terms apply:
*
* You may not use this file except in compliance with the License. You may
* obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
*
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/
/*
! Content:
! C++ example of principal component analysis (PCA) using the correlation
! method in the online 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 */
const size_t nBlocks = 4;
const string datasetFileNames[] =
{
"../data/online/covcormoments_csr_1.csv",
"../data/online/covcormoments_csr_2.csv",
"../data/online/covcormoments_csr_3.csv",
"../data/online/covcormoments_csr_4.csv"
};
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 4, &datasetFileNames[0], &datasetFileNames[1], &datasetFileNames[2], &datasetFileNames[3]);
/* Create an algorithm for principal component analysis using the correlation method */
pca::Online<> algorithm;
/* Use covariance algorithm for sparse data inside the PCA algorithm */
algorithm.parameter.covariance = services::SharedPtr<covariance::Online<algorithmFPType, covariance::fastCSR> >
(new covariance::Online<algorithmFPType, covariance::fastCSR>());
for(size_t i = 0; i < nBlocks; i++)
{
/* Read data from a file and create a numeric table to store input data */
CSRNumericTablePtr dataTable(createSparseTable<float>(datasetFileNames[i]));
/* Set input objects for the algorithm */
algorithm.input.set(pca::data, CSRNumericTablePtr(dataTable));
/* Update PCA decomposition */
algorithm.compute();
}
/* Finalize computations */
algorithm.finalizeCompute();
/* Print the results */
pca::ResultPtr result = algorithm.getResult();
printNumericTable(result->get(pca::eigenvalues), "Eigenvalues:");
printNumericTable(result->get(pca::eigenvectors), "Eigenvectors:");
return 0;
}

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