C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

kdtree_knn_dense_batch.cpp

/* file: kdtree_knn_dense_batch.cpp */
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/*
! Content:
! C++ example of k-Nearest Neighbor in the batch processing mode.
!******************************************************************************/
#include "daal.h"
#include "service.h"
#include <cstdio>
using namespace std;
using namespace daal;
using namespace daal::algorithms;
/* Input data set parameters */
string trainDatasetFileName = "../data/batch/k_nearest_neighbors_train.csv";
string testDatasetFileName = "../data/batch/k_nearest_neighbors_test.csv";
size_t nFeatures = 5;
kdtree_knn_classification::training::ResultPtr trainingResult;
classifier::prediction::ResultPtr predictionResult;
NumericTablePtr testGroundTruth;
void trainModel();
void testModel();
void printResults();
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 2, &trainDatasetFileName, &testDatasetFileName);
trainModel();
testModel();
printResults();
return 0;
}
void trainModel()
{
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
FileDataSource<CSVFeatureManager> trainDataSource(trainDatasetFileName,
DataSource::notAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Create Numeric Tables for training data and labels */
NumericTablePtr trainData(new HomogenNumericTable<>(nFeatures, 0, NumericTable::doNotAllocate));
NumericTablePtr trainGroundTruth(new HomogenNumericTable<>(1, 0, NumericTable::doNotAllocate));
NumericTablePtr mergedData(new MergedNumericTable(trainData, trainGroundTruth));
/* Retrieve the data from the input file */
trainDataSource.loadDataBlock(mergedData.get());
/* Create an algorithm object to train the KD-tree based kNN model */
kdtree_knn_classification::training::Batch<> algorithm;
/* Pass the training data set and dependent values to the algorithm */
algorithm.input.set(classifier::training::data, trainData);
algorithm.input.set(classifier::training::labels, trainGroundTruth);
/* Train the KD-tree based kNN model */
algorithm.compute();
/* Retrieve the results of the training algorithm */
trainingResult = algorithm.getResult();
}
void testModel()
{
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file */
FileDataSource<CSVFeatureManager> testDataSource(testDatasetFileName,
DataSource::notAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Create Numeric Tables for testing data and labels */
NumericTablePtr testData(new HomogenNumericTable<>(nFeatures, 0, NumericTable::doNotAllocate));
testGroundTruth = NumericTablePtr(new HomogenNumericTable<>(1, 0, NumericTable::doNotAllocate));
NumericTablePtr mergedData(new MergedNumericTable(testData, testGroundTruth));
/* Retrieve the data from input file */
testDataSource.loadDataBlock(mergedData.get());
/* Create algorithm objects for KD-tree based kNN prediction with the default method */
kdtree_knn_classification::prediction::Batch<> algorithm;
/* Pass the testing data set and trained model to the algorithm */
algorithm.input.set(classifier::prediction::data, testData);
algorithm.input.set(classifier::prediction::model, trainingResult->get(classifier::training::model));
/* Compute prediction results */
algorithm.compute();
/* Retrieve algorithm results */
predictionResult = algorithm.getResult();
}
void printResults()
{
printNumericTables<int, int>(testGroundTruth,
predictionResult->get(classifier::prediction::prediction),
"Ground truth", "Classification results",
"KD-tree based kNN classification results (first 20 observations):", 20);
}

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