纯python实现机器学习之kNN算法示例

def classify(inX, dataSet, labels, k):
  """
  定义knn算法分类器函数
  :param inX: 测试数据
  :param dataSet: 训练数据
  :param labels: 分类类别
  :param k: k值
  :return: 所属分类
  """

  dataSetSize = dataSet.shape[0] #shape（m, n）m列n个特征
  diffMat = np.tile(inX, (dataSetSize, 1)) - dataSet
  sqDiffMat = diffMat ** 2
  sqDistances = sqDiffMat.sum(axis=1)
  distances = sqDistances ** 0.5 #欧式距离
  sortedDistIndicies = distances.argsort() #排序并返回index

  classCount = {}
  for i in range(k):
    voteIlabel = labels[sortedDistIndicies[i]]
    classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1 #default 0

  sortedClassCount = sorted(classCount.items(), key=lambda d:d[1], reverse=True)
  return sortedClassCount[0][0]

def classify_two(inX, dataSet, labels, k):
  m, n = dataSet.shape  # shape（m, n）m列n个特征
  # 计算测试数据到每个点的欧式距离
  distances = []
  for i in range(m):
    sum = 0
    for j in range(n):
      sum += (inX[j] - dataSet[i][j]) ** 2
    distances.append(sum ** 0.5)

  sortDist = sorted(distances)

  # k 个最近的值所属的类别
  classCount = {}
  for i in range(k):
    voteLabel = labels[ distances.index(sortDist[i])]
    classCount[voteLabel] = classCount.get(voteLabel, 0) + 1 # 0:map default
  sortedClass = sorted(classCount.items(), key=lambda d:d[1], reverse=True)
  return sortedClass[0][0]

def createDataSet():
  group = np.array([[1, 1.1], [1, 1], [0, 0], [0, 0.1]])
  labels = ['A', 'A', 'B', 'B']
  return group, labels

if __name__ == '__main__':
  dataSet, labels = createDataSet()
  r = classify_two([0, 0.2], dataSet, labels, 3)
  print(r)

40920 8.326976 0.953952 3
14488 7.153469 1.673904 2
26052 1.441871 0.805124 1
75136 13.147394 0.428964 1
38344 1.669788 0.134296 1

# 文本转换成numpy
def file2matrix(filepath="datingSet.csv"):
  dataSet = np.loadtxt(filepath)
  returnMat = dataSet[:, 0:-1]
  classlabelVector = dataSet[:, -1:]
  return returnMat, classlabelVector

# 2， 3列数据进行分析
def show_2_3_fig():
  data, cls = file2matrix()
  fig = plt.figure()
  ax = fig.add_subplot(111)
  ax.scatter(data[:, 1], data[: ,2], c=cls)
  plt.xlabel("playing game")
  plt.ylabel("Icm Cream")
  plt.show()

# 数据归一化
def autoNorm(dataSet):
  minVal = dataSet.min(0)
  maxVal = dataSet.max(0)
  ranges = maxVal - minVal

  normDataSet = np.zeros(dataSet.shape)
  m, n = dataSet.shape # 行， 特征
  normDataSet = dataSet - minVal
  normDataSet = normDataSet / ranges
  return normDataSet, ranges, minVal

# 定义测试算法的函数
def datingClassTest(h=0.1):
  hoRatio = h
  datingDataMat, datingLabels = file2matrix()
  normMat, ranges, minVals = autoNorm(datingDataMat)
  m, n = normMat.shape
  numTestVecs = int(m * hoRatio) #测试数据行数
  errorCount = 0 # 错误分类数


  # 用前10%的数据做测试
  for i in range(numTestVecs):
    classifierResult = classify(normMat[i, :], normMat[numTestVecs:m, :], datingLabels[numTestVecs:m], 3)
    # print('the classifier came back with: %d,the real answer is: %d' % (int(classifierResult), int(datingLabels[i])))
    if classifierResult != datingLabels[i]:
      errorCount += 1
  print("the total error rate is: %f" % (errorCount / float(numTestVecs)))

# 简单进行预测
def classifypersion():
  resultList = ["none", 'not at all','in small doses','in large doses']
  # 模拟数据
  ffmiles = 15360
  playing_game = 8.545204
  ice_name = 1.340429

  datingDataMat, datingLabels = file2matrix()
  normMat, ranges, minVals = autoNorm(datingDataMat)
  inArr = np.array([ffmiles, playing_game, ice_name])
  # 预测数据归一化
  inArr = (inArr - minVals) / ranges
  classifierResult = classify(inArr, normMat, datingLabels, 3)
  print(resultList[int(classifierResult)])