Use descriptive parameter names in Gaussian Naive Bayes

Author: shivasubrahmanya

machine_learning/naive_bayes.py

@@ -8,11 +8,10 @@
 https://en.wikipedia.org/wiki/Naive_Bayes_classifier
 """
 
-from typing import Dict, List, Tuple
 import math
 
 
-def gaussian_probability(x: float, mean: float, variance: float) -> float:
+def gaussian_probability(value: float, mean: float, variance: float) -> float:
     """
     Calculate Gaussian probability density.
 
@@ -24,7 +23,7 @@ def gaussian_probability(x: float, mean: float, variance: float) -> float:
     if variance == 0.0:
         return 0.0
 
-    exponent = math.exp(-((x - mean) ** 2) / (2.0 * variance))
+    exponent = math.exp(-((value - mean) ** 2) / (2.0 * variance))
     coefficient = 1.0 / math.sqrt(2.0 * math.pi * variance)
     return coefficient * exponent
 
@@ -35,11 +34,11 @@ class GaussianNaiveBayes:
     """
 
     def __init__(self) -> None:
-        self.class_priors: Dict[int, float] = {}
-        self.means: Dict[int, List[float]] = {}
-        self.variances: Dict[int, List[float]] = {}
+        self.class_priors: dict[int, float] = {}
+        self.means: dict[int, list[float]] = {}
+        self.variances: dict[int, list[float]] = {}
 
-    def fit(self, features: List[List[float]], labels: List[int]) -> None:
+    def fit(self, features: list[list[float]], labels: list[int]) -> None:
         """
         Train the Gaussian Naive Bayes classifier.
 
@@ -53,7 +52,7 @@ def fit(self, features: List[List[float]], labels: List[int]) -> None:
         if len(features) != len(labels):
             raise ValueError("Features and labels must have the same length")
 
-        separated: Dict[int, List[List[float]]] = {}
+        separated: dict[int, list[list[float]]] = {}
         for feature_vector, label in zip(features, labels):
             separated.setdefault(label, []).append(feature_vector)
 
@@ -63,13 +62,14 @@ def fit(self, features: List[List[float]], labels: List[int]) -> None:
             self.class_priors[label] = len(rows) / total_samples
 
             columns = list(zip(*rows))
-            self.means[label] = [sum(col) / len(col) for col in columns]
+            self.means[label] = [sum(column) / len(column) for column in columns]
             self.variances[label] = [
-                sum((x - mean) ** 2 for x in col) / len(col)
-                for col, mean in zip(columns, self.means[label])
+                sum((feature_value - mean) ** 2 for feature_value in column)
+                / len(column)
+                for column, mean in zip(columns, self.means[label])
             ]
 
-    def predict(self, features: List[List[float]]) -> List[int]:
+    def predict(self, features: list[list[float]]) -> list[int]:
         """
         Predict class labels for input features.
 
@@ -83,10 +83,10 @@ def predict(self, features: List[List[float]]) -> List[int]:
         >>> model.predict([[1.5], [3.5]])
         [0, 1]
         """
-        predictions: List[int] = []
+        predictions: list[int] = []
 
         for row in features:
-            scores: List[Tuple[int, float]] = []
+            scores: list[tuple[int, float]] = []
 
             for label in self.class_priors:
                 log_likelihood = math.log(self.class_priors[label])