本文整理了Java中gov.sandia.cognition.math.matrix.Vector.set()方法的一些代码示例，展示了Vector.set()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。Vector.set()方法的具体详情如下：
包路径：gov.sandia.cognition.math.matrix.Vector
类名称：Vector
方法名：set

Vector.set介绍

[英]Sets the value for an element at the zero-based index from the vector. It throws an ArrayIndexOutOfBoundsException if the index is out of range. It is a convenience method for setElement.
[中]在向量的从零开始的索引处设置元素的值。如果索引超出范围，它会抛出ArrayIndexOutOfBoundsException。对于setElement来说，这是一种方便的方法。

代码示例

代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-common-core

/**
 * Copies the values from the given Collection. It does this by iterating
 * through collection and initializing by iterating through them
 * in the order specified by the collection. Thus, this should probably
 * not be used with collections that do not have a very well determined
 * iteration order, like a HashSet (as such, LinkedHashSet may be a better
 * option to preserve insertion order).
 * 
 * @param values 
 *      Values to create a vector from. Cannot be null and cannot contain
 *      null values.
 * @return 
 *      A vector of dimensionality equal to the size of the given
 *      collection and whose values are initialized to the values in the
 *      collection in order of iteration.
 */
public VectorType copyValues(
  final Collection<? extends Number> values)
{
  final VectorType v = this.createVector(values.size());
  int index = 0;
  for (final Number value : values)
  {
    v.set(index, value.doubleValue());
    index++;
  }
  return v;
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Copies the values from the given Collection. It does this by iterating
 * through collection and initializing by iterating through them
 * in the order specified by the collection. Thus, this should probably
 * not be used with collections that do not have a very well determined
 * iteration order, like a HashSet (as such, LinkedHashSet may be a better
 * option to preserve insertion order).
 * 
 * @param values 
 *      Values to create a vector from. Cannot be null and cannot contain
 *      null values.
 * @return 
 *      A vector of dimensionality equal to the size of the given
 *      collection and whose values are initialized to the values in the
 *      collection in order of iteration.
 */
public VectorType copyValues(
  final Collection<? extends Number> values)
{
  final VectorType v = this.createVector(values.size());
  int index = 0;
  for (final Number value : values)
  {
    v.set(index, value.doubleValue());
    index++;
  }
  return v;
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Copies the values from the given Collection. It does this by iterating
 * through collection and initializing by iterating through them
 * in the order specified by the collection. Thus, this should probably
 * not be used with collections that do not have a very well determined
 * iteration order, like a HashSet (as such, LinkedHashSet may be a better
 * option to preserve insertion order).
 * 
 * @param values 
 *      Values to create a vector from. Cannot be null and cannot contain
 *      null values.
 * @return 
 *      A vector of dimensionality equal to the size of the given
 *      collection and whose values are initialized to the values in the
 *      collection in order of iteration.
 */
public VectorType copyValues(
  final Collection<? extends Number> values)
{
  final VectorType v = this.createVector(values.size());
  int index = 0;
  for (final Number value : values)
  {
    v.set(index, value.doubleValue());
    index++;
  }
  return v;
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Creates a vector from the given map of indices to values.
 * 
 * @param   dimensionality
 *      The dimensionality of the vector. Cannot be negative.
 * @param   map
 *      The map of indices to values to fill in the vector. All indices
 *      must be 0-based and between 0 (inclusive) and dimensionality 
 *      (exclusive). Cannot be null.
 * @return 
 *      A new vector with elements initialized to the ones in the map.
 */
public VectorType copyMap(
  final int dimensionality,
  final Map<Integer, ? extends Number> map)
{
  ArgumentChecker.assertIsNotNull("map", map);
  
  final VectorType result = this.createVectorCapacity(dimensionality,
    map.size());
  for (final Map.Entry<Integer, ? extends Number> entry : map.entrySet())
  {
    result.set(entry.getKey(), entry.getValue().doubleValue());
  }
  return result;
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Creates a vector from the given map of indices to values.
 * 
 * @param   dimensionality
 *      The dimensionality of the vector. Cannot be negative.
 * @param   map
 *      The map of indices to values to fill in the vector. All indices
 *      must be 0-based and between 0 (inclusive) and dimensionality 
 *      (exclusive). Cannot be null.
 * @return 
 *      A new vector with elements initialized to the ones in the map.
 */
public VectorType copyMap(
  final int dimensionality,
  final Map<Integer, ? extends Number> map)
{
  ArgumentChecker.assertIsNotNull("map", map);
  
  final VectorType result = this.createVectorCapacity(dimensionality,
    map.size());
  for (final Map.Entry<Integer, ? extends Number> entry : map.entrySet())
  {
    result.set(entry.getKey(), entry.getValue().doubleValue());
  }
  return result;
}

代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-common-core

/**
 * Creates a vector from the given map of indices to values.
 * 
 * @param   dimensionality
 *      The dimensionality of the vector. Cannot be negative.
 * @param   map
 *      The map of indices to values to fill in the vector. All indices
 *      must be 0-based and between 0 (inclusive) and dimensionality 
 *      (exclusive). Cannot be null.
 * @return 
 *      A new vector with elements initialized to the ones in the map.
 */
public VectorType copyMap(
  final int dimensionality,
  final Map<Integer, ? extends Number> map)
{
  ArgumentChecker.assertIsNotNull("map", map);
  
  final VectorType result = this.createVectorCapacity(dimensionality,
    map.size());
  for (final Map.Entry<Integer, ? extends Number> entry : map.entrySet())
  {
    result.set(entry.getKey(), entry.getValue().doubleValue());
  }
  return result;
}

代码示例来源：origin: algorithmfoundry/Foundry

this.inputsTransposed.get(entry.getIndex()).set(i,
  entry.getValue());

代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core

this.inputsTransposed.get(entry.getIndex()).set(i,
  entry.getValue());

代码示例来源：origin: algorithmfoundry/Foundry

this.inputsTransposed.get(entry.getIndex()).set(i,
  entry.getValue());

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public Vector sample(
  final Random random)
{
  // Create the result vector.
  final int K = this.getParameters().getDimensionality();
  final Vector y = VectorFactory.getDenseDefault().createVector(K);
  double sum = 0.0;
  for (int i = 0; i < K; i++)
  {
    final double yi = GammaDistribution.sampleStandard(
      this.parameters.get(i), random);
    y.set(i, yi);
    sum += yi;
  }
  
  if (sum != 0.0)
  {
    y.scaleEquals(1.0 / sum);
  }
  
  return y;
}

代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core

@Override
public Vector sample(
  final Random random)
{
  // Create the result vector.
  final int K = this.getParameters().getDimensionality();
  final Vector y = VectorFactory.getDenseDefault().createVector(K);
  double sum = 0.0;
  for (int i = 0; i < K; i++)
  {
    final double yi = GammaDistribution.sampleStandard(
      this.parameters.get(i), random);
    y.set(i, yi);
    sum += yi;
  }
  
  if (sum != 0.0)
  {
    y.scaleEquals(1.0 / sum);
  }
  
  return y;
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public Vector sample(
  final Random random)
{
  // Create the result vector.
  final int K = this.getParameters().getDimensionality();
  final Vector y = VectorFactory.getDenseDefault().createVector(K);
  double sum = 0.0;
  for (int i = 0; i < K; i++)
  {
    final double yi = GammaDistribution.sampleStandard(
      this.parameters.get(i), random);
    y.set(i, yi);
    sum += yi;
  }
  
  if (sum != 0.0)
  {
    y.scaleEquals(1.0 / sum);
  }
  
  return y;
}

代码示例来源：origin: algorithmfoundry/Foundry

y.set(i, yin);
sum += yin;

代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core

y.set(i, yin);
sum += yin;

代码示例来源：origin: algorithmfoundry/Foundry

y.set(i, yin);
sum += yin;

代码示例来源：origin: algorithmfoundry/Foundry

final double actual = example.getOutput();
final double error = actual - prediction;
errors.set(i, error);
    (oldWeight * sumOfSquares + derivative.dot(errors))
    / (sumOfSquares + this.weightRegularization);
  weights.set(j, newWeight);
  for (int i = 0; i < this.dataSize; i++)
    factorTimesInput.set(i,
      this.dataList.get(i).getInput().dot(factorRow));

代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core

final double actual = example.getOutput();
final double error = actual - prediction;
errors.set(i, error);
    (oldWeight * sumOfSquares + derivative.dot(errors))
    / (sumOfSquares + this.weightRegularization);
  weights.set(j, newWeight);
  for (int i = 0; i < this.dataSize; i++)
    factorTimesInput.set(i,
      this.dataList.get(i).getInput().dot(factorRow));

代码示例来源：origin: algorithmfoundry/Foundry

final double actual = example.getOutput();
final double error = actual - prediction;
errors.set(i, error);
    (oldWeight * sumOfSquares + derivative.dot(errors))
    / (sumOfSquares + this.weightRegularization);
  weights.set(j, newWeight);
  for (int i = 0; i < this.dataSize; i++)
    factorTimesInput.set(i,
      this.dataList.get(i).getInput().dot(factorRow));