def singleStore(self, lst):
targLst = []
wallLst = []
a = heap.heap(1)
b = heap.heap(2)
for i in lst:
print(i.data)
# print(i.data)
if i.data[3] == "target":
targLst.append(i)
self.cursor.execute("SELECT * FROM search_type WHERE id = %s", (i.data[5],))
searchTerm = self.cursor.fetchone()[1]
c = bayesFilter.naivebayes(bayesFilter.getwords, searchTerm, self.database, self.cursor)
self.cursor.execute("SELECT * FROM search_type WHERE search = %s", (searchTerm,))
results = self.cursor.fetchall()
for j in results:
if j[3] == "walmart":
pass
else:
toup = (j.data[0], j.data[1], j.data[2], j.data[3], c.prob(j.data[0], 1), j.data[4])
a.push(toup)
targLst.append(a.sort()[0])
else:
wallLst.append(i)
self.cursor.execute("SELECT * FROM search_type WHERE id = %s", (i.data[5],))
searchTerm = self.cursor.fetchone()[1]
c = bayesFilter.naivebayes(bayesFilter.getwords, searchTerm, self.database, self.cursor)
self.cursor.execute("SELECT * FROM search_type WHERE search = %s", (searchTerm,))
results = self.cursor.fetchall()
# print(results)
for j in results:
self.cursor.execute("SELECT * FROM food WHERE id = %s", (j[0],))
r = self.cursor.fetchone()
if r[3] == "target":
pass
else:
toup = (r[0], r[1], r[2], r[3], c.prob(r[0], 1), r[4])
a.push(toup)
wallLst.append(a.sort()[0])
targTot = 0
wallTot = 0
for i in targLst:
targTot += i.data[4]
for i in wallLst:
wallTot += i.data[4]
print(targTot)
print(wallTot)
return (targLst,wallLst)
def training(self, item):
lst = self.search(item,5,2)
for i in lst:
print(i.data)
c = bayesFilter.naivebayes(bayesFilter.getwords, item, self.database, self.cursor)
print("\nbegin input\n")
for i in lst:
print(i.data)
yn = input("y/n")
if(yn == 'y'):
print("yay")
c.train(i.data[0],1)
elif(yn =='n'):
print("boo")
c.train(i.data[0], 0)
else:
for k in lst:
print(k.data)
print(str(c.prob(k.data[0],"good")))
# print(c.prob(k.data[0], "good"))
# print(k.data[0] + " : " + str(c.prob(k.data[0], "good")) + " : " + str(c.classify(k.data[0])))
try:
# print(k.data[0])
# print(str(k.data[0]) + " : " + str(c.prob(k.data[0],"good")))
pass
# print(k.data[0] + " : " + str(c.prob(k.data[0], "good")) + " : " + str(c.classify(k.data[0])))
except:
print("aww man I suck")
print(k.data)
return
# print(i.data[0] + " : " + str(c.prob(i.data[0], "good")) + " : " + str(c.classify(i.data[0])))
return
def search(self, searchTerm, glst, quantity):
c = bayesFilter.naivebayes(bayesFilter.getwords, searchTerm, self.database, self.cursor)
querySearchTerm = ("SELECT * FROM search WHERE name = %s")
self.cursor.execute(querySearchTerm, (searchTerm,))
results = self.cursor.fetchall()
if len(results) == 0:
self.buildData(searchTerm)
self.search(searchTerm,glst, quantity)
return
querySearch = ("SELECT * FROM search_type WHERE search = %s")
self.cursor.execute(querySearch, (searchTerm,))
curs1 = self.cursor.fetchall()
# results = cursor.fetchall()
lst = []
for i in curs1:
queryItem = ("SELECT * FROM food WHERE id = %s")
self.cursor.execute(queryItem, (i[0],))
r = self.cursor.fetchone()
# print(r)
toup = (r[0], r[1], r[2], r[3], r[1]/r[2], r[4])
lst.append(toup)
h = heap.heap(1)
# print("hi")
for i in lst:
h.push(i)
finalForm = h.sort()
for i in finalForm:
print(i.data)
yn = input("is this the item you are looking for? y/n")
if yn == "y":
c.train(i.data[0],1)
self.cursor.execute("INSERT INTO list_items "
"(food_id, list_id, quantity) "
"VALUES (%s,%s,%s)",(i.data[5],glst,quantity))
self.database.commit()
self.cursor.execute("UPDATE search_type SET points = points + 1 WHERE id = %s", (i.data[5],))
self.database.commit()
# print(finalForm)
return finalForm
c.train(i.data[0],0)
return finalForm
