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

EmGmmDenseBatch.java

/* file: EmGmmDenseBatch.java */
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/*
// Content:
// Java example of the expectation-maximization (EM) algorithm for the
// Gaussian mixture model (GMM)
*/
package com.intel.daal.examples.em;
import com.intel.daal.algorithms.em_gmm.*;
import com.intel.daal.algorithms.em_gmm.init.*;
import com.intel.daal.data_management.data.NumericTable;
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 EmGmmDenseBatch {
/* Input data set parameters */
private static final String dataset = "../data/batch/em_gmm.csv";
private static final int nComponents = 2;
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Retrieve the input data */
FileDataSource dataSource = new FileDataSource(context, dataset,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
dataSource.loadDataBlock();
NumericTable input = dataSource.getNumericTable();
/* Create an algorithm to initialize the EM algorithm for the GMM */
InitBatch initAlgorithm = new InitBatch(context, Float.class, InitMethod.defaultDense, nComponents);
/* Set an input object for the initialization algorithm */
initAlgorithm.input.set(InitInputId.data, input);
InitResult initResult = initAlgorithm.compute();
/* Create an algorithm for EM clustering */
Batch algorithm = new Batch(context, Float.class, Method.defaultDense, nComponents);
/* Set an input object for the algorithm */
algorithm.input.set(InputId.data, input);
algorithm.input.set(InputValuesId.inputValues, initResult);
/* Clusterize the data */
Result result = algorithm.compute();
NumericTable means = result.get(ResultId.means);
NumericTable weights = result.get(ResultId.weights);
/* Print the results */
Service.printNumericTable("Weights", weights);
Service.printNumericTable("Means", means);
for (int i = 0; i < nComponents; i++) {
NumericTable covariance = result.get(ResultCovariancesId.covariances, i);
Service.printNumericTable("Covariance", covariance);
}
context.dispose();
}
}

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