Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

KDTreeKNNDenseBatch.java

/* file: KDTreeKNNDenseBatch.java */
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/*
// Content:
// Java example of k nearest neighbors algorithm in the batch processing mode.
*/
package com.intel.daal.examples.kdtree_knn_classification;
import com.intel.daal.algorithms.kdtree_knn_classification.Model;
import com.intel.daal.algorithms.kdtree_knn_classification.prediction.*;
import com.intel.daal.algorithms.kdtree_knn_classification.training.*;
import com.intel.daal.algorithms.classifier.training.InputId;
import com.intel.daal.algorithms.classifier.training.TrainingResultId;
import com.intel.daal.algorithms.classifier.prediction.ModelInputId;
import com.intel.daal.algorithms.classifier.prediction.NumericTableInputId;
import com.intel.daal.algorithms.classifier.prediction.PredictionResultId;
import com.intel.daal.algorithms.classifier.prediction.PredictionResult;
import com.intel.daal.data_management.data.NumericTable;
import com.intel.daal.data_management.data.HomogenNumericTable;
import com.intel.daal.data_management.data.MergedNumericTable;
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 KDTreeKNNDenseBatch {
/* Input data set parameters */
private static final String trainDatasetFileName = "../data/batch/k_nearest_neighbors_train.csv";
private static final String testDatasetFileName = "../data/batch/k_nearest_neighbors_test.csv";
private static final int nFeatures = 5;
static Model model;
static NumericTable results;
static NumericTable testGroundTruth;
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
trainModel();
testModel();
printResults();
context.dispose();
}
private static void trainModel() {
/* Initialize FileDataSource to retrieve the input data from a .csv file */
FileDataSource trainDataSource = new FileDataSource(context, trainDatasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
/* Create Numeric Tables for training data and labels */
NumericTable trainData = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.DoNotAllocate);
NumericTable trainGroundTruth = new HomogenNumericTable(context, Float.class, 1, 0, NumericTable.AllocationFlag.DoNotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(trainData);
mergedData.addNumericTable(trainGroundTruth);
/* Retrieve the data from an input file */
trainDataSource.loadDataBlock(mergedData);
/* Create an algorithm object to train the k nearest neighbors model with the default dense method */
TrainingBatch kNearestNeighborsTrain = new TrainingBatch(context, Float.class, TrainingMethod.defaultDense);
kNearestNeighborsTrain.input.set(InputId.data, trainData);
kNearestNeighborsTrain.input.set(InputId.labels, trainGroundTruth);
/* Build the k nearest neighbors model */
TrainingResult trainingResult = kNearestNeighborsTrain.compute();
model = trainingResult.get(TrainingResultId.model);
}
private static void testModel() {
/* Initialize FileDataSource to retrieve the input data from a .csv file */
FileDataSource testDataSource = new FileDataSource(context, testDatasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
/* Create Numeric Tables for testing data and labels */
NumericTable testData = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.DoNotAllocate);
testGroundTruth = new HomogenNumericTable(context, Float.class, 1, 0, NumericTable.AllocationFlag.DoNotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(testData);
mergedData.addNumericTable(testGroundTruth);
/* Retrieve the data from an input file */
testDataSource.loadDataBlock(mergedData);
/* Create algorithm objects to predict values of k nearest neighbors with the default method */
PredictionBatch kNearestNeighborsPredict = new PredictionBatch(context, Float.class,
PredictionMethod.defaultDense);
kNearestNeighborsPredict.input.set(NumericTableInputId.data, testData);
kNearestNeighborsPredict.input.set(ModelInputId.model, model);
/* Compute prediction results */
PredictionResult predictionResult = kNearestNeighborsPredict.compute();
results = predictionResult.get(PredictionResultId.prediction);
}
private static void printResults() {
NumericTable expected = testGroundTruth;
Service.printClassificationResult(expected,results,"Ground truth","Classification results","KD-tree based kNN classification results (first 20 observations):",20);
System.out.println("");
}
}

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