76 lines
1.7 KiB
Markdown
76 lines
1.7 KiB
Markdown
# Podprojekt Szi
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### Opis
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Tematem podprojektu jest rozpoznawanie zamówień na podstawie historii zamówień.
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Użyłem drzew decyzyjnych.
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### Dane
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Potrawy, ich nazwa, rodzaj oraz charakterystyka.
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tree_format = ["dish", "served", "price", "origin", "cooked", "ingredients", "name"]
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Dane uczące:
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dish - (salad/soup/meal/coffee/tea/non-alcho drink)
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served - (cold/hot/warm)
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origin - (Worldwide/America/Europe/Asia)
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cooked - (baked/boiled/mixed)
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ingridients - (2/4)
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Dane testowe jest tworzone losowo w funkcji:
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def client_ordering():
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order = []
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dish = uniq_val_from_data(training_data, 0)
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temperature = uniq_val_from_data(training_data, 1)
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tmpr = random.sample(dish, 1)
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order.append(tmpr[0])
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tmpr = random.sample(temperature, 1)
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order.append(tmpr[0])
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order.append('order')
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return order
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### Implementacja
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#### Drzewo:
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Klasy:
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##### Question
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class Queestion:
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def __init__(self, col, value):
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self.col = col #column
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self.value = value #value of column
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def compare(self, example):
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#compare val in example with val in the question
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def __repr__(self):
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#just to print
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##### Node
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class Decision_Node():
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#contain the question and child nodes
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def __init__(self, quest, t_branch, f_branch):
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self.quest = quest
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self.t_branch = t_branch
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self.f_branch = f_branch
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##### Leaf
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class Leaf:
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#contain a number of how many times the label has appeared in dataset
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def __init__(self, rows):
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self.predicts = uniq_count(rows)
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### Biblioteki
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* random
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* numpy
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