51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
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from collections import Counter
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def tree_learn(examples, attributes, default_class):
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if len(examples) == 0:
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return default_class
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if all(examples[0][-1] == example[-1] for example in examples):
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return examples[0][-1]
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if len(attributes) == 0:
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class_counts = Counter(example[-1] for example in examples)
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majority_class = class_counts.most_common(1)[0][0]
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return majority_class
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# Choose the attribute A as the root of the decision tree
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A = select_attribute(attributes, examples)
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tree = {A: {}}
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new_attributes = [attr for attr in attributes if attr != A]
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new_default_class = Counter(example[-1] for example in examples).most_common(1)[0][0]
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for value in get_attribute_values(A):
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new_examples = [example for example in examples if example[attributes.index(A)] == value]
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subtree = tree_learn(new_examples, new_attributes, new_default_class)
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tree[A][value] = subtree
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return tree
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# Helper function: Select the best attribute based on a certain criterion (e.g., information gain)
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def select_attribute(attributes, examples):
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# Implement your attribute selection criterion here
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pass
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# Helper function: Get the possible values of an attribute from the examples
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def get_attribute_values(attribute):
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# Implement your code to retrieve the attribute values from the examples here
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pass
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# Example usage with coordinates
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examples = [
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[1, 2, 'A'],
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[3, 4, 'A'],
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[5, 6, 'B'],
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[7, 8, 'B']
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]
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attributes = ['x', 'y']
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default_class = 'unknown'
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decision_tree = tree_learn(examples, attributes, default_class)
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print(decision_tree)
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