局部敏感哈希(原始LSH)python实

最近短期计划是学习一下python，最好的学习方式当然是实践了，今天用Python实现了下lsh算法，代码比较简陋。。。（2016.1.17）

origionalLSH.py:
import random
class Bucket:
    features=[]
    name=[]
    def __init__(self):
       self.features=[]
       self.name=[]
    def addFeature(self,feature,nameID):
        self.features.append(feature)
        self.name.append(nameID)
    def size(self):
        return len(self.features)
class Table:
    buckets=[]
    size=0
    def __init__(self,tableSize):
        for i in xrange(tableSize):
            bucket=Bucket()
            self.buckets.append(bucket)
    def addFeature(self,feature,bucketID,nameID):
        self.buckets[bucketID].addFeature(feature,nameID)
    def size(self):
        return  self.size
class LSH:
    __m_k = 0
    __m_d = 0
    __m_l = 0
    __m_M = 0
    __m_FeatureDims = 0
    __m_hashFun_level1Subset =[]
    __m_hashFun_level2=[]
    __m_table = Table(0)
    __m_MaxValue = 0
    def __init__(self,maxValue,l,m,ratio,featureDims):
        self.__m_k = int(maxValue*featureDims*ratio)
        self.__m_d = maxValue*featureDims
        self.__m_l = l
        self.__m_M = m
        self.__m_FeatureDims = featureDims
        self.__m_MaxValue = maxValue
        self.__m_table = Table(self.__m_M)
        # __m_hashFun_level1Subset用初始化么？
        for i in xrange(m):
            temp0 =[]
            for j in xrange(featureDims):
                tem1 = []
                temp0.append(tem1)
            self.__m_hashFun_level1Subset.append(temp0)
        for i in xrange(self.__m_k):
            self.__m_hashFun_level2.append(random.randint(0,self.__m_M-1))

def __GetHashFun_level1(self): #生成一级哈希函数
     for i in xrange(self.__m_M):
        for j in xrange(self.__m_k):
            val = random.randint(0,self.__m_d-1)#随机选取位置
            pos = int(val/self.__m_MaxValue) #对应原始特征的哪一维
            self.__m_hashFun_level1Subset[i][pos].append(val)
def __Hash_level1(self,feature,tableID):
    value = []
    table = self.__m_hashFun_level1Subset[tableID]
    for i in xrange(len(table)):
        val0 = feature[i]
        one_num = 0
        zero_num = 0
        for j in xrange(len(table[i])):
            val1 = table[i][j]-self.__m_d*i
            if val1<=val0:
                one_num +=1
            else:
                zero_num +=1
        while one_num > 0:
            value.append(1)
            one_num -=1
        while zero_num > 0:
            value.append(0)
            zero_num -=1
    return value
def __HashLevel2(self,value,):
    buTKEtID = -1
    temp = 0
    for i in xrange(len(value)):
        temp += self.__m_hashFun_level2[i]*value[i]
    bucketID = temp % self.__m_M
    return  bucketID
def train(self,features):
    self.__GetHashFun_level1()
    num = len(features) #特征个数
    for  i in xrange(num):
        feature = features[i]
        for j in xrange(self.__m_l):
            value = self.__Hash_level1(feature,j)
            bucketID = self.__HashLevel2(value)
            self.__m_table.addFeature(feature,bucketID,i)
def search(self,feature):
    name = -1
    dist = -1
    minDist = 10000000
    buckets = []
    #hash 获取所有候选bucket
    for i in xrange(self.__m_l):
        value = self.__Hash_level1(feature,i)
        bucketID = self.__HashLevel2(value)
        buckets.append(bucketID)
    print"查找时遍历痛的个数为： %d" %(len(buckets))
    for i in xrange(len(buckets)):#遍历候选bucket
        tempFeatures = self.__m_table.buckets[i].features
        tempName = self.__m_table.buckets[i].name
        num = len(tempFeatures)
        print "该桶含有的特征个数为：%d" %(num)
        for j in xrange(num):
            dist = self.__calcDist(feature,tempFeatures[j])
            if dist < minDist:
                minDist = dist
                name = tempName[j]
    return name,minDist

def __calcDist(self,feature0,feature1):
    dist = 0
    length = len(feature0)
    for i in xrange(length):
        dist += abs(feature0[i]-feature1[i])
    return  dist
# code:utf-8

test.py:
from origionalLSH import *
featureNum = 10000
featureLength = 40
#step1: 生成特征
print "step1: 生成特征"
features = []
for i in xrange(featureNum):
    temp = []
    for j in xrange(featureLength):
        temp.append(random.randint(0,255))
    features.append(temp)

#step2: LSH初始化
print "step2: LSH初始化"
#LSH lsh(255,10,100,0.1,featureLength)
lsh =LSH(255,10,100,0.12,featureLength)
#step3: 开始训练
print "step3: 开始训练"
lsh.train(features)

#step4: search:
print"step4: search:"
name,dist = lsh.search(features[457])
print "最近的距离为：%d, 行号为%d" %(dist,name)