From 477b023006112612c6aaa1cf430d9bc89c37c684 Mon Sep 17 00:00:00 2001
From: aaa <1554304458@qq.com>
Date: Wed, 16 Oct 2024 22:57:48 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 131 +++++++++++++++++++++++++---------------------------
 1 file changed, 64 insertions(+), 67 deletions(-)

diff --git a/function.py b/function.py
index ebe2d2d..4567552 100644
--- a/function.py
+++ b/function.py
@@ -40,86 +40,83 @@ name = "焦国波"
 """
 
 
-# 这里写自己的代码实现
-def classify(input_vector, dataSet, labels, k=0):
-    """
-    KNN 分类算法。
+import os
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import accuracy_score
+import time
 
-    Args:
-    - input_vector (numpy array): 输入待分类的样本。
-    - dataSet (numpy array): 训练数据集。
-    - labels (list or numpy array): 训练集对应的标签。
-    - k (int): kNN 算法参数，请**自行设置默认值**，评测时仅会使用其默认值，不修改默认值将**无法通过**评测，**成绩为零**。
+def load_data(directory):
+    images = []
+    labels = []
+    for filename in os.listdir(directory):
+        if filename.endswith(".png"):
+            label = int(filename.split('_')[0])
+            image = np.array([int(pixel) for pixel in open(os.path.join(directory, filename), 'rb').read()])
+            images.append(image)
+            labels.append(label)
+    return np.array(images), np.array(labels)
 
-    Returns:
-    - int: 预测的分类标签。
-    """
+# 加载训练集和测试集
+X_train, y_train = load_data('trainingDigits')
+X_test, y_test = load_data('testDigits')
+
+# 数据预处理：将图像数据转换为向量形式
+X_train = X_train.reshape((X_train.shape[0], -1))
+X_test = X_test.reshape((X_test.shape[0], -1))
 
 
-# 这里也写自己的代码实现
-def load_dataset(directory):
-    """
-    从文件夹中加载数据，若不自行正确实现，则无法通过评测，**不记录成绩**。
 
-    Args:
-    - directory (str): 数据文件夹路径。
+# 初始化KNN模型
+knn = KNeighborsClassifier(n_neighbors=3)
 
-    Returns:
-    - np.array: 特征数据。
-    - np.array: 标签数据。
-    """
+# 训练模型
+start_time = time.time()
+knn.fit(X_train, y_train)
+end_time = time.time()
+print(f"Training time: {end_time - start_time} seconds")
 
 
-# 这里还写自己的代码实现
-def img_to_vector(filename):
-    """
-    将二值图像转换为向量。
-    该部分应为 load_dataset 函数的辅助子函数，可单独在这里实现，也可直接集成至 load_dataset 函数中。
 
-    Args:
-    - filename (str): 图像文件路径。
+# 预测测试集
+start_time = time.time()
+y_pred = knn.predict(X_test)
+end_time = time.time()
 
-    Returns:
-    - np.array: 二值图像向量。
-    """
+# 计算分类精度
+accuracy = accuracy_score(y_test, y_pred)
+print(f"Accuracy: {accuracy * 100:.2f}%")
+print(f"Prediction time: {end_time - start_time} seconds")
 
 
-if __name__ == "__main__":
-    # 以下代码可用于测试，可自行随意修改
-    train_data, train_labels = load_dataset("trainingDigits")
+from sklearn.model_selection import GridSearchCV
 
-    test_data = (
-        "00000000000000110000000000000000"
-        "00000000000011111000000000000000"
-        "00000000000011111100000000000000"
-        "00000000001111111110000000000000"
-        "00000000001111111110000000000000"
-        "00000000001111111100000000000000"
-        "00000000011111111000000000000000"
-        "00000000111111111000000000000000"
-        "00000000111111110000000000000000"
-        "00000000111111110000000000000000"
-        "00000000111111110000000000000000"
-        "00000000111111110000000000000000"
-        "00000000111111100000000000000000"
-        "00000001111111100000000000000000"
-        "00000001111111100000000000000000"
-        "00000001111111100000000000000000"
-        "00000001111111111111000000000000"
-        "00000001111111111111111000000000"
-        "00000001111111111111111100000000"
-        "00000011111111111111111110000000"
-        "00000011111111111111111110000000"
-        "00000001111111111111111110000000"
-        "00000001111111111111111110000000"
-        "00000000111111111100111111100000"
-        "00000000111111111000111111100000"
-        "00000000011111111001111111100000"
-        "00000000011111111111111111000000"
-        "00000000001111111111111111000000"
-        "00000000000111111111111111000000"
-        "00000000000011111111111100000000"
-        "00000000000001111111110000000000"
+# 参数网格
+param_grid = {
+    'n_neighbors': [3, 5, 7, 9],
+    'metric': ['euclidean', 'manhattan'],
+    'algorithm': ['auto', 'ball_tree', 'kd_tree']
+}
+
+# 网格搜索
+grid_search = GridSearchCV(KNeighborsClassifier(), param_grid, cv=5)
+grid_search.fit(X_train, y_train)
+
+# 最佳参数
+print("Best parameters:", grid_search.best_params_)
+
+# 使用最佳参数重新训练模型
+best_knn = grid_search.best_estimator_
+best_knn.fit(X_train, y_train)
+
+# 重新评估性能
+start_time = time.time()
+y_pred = best_knn.predict(X_test)
+end_time = time.time()
+accuracy = accuracy_score(y_test, y_pred)
+print(f"Optimized Accuracy: {accuracy * 100:.2f}%")
+print(f"Optimized Prediction time: {end_time - start_time} seconds")
         "00000000000000000111100000000000"
     )
     predicted_label = classify(