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

Nd4j.randn介绍

[英]Random normal N(0,1) with the specified shape and array order
[中]具有指定形状和数组顺序的随机法线N（0,1）

代码示例

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Random normal N(0,1) with the specified shape and array order
 *
 * @param order   the order of the output array
 * @param rows    the number of rows in the matrix
 * @param columns the number of columns in the matrix
 */
public static INDArray randn(char order, long rows, long columns) {
  INDArray ret = Nd4j.createUninitialized(new long[]{rows, columns}, order);
  logCreationIfNecessary(ret);
  return randn(ret);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Random normal N(0,1) with the specified shape and array order
 *
 * @param order order of the output ndarray
 * @param shape the shape of the ndarray
 */
public static INDArray randn(char order, long[] shape) {
  INDArray ret = Nd4j.createUninitialized(shape, order);
  logCreationIfNecessary(ret);
  return randn(ret);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Random normal N(0,1) with the specified shape and array order
 *
 * @param order order of the output ndarray
 * @param shape the shape of the ndarray
 */
public static INDArray randn(char order, int[] shape) {
  INDArray ret = Nd4j.createUninitialized(shape, order);
  logCreationIfNecessary(ret);
  return randn(ret);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Random normal using the specified seed
 *
 * @param shape the shape of the ndarray
 * @return
 */
public static INDArray randn(int[] shape, long seed) {
  INDArray ret = Nd4j.createUninitialized(shape, order());
  logCreationIfNecessary(ret);
  return randn(ret, seed);
}

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
 */
public static INDArray randn(int[] shape) {
  INDArray ret = Nd4j.createUninitialized(shape, order());
  logCreationIfNecessary(ret);
  return randn(ret);
}

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
 */
public static INDArray randn(long rows, long columns, long seed) {
  INDArray ret = Nd4j.createUninitialized(new long[]{rows, columns}, order());
  logCreationIfNecessary(ret);
  return randn(ret, seed);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Random normal using the given rng
 *
 * @param shape the shape of the ndarray
 * @param r     the random generator to use
 * @return
 */
public static INDArray randn(int[] shape, org.nd4j.linalg.api.rng.Random r) {
  final INDArray ret = Nd4j.createUninitialized(shape, order());
  logCreationIfNecessary(ret);
  return randn(ret, r);
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray randn(long[] shape) {
  INDArray ret = Nd4j.createUninitialized(shape, order());
  logCreationIfNecessary(ret);
  return randn(ret);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Random normal using the given rng
 *
 * @param shape the shape of the ndarray
 * @param r     the random generator to use
 * @return
 */
public static INDArray randn(long[] shape, org.nd4j.linalg.api.rng.Random r) {
  final INDArray ret = Nd4j.createUninitialized(shape, order());
  logCreationIfNecessary(ret);
  return randn(ret, r);
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray doCreate(long[] shape, INDArray paramsView) {
  return    Nd4j.randn(order(), shape).divi(FastMath.sqrt(fanIn));
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray doCreate(long[] shape, INDArray paramsView) {
  return Nd4j.randn(order(), shape).muli(FastMath.sqrt(2.0 / (fanIn + fanOut)));
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Random normal using the given rng
 *
 * @param rows    the number of rows in the matrix
 * @param columns the number of columns in the matrix
 * @param r       the random generator to use
 * @return
 */
public static INDArray randn(long rows, long columns, org.nd4j.linalg.api.rng.Random r) {
  INDArray ret = Nd4j.createUninitialized(new long[]{rows, columns}, order());
  logCreationIfNecessary(ret);
  return randn(ret, r);
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray doCreate(long[] shape, INDArray paramsView) {
  return    Nd4j.randn(order(), shape).divi(FastMath.sqrt(fanOut));
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray doCreate(long[] shape, INDArray paramsView) {
  return   Nd4j.randn(order(), shape).divi(FastMath.sqrt(fanIn));
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Random normal using the current time stamp
 * as the seed
 *
 * @param rows    the number of rows in the matrix
 * @param columns the number of columns in the matrix
 * @return
 */
public static INDArray randn(long rows, long columns) {
  INDArray ret = Nd4j.createUninitialized(new long[]{rows, columns}, order());
  logCreationIfNecessary(ret);
  return randn(ret);
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray doCreate(long[] shape, INDArray paramsView) {
  return Nd4j.randn(order(), shape).divi(FastMath.sqrt((fanIn + fanOut) / 2));
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray doCreate(long[] shape, INDArray paramsView) {
  return  Nd4j.randn(order(), shape).muli(FastMath.sqrt(2.0 / fanIn)); //N(0, 2/nIn);
}

代码示例来源：origin: deeplearning4j/dl4j-examples

System.out.println("Full precision of random value at position (0,0): " + uniformRandom.getDouble(0,0));
INDArray gaussianMeanZeroUnitVariance = Nd4j.randn(shape);
System.out.println("\nN(0,1) random array:");
System.out.println(gaussianMeanZeroUnitVariance);

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Generates a set of <i>count</i> random samples with the same variance and mean and eigenvector/values
 * as the data set used to initialize the PCA object, with same number of features <i>N</i>.
 * @param count The number of samples to generate
 * @return A matrix of size <i>count</i> rows by <i>N</i> columns
 */
public INDArray generateGaussianSamples(long count) {
  INDArray samples = Nd4j.randn(new long[] {count, eigenvalues.columns()});
  INDArray factors = Transforms.pow(eigenvalues, -0.5, true);
  samples.muliRowVector(factors);
  return Nd4j.tensorMmul(eigenvectors, samples, new int[][] {{1}, {1}}).transposei().addiRowVector(mean);
}

代码示例来源：origin: org.nd4j/nd4j-api

/**
 * Generates a set of <i>count</i> random samples with the same variance and mean and eigenvector/values
 * as the data set used to initialize the PCA object, with same number of features <i>N</i>.
 * @param count The number of samples to generate
 * @return A matrix of size <i>count</i> rows by <i>N</i> columns
 */
public INDArray generateGaussianSamples(int count) {
  INDArray samples = Nd4j.randn(count, eigenvalues.columns());
  INDArray factors = Transforms.pow(eigenvalues, -0.5, true);
  samples.muliRowVector(factors);
  return Nd4j.tensorMmul(eigenvectors, samples, new int[][] {{1}, {1}}).transposei().addiRowVector(mean);
}