Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

UniformDenseBatch.java

/* file: UniformDenseBatch.java */
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/*
// Content:
// Java example of uniform distribution
*/
package com.intel.daal.examples.distributions;
import com.intel.daal.algorithms.distributions.*;
import com.intel.daal.algorithms.distributions.uniform.*;
import com.intel.daal.data_management.data.NumericTable;
import com.intel.daal.data_management.data.HomogenNumericTable;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;
class UniformDenseBatch {
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Create input table to fill with random numbers */
HomogenNumericTable dataTable = new HomogenNumericTable(context, Float.class, 1, 10, NumericTable.AllocationFlag.DoAllocate);
/* Create the algorithm */
Batch uniform = new Batch(context, Float.class, Method.defaultDense, 0.0, 1.0);
/* Set the algorithm input */
uniform.input.set(InputId.tableToFill, dataTable);
/* Set the Mersenne Twister engine to the distribution */
com.intel.daal.algorithms.engines.mt19937.Batch eng = new com.intel.daal.algorithms.engines.mt19937.Batch(context, Float.class, com.intel.daal.algorithms.engines.mt19937.Method.defaultDense, 777);
uniform.parameter.setEngine(eng);
/* Perform computations */
uniform.compute();
/* Print the results */
Service.printNumericTable("Uniform distribution output:", dataTable);
context.dispose();
}
}

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