k近鄰算法--手寫識別系統
阿新 • • 發佈:2017-09-09
eal append 測試 users nes != tran text --
下面的例子來源為《機器學習實戰》,例子只能識別0-9。
首先需要將圖像二進制數據轉化為測試向量:
def imgTransformVector(filename): # 將 32x32 二進制圖像矩陣轉化為 1x1024 向量 returnVector = np.zeros((1,1024)) fr = open(filename) for i in range(32): lineStr = fr.readline() for j in range(32): returnVector[0,32*i+j] = int(lineStr[j]) return returnVector
接著是算法的實現代碼:
def handWritingTextTest(): handWritingLabels = [] # listdir 返回指定的文件夾包含的文件或文件夾的名字的列表 trainingFileList = os.listdir(‘/Users/Desktop/trainingDigits‘) trainingDataLen = len(trainingFileList) # 獲取訓練數據集的大小 trainingMatrix = np.zeros((trainingDataLen,1024)) for i in range(trainingDataLen -1): fileNameString = trainingFileList[i + 1] # 第i個訓練樣本的文件名 fileString = fileNameString.split(‘.‘)[0] # 截去.txt部分 classNumberString = int(fileString.split(‘_‘)[0]) #獲得分類數字 handWritingLabels.append(classNumberString) trainingMatrix[i,:] = imgTransformVector(‘/Users/Desktop/trainingDigits/%s‘%fileNameString) testFileList = os.listdir(‘/Users/Desktop/testDigits‘) errorCount = 0.0 testDataLen = len(testFileList) for i in range(testDataLen - 1): fileNameString = testFileList[i +1] fileString = fileNameString.split(‘.‘)[0] classNumberString = int(fileString.split(‘_‘)[0]) testDataVector = imgTransformVector(‘/Users/Desktop/testDigits/%s‘%fileNameString) classifierResult = classifyPerson(testDataVector,trainingMatrix,handWritingLabels,3) if (classifierResult != classNumberString): errorCount += 1 print(‘the classifier:%d, the real answer:%d‘ % (classifierResult, classNumberString)) print(‘\nthe total errorCount:%d‘%errorCount) print(‘\nthe total errorRate:%.d‘%(errorCount/float(testDataLen)))
k近鄰算法--手寫識別系統