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raport.md
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raport.md
@ -23,6 +23,7 @@ Potrawy, ich nazwa, rodzaj oraz charakterystyka.
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Dane uczące:
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Dane uczące:
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training_data = [
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training_data = [
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['meat','hot','Pork'],
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['meat','hot','Pork'],
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['salad','cold','Greek Salad'],
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['salad','cold','Greek Salad'],
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@ -30,30 +31,62 @@ Dane uczące:
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['drink','hot','Latte'],
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['drink','hot','Latte'],
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['drink','cold','Green Tea'],
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['drink','cold','Green Tea'],
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['meal','hot','Pizza'],
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['meal','hot','Pizza'],
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['meal','cold','Wheat Pita'],
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]
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]
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Dane testowe:
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Dane testowe jest tworzone losowo w funkcji:
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test_data = [
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['meat','hot','Latte'],
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def client_ordering():
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['salad','hot','Greek Salad'],
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order = []
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['drink','hot','Pork'],
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['drink','cold','Green Tea'],
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dish = uniq_val_from_data(training_data, 0)
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['drink','hot','Greek Salad'],
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temperature = uniq_val_from_data(training_data, 1)
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]
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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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### Implementacja
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Główna część:
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####Drzewo:
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In process
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Klasy:
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...
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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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### Biblioteki
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