Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

PCAMetricsDenseBatch.java

/* file: PCAMetricsDenseBatch.java */
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/*
// Content:
// Java example of PCA quality metrics
*/
package com.intel.daal.examples.quality_metrics;
import java.nio.DoubleBuffer;
import com.intel.daal.data_management.data.HomogenNumericTable;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;
import com.intel.daal.algorithms.pca.*;
import com.intel.daal.algorithms.pca.quality_metric.*;
import com.intel.daal.algorithms.pca.quality_metric_set.*;
import com.intel.daal.data_management.data.DataCollection;
import com.intel.daal.data_management.data_source.DataSource;
import com.intel.daal.data_management.data_source.FileDataSource;
class PCAMetricsDenseBatch {
/* Input data set parameters */
private static final String trainDatasetFileName = "../data/batch/pca_normalized.csv";
private static final long nVectors = 1000;
private static final long nComponents = 5;
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Initialize FileDataSource to retrieve the input data from a .csv file */
FileDataSource dataSource = new FileDataSource(context, trainDatasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
/* Retrieve the data from an input file */
dataSource.loadDataBlock(nVectors);
/* Create an algorithm for principal component analysis using the SVD method */
Batch pca = new Batch(context, Float.class, Method.svdDense);
/* Set the algorithm input data */
pca.input.set(InputId.data, dataSource.getNumericTable());
/* Compute results of the PCA algorithm */
Result pcaResult = pca.compute();
/* Create a quality metrics algorithm for explained variances, explained variances ratios and noise_variance */
QualityMetricSetBatch qms = new QualityMetricSetBatch(context, nComponents, 0);
ExplainedVarianceInput varianceMetrics = (ExplainedVarianceInput)qms.getInputDataCollection().getInput(QualityMetricId.explainedVariancesMetrics);
varianceMetrics.set(ExplainedVarianceInputId.eigenValues, pcaResult.get(ResultId.eigenValues));
/* Compute quality metrics of the PCA algorithm */
ResultCollection res = qms.compute();
/* Output quality metrics of the PCA algorithm */
ExplainedVarianceResult qmsResult = (ExplainedVarianceResult)res.getResult(QualityMetricId.explainedVariancesMetrics);
Service.printNumericTable("Explained variances:", qmsResult.get(ExplainedVarianceResultId.explainedVariances));
Service.printNumericTable("Explained variance ratios:", qmsResult.get(ExplainedVarianceResultId.explainedVariancesRatios));
Service.printNumericTable("Noise variance:", qmsResult.get(ExplainedVarianceResultId.noiseVariance));
context.dispose();
}
}

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