本文實例為大家分享了python實現knn算法的具體代碼，供大家參考，具體內容如下

knn算法描述

對需要分類的點依次執行以下操作：
1.計算已知類別數據集中每個點與該點之間的距離
2.按照距離遞增順序排序
3.選取與該點距離最近的k個點
4.確定前k個點所在類別出現的頻率
5.返回前k個點出現頻率最高的類別作為該點的預測分類

knn算法實現

數據處理

#從文件中讀取數據，返回的數據和分類均為二維數組def loadDataSet(filename):  dataSet = []  labels = []  fr = open(filename)  for line in fr.readlines():    lineArr = line.strip().split(",")    dataSet.append([float(lineArr[0]),float(lineArr[1])])    labels.append([float(lineArr[2])])  return dataSet , labels

knn算法

#計算兩個向量之間的歐氏距離def calDist(X1 , X2):  sum = 0  for x1 , x2 in zip(X1 , X2):    sum += (x1 - x2) ** 2  return sum ** 0.5def knn(data , dataSet , labels , k):  n = shape(dataSet)[0]  for i in range(n):    dist = calDist(data , dataSet[i])    #只記錄兩點之間的距離和已知點的類別    labels[i].append(dist)  #按照距離遞增排序  labels.sort(key=lambda x:x[1])  count = {}  #統計每個類別出現的頻率  for i in range(k):    key = labels[i][0]    if count.has_key(key):      count[key] += 1    else : count[key] = 1  #按頻率遞減排序  sortCount = sorted(count.items(),key=lambda item:item[1],reverse=True)  return sortCount[0][0]#返回頻率最高的key，即label

結果測試

已知類別數據（來源于西瓜書+虛構）

0.697,0.460,1
0.774,0.376,1
0.720,0.330,1
0.634,0.264,1
0.608,0.318,1
0.556,0.215,1
0.403,0.237,1
0.481,0.149,1
0.437,0.211,1
0.525,0.186,1
0.666,0.091,0
0.639,0.161,0
0.657,0.198,0
0.593,0.042,0
0.719,0.103,0
0.671,0.196,0
0.703,0.121,0
0.614,0.116,0

繪圖方法

def drawPoints(data , dataSet, labels):  xcord1 = [];  ycord1 = [];  xcord2 = [];  ycord2 = [];  for i in range(shape(dataSet)[0]):    if labels[i][0] == 0:      xcord1.append(dataSet[i][0])      ycord1.append(dataSet[i][1])    if labels[i][0] == 1:      xcord2.append(dataSet[i][0])      ycord2.append(dataSet[i][1])  fig = plt.figure()  ax = fig.add_subplot(111)  ax.scatter(xcord1, ycord1, s=30, c='blue', marker='s',label=0)  ax.scatter(xcord2, ycord2, s=30, c='green',label=1)  ax.scatter(data[0], data[1], s=30, c='red',label="testdata")  plt.legend(loc='upper right')  plt.show()

測試代碼

dataSet , labels = loadDataSet('dataSet.txt')data = [0.6767,0.2122]drawPoints(data , dataSet, labels)newlabels = knn(data, dataSet , labels , 5)print newlabels

運行結果