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.

menu = Context.fromstring(''' |meat|salad|meal|drink|cold|hot |
                   Pork       |  X |     |    |     |    |  X |
                   Espresso   |    |     |    |  X  |    |  X |
                   Latte      |    |     |    |  X  |    |  X |
                   Green Tea  |    |     |    |  X  | X  |    |
                   Greek Salad|    |  X  |    |     | X  |    |
                   Pizza      |    |     |  X |     |    |  X |''')

Dane uczące:

training_data = [
    ['meat','hot','Pork'],
    ['salad','cold','Greek Salad'],
    ['drink','hot','Espresso'],
    ['drink','hot','Latte'],
    ['drink','cold','Green Tea'],
    ['meal','hot','Pizza'],
    ['meal','cold','Wheat Pita'],
]

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:

#####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

#####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

• concepts
• random
• numpy