knn.zip_KNN 分类 python_knn_knn python实现_python knn

from numpy import * import os # classify using kNN def kNNClassify(newInput, dataSet, labels, k): numSamples = dataSet.shape[0] # shape[0] stands for the num of row ## step 1: calculate Euclidean distance # tile(A, reps): Construct an array by repeating A reps times # the following copy numSamples rows for dataSet diff = tile(newInput, (numSamples, 1)) - dataSet # Subtract element-wise squaredDiff = diff ** 2 # squared for the subtract squaredDist = sum(squaredDiff, axis = 1) # sum is performed by row distance = squaredDist ** 0.5 ## step 2: sort the distance # argsort() returns the indices that would sort an array in a ascending order sortedDistIndices = argsort(distance) classCount = {} # define a dictionary (can be append element) for i in range(k): ## step 3: choose the min k distance voteLabel = labels[sortedDistIndices[i]] ## step 4: count the times labels occur # when the key voteLabel is not in dictionary classCount, get() # will return 0 classCount[voteLabel] = classCount.get(voteLabel, 0) + 1 ## step 5: the max voted class will return maxCount = 0 for key, value in classCount.items(): if value > maxCount: maxCount = value maxIndex = key return maxIndex # convert image to vector def img2vector(filename): rows = 32 cols = 32 imgVector = zeros((1, rows * cols)) fileIn = open(filename) for row in range(rows): lineStr = fileIn.readline() for col in range(cols): imgVector[0, row * 32 + col] = int(lineStr[col]) return imgVector # load dataSet def loadDataSet(): ## step 1: Getting training set print("---Getting training set...") dataSetDir = 'E:/Python/Machine Learning in Action/' trainingFileList = os.listdir(dataSetDir + 'trainingDigits') # load the training set numSamples = len(trainingFileList) train_x = zeros((numSamples, 1024)) train_y = [] for i in range(numSamples): filename = trainingFileList[i] # get train_x train_x[i, :] = img2vector(dataSetDir + 'trainingDigits/%s' % filename) # get label from file name such as "1_18.txt" label = int(filename.split('_')[0]) # return 1 train_y.append(label) ## step 2: Getting testing set print("---Getting testing set...") testingFileList = os.listdir(dataSetDir + 'testDigits') # load the testing set numSamples = len(testingFileList) test_x = zeros((numSamples, 1024)) test_y = [] for i in range(numSamples): filename = testingFileList[i] # get train_x test_x[i, :] = img2vector(dataSetDir + 'testDigits/%s' % filename) # get label from file name such as "1_18.txt" label = int(filename.split('_')[0]) # return 1 test_y.append(label) return train_x, train_y, test_x, test_y ## step 1: load data print("step 1: load data...") train_x, train_y, test_x, test_y = loadDataSet() ## step 2: training... print("step 2: training...") pass ## step 3: testing print("step 3: testing...") numTestSamples = test_x.shape[0] matchCount = 0 for i in range(numTestSamples): predict = kNNClassify(test_x[i], train_x, train_y, 3) if predict == test_y[i]: matchCount += 1 accuracy = float(matchCount) / numTestSamples ## step 4: show the result print("step 4: show the result...") print('The classify accuracy is: %.2f%%' % (accuracy * 100))