AI-2020/raport.md

Podprojekt Szi

Opis

Tematem podprojektu jest rozpoznawanie zamówień na podstawie historii zamówień. Użyłem drzew decyzyjnych.

Dane

Potrawy, ich nazwa, rodzaj oraz charakterystyka.

tree_format = ["dish", "served", "price", "origin", "cooked", "ingredients", "name"]

Dane uczące:

dish -  (salad/soup/meal/coffee/tea/non-alcho drink)
served - (cold/hot/warm)
origin - (Worldwide/America/Europe/Asia)
cooked - (baked/boiled/mixed)
ingridients - (2/4)

Dane testowe jest tworzone losowo w funkcji:

def client_ordering():
    order = []

    dish = uniq_val_from_data(training_data, 0)
    temperature = uniq_val_from_data(training_data, 1)

    tmpr = random.sample(dish, 1)
    order.append(tmpr[0])

    tmpr = random.sample(temperature, 1)
    order.append(tmpr[0])
    order.append('order')
    return order

Implementacja

####Drzewo:

Klasy: Klasa Question #####Question class Queestion: def init(self, col, value): self.col = col #column self.value = value #value of column

    def compare(self, example):
    #compare val in example with val in the question
        
    def __repr__(self):
    #just to print

Klasa Node
#####Node class Decision_Node(): #contain the question and child nodes def init(self, quest, t_branch, f_branch): self.quest = quest self.t_branch = t_branch self.f_branch = f_branch

#####Leaf class Leaf: #contain a number of how many times the label has appeared in dataset def init(self, rows): self.predicts = uniq_count(rows)

Biblioteki

• random
• numpy