本文整理了Java中org.nd4j.linalg.factory.Nd4j.getDataBufferFactory()方法的一些代码示例，展示了Nd4j.getDataBufferFactory()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Nd4j.getDataBufferFactory()方法的具体详情如下：
包路径：org.nd4j.linalg.factory.Nd4j
类名称：Nd4j
方法名：getDataBufferFactory

Nd4j.getDataBufferFactory介绍

暂无

代码示例

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Return the minor pointers. (columns for CSR, rows for CSC,...)
 * */
public DataBuffer getVectorCoordinates() {
  return Nd4j.getDataBufferFactory().create(columnsPointers, 0, length());
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public DataBuffer data() {
  return Nd4j.getDataBufferFactory().create(values, 0, length());
}

代码示例来源：origin: deeplearning4j/nd4j

public DataBuffer getPointerE() {
  return Nd4j.getDataBufferFactory().create(pointerE, 0, rows());
}

代码示例来源：origin: deeplearning4j/nd4j

DataBuffer dataBuffer = Nd4j.getDataBufferFactory().createInt(result);
return dataBuffer;

代码示例来源：origin: deeplearning4j/nd4j

public DataBuffer getPointerB() {
  return Nd4j.getDataBufferFactory().create(pointerB, 0, rows());
}

代码示例来源：origin: deeplearning4j/nd4j

public BaseSparseNDArrayCSR(DataBuffer data, int[] columnsPointers, int[] pointerB, int[] pointerE, int[] shape) {
  checkArgument(pointerB.length == pointerE.length);
  setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(shape));
  init(shape);
  this.values = data;
  this.columnsPointers = Nd4j.getDataBufferFactory().createInt(data.length());
  this.columnsPointers.setData(columnsPointers);
  this.length = columnsPointers.length;
  // The size of these pointers are constant
  int pointersSpace = rows;
  this.pointerB = Nd4j.getDataBufferFactory().createInt(pointersSpace);
  this.pointerB.setData(pointerB);
  this.pointerE = Nd4j.getDataBufferFactory().createInt(pointersSpace);
  this.pointerE.setData(pointerE);
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray bitmapEncode(INDArray indArray, double threshold) {
  DataBuffer buffer = Nd4j.getDataBufferFactory().createInt(indArray.length() / 16 + 5);
  INDArray ret = Nd4j.createArrayFromShapeBuffer(buffer, indArray.shapeInfoDataBuffer());
  bitmapEncode(indArray, ret, threshold);
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

init(shape);
int valuesSpace = (int) (data.length * THRESHOLD_MEMORY_ALLOCATION);
this.values = Nd4j.getDataBufferFactory().createDouble(valuesSpace);
this.values.setData(data);
this.columnsPointers = Nd4j.getDataBufferFactory().createInt(valuesSpace);
this.columnsPointers.setData(columnsPointers);
this.length = columnsPointers.length;
int pointersSpace = rows;
this.pointerB = Nd4j.getDataBufferFactory().createInt(pointersSpace);
this.pointerB.setData(pointerB);
this.pointerE = Nd4j.getDataBufferFactory().createInt(pointersSpace);
this.pointerE.setData(pointerE);

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Returns the underlying indices of the element of the given index
 * such as there really are in the original ndarray
 *
 * @param i the index of the element+
 * @return a dataBuffer containing the indices of element
 * */
public DataBuffer getUnderlyingIndicesOf(int i) {
  int from = underlyingRank() * i;
  //int to = from + underlyingRank();
  int[] res = new int[underlyingRank()];
  for(int j = 0; j< underlyingRank(); j++){
    res[j] = indices.getInt(from + j);
  }
  ///int[] arr = Arrays.copyOfRange(indices.asInt(), from, to);
  return Nd4j.getDataBufferFactory().createInt(res);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Returns the indices of the element of the given index in the array context
 *
 * @param i the index of the element
 * @return a dataBuffer containing the indices of element
 * */
public DataBuffer getIndicesOf(int i) {
  int from = underlyingRank() * i;
  int to = from + underlyingRank(); //not included
  int[] arr = new int[rank];
  int j = 0; // iterator over underlying indices
  int k = 0; //iterator over hiddenIdx
  for (int dim = 0; dim < rank; dim++) {
    if (k < hiddenDimensions().length && hiddenDimensions()[k] == j) {
      arr[dim] = 0;
      k++;
    } else {
      arr[dim] = indices.getInt(j);
      j++;
    }
  }
  return Nd4j.getDataBufferFactory().createInt(arr);
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray getrf(INDArray A) {
  // FIXME: int cast
  if (A.rows() > Integer.MAX_VALUE || A.columns() > Integer.MAX_VALUE)
    throw new ND4JArraySizeException();
  int m = (int) A.rows();
  int n = (int) A.columns();
  INDArray INFO = Nd4j.createArrayFromShapeBuffer(Nd4j.getDataBufferFactory().createInt(1),
          Nd4j.getShapeInfoProvider().createShapeInformation(new int[] {1, 1}).getFirst());
  int mn = Math.min(m, n);
  INDArray IPIV = Nd4j.createArrayFromShapeBuffer(Nd4j.getDataBufferFactory().createInt(mn),
          Nd4j.getShapeInfoProvider().createShapeInformation(new int[] {1, mn}).getFirst());
  if (A.data().dataType() == DataBuffer.Type.DOUBLE)
    dgetrf(m, n, A, IPIV, INFO);
  else if (A.data().dataType() == DataBuffer.Type.FLOAT)
    sgetrf(m, n, A, IPIV, INFO);
  else
    throw new UnsupportedOperationException();
  if (INFO.getInt(0) < 0) {
    throw new Error("Parameter #" + INFO.getInt(0) + " to getrf() was not valid");
  } else if (INFO.getInt(0) > 0) {
    log.warn("The matrix is singular - cannot be used for inverse op. Check L matrix at row " + INFO.getInt(0));
  }
  return IPIV;
}

代码示例来源：origin: deeplearning4j/nd4j

public NativeRandom(long seed, long numberOfElements) {
  this.amplifier = seed;
  this.generation = 1;
  this.seed = seed;
  this.numberOfElements = numberOfElements;
  nativeOps = NativeOpsHolder.getInstance().getDeviceNativeOps();
  stateBuffer = Nd4j.getDataBufferFactory().createDouble(numberOfElements);
  init();
  hostPointer = new LongPointer(stateBuffer.addressPointer());
  deallocator = NativeRandomDeallocator.getInstance();
  pack = new NativePack(statePointer.address(), statePointer);
  deallocator.trackStatePointer(pack);
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public void potrf(INDArray A, boolean lower) {
  // FIXME: int cast
  if (A.columns() > Integer.MAX_VALUE)
    throw new ND4JArraySizeException();
  byte uplo = (byte) (lower ? 'L' : 'U'); // upper or lower part of the factor desired ?
  int n = (int) A.columns();
  INDArray INFO = Nd4j.createArrayFromShapeBuffer(Nd4j.getDataBufferFactory().createInt(1),
          Nd4j.getShapeInfoProvider().createShapeInformation(new int[] {1, 1}).getFirst());
  if (A.data().dataType() == DataBuffer.Type.DOUBLE)
    dpotrf(uplo, n, A, INFO);
  else if (A.data().dataType() == DataBuffer.Type.FLOAT)
    spotrf(uplo, n, A, INFO);
  else
    throw new UnsupportedOperationException();
  if (INFO.getInt(0) < 0) {
    throw new Error("Parameter #" + INFO.getInt(0) + " to potrf() was not valid");
  } else if (INFO.getInt(0) > 0) {
    throw new Error("The matrix is not positive definite! (potrf fails @ order " + INFO.getInt(0) + ")");
  }
  return;
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public void gesvd(INDArray A, INDArray S, INDArray U, INDArray VT) {
  // FIXME: int cast
  if (A.rows() > Integer.MAX_VALUE || A.columns() > Integer.MAX_VALUE)
    throw new ND4JArraySizeException();
  int m = (int) A.rows();
  int n = (int) A.columns();
  byte jobu = (byte) (U == null ? 'N' : 'A');
  byte jobvt = (byte) (VT == null ? 'N' : 'A');
  INDArray INFO = Nd4j.createArrayFromShapeBuffer(Nd4j.getDataBufferFactory().createInt(1),
          Nd4j.getShapeInfoProvider().createShapeInformation(new int[] {1, 1}).getFirst());
  if (A.data().dataType() == DataBuffer.Type.DOUBLE)
    dgesvd(jobu, jobvt, m, n, A, S, U, VT, INFO);
  else if (A.data().dataType() == DataBuffer.Type.FLOAT)
    sgesvd(jobu, jobvt, m, n, A, S, U, VT, INFO);
  else
    throw new UnsupportedOperationException();
  if (INFO.getInt(0) < 0) {
    throw new Error("Parameter #" + INFO.getInt(0) + " to gesvd() was not valid");
  } else if (INFO.getInt(0) > 0) {
    log.warn("The matrix contains singular elements. Check S matrix at row " + INFO.getInt(0));
  }
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public void geqrf(INDArray A, INDArray R) {
  // FIXME: int cast
  if (A.rows() > Integer.MAX_VALUE || A.columns() > Integer.MAX_VALUE)
    throw new ND4JArraySizeException();
  int m = (int) A.rows();
  int n = (int) A.columns();
  INDArray INFO = Nd4j.createArrayFromShapeBuffer(Nd4j.getDataBufferFactory().createInt(1),
          Nd4j.getShapeInfoProvider().createShapeInformation(new int[] {1, 1}).getFirst());
  if (R.rows() != A.columns() || R.columns() != A.columns()) {
    throw new Error("geqrf: R must be N x N (n = columns in A)");
  }
  if (A.data().dataType() == DataBuffer.Type.DOUBLE) {
    dgeqrf(m, n, A, R, INFO);
  } else if (A.data().dataType() == DataBuffer.Type.FLOAT) {
    sgeqrf(m, n, A, R, INFO);
  } else {
    throw new UnsupportedOperationException();
  }
  if (INFO.getInt(0) < 0) {
    throw new Error("Parameter #" + INFO.getInt(0) + " to getrf() was not valid");
  }
}

代码示例来源：origin: deeplearning4j/nd4j

public static Cloner newCloner() {
  Cloner cloner = new Cloner();
  //Implement custom cloning for INDArrays (default can have problems with off-heap and pointers)
  //Sadly: the cloner library does NOT support interfaces here, hence we need to use the actual classes
  //cloner.registerFastCloner(INDArray.class, new INDArrayFastCloner());  //Does not work due to interface
  IFastCloner fc = new INDArrayFastCloner();
  cloner.registerFastCloner(Nd4j.getBackend().getNDArrayClass(), fc);
  cloner.registerFastCloner(Nd4j.getBackend().getComplexNDArrayClass(), fc);
  //Same thing with DataBuffers: off heap -> cloner library chokes on them, but need to know the concrete
  // buffer classes, not just the interface
  IFastCloner fc2 = new DataBufferFastCloner();
  DataBufferFactory d = Nd4j.getDataBufferFactory();
  doReg(cloner, fc2, d.intBufferClass());
  doReg(cloner, fc2, d.longBufferClass());
  doReg(cloner, fc2, d.halfBufferClass());
  doReg(cloner, fc2, d.floatBufferClass());
  doReg(cloner, fc2, d.doubleBufferClass());
  doReg(cloner, fc2, CompressedDataBuffer.class);
  return cloner;
}

代码示例来源：origin: org.nd4j/nd4j-cuda-7.5

protected <T extends Aggregate> DataBuffer getBuffer(Batch<T> batch) {
  DataBuffer buffer = Nd4j.getDataBufferFactory().createInt(batch.getSample().getRequiredBatchMemorySize() * 4,
      false);
  batch.setParamsSurface(buffer);
  return buffer;
}

代码示例来源：origin: org.nd4j/nd4j-cuda-10.0

protected <T extends Aggregate> DataBuffer getBuffer(Batch<T> batch) {
  DataBuffer buffer = Nd4j.getDataBufferFactory().createInt(batch.getSample().getRequiredBatchMemorySize() * 4,
      false);
  batch.setParamsSurface(buffer);
  return buffer;
}

代码示例来源：origin: org.nd4j/nd4j-api

@Override
public INDArray bitmapEncode(INDArray indArray, double threshold) {
  DataBuffer buffer = Nd4j.getDataBufferFactory().createInt(indArray.length() / 16 + 5);
  INDArray ret = Nd4j.createArrayFromShapeBuffer(buffer, indArray.shapeInfoDataBuffer());
  bitmapEncode(indArray, ret, threshold);
  return ret;
}

代码示例来源：origin: org.nd4j/nd4j-native-api

public NativeRandom(long seed, long numberOfElements) {
  this.amplifier = seed;
  this.generation = 1;
  this.seed = seed;
  this.numberOfElements = numberOfElements;
  nativeOps = NativeOpsHolder.getInstance().getDeviceNativeOps();
  stateBuffer = Nd4j.getDataBufferFactory().createDouble(numberOfElements);
  init();
  hostPointer = new LongPointer(stateBuffer.addressPointer());
  deallocator = NativeRandomDeallocator.getInstance();
  pack = new NativePack(statePointer.address(), statePointer);
  deallocator.trackStatePointer(pack);
}