com.linkedin.feathr.common.tensor.dense

Class DenseLongTensor

java.lang.Object

com.linkedin.feathr.common.tensor.DenseTensor

com.linkedin.feathr.common.tensor.dense.ByteBufferDenseTensor

com.linkedin.feathr.common.tensor.dense.DenseLongTensor

All Implemented Interfaces:

TensorData

public class DenseLongTensor
extends ByteBufferDenseTensor

Dense tensor which can be converted from/to a tensorflow tensor object. The underlying data is backed up by LongBuffer.

Field Summary

Fields inherited from class com.linkedin.feathr.common.tensor.dense.ByteBufferDenseTensor

_byteBuffer, _cardinality, _limit, _shape, _start, _types

Constructor Summary

Constructors

Constructor and Description
DenseLongTensor(java.nio.ByteBuffer byteBuffer, long[] shape)
DenseLongTensor(java.nio.ByteBuffer byteBuffer, long[] shape, Representable[] types, int cardinality)

Method Summary

Modifier and Type	Method and Description
java.util.List<?>	asList()
TensorIterator	iterator()

Methods inherited from class com.linkedin.feathr.common.tensor.dense.ByteBufferDenseTensor

cardinality, estimatedCardinality, getByteBuffer, getByteBuffer, getShape, getTypes

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface com.linkedin.feathr.common.tensor.TensorData

getArity, isEmpty

Constructor Detail

DenseLongTensor

public DenseLongTensor(java.nio.ByteBuffer byteBuffer,
                       long[] shape)

DenseLongTensor

public DenseLongTensor(java.nio.ByteBuffer byteBuffer,
                       long[] shape,
                       Representable[] types,
                       int cardinality)

Method Detail

iterator

public TensorIterator iterator()

Returns:

an iterator over entries in the mapping from dimensions to the value.

asList

public java.util.List<?> asList()

Specified by:

asList in class DenseTensor

Returns:

the list of values in their unraveled order. This in combination with the shape is enough to restore the entire tensor, as the dimensions can be calculated from the shape.