BootstrapEstimationPolicy

Value Members

1. final def !=(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

   AnyRef → Any

3. final def ==(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

4. val NumBootstraps: Int

   

5. def allow_early_termination(): Boolean

   

   Determines whether a policy allows for canceling running tasks.

   Determines whether a policy allows for canceling running tasks. If true, is_done will be called more often, increasing the required confidence level to terminate. Note that an early termination check is always conducted when timeouts occur regardless of this setting.

   Definition Classes

   AggregationPolicy

6. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

7. def blacklisted_workers(tasks: List[Task]): List[String]

   

   Returns a list of blacklisted worker_ids given a set of tasks, completed or not.

   Returns a list of blacklisted worker_ids given a set of tasks, completed or not.

   tasks

   The complete list of tasks.

   returns

   A list of worker IDs.

   Definition Classes

   AggregationPolicy

8. var bootCache: Map[(Seq[Double], Int, Double), (Double, Double, Double)]

   

9. def bootstrap(statistic: (Seq[Double]) ⇒ Double, X: Seq[Double], B: Int, alpha: Double): (Double, Double, Double)

   

   Computes statistic and confidence intervals specified.

   Computes statistic and confidence intervals specified.

   statistic

   An arbitrary function of the data.

   X

   an input vector

   B

   the number of bootstrap replications to perform

   alpha

   the margin of error

10. def clone(): AnyRef

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

11. def completed_tasks(tasks: List[Task]): List[Task]

    

    Definition Classes

    AggregationPolicy

12. def completed_workerunique_tasks(tasks: List[Task]): List[Task]

    

    Definition Classes

    AggregationPolicy

13. final def eq(arg0: AnyRef): Boolean

    

    Definition Classes

    AnyRef

14. def equals(arg0: Any): Boolean

    

    Definition Classes

    AnyRef → Any

15. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

16. final def getClass(): Class[_]

    

    Definition Classes

    AnyRef → Any

17. def getDistribution(tasks: List[Task]): Array[Response[A]]

    

    Definition Classes

    AggregationPolicy

18. def hasUnmarkedDuplicate(tasks: List[Task]): Boolean

    

19. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

20. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

21. def is_done(tasks: List[Task], num_comparisons: Int): (Boolean, Int)

    

    Returns true if the strategy has enough data to stop scheduling work.

    Returns true if the strategy has enough data to stop scheduling work.

    tasks

    The complete list of scheduled tasks.

    num_comparisons

    The number of times this function has been called, inclusive.

    returns

    true iff done.

    Definition Classes

    BootstrapEstimationPolicy → AggregationPolicy

22. final def ne(arg0: AnyRef): Boolean

    

    Definition Classes

    AnyRef

23. def not_final(task: Task): Boolean

    

    Definition Classes

    AggregationPolicy

24. final def notify(): Unit

    

    Definition Classes

    AnyRef

25. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

26. def num_to_run(tasks: List[Task], num_comparisons: Int, reward: BigDecimal): Int

    

    Calculate the number of new tasks to schedule.

    Calculate the number of new tasks to schedule.

    Attributes

    protected[edu.umass.cs.automan.core.policy]

    Definition Classes

    BootstrapEstimationPolicy → AggregationPolicy

27. def outstanding_tasks(tasks: List[Task]): List[Task]

    

    Definition Classes

    AggregationPolicy

28. def partition_duplicates(tasks: List[Task]): (List[Task], List[Task])

    

    Partitions a set of tasks into those that should be marked as duplicate and those that should not be.

    Partitions a set of tasks into those that should be marked as duplicate and those that should not be. All tasks passed in are passed back out.

    tasks

    A list of tasks

    returns

    (list of non-duplicate tasks, list of duplicate tasks)

    Definition Classes

    AggregationPolicy

29. def rejection_response(tasks: List[Task]): String

    

    Returns a string explaining why the worker's answer was not accepted.

    Returns a string explaining why the worker's answer was not accepted.

    tasks

    The list of accepted tasks. Used to determine the correct answer.

    returns

    Explanation string.

    Definition Classes

    BootstrapEstimationPolicy → AggregationPolicy

30. def select_answer(tasks: List[Task], num_comparisons: Int): AA

    

    Returns the top answer.

    Returns the top answer.

    tasks

    The complete list of tasks.

    num_comparisons

    The number of times is_done has been called.

    returns

    Top answer

    Definition Classes

    BootstrapEstimationPolicy → AggregationPolicy

31. def select_over_budget_answer(tasks: List[Task], need: BigDecimal, have: BigDecimal, num_comparisons: Int): AA

    

    Returns an appropriate response for when the computation ran out of money.

    Returns an appropriate response for when the computation ran out of money.

    tasks

    The complete list of tasks.

    need

    The smallest amount of money needed to complete the computation under optimistic assumptions.

    have

    The amount of money we have.

    num_comparisons

    The number of times is_done has been called.

    returns

    A low-confidence or over-budget answer.

    Definition Classes

    BootstrapEstimationPolicy → AggregationPolicy

32. def spawn(tasks: List[Task], suffered_timeout: Boolean, num_comparisons: Int): List[Task]

    

    Computes the number of tasks needed to satisfy the quality-control algorithm given the already-collected list of tasks.

    Computes the number of tasks needed to satisfy the quality-control algorithm given the already-collected list of tasks. Returns only newly-created tasks.

    tasks

    The complete list of previously-scheduled tasks

    suffered_timeout

    True if any of the latest batch of tasks suffered a timeout.

    returns

    A list of new tasks to schedule on the backend.

    Definition Classes

    AggregationPolicy

33. final def synchronized[T0](arg0: ⇒ T0): T0

    

    Definition Classes

    AnyRef

34. def tasks_to_accept(tasks: List[Task]): List[Task]

    

    Definition Classes

    BootstrapEstimationPolicy → AggregationPolicy

35. def tasks_to_accept_on_failure(tasks: List[Task]): List[Task]

    

    Definition Classes

    AggregationPolicy

36. def tasks_to_cancel(tasks: List[Task]): List[Task]

    

    Definition Classes

    AggregationPolicy

37. def tasks_to_reject(tasks: List[Task]): List[Task]

    

    Definition Classes

    BootstrapEstimationPolicy → AggregationPolicy

38. def toString(): String

    

    Definition Classes

    AnyRef → Any

39. def unique_by_date(ts: List[Task]): List[Task]

    

    Attributes

    protected

    Definition Classes

    AggregationPolicy

40. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

41. final def wait(arg0: Long, arg1: Int): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

42. final def wait(arg0: Long): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )