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added proper implementation for learn in decisiontree

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Lukas K
2024-10-26 13:35:05 +02:00
parent 3ff4a05d7f
commit e5099b5acc
2 changed files with 50 additions and 24 deletions
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45
src/classes/decision_tree.py
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@@ -17,40 +17,49 @@ class DecisionTree:
# TODO
# IMPLEMENTATION IS NOT WORKING
def classify(self, feature_vector: FeatureVector) -> tuple[Concept, int]:
def classify(self, feature_vector: FeatureVector) -> Concept:
if len(self.children) == 0:
return self.concept, self.depth
return self.concept
feature_value = feature_vector.features_list[self.depth] # get feature value of vector based on current depth
try:
new_decision_tree: Self = self.children[feature_value] # get sub decition tree for feature
except KeyError:
return Concept.UNKNOWN, self.depth
return Concept.UNKNOWN
return new_decision_tree.classify(feature_vector) # classify new desicion tree
def learn(self, training_set: list[FeatureVector]) -> None:
i = 0
while not self._finished(i, len(training_set)*2):
while not self._finished(i, len(training_set)):
vector: FeatureVector = training_set[i % len(training_set)]
concept, depth = self.classify(vector)
print(f"concept is {vector.concept}, was classified as {concept} in depth: {depth}")
if concept == Concept.UNKNOWN:
self.concept = vector.concept
i+=1
elif concept == vector.concept:
i+=1
concept = self.classify(vector)
#print(f"{i}\t: actual: {vector.concept} \t classified as: {concept}")
if concept == vector.concept:
i += 1
else:
self.concept = Concept.UNKNOWN
self.children.update({vector.features_list[depth]: DecisionTree(concept=vector.concept, parent=self, depth=depth+1, children={})})
self._insert(vector)
#i += 1
def _insert(self, vector: FeatureVector) -> None:
if self.concept == Concept.UNKNOWN:
self.concept = vector.concept
else:
feature_value = vector.features_list[self.depth]
try:
sub_decision_tree: Self = self.children[feature_value] # get sub decition tree for feature
sub_decision_tree._insert(vector)
except KeyError:
self.children.update({vector.features_list[self.depth]: DecisionTree(concept=vector.concept, parent=self, depth=self.depth+1, children={})})
def _finished(self, i: int, num_training_data: int):
# TODO implement proper finished condition
if i < num_training_data:
if i < num_training_data*3:
return False
return True