k-近鄰算法，屬於有監督的分類算法。
思想：利用輸入數據特徵值和訓練樣本數據特徵值之間的距離分類，挑出距離最小的k個訓練樣本的類別頻率，作爲預測的分類估計。
'''
k-近鄰算法是基於實例的學習
1 使用時要保存全部的數據集，佔存儲空間
2 要對每個訓練數據計算距離值，實際使用時非常耗時
'''
import numpy as np
import operator
def classify0(x, dataSet, labels, k):
dataSetSize = dataSet.shape[0]
diffMat = np.tile(x, (dataSetSize,1)) - dataSet
sqDiff = diffMat**2
sqDist = sqDiff.sum(axis=1)
distances = sqDist**0.5 # 一行數據的平方根
sortedDistInd = distances.argsort() # 向量元素從小到大對應的索引號
classCount = {}
for i in range(k): # 前k個,也就是最近的k個; 統計類出現的頻率
vLabel = labels[sortedDistInd[i]]
classCount[vLabel] = classCount.get(vLabel,0)+1
sortedClassCount = sorted(classCount.items(), # 轉成dict_items:[(key1,cnt1),(key2,cnt2),..]
key=operator.itemgetter(1), # 排序,依據tuple第二個元素;reverse,由大到小
reverse=True)
return sortedClassCount[0][0]
def img2vec(filename): # 32x32的矩陣數據轉成向量
vec = np.zeros((1,1024))
fr = open(filename) # (如果是txt文件的話)
for i in range(32):
lineStr = fr.readline()
for j in range(32):
vec[0, 32*i+j] = int(lineStr[j])
return vec
def handwritingClassify():
trainLab = []
trainFileList = listdir('trainingDigits') # 訓練數據目錄
m = len(trainFileList)
trainMat = zeros((m,1024)) # 訓練數據存成一個矩陣
for i in range(m):
filenameStr = trainFileList[i]
fileStr = filenameStr.split('.')[0]
classStr = int(fileStr.split('_')[0])
trainLab.append(classStr)
trainMat[i,:] = img2vec('trainingDigits/%s' % filenameStr)
#------------------------ 測試數據 -------------------------
errorCount = 0.0
testFileList = listdir('testDigits') # 測試數據目錄
n = len(testFileList)
for i in range(n):
filenameStr = testFileList[i]
fileStr = filenameStr.split('.')[0]
classStr = int(fileStr.split('_')[0])
vecTest = img2vec('testDigits/%s' % filenameStr)
classTest = classify0(vecTest, trainMat, trainLab, 3) # 測試數據的直接分類
print("the classifier predicts : %d, the real is : %d" % (classTest,classStr))
if(classTest!=classStr):
error += 1.0
print("\n the total numbers of errors is: %d" % errorCount)
print("\n the total error rate is: %d" % (error/float(n)))
