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

Nd4j.create介绍

[英]Creates a row vector with the specified number of columns
[中]创建具有指定列数的行向量

代码示例

代码示例来源：origin: deeplearning4j/nd4j

protected INDArray create(DataBuffer data, int[] shape, long offset) {
  if (this instanceof IComplexNDArray)
    return Nd4j.createComplex(data, shape, offset);
  else
    return Nd4j.create(data, shape, offset);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 *
 * @param shape
 * @param stride
 * @param ordering
 * @return
 */
public static INDArray zeros(int[] shape, int[] stride, char ordering) {
  return create(shape, stride, ordering);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Creates a row vector with the specified number of columns
 *
 * @param columns the columns of the ndarray
 * @return the created ndarray
 */
public static INDArray create(int columns) {
  return create(columns, order());
}

代码示例来源：origin: deeplearning4j/nd4j

protected INDArray create(DataBuffer data, int[] newShape, int[] newStrides, long offset, char ordering) {
  if (this instanceof IComplexNDArray)
    return Nd4j.createComplex(data, newShape, newStrides, offset, ordering);
  else
    return Nd4j.create(data, newShape, newStrides, offset, ordering);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Creates an ndarray with the specified shape
 *
 * @param rows    the rows of the ndarray
 * @param columns the columns of the ndarray
 * @param stride  the stride for the ndarray
 * @return the instance
 */
public static INDArray create(int rows, int columns, int[] stride) {
  return create(rows, columns, stride, order());
}

代码示例来源：origin: deeplearning4j/nd4j

protected INDArray create(DataBuffer data, int[] newShape, int[] newStrides, long offset) {
  if (this instanceof IComplexNDArray)
    return Nd4j.createComplex(data, newShape, newStrides, offset);
  else
    return Nd4j.create(data, newShape, newStrides, offset);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Creates an ndarray with the specified shape
 *
 * @param rows    the rows of the ndarray
 * @param columns the columns of the ndarray
 * @return the instance
 */
public static INDArray create(int rows, int columns) {
  return create(rows, columns, order());
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Computes the eigenvalues of a general matrix.
 */
public static IComplexNDArray eigenvalues(INDArray A) {
  assert A.rows() == A.columns();
  INDArray WR = Nd4j.create(A.rows(), A.rows());
  INDArray WI = WR.dup();
  Nd4j.getBlasWrapper().geev('N', 'N', A.dup(), WR, WI, dummy, dummy);
  return Nd4j.createComplex(WR, WI);
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray sample(long[] shape) {
  INDArray ret = Nd4j.create(shape);
  return sample(ret);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Creates a row vector with the data
 *
 * @param data the columns of the ndarray
 * @return the created ndarray
 */
public static INDArray create(float[] data) {
  return create(data, order());
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Creates a row vector with the data
 *
 * @param data the columns of the ndarray
 * @return the created ndarray
 */
public static INDArray create(double[] data) {
  return create(data, order());
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Creates an ndarray with the specified shape
 *
 * @param shape the shape of the ndarray
 * @return the instance
 */
public static INDArray create(long... shape) {
  return create(shape, order());
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray sample(int[] shape) {
  INDArray ret = Nd4j.create(shape);
  return sample(ret);
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray toNDArray(int[][] nums) {
  if (Nd4j.dataType() == DataBuffer.Type.DOUBLE) {
    double[] doubles = ArrayUtil.toDoubles(nums);
    INDArray create = Nd4j.create(doubles, new int[] {nums[0].length, nums.length});
    return create;
  } else {
    float[] doubles = ArrayUtil.toFloats(nums);
    INDArray create = Nd4j.create(doubles, new int[] {nums[0].length, nums.length});
    return create;
  }
}

代码示例来源：origin: deeplearning4j/nd4j

private void growCapacity(int idx) {
  if(container == null) {
    container = Nd4j.create(10);
  }
  else if(idx >= container.length()) {
    val max = Math.max(container.length() * 2,idx);
    INDArray newContainer = Nd4j.create(max);
    newContainer.put(new INDArrayIndex[]{NDArrayIndex.interval(0,container.length())},container);
    container = newContainer;
  }
}

代码示例来源：origin: deeplearning4j/nd4j

protected INDArray createScalarForIndex(long i, boolean applyOffset) {
  if(isVector())
    return getScalar(i);
  return Nd4j.create(data(), new long[] {1, 1}, new long[] {1, 1}, i);
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray nextFloat(char order, long[] shape) {
  long length = ArrayUtil.prodLong(shape);
  INDArray ret = Nd4j.create(shape, order);
  DataBuffer data = ret.data();
  for (long i = 0; i < length; i++) {
    data.put(i, nextFloat());
  }
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray convertFromApacheMatrix(RealMatrix matrix) {
  int[] shape = new int[] {matrix.getRowDimension(), matrix.getColumnDimension()};
  INDArray out = Nd4j.create(shape);
  for (int i = 0; i < shape[0]; i++) {
    for (int j = 0; j < shape[1]; j++) {
      double value = matrix.getEntry(i, j);
      out.putScalar(new int[] {i, j}, value);
    }
  }
  return out;
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray getReal() {
  INDArray result = Nd4j.create(shape());
  IComplexNDArray linearView = linearView();
  INDArray linearRet = result.linearView();
  for (int i = 0; i < linearView.length(); i++) {
    linearRet.putScalar(i, linearView.getReal(i));
  }
  return result;
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray im2col(INDArray img, int kh, int kw, int sy, int sx, int ph, int pw, int dh, int dw, boolean isSameMode) {
  Nd4j.getCompressor().autoDecompress(img);
  //Input: NCHW format
  // FIXME: int cast
  int outH = outputSize((int) img.size(2), kh, sy, ph, dh, isSameMode);
  int outW = outputSize((int) img.size(3), kw, sx, pw, dw, isSameMode);
  //[miniBatch,depth,kH,kW,outH,outW]
  INDArray out = Nd4j.create(new long[]{img.size(0), img.size(1), kh, kw, outH, outW}, 'c');
  return im2col(img, kh, kw, sy, sx, ph, pw, dh, dw, isSameMode, out);
}