From 52d3419832d35905692205b13a3fe41b7dd93b58 Mon Sep 17 00:00:00 2001
From: kxz <2897100651@qq.com>
Date: Tue, 8 Oct 2024 20:34:57 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 25 ++++++++++++++++++++++++-
 1 file changed, 24 insertions(+), 1 deletion(-)

diff --git a/function.py b/function.py
index 907ba41..8d0f08f 100644
--- a/function.py
+++ b/function.py
@@ -83,10 +83,25 @@ 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)
 
@@ -110,5 +125,13 @@ if __name__ == "__main__":
 
     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}")
+