本文整理了Java中org.nd4j.linalg.factory.Nd4j.createUninitialized()方法的一些代码示例，展示了Nd4j.createUninitialized()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.createUninitialized()方法的具体详情如下：
包路径：org.nd4j.linalg.factory.Nd4j
类名称：Nd4j
方法名：createUninitialized

Nd4j.createUninitialized介绍

[英]This method creates an uninitialized ndarray of specified length and default ordering. PLEASE NOTE: Do not use this method unless you're 100% sure why you use it.
[中]此方法创建指定长度和默认顺序的未初始化数据数组。请注意：除非你100%确定为什么要使用这种方法，否则不要使用这种方法。

代码示例

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray nextGaussian(char order, int[] shape) {
  INDArray array = Nd4j.createUninitialized(shape, order);
  GaussianDistribution op = new GaussianDistribution(array, 0.0, 1.0);
  Nd4j.getExecutioner().exec(op, this);
  return array;
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray nextGaussian(char order, long[] shape) {
  INDArray array = Nd4j.createUninitialized(shape, order);
  GaussianDistribution op = new GaussianDistribution(array, 0.0, 1.0);
  Nd4j.getExecutioner().exec(op, this);
  return array;
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray nextDouble(char order, int[] shape) {
  INDArray array = Nd4j.createUninitialized(shape, order);
  UniformDistribution op = new UniformDistribution(array, 0.0, 1.0);
  Nd4j.getExecutioner().exec(op, this);
  return array;
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray nextFloat(char order, long[] shape) {
  INDArray array = Nd4j.createUninitialized(shape, order);
  UniformDistribution op = new UniformDistribution(array, 0.0, 1.0);
  Nd4j.getExecutioner().exec(op, this);
  return array;
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray nextFloat(char order, int[] shape) {
  INDArray array = Nd4j.createUninitialized(shape, order);
  UniformDistribution op = new UniformDistribution(array, 0.0, 1.0);
  Nd4j.getExecutioner().exec(op, this);
  return array;
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray nextDouble(char order, long[] shape) {
  INDArray array = Nd4j.createUninitialized(shape, order);
  UniformDistribution op = new UniformDistribution(array, 0.0, 1.0);
  Nd4j.getExecutioner().exec(op, this);
  return array;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Atan2 operation, new INDArray instance will be returned
 * Note the order of x and y parameters is opposite to that of java.lang.Math.atan2
 *
 * @param x the abscissa coordinate
 * @param y the ordinate coordinate
 * @return the theta from point (r, theta) when converting (x,y) from to cartesian to polar coordinates
 */
public static INDArray atan2(@NonNull INDArray x, @NonNull INDArray y) {
  return Nd4j.getExecutioner()
          .execAndReturn(new OldAtan2Op(x, y, Nd4j.createUninitialized(x.shape(), x.ordering())));
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray and(INDArray x, INDArray y) {
  INDArray z = Nd4j.createUninitialized(x.shape(), x.ordering());
  Nd4j.getExecutioner().exec(new And(x, y, z, 0.0));
  return z;
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray not(INDArray x) {
  INDArray z = Nd4j.createUninitialized(x.shape(), x.ordering());
  Nd4j.getExecutioner().exec(new Not(x, z, 0.0));
  return z;
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray xor(INDArray x, INDArray y) {
  INDArray z = Nd4j.createUninitialized(x.shape(), x.ordering());
  Nd4j.getExecutioner().exec(new Xor(x, y, z, 0.0));
  return z;
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray or(INDArray x, INDArray y) {
  INDArray z = Nd4j.createUninitialized(x.shape(), x.ordering());
  Nd4j.getExecutioner().exec(new Or(x, y, z, 0.0));
  return z;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Negate each element.
 */
@Override
public INDArray neg() {
  return Nd4j.getExecutioner().exec(new Negative(this, Nd4j.createUninitialized(this.shape(), this.ordering())))
      .z();
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray reverse(INDArray x, boolean dup) {
  return Nd4j.getExecutioner().exec(new OldReverse(x, dup ? Nd4j.createUninitialized(x.shape(), x.ordering()) : x))
          .z();
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Like the scipy function tri.
 * From the scipy documentation:
 *  An array with ones at and below the given diagonal and zeros elsewhere.
 * @param n number of rows in the array
 * @param m number of columns in the array ( can be just equal to n)
 * @param k    The sub-diagonal at and below which the array is filled.
`k` = 0 is the main diagonal, while `k` < 0 is below it,
and `k` > 0 is above.  The default is 0.
 * @return
 */
public static INDArray tri(int n,int m,int k) {
  /*
  INDArray mRet = Transforms.greaterThanOrEqual(arange(n),arange(-k,m - k));
  return mRet;
  */
  INDArray ret = Nd4j.createUninitialized(n, m);
  val op = DynamicCustomOp.builder("tri")
      .addIntegerArguments(n, m, k)
      .addOutputs(ret)
      .build();
  Nd4j.getExecutioner().exec(op);
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Meshgrid op. Returns a pair of arrays where values are broadcast on a 2d grid.<br>
 * For example, if x = [1,2,3,4] and y = [5,6,7], then:<br>
 * out[0] =<br>
 * [1,2,3,4]<br>
 * [1,2,3,4]<br>
 * [1,2,3,4]<br>
 * <br>
 * out[1] =<br>
 * [5,5,5,5]<br>
 * [6,6,6,6]<br>
 * [7,7,7,7]<br>
 * <br>
 *
 * @param x X array input
 * @param y Y array input
 * @return INDArray[] of length 2, shape [y.length, x.length]
 */
public static INDArray[] meshgrid(@NonNull INDArray x, @NonNull INDArray y){
  Preconditions.checkArgument(x.isVectorOrScalar(), "X must be a vector");
  Preconditions.checkArgument(y.isVectorOrScalar(), "Y must be a vector");
  INDArray xOut = Nd4j.createUninitialized(y.length(), x.length());
  INDArray yOut = Nd4j.createUninitialized(y.length(), x.length());
  CustomOp op = DynamicCustomOp.builder("meshgrid")
      .addInputs(x, y)
      .addOutputs(xOut, yOut)
      .build();
  Nd4j.getExecutioner().exec(op);
  return new INDArray[]{xOut, yOut};
}

代码示例来源：origin: deeplearning4j/nd4j

INDArray result = Nd4j.createUninitialized(m.shape());
    .build();
Nd4j.getExecutioner().exec(op);

代码示例来源：origin: deeplearning4j/nd4j

return Nd4j.createUninitialized(shape).assign(this.getDouble(0));
  Nd4j.getExecutioner().exec(new Tile(new INDArray[]{this.dup(this.ordering())},new INDArray[]{result},repeat));
} else
  Nd4j.getExecutioner().exec(new Tile(new INDArray[]{this},new INDArray[]{result},repeat));

代码示例来源：origin: deeplearning4j/nd4j

int oW = (int) Math.ceil(img.size(3) * 1.f / sx);
  output = Nd4j.createUninitialized(new long[] {img.size(0), img.size(1), kh, kw, oH, oW}, 'c');
  int oW = ((int) img.size(3) - (kw + (kw-1)*(1-1)) + 2*pw)/sx + 1;
  output = Nd4j.createUninitialized(new long[] {img.size(0), img.size(1), kh, kw, oH, oW}, 'c');
        .build()).build();
Nd4j.getExecutioner().exec(im2col);
return im2col.outputArguments()[0];

代码示例来源：origin: deeplearning4j/nd4j

Nd4j.getExecutioner().commit();
    Nd4j.getExecutioner().commit();
    DataBuffer buffer = Nd4j.createBuffer(this.lengthLong(), false);
    INDArray copy = Nd4j.createUninitialized(this.shape(), this.ordering());
    copy.assign(this);
    Nd4j.getExecutioner().commit();
    copy = Nd4j.createUninitialized(this.shape(), this.ordering());
    copy.assign(this);
    Nd4j.getExecutioner().commit();

代码示例来源：origin: deeplearning4j/nd4j

/**
 * in place addition of two matrices
 *
 * @param other  the second ndarray to add
 * @param result the result ndarray
 * @return the result of the addition
 */
@Override
public INDArray addi(INDArray other, INDArray result) {
  if (other.isScalar()) {
    return result.addi(other.getDouble(0), result);
  }
  if (isScalar()) {
    return other.addi(getDouble(0), result);
  }
  if(!Shape.shapeEquals(this.shape(),other.shape())) {
    int[] broadcastDimensions = Shape.getBroadcastDimensions(this.shape(),other.shape());
    result = Nd4j.createUninitialized(Shape.broadcastOutputShape(this.shape(),other.shape()));
    Nd4j.getExecutioner().exec(new BroadcastAddOp(this,other,result,broadcastDimensions),broadcastDimensions);
    return result;
  }
  LinAlgExceptions.assertSameShape(other, result);
  Nd4j.getExecutioner().exec(new OldAddOp(this, other, result, length()));
  if (Nd4j.ENFORCE_NUMERICAL_STABILITY)
    Nd4j.clearNans(result);
  return result;
}