简化

luokn · Jul 21, 2021 · c1c39bd · c1c39bd
1 parent cab2970
commit c1c39bd
Showing 1 changed file with 7 additions and 7 deletions.
diff --git a/AdaBoost/adaboost.py b/AdaBoost/adaboost.py
@@ -3,6 +3,7 @@
 # @Author: Luokun
 # @Email : olooook@outlook.com
 
+
 import numpy as np
 from matplotlib import pyplot as plt
 
@@ -42,18 +43,17 @@ def __call__(self, X: np.ndarray):
 class WeakEstimator:  # 弱分类器, 一阶决策树
     def __init__(self, lr: float):
         self.lr = lr
-        # 划分特征、划分阈值，符号{-1，1}
-        self.feature, self.threshold, self.sign = None, None, None
+        self.feature, self.threshold, self.sign = None, None, None  # 划分特征、划分阈值，符号{-1，1}
 
     def fit(self, X: np.ndarray, Y: np.ndarray, weights: np.ndarray):
-        error = float('inf')  # 最小带权误差
+        min_error = float('inf')  # 最小带权误差
         for feature, x in enumerate(X.T):
             for threshold in np.arange(np.min(x) - self.lr, np.max(x) + self.lr, self.lr):
                 for sign in [1, -1]:
-                    e = np.sum(weights[np.where(x > threshold, sign, -sign) != Y])  # 取分类错误的样本权重求和
-                    if e < error:
-                        self.feature, self.threshold, self.sign, error = feature, threshold, sign, e
-        return error
+                    error = np.sum(weights[np.where(x > threshold, sign, -sign) != Y])  # 取分类错误的样本权重求和
+                    if error < min_error:
+                        self.feature, self.threshold, self.sign, min_error = feature, threshold, sign, error
+        return min_error
 
     def __call__(self, X: np.ndarray):
         return np.where(X[:, self.feature] > self.threshold, self.sign, -self.sign)