kNN_test04.py

# @Time    : 2017/9/24 20:31
# @Author  : Jalin Hu
# @File    : kNN_test04.py
# @Software: PyCharm
import numpy as np
import numpy
from os import listdir
from sklearn.neighbors import KNeighborsClassifier as kNN


def img2vector(filename):
    targetvector = numpy.zeros((1, 1024))
    with open(filename) as file:
        for i in range(32):
            line = file.readline()
            for j in range(32):
                targetvector[0, i * 32 + j] = int(line[j])
    return targetvector


#
# def img2vector(filename):
#     # 创建1x1024零向量
#     returnVect = np.zeros((1, 1024))
#     # 打开文件
#     fr = open(filename)
#     # 按行读取
#     for i in range(32):
#         # 读一行数据
#         lineStr = fr.readline()
#         # 每一行的前32个元素依次添加到returnVect中
#         for j in range(32):
#             returnVect[0, 32 * i + j] = int(lineStr[j])
#     # 返回转换后的1x1024向量
#     return returnVect


def handwritingClassTest1():
    # 测试集的Labels
    hwLabels = []
    # 返回trainingDigits目录下的文件名
    trainingFileList = listdir('trainingDigits')
    # 返回文件夹下文件的个数
    m = len(trainingFileList)
    # 初始化训练的Mat矩阵,测试集
    trainingMat = np.zeros((m, 1024))
    # 从文件名中解析出训练集的类别
    for i in range(m):
        # 获得文件的名字
        fileNameStr = trainingFileList[i]
        # 获得分类的数字
        classNumber = int(fileNameStr.split('_')[0])
        # 将获得的类别添加到hwLabels中
        hwLabels.append(classNumber)
        # 将每一个文件的1x1024数据存储到trainingMat矩阵中
        trainingMat[i, :] = img2vector('trainingDigits/%s' % (fileNameStr))
    return hwLabels, trainingMat


def handwritingClassTest2(hwLabels, trainingMat):
    # 构建kNN分类器
    neigh = kNN(n_neighbors=3, algorithm='auto', weights='distance', n_jobs=1)
    # 拟合模型, trainingMat为测试矩阵,hwLabels为对应的标签
    neigh.fit(trainingMat, hwLabels)
    # 返回testDigits目录下的文件列表
    testFileList = listdir('testDigits')
    # 错误检测计数
    errorCount = 0.0
    # 测试数据的数量
    mTest = len(testFileList)
    # 从文件中解析出测试集的类别并进行分类测试
    for i in range(mTest):
        # 获得文件的名字
        fileNameStr = testFileList[i]
        # 获得分类的数字
        classNumber = int(fileNameStr.split('_')[0])
        # 获得测试集的1x1024向量,用于训练
        vectorUnderTest = img2vector('testDigits/%s' % (fileNameStr))
        # 获得预测结果
        # classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
        classifierResult = neigh.predict(vectorUnderTest)
        print("分类返回结果为%d\t真实结果为%d" % (classifierResult, classNumber))
        if (classifierResult != classNumber):
            errorCount += 1.0
    print("总共错了%d个数据\n错误率为%f%%" % (errorCount, errorCount / mTest * 100))


if __name__ == '__main__':
    hwLabels, trainingMat = handwritingClassTest1()
    handwritingClassTest2(hwLabels, trainingMat)