from Seller import Seller
from FloorBoss import FloorBoss
from Owner import Owner
owner = Owner()
boss = FloorBoss(owner)
seller = Seller(boss)
while True:
quantity = int(input())
price = int(input())
print(seller.charge(quantity, price))
def find_income(self, surname):
self.seller_income = Seller.seller_income_by_surname(surname.get())
return ", income: " + self.seller_income
def update_list(self, listbox):
listbox.delete(0, END)
self.seller_list = Seller.show_all_sellers().split('\n')
for seller in self.seller_list:
self.allseller_listbox.insert(END, seller)
return
def update_percent(self, surname, percent):
Seller.update_percent(surname, float(percent.get()))
self.update_list(self.allseller_listbox)
def __init__(self, *args, **kwargs):
Page.__init__(self, *args, **kwargs)
window = Toplevel(self)
window.title("Seller window")
window.state('zoomed')
all_sellers = Frame(window)
all_sellers.pack(side=LEFT, fill=Y)
Label(all_sellers, text="All avaliable sellers").pack()
self.allseller_listbox = Listbox(all_sellers, width=80)
self.allseller_listbox.bind('<Double-1>', self.all_list_on_click)
self.seller_list = Seller.show_all_sellers().split("\n")
self.seller_list.pop(-1)
for seller in self.seller_list:
self.allseller_listbox.insert(END, seller)
self.allseller_listbox.pack(side="top", fill="both")
all_docx = Button(all_sellers,
text="Export info in docx about all sellers",
command=self.form_docx_report())
all_docx.pack()
all_exel = Button(all_sellers,
text="Export info in exel about all sellers",
command=self.form_exel_report())
all_exel.pack()
search_seller = Frame(window)
search_seller.pack(side=LEFT, fill=Y)
Label(search_seller, text="Search seller by surname").pack()
enter_name = Entry(search_seller)
enter_name.pack()
self.find_listbox = Listbox(search_seller, width=80)
self.find_listbox.bind('<Double-1>', self.find_list_on_click)
find_prod = partial(self.search_seller_on_click, self.find_listbox,
enter_name)
search_btn = Button(search_seller, text="Search", command=find_prod)
search_btn.pack()
self.find_listbox.pack()
add_seller = Frame(window)
add_seller.pack(side=LEFT, fill=Y)
Label(add_seller, text="Add new seller").grid(row=0,
column=0,
columnspan=3)
name_label = Label(add_seller, text="Enter sellers` name:")
surname_label = Label(add_seller, text="Enter sellers` surname:")
middle_name_label = Label(add_seller,
text="Enter sellers middle name:")
income_percent_label = Label(add_seller,
text="Enter sellers` income price:")
name_entry = Entry(add_seller)
surname_entry = Entry(add_seller)
middle_name_entry = Entry(add_seller)
income_percent_entry = Entry(add_seller)
add_sel = partial(self.add_seller, name_entry, surname_entry,
middle_name_entry, income_percent_entry,
self.allseller_listbox)
add_btn = Button(add_seller, text="Add", command=add_sel)
name_label.grid(row=1, column=0)
surname_label.grid(row=2, column=0)
middle_name_label.grid(row=3, column=0)
income_percent_label.grid(row=4, column=0)
name_entry.grid(row=1, column=1)
surname_entry.grid(row=2, column=1)
middle_name_entry.grid(row=3, column=1)
income_percent_entry.grid(row=4, column=1)
add_btn.grid(row=5, column=1)
from tensorflow.keras.datasets import cifar100, cifar10
import math
from DModel import DModel
from EA import EA_Buyer
import random
import Utils
dataset = "CIFAR10"
n_classes = 10
u = 1
(trainImages, trainLabels), features, (testX, testY) = Utils.load_CIFAR10()
init_image_ids = np.loadtxt("./" + dataset + "/" + str(u) + "/init.csv")
init_image_ids = [int(v) for v in init_image_ids]
seller = Seller(n_classes, trainImages, trainLabels, init_image_ids)
alloc_strat = 'Squareroot'
for budget in [3000, 5000, 6000, 8000, 10000, 20000, 30000, 39000]:
# for budget in [5000,10000,20000]:
# for l in [0.01,0.03,0.05,0.07,0.09]:
for l in [0.05001]:
seller.reset()
buyer = EA_Buyer(budget, n_classes, features, trainImages, trainLabels,
init_image_ids, seller)
buyer.l = l
buyer.allocation_strategy = alloc_strat
purchase_list = buyer.process()
write_file = open(
"./" + dataset + "/" + str(u) + "/EA-" + alloc_strat + "-" +
def load_dataset():
(trainX, trainY), (testX, testY) = Utils.read_Crop()
features = trainX.copy()
return (trainX, trainY), features, (testX, testY)
(trainX, trainY), features, (testX, testY) = load_dataset()
init_image_ids = np.loadtxt(folder + "init.csv")
init_image_ids = [int(v) for v in init_image_ids]
total_budget = 100001
budget_list = list(np.arange(2000, 100001, 5000))
seller = Seller(n_classes, trainX, trainY, init_image_ids)
# purchase step
for _ in range(10):
seller.reset()
model = CModel()
buyer = RD_Buyer(total_budget, n_classes, features, trainX, trainY,
init_image_ids, model, seller)
buyer.batch_size = 100
purchase_list = buyer.process()
for budget in budget_list:
write_file = open(folder + "RD-" + str(budget), 'a')
write_file.write(' '.join([str(v)
for v in purchase_list[:budget]]) + "\n")
write_file.flush()
write_file.close()
import Utils
from RModel import RModel
import random
import Utils
u=1
n_regions = 16
(trainX, trainY), features, (testX, testY) = Utils.load_RoadNetd()
folder = "./roadnet/roadnet" + str(n_regions) + "/"
init_image_ids = np.loadtxt(folder + str(u) + "/init.csv")
init_image_ids = [int(v) for v in init_image_ids]
all_budgets = list(range(2000,300000,2000))
alloc_strat = 'Linear'
seller = Seller(n_regions, trainX, trainY, init_image_ids, False)
for l in [0.001]:
# data acquisition
for budget in all_budgets:
write_file = open(folder + str(u) + "/EA-" + str(budget), 'w')
for _ in range(10):
seller.reset()
buyer = EA_Buyer(budget, n_regions, features, trainX, trainY, init_image_ids, seller)
buyer.chenge_to_regression()
buyer.allocation_strategy = alloc_strat
buyer.l = l
purchase_list = buyer.process()
write_file.write(' '.join([str(v) for v in purchase_list]))
write_file.flush()
write_file.close()