本文整理汇总了Python中gsp.GSP.compute方法的典型用法代码示例。如果您正苦于以下问题：Python GSP.compute方法的具体用法？Python GSP.compute怎么用？Python GSP.compute使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类gsp.GSP的用法示例。
在下文中一共展示了GSP.compute方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: expected_utils
# 需要导入模块: from gsp import GSP [as 别名]
# 或者: from gsp.GSP import compute [as 别名]
def expected_utils(self, t, history, reserve):
"""
Figure out the expected utility of bidding such that we win each
slot, assuming that everyone else keeps their bids constant from
the previous round.
returns a list of utilities per slot.
"""
# TODO: Fill this in
last_round = history.round(t-1)
last_round_bids = filter(lambda (ID, bid):ID != self.id, last_round.bids)
last_round_clicks = last_round.clicks
allocation, _ = GSP.compute(last_round_clicks, reserve, last_round_bids)
num_slots = len(allocation)
utilities = []
for slot in range(num_slots):
last_round_k = allocation[slot] #take the person from last round in slot k
bid_i = reserve # default bid to reserve price
for ID, bid in last_round_bids: # go through each of last rounds' bids, and set that slot's bid
if last_round_k == ID:
bid_i = bid
utilities += [(self.value - bid_i) * last_round_clicks[slot]]
if utilities == []:
return [0]
return utilities示例2: runSecondPriceAuction
# 需要导入模块: from gsp import GSP [as 别名]
# 或者: from gsp.GSP import compute [as 别名]
def runSecondPriceAuction(self, reqList, sellerGraph):
'''
Function to call GSP mechanism
'''
allocation = []
self.__logger.debug("[AdExchange][runSecondPriceAuction]")
self.__logger.debug("Fiber allocation decisions.")
allocationDict = {}
for key, v in reqList.items():
k1,k2=key.split("#")
# n denotes the number of customers bidding for that conduit
n = len(v)
slot_click = [1] * n
shortestPath = nx.shortest_path(sellerGraph, source=k1, target=k2)
gCoP = self.getCostOfPath(shortestPath, sellerGraph)
lIP = self.linksInPath(shortestPath)
k = len(lIP)
reserve = max(self.__reserve, gCoP/k)
bids = []
for item in v:
bids.append((item.clientName, item.bidPerStrand))
if nx.has_path(sellerGraph, k1, k2) and self.resourceAvailable(sellerGraph, shortestPath, v):
(alloc, payments) = GSP.compute(slot_click, reserve, bids)
allocation.extend(zip(alloc, [i * k for i in payments]))
for (kTest, vTest) in allocation:
allocationDict[kTest] = vTest
# Updates sellerGraph with the allocation
self.__logger.debug("Before > {}".format(self.availableAttributes(shortestPath, sellerGraph)))
self.updateSellerGraph(sellerGraph, shortestPath, v)
self.__logger.debug("After > {}".format(self.availableAttributes(shortestPath, sellerGraph)))
else:
self.__logger.info("Link does not exists or No resources available for the request")
return self.updateRequestList(reqList, allocationDict)示例3: test_mechanism
# 需要导入模块: from gsp import GSP [as 别名]
# 或者: from gsp.GSP import compute [as 别名]
def test_mechanism():
num_slots = 4
slot_clicks = [1] * 4 # don't actually matter for gsp
bids = zip(range(1,6), [10, 12, 18, 14, 20])
reserve = 0
(alloc, payments) = GSP.compute(slot_clicks, reserve, bids)
assert alloc == [5,3,4,2]
assert payments == [18, 14, 12, 10]
reserve = 11
(alloc, payments) = GSP.compute(slot_clicks, reserve, bids)
assert alloc == [5,3,4,2]
assert payments == [18, 14, 12, 11]
reserve = 14
(alloc, payments) = GSP.compute(slot_clicks, reserve, bids)
assert alloc == [5,3,4]
assert payments == [18, 14, 14]
reserve = 15
(alloc, payments) = GSP.compute(slot_clicks, reserve, bids)
assert alloc == [5,3]
assert payments == [18, 15]
reserve = 19
(alloc, payments) = GSP.compute(slot_clicks, reserve, bids)
assert alloc == [5]
assert payments == [19]
reserve = 22
(alloc, payments) = GSP.compute(slot_clicks, reserve, bids)
assert alloc == []
assert payments == []