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

Nd4j.tensorMmul介绍

[英]Tensor matrix multiplication. Both tensors must be the same rank
[中]张量矩阵乘法。两个张量的秩必须相同

代码示例

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Take the data that has been transformed to the principal components about the mean and
 * transform it back into the original feature set.  Make sure to fill in zeroes in columns
 * where components were dropped!
 * @param data Data of the same features used to construct the PCA object but as the components
 * @return The records in terms of the original features
 */
public INDArray convertBackToFeatures(INDArray data) {
  return Nd4j.tensorMmul(eigenvectors, data, new int[][] {{1}, {1}}).transposei().addiRowVector(mean);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Takes a set of data on each row, with the same number of features as the constructing data
 * and returns the data in the coordinates of the basis set about the mean.
 * @param data Data of the same features used to construct the PCA object
 * @return The record in terms of the principal component vectors, you can set unused ones to zero.
 */
public INDArray convertToComponents(INDArray data) {
  INDArray dx = data.subRowVector(mean);
  return Nd4j.tensorMmul(eigenvectors.transpose(), dx, new int[][] {{1}, {1}}).transposei();
}

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

@Override
public void exec() {
  if(this.z != null)
    this.z.assign(Nd4j.tensorMmul(x,y,z,axes));
  else
    this.z = Nd4j.tensorMmul(x,y,axes);
}

代码示例来源：origin: improbable-research/keanu

@Override
public IntegerTensor tensorMultiply(IntegerTensor value, int[] dimLeft, int[] dimsRight) {
  INDArray tensorMmulResult = Nd4j.tensorMmul(tensor, unsafeGetNd4J(value), new int[][]{dimLeft, dimsRight});
  return new Nd4jIntegerTensor(tensorMmulResult);
}

代码示例来源：origin: improbable-research/keanu

@Override
public DoubleTensor tensorMultiply(DoubleTensor value, int[] dimsLeft, int[] dimsRight) {
  INDArray tensorMmulResult = Nd4j.tensorMmul(tensor, unsafeGetNd4J(value), new int[][]{dimsLeft, dimsRight});
  return new Nd4jDoubleTensor(tensorMmulResult);
}

代码示例来源：origin: org.nd4j/nd4j-api

/**
 * Take the data that has been transformed to the principal components about the mean and
 * transform it back into the original feature set.  Make sure to fill in zeroes in columns
 * where components were dropped!
 * @param data Data of the same features used to construct the PCA object but as the components
 * @return The records in terms of the original features
 */
public INDArray convertBackToFeatures(INDArray data) {
  return Nd4j.tensorMmul(eigenvectors, data, new int[][] {{1}, {1}}).transposei().addiRowVector(mean);
}

代码示例来源：origin: org.nd4j/nd4j-api

/**
 * Takes a set of data on each row, with the same number of features as the constructing data
 * and returns the data in the coordinates of the basis set about the mean.
 * @param data Data of the same features used to construct the PCA object
 * @return The record in terms of the principal component vectors, you can set unused ones to zero.
 */
public INDArray convertToComponents(INDArray data) {
  INDArray dx = data.subRowVector(mean);
  return Nd4j.tensorMmul(eigenvectors.transpose(), dx, new int[][] {{1},{1}}).transposei();
}

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);
}