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dataGen.py
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from numpy.random import choice
import pandas as pd
def normalize(arr):
m = sum(arr)
return [float(e) / m for e in arr]
def grade_food(foods, age, gender, eth):
l = []
for food in foods:
p = 0.1
if "chicken" in food.lower():
p += 1
if "nuggets" in food.lower():
p += 0.6
if age < 12:
p += 0.4
if "sandwich" in food.lower():
p += 0.8
if "salad" in food.lower():
p += 0.2
if gender == "female":
p += 0.2
if eth == "indian":
p += 0.4
if age < 12:
p -= 0.2
if "deluxe" in food.lower():
p -= 0.4
if "soup" in food.lower():
p -= 0.7
l.append(p)
return normalize(l)
food = [
"Spicy Chicken Sandwich",
"Chicken Sandwich",
"Chiken Nuggets 10pc",
"Chiken Nuggets 6pc",
"Salad Meal",
"Grilled Chicken Sandwich",
"Spicy Deluxe Sandwich",
"chicken noodle soup",
"chicken tortilla soup",
"nothing",
]
sides = [
"Hashbrowns",
"Fruit Cup",
# "Hashbrown scramble burrito",
# "Hashbrown scramble bowl",
"side salad",
"waffle fries",
"superfood side",
"nothing",
]
def grade_side(sides, age, gender, ethnic):
l = []
for side in sides:
p = 0.3
if "fries" in side.lower():
if age < 12:
p += 0.3
p += 2
if "hashbrown" in side.lower():
p += 1
if "nothing" in side.lower():
if age > 40:
p += 0.4
p += 0.5
l.append(p)
return normalize(l)
drink = ["lemonade", "Milkshake", "soda", "water", "nothing", "tea"]
def grade_drink(age, gender, eth):
if gender == "male":
if age < 15:
return normalize([4, 7, 8, 3, 2, 1])
return normalize([3, 5, 7, 5, 3, 3])
else:
if age < 15:
return normalize([6, 9, 5, 4, 2, 6])
return normalize([4, 10, 3, 6, 2, 8])
def grade_age(age):
if age < 5:
return 1
elif age < 10:
return 2
elif age < 15:
return 4
elif age < 20:
return 9
elif age < 35:
return 20
elif age < 45:
return 8
elif age < 65:
return 3
else:
return 0.5
ages = range(100)
age_p = normalize([grade_age(e) for e in ages])
ethnic = ["white", "black", "india", "asia"]
eth_mapper = {}
for i, e in enumerate(ethnic):
eth_mapper[e] = i
ethnic_p = normalize([18, 8, 5, 5])
gender = ["male", "female"]
gender_p = [0.6, 0.4]
lista = [["age", "gender", "ethnic_origin", "food", "side", "drink"]]
for i in range(1000):
i_age = choice(ages, p=age_p)
i_gender = choice(gender, p=gender_p)
i_ethnic = choice(ethnic, p=ethnic_p)
i_food = choice(food, p=grade_food(food, i_age, i_gender, i_ethnic))
i_sides = choice(sides, p=grade_side(sides, i_age, i_gender, i_ethnic))
i_drink = choice(drink, p=grade_drink(i_age, i_gender, i_ethnic))
lista.append([i_age, i_gender, i_ethnic, i_food, i_sides, i_drink])
df = pd.DataFrame(lista[1:], columns=lista[0])
df[["age", "gender", "ethnic_origin", "drink"]].to_csv("drink.csv", index=False)
df[["age", "gender", "ethnic_origin", "food"]].to_csv("food.csv", index=False)
df[["age", "gender", "ethnic_origin", "side"]].to_csv("side.csv", index=False)
# def longestSequence(node, maxi=float('-inf'), cur_count=0):
# maxx = maxi
# if cur_count > maxi:
# maxx = cur_count
# if node.right != None and node.left != None:
# return max(longestSequence(node.right, maxi=maxx, cur_count=cur_count + 1), longestSequence(node.left, maxi=maxx))
# elif node.right != None:
# return longestSequence(node.right, maxi=maxx, cur_count=cur_count + 1)
# elif node.left != None:
# return longestSequence(node.left, maxi=maxx)
# else:
# return maxx + 1
# def longestSequence(node):
# if node.right != None and node.left != None:
# return max(longestSequence(node.right) + 1, longestSequence(node.left))
# elif node.right != None:
# return longestSequence(node.right) + 1
# elif node.left != None:
# return longestSequence(node.left, maxi=maxx)
# else:
# return 1