DistributionValidationPolicy

abstract def spawn(tasks: List[Task], suffered_timeout: Boolean): List[Task]

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

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

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

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

def clone(): AnyRef

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

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

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def is_done(tasks: List[Task]): Boolean

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

def mark_duplicates(tasks: List[Task]): List[Task]

Given a list of tasks, this method returns the same list with all but one task marked as DUPLICATE for each subset submitted by each distinct worker.

final def ne(arg0: AnyRef): Boolean

def not_final(task: Task): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

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

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

def select_answer(tasks: List[Task]): AA

Returns the top answer.

def select_over_budget_answer(tasks: List[Task], need: BigDecimal, have: BigDecimal): AA

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

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

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

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

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

def toString(): String

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

final def wait(): Unit

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

final def wait(arg0: Long): Unit