From 316a137544cd4ac0d100854e655fcce384f3f9ee Mon Sep 17 00:00:00 2001
From: kxz <2897100651@qq.com>
Date: Tue, 8 Oct 2024 20:45:55 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 24 +-----------------------
 1 file changed, 1 insertion(+), 23 deletions(-)

diff --git a/function.py b/function.py
index 30f316d..2496580 100644
--- a/function.py
+++ b/function.py
@@ -83,25 +83,11 @@ def classify0(inX, dataSet, labels, k=3):
     else:
         return -1
 
-def load_data(file_path):
-    data = np.loadtxt(file_path, delimiter="\t")
-    features = data[:, :-1]
-    labels = data[:, -1].astype(int)
-    return features, labels
-
-def normalize(features):
-    min_vals = features.min(axis=0)
-    max_vals = features.max(axis=0)
-    ranges = max_vals - min_vals
-    normalized = (features - min_vals) / ranges
-    normalized[ranges == 0] = 0  # 防止除以零的情况
-    return normalized
 
 if __name__ == "__main__":
+    # 假设 load_data 和 normalize 函数已经定义
     train_features, train_labels = load_data("datingTrainSet2.tsv")
     test_features, test_labels = load_data("datingTestSet2.tsv")
-
-    # 在这里进行归一化处理
     train_features = normalize(train_features)
     test_features = normalize(test_features)
 
@@ -124,11 +110,3 @@ if __name__ == "__main__":
             best_k = k
 
     print(f"Best K: {best_k}, Best Accuracy: {best_accuracy:.2f}")
-
-        accuracy = correct_count / len(test_features)
-        print(f"Accuracy with k={k}: {accuracy:.2f}")
-        if accuracy > best_accuracy:
-            best_accuracy = accuracy
-            best_k = k
-
-    print(f"Best K: {best_k}, Best Accuracy: {best_accuracy:.2f}")
\ No newline at end of file