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

Nd4j.getRandom介绍

[英]Get the current random generator
[中]获取当前随机生成器

代码示例

代码示例来源：origin: deeplearning4j/nd4j

public LogNormalDistribution(INDArray mean, double std) {
  this.means = mean;
  this.standardDeviation = std;
  this.random = Nd4j.getRandom();
}

代码示例来源：origin: deeplearning4j/nd4j

public NormalDistribution(INDArray mean, double std) {
  this.means = mean;
  this.standardDeviation = std;
  this.random = Nd4j.getRandom();
}

代码示例来源：origin: deeplearning4j/nd4j

public BinomialDistribution(int n, INDArray p) {
  this.random = Nd4j.getRandom();
  this.numberOfTrials = n;
  this.p = p;
}

代码示例来源：origin: deeplearning4j/nd4j

public ConstantDistribution(double value) {
    this.value = value;
    this.random = Nd4j.getRandom();
  }
/*

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Sample without replacement and a random rng
 *
 * @param numSamples the number of samples to getFromOrigin
 * @return a sample data transform without replacement
 */
@Override
public DataSet sample(int numSamples) {
  return sample(numSamples, Nd4j.getRandom());
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Generate a random normal N(0,1) with the specified order and shape
 * @param order   Order of the output array
 * @param rows    the number of rows in the matrix
 * @param columns the number of columns in the matrix
 * @return
 */
@Override
public INDArray randn(char order, long rows, long columns) {
  return Nd4j.getRandom().nextGaussian(order, new long[] {rows, columns});
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Random normal using the specified seed
 *
 * @param rows    the number of rows in the matrix
 * @param columns the number of columns in the matrix
 * @return
 */
@Override
public INDArray randn(long rows, long columns, long seed) {
  Nd4j.getRandom().setSeed(seed);
  return randn(new long[] {rows, columns}, Nd4j.getRandom());
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Random normal using the current time stamp
 * as the seed
 *
 * @param shape the shape of the ndarray
 * @return
 */
@Override
public INDArray randn(char order, int[] shape) {
  return Nd4j.getRandom().nextGaussian(order, shape);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * This method executes specified RandomOp using default RNG available via Nd4j.getRandom()
 *
 * @param op
 */
@Override
public INDArray exec(RandomOp op) {
  return exec(op, Nd4j.getRandom());
}

代码示例来源：origin: deeplearning4j/nd4j

public static List<Pair<INDArray, String>> get4dPermutedWithShape(int seed, int... shape) {
  Nd4j.getRandom().setSeed(seed);
  int[] createdShape = {shape[1], shape[3], shape[2], shape[0]};
  INDArray arr = Nd4j.rand(createdShape);
  INDArray permuted = arr.permute(3, 0, 2, 1);
  return Collections.singletonList(new Pair<>(permuted,
          "get4dPermutedWithShape(" + seed + "," + Arrays.toString(shape) + ").get(0)"));
}

代码示例来源：origin: deeplearning4j/nd4j

public static List<Pair<INDArray, String>> get6dPermutedWithShape(int seed, int... shape) {
  Nd4j.getRandom().setSeed(seed);
  int[] createdShape = {shape[1], shape[4], shape[5], shape[3], shape[2], shape[0]};
  INDArray arr = Nd4j.rand(createdShape);
  INDArray permuted = arr.permute(5, 0, 4, 3, 1, 2);
  return Collections.singletonList(new Pair<>(permuted,
          "get6dPermutedWithShape(" + seed + "," + Arrays.toString(shape) + ").get(0)"));
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Fill the given ndarray with random numbers drawn from a uniform distribution
 *
 * @param target  target array
 * @return the given target array
 */
public static INDArray rand(INDArray target) {
  return getExecutioner().exec(new UniformDistribution(target), Nd4j.getRandom());
}

代码示例来源：origin: deeplearning4j/nd4j

public static List<Pair<INDArray, String>> get6dReshapedWithShape(int seed, int... shape) {
  Nd4j.getRandom().setSeed(seed);
  int[] shape3d = {shape[0] * shape[2], shape[4] * shape[5], shape[1] * shape[3]};
  INDArray array3d = Nd4j.rand(shape3d);
  INDArray array6d = array3d.reshape(ArrayUtil.toLongArray(shape));
  return Collections.singletonList(new Pair<>(array6d,
          "get6dReshapedWithShape(" + seed + "," + Arrays.toString(shape) + ").get(0)"));
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray rand(long rows, long columns, double min, double max, org.nd4j.linalg.api.rng.Random rng) {
  Nd4j.getRandom().setSeed(rng.getSeed());
  return rand(new long[] {rows, columns}, min, max, rng);
}

代码示例来源：origin: deeplearning4j/nd4j

public static List<Pair<INDArray, String>> get3dPermutedWithShape(long seed, long... shape) {
  Nd4j.getRandom().setSeed(seed);
  long[] createdShape = {shape[1], shape[2], shape[0]};
  int lencreatedShape = ArrayUtil.prod(createdShape);
  INDArray arr = Nd4j.linspace(1, lencreatedShape, lencreatedShape).reshape(createdShape);
  INDArray permuted = arr.permute(2, 0, 1);
  return Collections.singletonList(new Pair<>(permuted,
          "get3dPermutedWithShape(" + seed + "," + Arrays.toString(shape) + ").get(0)"));
}

代码示例来源：origin: deeplearning4j/nd4j

public static List<Pair<INDArray, String>> get3dReshapedWithShape(long seed, long... shape) {
  Nd4j.getRandom().setSeed(seed);
  long[] shape2d = {shape[0] * shape[2], shape[1]};
  int lenshape2d = ArrayUtil.prod(shape2d);
  INDArray array2d = Nd4j.linspace(1, lenshape2d, lenshape2d).reshape(shape2d);
  INDArray array3d = array2d.reshape(shape);
  return Collections.singletonList(new Pair<>(array3d,
          "get3dReshapedWithShape(" + seed + "," + Arrays.toString(shape) + ").get(0)"));
}

代码示例来源：origin: deeplearning4j/nd4j

public static Pair<INDArray, String> getTransposedMatrixWithShape(char ordering, int rows, int cols, int seed) {
  Nd4j.getRandom().setSeed(seed);
  INDArray out = Nd4j.linspace(1, rows * cols, rows * cols).reshape(ordering, cols, rows);
  return new Pair<>(out.transpose(), "getTransposedMatrixWithShape(" + rows + "," + cols + "," + seed + ")");
}

代码示例来源：origin: deeplearning4j/nd4j

public static Pair<INDArray, String> getPermutedWithShape(char ordering, long rows, long cols, long seed) {
  Nd4j.getRandom().setSeed(seed);
  long len = rows * cols;
  INDArray arr = Nd4j.linspace(1, len, len).reshape(cols, rows);
  return new Pair<>(arr.permute(1, 0), "getPermutedWithShape(" + rows + "," + cols + "," + seed + ")");
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray rand(int[] shape, double min, double max, org.nd4j.linalg.api.rng.Random rng) {
  Nd4j.getRandom().setSeed(rng.getSeed());
  return Nd4j.getDistributions().createUniform(min, max).sample(shape);
}

代码示例来源：origin: deeplearning4j/nd4j

public static Pair<INDArray, String> getTransposedMatrixWithShape(long rows, long cols, long seed) {
  Nd4j.getRandom().setSeed(seed);
  INDArray out = Nd4j.linspace(1, rows * cols, rows * cols).reshape(cols, rows);
  return new Pair<>(out.transpose(), "getTransposedMatrixWithShape(" + rows + "," + cols + "," + seed + ")");
}