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:

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

random
numpy

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