Class WeightedChoice<T>

java.lang.Object
edu.uci.ics.jung.algorithms.util.WeightedChoice<T>

public class WeightedChoice<T> extends Object
Selects items according to their probability in an arbitrary probability distribution. The distribution is specified by a Map from items (of type T) to weights of type Number, supplied to the constructor; these weights are normalized internally to act as probabilities.

This implementation selects items in O(1) time, and requires O(n) space.

  • Field Details

    • item_pairs

      private List<WeightedChoice<T>.ItemPair> item_pairs
    • random

      private Random random
    • DEFAULT_THRESHOLD

      public static final double DEFAULT_THRESHOLD
      The default minimum value that is treated as a valid probability (as opposed to rounding error from floating-point operations).
      See Also:
  • Constructor Details

    • WeightedChoice

      public WeightedChoice(Map<T,? extends Number> item_weights)
      Equivalent to this(item_weights, new Random(), DEFAULT_THRESHOLD).
      Parameters:
      item_weights - a map from items to their weights
    • WeightedChoice

      public WeightedChoice(Map<T,? extends Number> item_weights, double threshold)
      Equivalent to this(item_weights, new Random(), threshold).
      Parameters:
      item_weights - a map from items to their weights
      threshold - the minimum value that is treated as a probability (anything smaller will be considered equivalent to a floating-point rounding error)
    • WeightedChoice

      public WeightedChoice(Map<T,? extends Number> item_weights, Random random)
      Equivalent to this(item_weights, random, DEFAULT_THRESHOLD).
      Parameters:
      item_weights - a map from items to their weights
      random - the Random instance to use for selection
    • WeightedChoice

      public WeightedChoice(Map<T,? extends Number> item_weights, Random random, double threshold)
      Creates an instance with the specified mapping from items to weights, random number generator, and threshold value.

      The mapping defines the weight for each item to be selected; this will be proportional to the probability of its selection.

      The random number generator specifies the mechanism which will be used to provide uniform integer and double values.

      The threshold indicates default minimum value that is treated as a valid probability (as opposed to rounding error from floating-point operations).

      Parameters:
      item_weights - a map from items to their weights
      random - the Random instance to use for selection
      threshold - the minimum value that is treated as a probability (anything smaller will be considered equivalent to a floating-point rounding error)
  • Method Details

    • enqueueItem

      private void enqueueItem(T key, double value, double threshold, Queue<WeightedChoice<T>.ItemPair> light_weights, Queue<WeightedChoice<T>.ItemPair> heavy_weights)
      Adds key/value to the appropriate queue. Keys with values less than the threshold get added to light_weights, all others get added to heavy_weights.
    • setRandomSeed

      public void setRandomSeed(long seed)
      Parameters:
      seed - the seed to be used by the internal random number generator
    • nextItem

      public T nextItem()
      Retrieves an item with probability proportional to its weight in the Map provided in the input.
      Returns:
      an item chosen randomly based on its specified weight