Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

DataStructuresHomogenTensor.java

/* file: DataStructuresHomogenTensor.java */
/*******************************************************************************
* Copyright 2014-2019 Intel Corporation.
*
* This software and the related documents are Intel copyrighted materials, and
* your use of them is governed by the express license under which they were
* provided to you (License). Unless the License provides otherwise, you may not
* use, modify, copy, publish, distribute, disclose or transmit this software or
* the related documents without Intel's prior written permission.
*
* This software and the related documents are provided as is, with no express
* or implied warranties, other than those that are expressly stated in the
* License.
*******************************************************************************/
/*
// Content:
// Java example of using homogeneous data structures
*/
package com.intel.daal.examples.datasource;
import java.nio.FloatBuffer;
import com.intel.daal.data_management.data.HomogenTensor;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;
class DataStructuresHomogenTensor {
private static DaalContext context = new DaalContext();
public static void main(String[] args) {
int readFeatureIdx;
float[] data = {1,2,3,4,5,6,7,8,9,11,12,13,14,15,16,17,18,19,21,22,23,24,25,26,27,28,29};
long[] dims = {3,3,3};
System.out.println("Initial data:");
for(int i= 0;i<dims[0]*dims[1]*dims[2];i++)
{
System.out.format("% 5.1f ", data[i]);
}
System.out.println("");
HomogenTensor dataTensor = new HomogenTensor(context, dims, data);
long fDims[] = {0,1};
FloatBuffer dataFloatSubtensor = FloatBuffer.allocate(2);
dataTensor.getSubtensor(fDims, 1, 2, dataFloatSubtensor);
int sub_demension = dataFloatSubtensor.arrayOffset() + 1;
int sub_size = dataFloatSubtensor.capacity();
System.out.format("Subtensor dimensions: % 5.1f\n", (float)sub_demension);
System.out.format("Subtensor size: % 5.1f\n", (float)sub_size);
System.out.println("Subtensor data:");
for(int i= 0;i<sub_size;i++)
{
System.out.format("% 5.1f ", dataFloatSubtensor.get(i));
}
System.out.println("");
dataFloatSubtensor.put(-1);
dataTensor.releaseSubtensor(fDims, 1, 2, dataFloatSubtensor);
System.out.println("Data after modification:");
for(int i= 0;i<dims[0]*dims[1]*dims[2];i++)
{
System.out.format("% 5.1f ", data[i]);
}
System.out.println("");
context.dispose();
}
private static void printFloatBuffer(FloatBuffer buf, long nColumns, long nRows, String message) {
int step = (int) nColumns;
System.out.println(message);
for (int i = 0; i < nRows; i++) {
for (int j = 0; j < nColumns; j++) {
System.out.format("%6.3f ", buf.get(i * step + j));
}
System.out.println("");
}
System.out.println("");
}
private static void printFloatArray(float[] buf, long nColumns, long nRows, String message) {
int step = (int) nColumns;
System.out.println(message);
for (int i = 0; i < nRows; i++) {
for (int j = 0; j < nColumns; j++) {
System.out.format("%6.3f ", buf[i * step + j]);
}
System.out.println("");
}
System.out.println("");
}
}

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