generated from LufsX/Experiment-kNN
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+57
-53
@@ -25,8 +25,8 @@ 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 = "202210423109"
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name = "孔祥芷"
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"""
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@@ -41,25 +41,39 @@ 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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def classify(input_vector, dataSet, labels, k=3):
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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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- k (int): kNN 算法参数，默认值为3。
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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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# 计算输入向量与数据集中每个向量之间的欧式距离
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diffMat = np.tile(input_vector, (dataSet.shape[0], 1)) - dataSet
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sqDiffMat = diffMat**2
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sqDistances = sqDiffMat.sum(axis=1)
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distances = sqDistances**0.5
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# 获取k个最近邻的索引
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sortedDistIndices = distances.argsort()
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classCount = {}
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for i in range(k):
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voteIlabel = labels[sortedDistIndices[i]]
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classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
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# 以出现次数最多的类别作为预测类别
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sortedClassCount = sorted(classCount.items(), key=lambda x: x[1], reverse=True)
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return sortedClassCount[0][0]
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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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从文件夹中加载数据。
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Args:
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- directory (str): 数据文件夹路径。
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@@ -68,13 +82,22 @@ def load_dataset(directory):
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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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fileList = os.listdir(directory) # 使用 os.listdir
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numFiles = len(fileList)
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trainingMat = np.zeros((numFiles, 1024))
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hwLabels = []
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for i in range(numFiles):
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fileNameStr = fileList[i]
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fileStr = fileNameStr.split(',')[0]
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classNumStr = int(fileStr.split('_')[0])
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hwLabels.append(classNumStr)
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returnVect = img_to_vector(os.path.join(directory, fileNameStr)) # 使用 os.path.join
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trainingMat[i, :] = returnVect
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return trainingMat, np.array(hwLabels)
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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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@@ -82,50 +105,31 @@ def img_to_vector(filename):
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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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returnVect = np.zeros((1, 1024))
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with open(filename, 'r') as fr:
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for i in range(32):
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lineStr = fr.readline()
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for j in range(32):
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returnVect[0, 32 * i + j] = int(lineStr[j])
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return returnVect.flatten()
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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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train_data, train_labels = load_dataset("trainingDigits")
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# 加载训练集和测试集数据
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train_data, train_labels = load_dataset("C:\\Users\\kxz\\Desktop\\kxz\\ds\\ML\\sy1\\trainingDigits")
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test_data, test_labels = load_dataset("C:\\Users\\kxz\\Desktop\\kxz\\ds\\ML\\sy1\\testDigits")
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# Test the classifier
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errorCount = 0.0
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mTest = len(test_data)
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for i in range(mTest):
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vectorUnderTest = test_data[i, :]
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classifierResult = classify(vectorUnderTest, train_data, train_labels, 3)
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print(f"KNN classified as: {classifierResult}, Actual: {test_labels[i]}")
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if (classifierResult != test_labels[i]):
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errorCount += 1.0
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print(f"\nError Count: {errorCount}")
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print(f"\nAccuracy: {(1 - errorCount / float(mTest)) * 100}%")
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test_data = (
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"00000000000000110000000000000000"
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"00000000000011111000000000000000"
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"00000000000011111100000000000000"
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"00000000001111111110000000000000"
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"00000000001111111110000000000000"
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"00000000001111111100000000000000"
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"00000000011111111000000000000000"
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"00000000111111111000000000000000"
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"00000000111111110000000000000000"
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"00000000111111110000000000000000"
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"00000000111111110000000000000000"
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"00000000111111110000000000000000"
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"00000000111111100000000000000000"
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"00000001111111100000000000000000"
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"00000001111111100000000000000000"
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"00000001111111100000000000000000"
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"00000001111111111111000000000000"
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"00000001111111111111111000000000"
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"00000001111111111111111100000000"
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"00000011111111111111111110000000"
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"00000011111111111111111110000000"
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"00000001111111111111111110000000"
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"00000001111111111111111110000000"
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"00000000111111111100111111100000"
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"00000000111111111000111111100000"
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"00000000011111111001111111100000"
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"00000000011111111111111111000000"
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"00000000001111111111111111000000"
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"00000000000111111111111111000000"
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"00000000000011111111111100000000"
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"00000000000001111111110000000000"
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"00000000000000000111100000000000"
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)
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predicted_label = classify(
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np.array([int(char) for char in test_data]).reshape(1, 1024),
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train_data,
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train_labels,
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)
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print(f"预测的标签: {predicted_label}")
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