Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

AdagradDenseBatch.java

/* file: AdagradDenseBatch.java */
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/*
// Content:
// Java example of dense Adagrad in the batch processing mode
// processing mode
*/
package com.intel.daal.examples.optimization_solvers;
import com.intel.daal.algorithms.optimization_solver.adagrad.*;
import com.intel.daal.algorithms.optimization_solver.iterative_solver.InputId;
import com.intel.daal.algorithms.optimization_solver.iterative_solver.Result;
import com.intel.daal.algorithms.optimization_solver.iterative_solver.ResultId;
import com.intel.daal.data_management.data.HomogenNumericTable;
import com.intel.daal.data_management.data.NumericTable;
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 AdagradDenseBatch {
private static final long nFeatures = 3;
private static final double accuracyThreshold = 0.0000001;
private static final long nIterations = 1000;
private static final long batchSize = 1;
private static final double learningRate = 1;
private static double[] startPoint = {8, 2, 1, 4};
/* Input data set parameters */
private static final String dataFileName = "../data/batch/mse.csv";
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Retrieve the data from input data sets */
FileDataSource dataSource = new FileDataSource(context, dataFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
/* Create Numeric Tables for data and values for dependent variable */
NumericTable data = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.DoNotAllocate);
NumericTable dataDependents = new HomogenNumericTable(context, Float.class, 1, 0, NumericTable.AllocationFlag.DoNotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(data);
mergedData.addNumericTable(dataDependents);
/* Retrieve the data from an input file */
dataSource.loadDataBlock(mergedData);
/* Create an MSE objective function to compute a Adagrad */
com.intel.daal.algorithms.optimization_solver.mse.Batch mseFunction =
new com.intel.daal.algorithms.optimization_solver.mse.Batch(context, Float.class,
com.intel.daal.algorithms.optimization_solver.mse.Method.defaultDense, data.getNumberOfRows());
mseFunction.getInput().set(com.intel.daal.algorithms.optimization_solver.mse.InputId.data, data);
mseFunction.getInput().set(com.intel.daal.algorithms.optimization_solver.mse.InputId.dependentVariables, dataDependents);
/* Create algorithm objects to compute Adagrad results */
Batch adagradAlgorithm = new Batch(context, Float.class, Method.defaultDense);
adagradAlgorithm.parameter.setFunction(mseFunction);
adagradAlgorithm.parameter.setLearningRate(new HomogenNumericTable(context, Float.class, 1, 1, NumericTable.AllocationFlag.DoAllocate, learningRate));
adagradAlgorithm.parameter.setNIterations(nIterations);
adagradAlgorithm.parameter.setAccuracyThreshold(accuracyThreshold);
adagradAlgorithm.parameter.setBatchSize(batchSize);
adagradAlgorithm.input.set(InputId.inputArgument, new HomogenNumericTable(context, startPoint, 1, nFeatures + 1));
/* Compute the Adagrad result for MSE objective function matrix */
Result result = adagradAlgorithm.compute();
Service.printNumericTable("Minimum:", result.get(ResultId.minimum));
Service.printNumericTable("Number of iterations performed:", result.get(ResultId.nIterations));
context.dispose();
}
}

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