data and dream

1 用通俗的语言介绍下线性回归->逻辑回归->SVM之间的区别和联系。

2 聚类算法的应用场景，以及k-means中的k值怎么确定。

def center(data):
    
    center = []
    for num in data:
        sumX = 0; sumY = 0
        for j in num:
            sumX += j[0]
            sumY += j[1]
            x = float(sumX) / len(data)
            y = float(sumY) / len(data)
        center.append([x, y])
        
    return center

def distance(one, two):
    
    sumT = 0
    for i in range(len(one)):
        sumT += pow((one[i] - two[i]), 2)
    
    return pow(sumT, 0.5)
    
def update(data, kcenter):
    
    
    length = len(kcenter)
    ret = [0] * length
    for i in range(length):
        ret[i] = []
        
    for num in data:
        tmp = []
        for point in kcenter:
           tmp.append(distance(num, point))
        ret[tmp.index(min(tmp))].append(num)
    
    return ret

if __name__ == '__main__': 
    
    data = [(1, 2), (2, 3), (1, 6), (8, 9)]
    kcenter = [[0.2, 1.2], [2, 3]]
    error = 0.0000001
    
    while True:
      rt = update(data, kcenter)
      tmp = center(rt)
      sume = 0
      for sa in range(len(kcenter)):
         sume += distance(tmp[sa], kcenter[sa])
      if sume < error:
          print rt
          break
      else:
          kcenter = tmp
     

3 协同过滤中评分矩阵中的元素怎么确定。大矩阵怎么分解。

4 文本挖掘怎么处理。