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

LBFGSDenseBatch.java

/* file: LBFGSDenseBatch.java */
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/*
// Content:
// Java example of dense LBFGS algorithm in the batch
// processing mode
*/
package com.intel.daal.examples.optimization_solvers;
import com.intel.daal.algorithms.optimization_solver.lbfgs.*;
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.MergedNumericTable;
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 LBFGSDenseBatch {
private static final long nFeatures = 10;
private static final long nIterations = 1000;
private static final double stepLength = 1.0e-4;
private static double[] initialPoint = {100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100};
private static double[] expectedPoint = { 11, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
private static final String datasetFileName = "../data/batch/lbfgs.csv";
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, datasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
/* Create Numeric Tables for input data and dependent variables */
NumericTable data = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.DoNotAllocate);
NumericTable dependentVariables = new HomogenNumericTable(context, Float.class, 1, 0,
NumericTable.AllocationFlag.DoNotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(data);
mergedData.addNumericTable(dependentVariables);
/* Retrieve the data from input file */
dataSource.loadDataBlock(mergedData);
/* Create an MSE objective function for LBFGS */
com.intel.daal.algorithms.optimization_solver.mse.Batch mseObjectiveFunction =
new com.intel.daal.algorithms.optimization_solver.mse.Batch(context, Float.class,
com.intel.daal.algorithms.optimization_solver.mse.Method.defaultDense, data.getNumberOfRows());
mseObjectiveFunction.getInput().set(com.intel.daal.algorithms.optimization_solver.mse.InputId.data, data);
mseObjectiveFunction.getInput().set(com.intel.daal.algorithms.optimization_solver.mse.InputId.dependentVariables,
dependentVariables);
/* Create objects to compute LBFGS result using the default method */
Batch algorithm = new Batch(context, Float.class, Method.defaultDense);
algorithm.parameter.setFunction(mseObjectiveFunction);
algorithm.parameter.setNIterations(nIterations);
algorithm.parameter.setStepLengthSequence(
new HomogenNumericTable(context, Float.class, 1, 1, NumericTable.AllocationFlag.DoAllocate, stepLength));
algorithm.input.set(InputId.inputArgument, new HomogenNumericTable(context, initialPoint, 1, nFeatures + 1));
/* Compute LBFGS result */
Result result = algorithm.compute();
NumericTable expected = new HomogenNumericTable(context, expectedPoint, 1, nFeatures + 1);
Service.printNumericTable("Expected coefficients:", expected);
Service.printNumericTable("Resulting coefficients:", result.get(ResultId.minimum));
Service.printNumericTable("Number of iterations performed:", result.get(ResultId.nIterations));
}
}

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