工作原理:

  给定一个训练数据集,对新的输入实例,在训练数据集中找到与该实例最邻近的K个实例(也就是上面所说的K个邻居), 这K个实例的多数属于某个类,就把该输入实例分类到这个类中。

代码实例:

kNN.py

from numpy import *
import operator

def createDataSet():
    group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
    labels= ['B','B','B','B']
    return group,labels

 

>>> import kNN
>>> group,labels = kNN.createDataSet()
>>> group
array([[ 1. ,  1.1],
       [ 1. ,  1. ],
       [ 0. ,  0. ],
       [ 0. ,  0.1]])
>>> labels
['B', 'B', 'B', 'B']

 

数据读取

矩阵第一维的长度
>>> group.shape[0]
4

 

矩阵操作:

>>> import numpy as np
>>> a = np.matrix('1 2 7;3 4 8;0 3 10')
>>> a.min(0)
matrix([[0, 2, 7]])
>>> a.min(1)
matrix([[1],
        [3],
        [0]])

min([axis, out]) :返回指定轴的最小值

 

复制数组

>>> from numpy import *
>>> a=[0,1,2]
>>> b=tile(a,2)
>>> b
array([0, 1, 2, 0, 1, 2])
>>> b=tile(a,(1,2))
>>> b
array([[0, 1, 2, 0, 1, 2]])
>>> b=tile(a,(2,1))
>>> b
array([[0, 1, 2],
       [0, 1, 2]])
>>> b=tile(a,(2,2))
>>> b
array([[0, 1, 2, 0, 1, 2],
       [0, 1, 2, 0, 1, 2]])

 

构建数组

>>> zeros((3,4))
array([[ 0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.]])

 

sum

>>> c = np.array([[1,2,3],[4,5,6],[7,8,9]] )
>>> print c.sum()
45
>>> print c.sum(axis=0)
[12 15 18]
>>> print c.sum(axis=1)
[ 6 15 24]

 argsort

返回从小到大排序的索引值

>>> x=np.array([3,1,2])
>>> np.argsort(x)
array([1, 2, 0])

 sorted

根据key排序

>>> students = [('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10),]  
>>> from operator import itemgetter, attrgetter  
>>> sorted(students, key=itemgetter(2))  
[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
>>> sorted(students, key=itemgetter(1))  
[('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10)]

 

get

获取列表键对应的值

>>> a = {1: 1, 3: 1}
>>> a.get(1,0)
1
>>> a.get(3,0)
1
>>> a.get(4,0)
0

 

posted on 2016-01-22 11:38  充实自己  阅读(157)  评论(0编辑  收藏  举报