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

Nd4j.trueScalar介绍

[英]This method creates new 0D INDArray, aka scalar. PLEASE NOTE: Temporary method, added to ensure backward compatibility
[中]

代码示例

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray getScalar(long... indexes) {
  return Nd4j.trueScalar(getDouble(indexes));
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray createUninitializedDetached(long[] shape, char ordering) {
  if (shape.length == 0)
    return trueScalar(0.0);
  //ensure shapes that wind up being scalar end up with the write shape
  if (shape.length == 1 && shape[0] == 0) {
    shape = new long[] {1, 1};
  } else if (shape.length == 1) {
    shape = new long[] {1, shape[0]};
  }
  checkShapeValues(shape);
  INDArray ret = INSTANCE.createUninitializedDetached(shape, ordering);
  logCreationIfNecessary(ret);
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Creates an ndarray with the specified value
 * as the  only value in the ndarray.
 * Some people may know this as np.full
 *
 * @param shape the shape of the ndarray
 * @param value the value to assign
 * @return the created ndarray
 */
public static INDArray valueArrayOf(int[] shape, double value) {
  if (shape.length == 0)
    return trueScalar(value);
  checkShapeValues(shape);
  INDArray ret = INSTANCE.valueArrayOf(shape, value);
  logCreationIfNecessary(ret);
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Creates an ndarray with the specified value
 * as the  only value in the ndarray.
 * Some people may know this as np.full
 *
 * @param shape the shape of the ndarray
 * @param value the value to assign
 * @return the created ndarray
 */
public static INDArray valueArrayOf(long[] shape, double value) {
  if (shape.length == 0)
    return trueScalar(value);
  checkShapeValues(shape);
  INDArray ret = INSTANCE.valueArrayOf(shape, value);
  logCreationIfNecessary(ret);
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Cretes uninitialized INDArray detached from any (if any) workspace
 *
 * @param shape
 * @param ordering
 * @return
 */
public static INDArray createUninitializedDetached(int[] shape, char ordering) {
  if (shape.length == 0)
    return trueScalar(0.0);
  //ensure shapes that wind up being scalar end up with the write shape
  if (shape.length == 1 && shape[0] == 0) {
    shape = new int[] {1, 1};
  } else if (shape.length == 1) {
    shape = new int[] {1, shape[0]};
  }
  checkShapeValues(shape);
  INDArray ret = INSTANCE.createUninitializedDetached(shape, ordering);
  logCreationIfNecessary(ret);
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

String shapeString = line.split(":")[1].replace("[", "").replace("],", "");
if (shapeString.isEmpty()) {
  newArr = Nd4j.trueScalar(0);
} else {
  String[] shapeArr = shapeString.split(",");

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray createUninitialized(long[] shape, char ordering) {
  if (shape.length == 0)
    return trueScalar(0.0);
  shape = getEnsuredShape(shape);
  // now we allow 1D vectors
  /*else if (shape.length == 1) {
    shape = new int[] {1, shape[0]};
  }
  */
  checkShapeValues(shape);
  INDArray ret = INSTANCE.createUninitialized(shape, ordering);
  logCreationIfNecessary(ret);
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray create(float[] data, long[] shape) {
  if (shape.length == 0 && data.length == 1) {
    return trueScalar(data[0]);
  }
  shape = getEnsuredShape(shape);
  if (shape.length == 1) {
    if (shape[0] != data.length)
      throw new ND4JIllegalStateException("Shape of the new array doesn't match data length");
  }
  checkShapeValues(data.length, shape);
  INDArray ret = INSTANCE.create(data, shape);
  logCreationIfNecessary(ret);
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Creates an ndarray with the specified shape
 *
 * @param shape  the shape of the ndarray
 * @param stride the stride for the ndarray
 * @param offset the offset of the ndarray
 * @return the instance
 */
public static INDArray create(double[] data, int[] shape, int[] stride, long offset, char ordering) {
  if (data.length == 1 && shape.length == 0)
    return trueScalar(data[0]);
  shape = getEnsuredShape(shape);
  if (shape.length == 1) {
    if (shape[0] != data.length)
      throw new ND4JIllegalStateException("Shape of the new array " + Arrays.toString(shape)
          + " doesn't match data length: " + data.length);
  }
  checkShapeValues(data.length, shape);
  INDArray ret = INSTANCE.create(data, shape, stride, offset, ordering);
  logCreationIfNecessary(ret);
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

if (rank == 0) {
  all.add(new Pair<>(Nd4j.trueScalar(Nd4j.rand(1, 1).getDouble(0)), "{}"));
  return all;

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray create(double[] data, long[] shape) {
  if (shape.length == 0 && data.length == 1) {
    return trueScalar(data[0]);
  }
  shape = getEnsuredShape(shape);
  if (shape.length == 1) {
    if (shape[0] != data.length)
      throw new ND4JIllegalStateException("Shape of the new array doesn't match data length");
  }
  checkShapeValues(data.length, shape);
  INDArray ret = INSTANCE.create(data, shape);
  logCreationIfNecessary(ret);
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Creates an *uninitialized* ndarray with the specified shape and ordering.<br>
 * <b>NOTE</b>: The underlying memory (DataBuffer) will not be initialized. Don't use this unless you know what you are doing.
 *
 * @param shape the shape of the ndarray
 * @param ordering the order of the ndarray
 * @return the instance
 */
public static INDArray createUninitialized(int[] shape, char ordering) {
  if (shape.length == 0)
    return trueScalar(0.0);
  shape = getEnsuredShape(shape);
  // now we allow 1D vectors
  /*else if (shape.length == 1) {
    shape = new int[] {1, shape[0]};
  }
  */
  checkShapeValues(shape);
  INDArray ret = INSTANCE.createUninitialized(shape, ordering);
  logCreationIfNecessary(ret);
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Create an ndrray with the specified shape
 *
 * @param data  the data to use with tne ndarray
 * @param shape the shape of the ndarray
 * @return the created ndarray
 */
public static INDArray create(float[] data, int[] shape) {
  if (shape.length == 0 && data.length == 1) {
    return trueScalar(data[0]);
  }
  shape = getEnsuredShape(shape);
  if (shape.length == 1) {
    if (shape[0] != data.length)
      throw new ND4JIllegalStateException("Shape of the new array doesn't match data length");
  }
  checkShapeValues(data.length, shape);
  INDArray ret = INSTANCE.create(data, shape);
  logCreationIfNecessary(ret);
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Note that iArgs (integer arguments) and  tArgs(double/float arguments)
 * may end up being used under the following conditions:
 * scalar operations (if a scalar is specified the you do not need to specify an ndarray)
 * otherwise, if an ndarray is needed as a second input then put it in the inputs
 *
 * Usually, you only need 1 input (the equivalent of the array you're trying to do indexing on)
 *
 * @param inputs the inputs in to the op
 * @param iArgs the integer arguments as needed
 * @param tArgs the arguments
 * @param condition the condition to filter on
 */
public Choose(INDArray[] inputs,List<Integer> iArgs, List<Double> tArgs,Condition condition) {
  super(null, inputs, null);
  if(condition == null) {
    throw new ND4JIllegalArgumentException("Must specify a condition.");
  }
  if(!iArgs.isEmpty())
    addIArgument(Ints.toArray(iArgs));
  if(!tArgs.isEmpty())
    addTArgument(Doubles.toArray(tArgs));
  addIArgument(condition.condtionNum());
  addOutputArgument(Nd4j.create(inputs[0].shape(), inputs[0].ordering()),Nd4j.trueScalar(1.0));
}

代码示例来源：origin: deeplearning4j/nd4j

return Nd4j.trueScalar(0.0);
    return Nd4j.trueScalar(fa[0]);
    return Nd4j.trueScalar(fa[0]);
    return Nd4j.trueScalar(0.0);
  INDArray array = Nd4j.trueScalar(val);
  return array;
} else if (tfTensor.getDoubleValCount() > 0) {
    return Nd4j.trueScalar(da[0]);
    return Nd4j.trueScalar(0.0);
  INDArray array = Nd4j.trueScalar(val);
  return array;
} else if (tfTensor.getInt64ValCount() > 0)  {
    return Nd4j.trueScalar(fa[0]);

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

public static INDArray scalar(double scalarValue, DataBuffer.Type bufferType) {
  Nd4j.setDataType(bufferType);
  return Nd4j.trueScalar(scalarValue);
}