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docs: 📝 shorten comment lines length to <80

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Ricardo Montañana Gómez
2023-01-30 19:27:27 +01:00
parent 6f9488f281
commit 8089e4fd57
1 changed files with 14 additions and 7 deletions
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bayesclass/clfs.py
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@@ -260,7 +260,8 @@ class TAN(BayesBase):
return X, y
def _build(self):
# est = TreeSearch(self.dataset_, root_node=self. feature_names_in_[self.head_])
# est = TreeSearch(self.dataset_,
# root_node=self.feature_names_in_[self.head_])
# self.dag_ = est.estimate(
# estimator_type="tan",
# class_node=self.class_name_,
@@ -326,17 +327,23 @@ class KDB(BayesBase):
def _build(self):
"""
1. For each feature Xi, compute mutual information, I(X;;C), where C is the class.
2. Compute class conditional mutual information I(Xi;XjIC), f or each pair of features Xi and Xj, where i#j.
1. For each feature Xi, compute mutual information, I(X;;C),
where C is the class.
2. Compute class conditional mutual information I(Xi;XjIC), f or each
pair of features Xi and Xj, where i#j.
3. Let the used variable list, S, be empty.
4. Let the Bayesian network being constructed, BN, begin with a single class node, C.
4. Let the Bayesian network being constructed, BN, begin with a single
class node, C.
5. Repeat until S includes all domain features
5.1. Select feature Xmax which is not in S and has the largest value I(Xmax;C).
5.1. Select feature Xmax which is not in S and has the largest value
I(Xmax;C).
5.2. Add a node to BN representing Xmax.
5.3. Add an arc from C to Xmax in BN.
5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with the highest value for I(Xmax;X,jC).
5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with
the highest value for I(Xmax;X,jC).
5.5. Add Xmax to S.
Compute the conditional probabilility infered by the structure of BN by using counts from DB, and output BN.
Compute the conditional probabilility infered by the structure of BN by
using counts from DB, and output BN.
"""
# 1. get the mutual information between each feature and the class