def selection(inventory, revenue):
"Takes in the item and time and checks its corresponding inventory"
print "Selection called"
bernoulli_n = 1
if (inventory.cur < inventory.maxi):
decision = 0
number_items_dp = 0
else: # add item to selection with some probability
number_items_dp = inventory.cur - inventory.maxi
# Commpute the probability
prob = (float)(revenue.dp / (revenue.dp + revenue.cp))
# make a Bernoulli draw with probability "prob"
#decision=bernoulli.rvs(prob,loc=0,size=1) # part of scipy
decision = binomial(bernoulli_n, prob, 1)
it = d.get1(db_name, 'product', inventory.id)
it['number_items_dp'] = number_items_dp
it['decision'] = it['decision']
d.replace1(db_name, 'product', it, it['id'])
return decision
def selection(inventory,revenue):
"Takes in the item and time and checks its corresponding inventory"
print "Selection called"
bernoulli_n=1
if(inventory.cur<inventory.maxi):
decision=0
number_items_dp=0
else:# add item to selection with some probability
number_items_dp=inventory.cur-inventory.maxi
# Commpute the probability
prob=(float)(revenue.dp/(revenue.dp+revenue.cp))
# make a Bernoulli draw with probability "prob"
#decision=bernoulli.rvs(prob,loc=0,size=1) # part of scipy
decision=binomial(bernoulli_n,prob,1)
it=d.get1(db_name,'product',inventory.id)
it['number_items_dp']=number_items_dp
it['decision']=it['decision']
d.replace1(db_name,'product',it,it['id'])
return decision
def bidding(item, buyer_offer, customer_loyalty, buyer_demand, profit_margin):
it = d.get1(db_name, 'product', item)
return bidding_compute(it, buyer_offer, customer_loyalty, buyer_demand,
profit_margin)
def bidding(item, buyer_offer,customer_loyalty,buyer_demand,profit_margin):
it=d.get1(db_name,'product',item)
bidding_compute(it,buyer_offer,customer_loyalty,buyer_demand,profit_margin)
return