generated from LufsX/Experiment-kNN
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+25
-34
@@ -1,6 +1,7 @@
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# function.py
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import numpy as np
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import os
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from collections import Counter
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"""
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实验说明:
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@@ -25,8 +26,8 @@ import os
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"""
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请在这里填写学号和姓名,不填写即成绩无效。
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"""
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id = "202210423xxx"
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name = "test_name"
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id = "202210423204"
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name = "陈垣豪"
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"""
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@@ -41,48 +42,38 @@ name = "test_name"
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# 这里写自己的代码实现
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def classify(input_vector, dataSet, labels, k=0):
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"""
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KNN 分类算法。
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Args:
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- input_vector (numpy array): 输入待分类的样本。
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- dataSet (numpy array): 训练数据集。
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- labels (list or numpy array): 训练集对应的标签。
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- 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**。
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Returns:
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- int: 预测的分类标签。
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"""
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def classify(input_vector, dataSet, labels, k=3):
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distances = np.linalg.norm(dataSet - input_vector, axis=1)
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nearest_indices = np.argsort(distances)[:k]
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nearest_labels = [labels[i] for i in nearest_indices]
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most_common_label = Counter(nearest_labels).most_common(1)[0][0]
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return most_common_label
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# 这里也写自己的代码实现
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def load_dataset(directory):
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"""
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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**。
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file_list = os.listdir(directory)
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num_files = len(file_list)
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dataSet = np.zeros((num_files, 1024))
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labels = []
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Args:
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- directory (str): 数据文件夹路径。
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for i, filename in enumerate(file_list):
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label = int(filename.split('_')[0])
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labels.append(label)
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dataSet[i, :] = img_to_vector(os.path.join(directory, filename))
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Returns:
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- np.array: 特征数据。
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- np.array: 标签数据。
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"""
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return dataSet, np.array(labels)
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# 这里还写自己的代码实现
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def img_to_vector(filename):
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"""
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将二值图像转换为向量。
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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。
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Args:
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- filename (str): 图像文件路径。
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Returns:
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- np.array: 二值图像向量。
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"""
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return_vector = np.zeros((1, 1024))
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with open(filename) as f:
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for i in range(32):
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line_str = f.readline()
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for j in range(32):
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return_vector[0, i * 32 + j] = int(line_str[j])
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return return_vector
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if __name__ == "__main__":
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# 以下代码可用于测试,可自行随意修改
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