def generateReligions(self, locality):
Catholic = rel.Catholicism()
Protestant = rel.Protestantism()
localCatholic = bu.Business([], "Catholic", locality, 10000000)
localProtestant = bu.Business([], "Protestant", locality, 100000000)
Catholic.addBusiness(localCatholic)
Protestant.addBusiness(localProtestant)
religionList = [Catholic, Protestant]
return religionList
def importBusinesses(businessDict):
businessDataset = open(
"/globalscratch/plingras/matt/original/yelp_academic_dataset_business.json",
"r")
for line in businessDataset:
currBusiness = business.Business(line)
businessDict[currBusiness.data["business_id"]] = currBusiness
return businessDict
def test_kmeansBase(self):
data = [
business.Business(10, [0, 0, 0]),
business.Business(20, [1, 1, 1]),
business.Business(30, [2, 2, 2]),
business.Business(411, [10, 10, 10]),
business.Business(511, [11, 11, 11]),
business.Business(611, [12, 12, 12]),
business.Business(7123, [110, 110, 110]),
business.Business(8123, [111, 111, 111]),
business.Business(9123, [112, 112, 112])
]
expected = [
[0, 1, 2],
[3, 4, 5],
[6, 7, 8]
]
manhattan = lambda a, b: distance.manhattan([a], [b])
kMeans = clustering.KMeans(3, featureDistanceMap.FeatureDistanceMap([manhattan, manhattan, manhattan]))
self.assertEqual(sorted(kMeans.cluster(data)), sorted(expected))
def newBusiness(self, boss, busiName=None, capital=1000000):
newBus = None
if busiName is None:
busiName = boss.lastname + " " + random.choice(d.busiNameList)
if boss.capital >= capital:
boss.capital -= capital
newBus = bu.Business([boss], busiName, boss.locality, capital)
boss.businesses.append(newBus)
startupHQ = boss.getHome()
OwnerJob = jobs.Owner(newBus, startupHQ)
self.model.hirer.jobApplication(boss, OwnerJob)
if boss != self.model.char:
i_build = self.model.startupAI.whatToBuild(boss)
self.model.builder.buildChain(boss.businesses[0], i_build)
return newBus
def extractFeatures(rawBusiness, attributeMap, categoryMap, topWordMap, keyWordMap, openHoursMap):
featureVector = []
numericColumns = [
COLUMN_STARS,
COLUMN_TOTAL_REVIEW_COUNT,
COLUMN_AVAILABLE_REVIEW_COUNT,
COLUMN_MEAN_REVIEW_LEN,
COLUMN_MEAN_WORD_LEN,
COLUMN_NUM_WORDS,
COLUMN_MEAN_WORD_COUNT,
COLUMN_TOTAL_HOURS
]
for column in numericColumns:
featureVector.append(rawBusiness[column])
attributes = [attributeMap[ele] for ele in splitWithEmpty(rawBusiness[COLUMN_ATTRIBUTES])]
featureVector.append(attributes)
categories = [categoryMap[ele] for ele in splitWithEmpty(rawBusiness[COLUMN_CATEGORIES])]
featureVector.append(categories)
topWords = [topWordMap[ele] for ele in splitWithEmpty(rawBusiness[COLUMN_TOP_WORDS])]
featureVector.append(topWords)
keyWords = [keyWordMap[ele] for ele in splitWithEmpty(rawBusiness[COLUMN_KEY_WORDS])]
featureVector.append(keyWords)
openHours = [openHoursMap[ele] for ele in splitWithEmpty(rawBusiness[COLUMN_OPEN_HOURS])]
featureVector.append(openHours)
otherInfo = {
"yelpId": rawBusiness[COLUMN_YELP_ID],
"name": rawBusiness[COLUMN_NAME]
}
return business.Business(rawBusiness[COLUMN_ID], featureVector, otherInfo)
import sys
#main
#world generator
worldGen = gen.generator()
BraveNewWorld = worldGen.generateWorld(30, 3, 3)
BraveNewWorld.printMap()
Jonestown = d.getLocality()[0]
#people, unit, business test
JohnDoe = d.peopleList[0]
JaneDoe = d.peopleList[1]
SusanSmith = d.peopleList[2]
MillersMill = b.Business("Miller's Mill", 30)
TheMill = u.Mill("The old mill", Jonestown, (1, 1), MillersMill)
MillersWarehouse = u.Warehouse("Miller's Warehouse", Jonestown, (7, 3),
MillersMill)
Bakery = u.Market("Ye Olde Bakery", Jonestown, (7, 1), MillersMill)
Deli = u.Market("Ye Delicious Deli", Jonestown, (1, 3), MillersMill)
MillingJob = j.Miller(10, MillersMill, TheMill)
Transporter = j.Carrier(1, MillersMill, MillersWarehouse)
ManagerJob = j.Manager(1, MillersMill, MillersWarehouse)
BraveNewWorld.printMap()
Jonestown.printMap()
#jobs test
#world generator
worldGen = gen.generator()
BraveNewWorld = worldGen.generateWorld(30000, 10, 10)
Jonestown = d.getLocality()[0]
Jonestown.week.machine.set_state('Friday')
#people, unit, business test
JohnDoe = d.peopleList[0]
JaneDoe = d.peopleList[1]
JimSmith = d.peopleList[2]
SusanSmith = d.peopleList[3]
JoeJohnson = d.peopleList[4]
SallyJohnson = d.peopleList[5]
MillersMill = b.Business("Miller's Mill", Jonestown, 1000000)
TheMill = u.Mill("The old mill", Jonestown, (1,1), MillersMill)
MillersWarehouse = u.Warehouse("Miller's Warehouse", Jonestown, (7,3), MillersMill)
Bakery = u.Bakery("Ye Olde Bakery", Jonestown, (7,1), MillersMill)
WheatFarm = u.Farm("Johnson's Wheat Farm", Jonestown, (5,1), MillersMill)
BraveNewWorld.printMap()
Jonestown.printMap()
#jobs test
MillingJob = j.Miller(9000, MillersMill, TheMill, 400)
Transporter = j.Carrier(1, MillersMill, MillersWarehouse, 400)
ManagerJob = j.Manager(MillersMill, Bakery, 400)
BakerJob = j.Baker(9000, MillersMill, Bakery, 400)
FarmerJob = j.Farmer(9000,MillersMill, WheatFarm, 400)
import business
my_store = business.Business("Vinyl Store")
my_store.build_customers("customers.txt")
my_store.build_inventory("albums.txt")
my_store.process_commands("commands.txt")
big = max(i, j)
self._distances[self._calcIndex(small, big)] = val
def get(self, i, j):
if (i == j):
return 0
small = min(i, j)
big = max(i, j)
return self._distances[self._calcIndex(small, big)]
if __name__ == '__main__':
data = [
business.Business(1, [0, 0, 0]),
business.Business(2, [1, 1, 1]),
business.Business(3, [2, 2, 2]),
business.Business(4, [10, 10, 10]),
business.Business(5, [11, 11, 11]),
business.Business(6, [12, 12, 12]),
business.Business(7, [110, 110, 110]),
business.Business(8, [111, 111, 111]),
business.Business(9, [112, 112, 112])
]
manhattan = lambda a, b: distance.manhattan([a], [b])
kMeans = KMeans(3, featureDistanceMap.FeatureDistanceMap([manhattan, manhattan, manhattan]))
clusters = kMeans.cluster(data)
