si23traktor/learn_tree.py
2023-06-04 10:35:05 +02:00

51 lines
1.6 KiB
Python

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