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通过顾客的会员卡可获取已定的折扣，商店也可以了解客户购买的商品; 不买会员卡，商店也可以查用顾客一起购买的物品，找出商品之间的关系。 + * 例如： 尿布和啤酒的故事 +* 关联关系(associati analysis) 或 关联规则学习(association rule learning) + * 从大规模数据集中寻找物品间的隐含关系呗称作关联关系。 +* 关联分析： + * 优点：易编码实现 + * 缺点：在大数据集上可能较慢 + * 适用数据类型：数值型 或者 标称型数据。 +* 关联分析有2种形式： + * 1.频繁项集(frequent item sets): 经常出现在一块的物品集合 + * 2.关联规则(association rules): 暗示两种物品之间可能存在很强的关系 + * 总结：首先需要找到频繁项集，才能找到关联规则。 + * 如下图： + * ![交易清单](./11.交易清单.png) +* 支持度(support) + * 数据集中包含该项集的记录所占的比例 + * 例如上图中：{豆奶}的支持度=4/5， {豆奶，尿布}的支持度=3/5 +* 置信度（confidence) + * 置信度({A}->{B}) = 支持度{A,B}/支持度{A} + * 例如上图中：{尿布，葡萄酒}的支持度=3/5， {尿布}的支持度=4/5， 所以 尿布->葡萄酒的可信度=3/4 +* Apriori原理 + * 如果某个项集是频繁的，那么它的所有子集也是频繁的，反之，一个项集是非频繁的，那么它的所有超集也是非频繁的。 + * 例如： 我们假设知道{2, 3}是非频繁项，那么{0, 2, 3}, {1, 2, 3}, {0, 1, 2, 3}都是非频繁项。 + * 如下图： + * ![非频繁项集](./11.非频繁项集.png) diff --git a/docs/11.非频繁项集.png b/docs/11.非频繁项集.png new file mode 100644 index 0000000000000000000000000000000000000000..da7834baf901f5ef43d4d1b4bfd81905362206f0 GIT binary patch literal 414354 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zW8HWko$Wfl=IDf8^3av<^2`N)o4nE;;iu$G**F$cj>bBgQ}uK3XpVg5V5{ur_By|B zgH!Qyy*jg*f}_r>66-a-+caXt*;U?y;^T+L*lD$VSD6kQzUndUt!3Nto5S6fR`9pw zcNB1uwaSvRqvSV5n+%orct3?RmR0<*?lAAC^fkr1mpyFBx?YM(cRl3(YImWZ_Ea3# z%4^vuN$M(Gy&mDm`|zmckww{5eTB_++Po=#m3W&HK#`e_}jFl*k+z', conseq, 'conf:', conf + brl.append((freqSet-conseq, conseq, conf)) + prunedH.append(conseq) + return prunedH + + +def rulesFromConseq(freqSet, H, supportData, brl, minConf=0.7): + """rulesFromConseq + + Args: + freqSet 每一组的各个元素 + H 将元素变成set集合 + supportData 所有元素的支持度全集 + brl bigRuleList的空数组 + minConf 置信度的阈值 + Returns: + prunedH 记录 可信度大于阈值的集合 + """ + # 去除list列表中第一个出现的冻结的set集合 + m = len(H[0]) + # 判断，freqSet的长度是否>组合的长度+1 + if (len(freqSet) > (m + 1)): + # 合并相邻的集合，组合为2/3/..n的集合 + Hmp1 = aprioriGen(H, m+1) + Hmp1 = calcConf(freqSet, Hmp1, supportData, brl, minConf) + # 如果有2个结果都可以，直接返回结果就行，下面这个判断是多余，我个人觉得 + print 'Hmp1=', Hmp1 + if (len(Hmp1) > 1): + # print '-------' + # print len(freqSet), len(Hmp1[0]) + 1 + rulesFromConseq(freqSet, Hmp1, supportData, brl, minConf) + + +def generateRules(L, supportData, minConf=0.7): + """generateRules + + Args: + L 频繁项集的全集 + supportData 所有元素的支持度全集 + minConf 可信度的阈值 + Returns: + bigRuleList 关于 (A->B+置信度) 3个字段的组合 + """ + bigRuleList = [] + # 循环L频繁项集，所有的统一大小组合（2/../n个的组合，从第2组开始） + for i in range(1, len(L)): + # 获取频繁项集中每个组合的所有元素 + for freqSet in L[i]: + # 组合总的元素并遍历子元素，并转化为冻结的set集合，再存放到list列表中 + H1 = [frozenset([item]) for item in freqSet] + # 2个的组合，走else, 2个以上的组合，走if + if (i > 1): + rulesFromConseq(freqSet, H1, supportData, bigRuleList, minConf) + else: + calcConf(freqSet, H1, supportData, bigRuleList, minConf) + return bigRuleList + + def main(): # project_dir = os.path.dirname(os.path.dirname(os.getcwd())) # 1.收集并准备数据 @@ -139,8 +218,10 @@ def main(): print(dataSet) # 调用 apriori 做购物篮分析 # 支持度满足阈值的key集合L，和所有key的全集suppoerData - L, supportData = apriori(dataSet, minSupport=0.7) - print L, supportData + L, supportData = apriori(dataSet, minSupport=0.5) + # print L, supportData + print '\ngenerateRules\n' + generateRules(L, supportData, minConf=0.05) if __name__ == "__main__": @@ -171,39 +252,6 @@ if __name__ == "__main__": - - - - -def generateRules(L, supportData, minConf=0.7): #supportData is a dict coming from scanD - bigRuleList = [] - for i in range(1, len(L)):#only get the sets with two or more items - for freqSet in L[i]: - H1 = [frozenset([item]) for item in freqSet] - if (i > 1): - rulesFromConseq(freqSet, H1, supportData, bigRuleList, minConf) - else: - calcConf(freqSet, H1, supportData, bigRuleList, minConf) - return bigRuleList - -def calcConf(freqSet, H, supportData, brl, minConf=0.7): - prunedH = [] #create new list to return - for conseq in H: - conf = supportData[freqSet]/supportData[freqSet-conseq] #calc confidence - if conf >= minConf: - print freqSet-conseq,'-->',conseq,'conf:',conf - brl.append((freqSet-conseq, conseq, conf)) - prunedH.append(conseq) - return prunedH - -def rulesFromConseq(freqSet, H, supportData, brl, minConf=0.7): - m = len(H[0]) - if (len(freqSet) > (m + 1)): #try further merging - Hmp1 = aprioriGen(H, m+1)#create Hm+1 new candidates - Hmp1 = calcConf(freqSet, Hmp1, supportData, brl, minConf) - if (len(Hmp1) > 1): #need at least two sets to merge - rulesFromConseq(freqSet, Hmp1, supportData, brl, minConf) - def pntRules(ruleList, itemMeaning): for ruleTup in ruleList: for item in ruleTup[0]: From 3f92837e56e398db0f5fdf03041aeb7817eaafc1 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 17 Mar 2017 12:53:24 +0800 Subject: [PATCH 05/11] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E5=AE=8C2=E4=B8=AA?= =?UTF-8?q?=E5=85=B3=E4=BA=8EApriori=E6=89=80=E6=9C=89=E7=9A=84=E9=A1=B9?= =?UTF-8?q?=E7=9B=AE=E6=A1=88=E4=BE=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/11.使用Apriori算法进行关联分析.md | 3 +- src/python/11.Apriori/apriori.py | 209 +- testData/Apriori_mushroom.dat | 8124 ++++++++++++++++++++++++ testData/Apriori_recent20bills.txt | 20 + 4 files changed, 8254 insertions(+), 102 deletions(-) create mode 100755 testData/Apriori_mushroom.dat create mode 100755 testData/Apriori_recent20bills.txt diff --git a/docs/11.使用Apriori算法进行关联分析.md b/docs/11.使用Apriori算法进行关联分析.md index feee8369..0326a153 100644 --- a/docs/11.使用Apriori算法进行关联分析.md +++ b/docs/11.使用Apriori算法进行关联分析.md @@ -33,5 +33,4 @@ * 2.接下来合并所有剩余规则来创建一个新的规则列表，其中规则右部包含两个元素。 * 如下图： * ![所有可能的项集组合](./11.所有可能的项集组合.png) - - +* 最后： 每次增加频繁项集的大小，Apriori算法都会重新扫描整个数据集，是否有优化空间呢？ 下一章：FP-growth算法等着你的到来 diff --git a/src/python/11.Apriori/apriori.py b/src/python/11.Apriori/apriori.py index f0ddc49b..03be65bb 100644 --- a/src/python/11.Apriori/apriori.py +++ b/src/python/11.Apriori/apriori.py @@ -87,7 +87,7 @@ def aprioriGen(Lk, k): # if first k-2 elements are equal if L1 == L2: # set union - print 'union=', Lk[i] | Lk[j], Lk[i], Lk[j] + # print 'union=', Lk[i] | Lk[j], Lk[i], Lk[j] retList.append(Lk[i] | Lk[j]) return retList @@ -108,7 +108,7 @@ def apriori(dataSet, minSupport=0.5): # 计算支持support， L1表示满足support的key, supportData表示全集的集合 L1, supportData = scanD(D, C1, minSupport) - print "L1=", L1, "\n", "outcome: ", supportData + # print "L1=", L1, "\n", "outcome: ", supportData L = [L1] k = 2 @@ -176,7 +176,7 @@ def rulesFromConseq(freqSet, H, supportData, brl, minConf=0.7): Hmp1 = aprioriGen(H, m+1) Hmp1 = calcConf(freqSet, Hmp1, supportData, brl, minConf) # 如果有2个结果都可以，直接返回结果就行，下面这个判断是多余，我个人觉得 - print 'Hmp1=', Hmp1 + # print 'Hmp1=', Hmp1 if (len(Hmp1) > 1): # print '-------' # print len(freqSet), len(Hmp1[0]) + 1 @@ -208,108 +208,117 @@ def generateRules(L, supportData, minConf=0.7): return bigRuleList +def getActionIds(): + from time import sleep + from votesmart import votesmart + # votesmart.apikey = 'get your api key first' + votesmart.apikey = 'a7fa40adec6f4a77178799fae4441030' + actionIdList = [] + billTitleList = [] + fr = open('testData/Apriori_recent20bills.txt') + for line in fr.readlines(): + billNum = int(line.split('\t')[0]) + try: + billDetail = votesmart.votes.getBill(billNum) # api call + for action in billDetail.actions: + if action.level == 'House' and (action.stage == 'Passage' or action.stage == 'Amendment Vote'): + actionId = int(action.actionId) + print 'bill: %d has actionId: %d' % (billNum, actionId) + actionIdList.append(actionId) + billTitleList.append(line.strip().split('\t')[1]) + except: + print "problem getting bill %d" % billNum + sleep(1) # delay to be polite + return actionIdList, billTitleList + + +def getTransList(actionIdList, billTitleList): #this will return a list of lists containing ints + itemMeaning = ['Republican', 'Democratic']#list of what each item stands for + for billTitle in billTitleList:#fill up itemMeaning list + itemMeaning.append('%s -- Nay' % billTitle) + itemMeaning.append('%s -- Yea' % billTitle) + transDict = {}#list of items in each transaction (politician) + voteCount = 2 + for actionId in actionIdList: + sleep(3) + print 'getting votes for actionId: %d' % actionId + try: + voteList = votesmart.votes.getBillActionVotes(actionId) + for vote in voteList: + if not transDict.has_key(vote.candidateName): + transDict[vote.candidateName] = [] + if vote.officeParties == 'Democratic': + transDict[vote.candidateName].append(1) + elif vote.officeParties == 'Republican': + transDict[vote.candidateName].append(0) + if vote.action == 'Nay': + transDict[vote.candidateName].append(voteCount) + elif vote.action == 'Yea': + transDict[vote.candidateName].append(voteCount + 1) + except: + print "problem getting actionId: %d" % actionId + voteCount += 2 + return transDict, itemMeaning + + +# 暂时没用上 +# def pntRules(ruleList, itemMeaning): +# for ruleTup in ruleList: +# for item in ruleTup[0]: +# print itemMeaning[item] +# print " -------->" +# for item in ruleTup[1]: +# print itemMeaning[item] +# print "confidence: %f" % ruleTup[2] +# print #print a blank line + + def main(): + # 以前的测试 # project_dir = os.path.dirname(os.path.dirname(os.getcwd())) - # 1.收集并准备数据 + # 收集并准备数据 # dataMat, labelMat = loadDataSet("%s/resources/Apriori_testdata.txt" % project_dir) - # 1. 加载数据 - dataSet = loadDataSet() - print(dataSet) - # 调用 apriori 做购物篮分析 - # 支持度满足阈值的key集合L，和所有key的全集suppoerData - L, supportData = apriori(dataSet, minSupport=0.5) - # print L, supportData - print '\ngenerateRules\n' - generateRules(L, supportData, minConf=0.05) + # 现在的的测试 + # # 1. 加载数据 + # dataSet = loadDataSet() + # print(dataSet) + # # 调用 apriori 做购物篮分析 + # # 支持度满足阈值的key集合L，和所有key的全集suppoerData + # L, supportData = apriori(dataSet, minSupport=0.5) + # # print L, supportData + # print '\ngenerateRules\n' + # rules = generateRules(L, supportData, minConf=0.05) + # print rules + + # 项目实战 + # 构建美国国会投票记录的事务数据集 + # actionIdList, billTitleList = getActionIds() + # # 测试前2个 + # # transDict, itemMeaning = getTransList(actionIdList[: 2], billTitleList[: 2]) + # # transDict 表示 action_id的集合，transDict[key]这个就是action_id对应的选项，例如 [1, 2, 3] + # transDict, itemMeaning = getTransList(actionIdList, billTitleList) + # # 得到全集的数据 + # dataSet = [transDict[key] for key in transDict.keys()] + # L, supportData = apriori(dataSet, minSupport=0.3) + # rules = generateRules(L, supportData, minConf=0.95) + # print rules + + # 项目实战 + # 发现毒蘑菇的相似特性 + # 得到全集的数据 + dataSet = [line.split() for line in open("testData/Apriori_mushroom.dat").readlines()] + L, supportData = apriori(dataSet, minSupport=0.3) + # 2表示毒蘑菇，1表示可食用的蘑菇 + # 找出关于2的频繁子项出来，就知道如果是毒蘑菇，那么出现频繁的也可能是毒蘑菇 + for item in L[1]: + if item.intersection('2'): + print item + + for item in L[2]: + if item.intersection('2'): + print item if __name__ == "__main__": main() - - - - - - - - - - - - - - - - - - - - - - - - - - -def pntRules(ruleList, itemMeaning): - for ruleTup in ruleList: - for item in ruleTup[0]: - print itemMeaning[item] - print " -------->" - for item in ruleTup[1]: - print itemMeaning[item] - print "confidence: %f" % ruleTup[2] - print #print a blank line - - -# from time import sleep -# from votesmart import votesmart -# votesmart.apikey = 'a7fa40adec6f4a77178799fae4441030' -# #votesmart.apikey = 'get your api key first' -# def getActionIds(): -# actionIdList = []; billTitleList = [] -# fr = open('recent20bills.txt') -# for line in fr.readlines(): -# billNum = int(line.split('\t')[0]) -# try: -# billDetail = votesmart.votes.getBill(billNum) #api call -# for action in billDetail.actions: -# if action.level == 'House' and \ -# (action.stage == 'Passage' or action.stage == 'Amendment Vote'): -# actionId = int(action.actionId) -# print 'bill: %d has actionId: %d' % (billNum, actionId) -# actionIdList.append(actionId) -# billTitleList.append(line.strip().split('\t')[1]) -# except: -# print "problem getting bill %d" % billNum -# sleep(1) #delay to be polite -# return actionIdList, billTitleList -# -# def getTransList(actionIdList, billTitleList): #this will return a list of lists containing ints -# itemMeaning = ['Republican', 'Democratic']#list of what each item stands for -# for billTitle in billTitleList:#fill up itemMeaning list -# itemMeaning.append('%s -- Nay' % billTitle) -# itemMeaning.append('%s -- Yea' % billTitle) -# transDict = {}#list of items in each transaction (politician) -# voteCount = 2 -# for actionId in actionIdList: -# sleep(3) -# print 'getting votes for actionId: %d' % actionId -# try: -# voteList = votesmart.votes.getBillActionVotes(actionId) -# for vote in voteList: -# if not transDict.has_key(vote.candidateName): -# transDict[vote.candidateName] = [] -# if vote.officeParties == 'Democratic': -# transDict[vote.candidateName].append(1) -# elif vote.officeParties == 'Republican': -# transDict[vote.candidateName].append(0) -# if vote.action == 'Nay': -# transDict[vote.candidateName].append(voteCount) -# elif vote.action == 'Yea': -# transDict[vote.candidateName].append(voteCount + 1) -# except: -# print "problem getting actionId: %d" % actionId -# voteCount += 2 -# return transDict, itemMeaning diff --git a/testData/Apriori_mushroom.dat b/testData/Apriori_mushroom.dat new 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83 85 87 90 93 105 110 119 +2 4 11 16 24 28 34 37 39 44 52 58 61 66 67 76 85 86 91 93 102 108 114 +2 6 9 13 24 28 35 36 39 51 52 58 59 63 73 83 85 88 90 93 104 112 119 +2 4 9 13 24 28 35 36 39 51 52 58 59 63 73 83 85 87 90 93 105 112 119 +2 6 9 13 24 28 35 36 39 51 52 58 59 63 73 83 85 87 90 93 104 110 119 +2 4 11 16 24 28 34 37 39 42 52 58 59 63 67 76 85 86 91 93 102 107 114 +1 7 10 17 24 29 34 36 38 48 53 58 59 63 67 76 85 86 90 94 102 110 119 +1 7 9 13 24 32 34 36 38 48 53 58 59 66 69 76 85 86 90 94 102 110 116 +1 7 10 13 24 29 34 36 38 48 53 58 61 63 67 76 85 86 90 94 102 110 117 +1 7 9 17 24 32 34 36 38 48 53 58 61 66 69 77 85 86 90 94 102 110 117 +1 7 10 13 24 31 34 36 38 48 53 58 59 63 67 76 85 86 90 94 102 110 119 +2 4 11 16 24 28 34 37 39 43 52 58 61 66 67 76 85 86 91 93 102 107 114 +2 7 11 15 24 28 34 37 39 42 52 58 59 66 67 76 85 86 91 93 102 107 114 +2 7 9 13 24 28 35 36 39 41 52 58 59 63 73 83 85 87 90 93 104 110 119 +1 3 9 17 24 29 34 36 38 48 53 58 61 63 67 77 85 86 90 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+2 3 9 13 24 28 35 36 39 50 52 58 59 63 73 83 85 87 90 93 99 112 119 +2 7 9 13 24 28 35 36 39 50 52 58 59 63 73 83 85 87 90 93 104 112 119 +2 7 9 13 24 28 35 36 39 50 52 58 59 63 73 83 85 88 90 93 99 110 119 +2 7 9 13 24 28 35 36 39 50 52 58 59 63 73 83 85 87 90 93 105 110 119 +2 7 9 13 24 28 35 36 39 51 52 58 59 63 73 83 85 88 90 93 99 110 119 +2 3 9 13 24 28 35 36 39 50 52 58 59 63 73 83 85 88 90 93 99 112 119 +1 7 10 17 24 31 34 36 38 48 53 58 61 63 69 76 85 86 90 94 102 110 119 +2 4 9 15 24 28 34 37 39 44 52 58 59 63 67 76 85 86 91 93 102 108 114 +2 3 9 13 24 28 35 36 39 51 52 58 59 63 73 83 85 88 90 93 99 110 119 +2 7 9 15 24 28 34 37 39 43 52 58 59 63 67 76 85 86 91 93 102 108 114 +2 7 9 13 24 28 35 36 39 51 52 58 59 63 73 83 85 87 90 93 106 110 119 +1 7 10 17 24 31 34 36 38 48 53 58 61 66 69 77 85 86 90 94 102 110 116 +1 6 10 21 24 33 35 36 39 50 52 55 61 65 74 84 85 86 92 97 102 112 116 +2 3 9 13 24 28 35 36 39 50 52 58 59 63 73 83 85 88 90 93 104 110 119 +1 7 10 13 24 32 34 36 38 48 53 58 59 66 69 76 85 86 90 94 102 110 119 +1 7 9 17 24 31 34 36 38 48 53 58 61 63 69 76 85 86 90 94 102 110 116 +1 7 10 13 24 29 34 36 38 48 53 58 61 63 69 76 85 86 90 94 102 110 116 +2 7 9 13 24 28 35 36 39 50 52 58 59 63 73 83 85 88 90 93 106 112 119 +2 3 9 13 24 28 35 36 39 50 52 58 59 63 73 83 85 87 90 93 106 110 119 +2 6 9 13 24 28 35 36 39 41 52 58 59 63 73 83 85 88 90 93 106 112 119 +1 7 10 13 24 31 34 36 38 48 53 58 59 66 67 76 85 86 90 94 102 110 119 +2 3 9 13 24 28 35 36 39 50 52 58 59 63 73 83 85 88 90 93 104 112 119 diff --git a/testData/Apriori_recent20bills.txt b/testData/Apriori_recent20bills.txt new file mode 100755 index 00000000..6f1c6a92 --- /dev/null +++ b/testData/Apriori_recent20bills.txt @@ -0,0 +1,20 @@ +12939 Prohibiting Federal Funding of National Public Radio +12940 Removing Troops from Afghanistan +12830 Prioritizing Payment of Public Debt +12857 Calling for a Balanced Budget Constitutional Amendment +12988 Terminating the Home Affordable Modification Program +12040 Repealing Business Transaction Reporting Requirements +12465 Repealing the Health Care Bill +11451 Science and Technology Funding +11364 Credit Default Swap Regulations +11820 "Whistleblower Protection" for Offshore Oil Workers +12452 Treaty with Russia to Reduce and Limit Offensive Arms +11318 Derivatives Regulation Modifications +11414 Repealing "Don't Ask, Don't Tell" After Military Review and Certification +11719 Unemployment Benefits Extension +11205 Prohibiting 2010- 2011 Congressional Cost-of-Living Pay Increase +12747 Prohibiting Use of Federal Funds For Planned Parenthood +12792 Reducing Federal Funding of the US Institute of Peace +12827 Prohibiting the Use of Federal Funds for NASCAR Sponsorships +12445 Mine Safety Act +12049 2010-2011 Defense Authorizations \ No newline at end of file From 53dee673371fb656b534c713289394a906d55b4f Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 17 Mar 2017 19:56:37 +0800 Subject: [PATCH 06/11] =?UTF-8?q?=E6=9B=B4=E8=A1=8C=E5=86=B3=E7=AD=96?= =?UTF-8?q?=E6=A0=91=E7=9A=84=E9=83=A8=E5=88=86=E5=86=85=E5=AE=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../12.使用FP-growth算法来高效发现频繁项集.md | 20 +- src/python/03.DecisionTree/DecisionTree.py | 35 ++-- .../03.DecisionTree/DecisionTreePlot.py | 4 +- src/python/07.AdaBoost/adaboost.py | 2 +- src/python/12.FrequentPattemTree/apriori.py | 12 -- src/python/12.FrequentPattemTree/fpGrowth.py | 177 +++++++++++++++++- testResult/tree.pdf | Bin 16177 -> 16281 bytes 7 files changed, 203 insertions(+), 47 deletions(-) delete mode 100644 src/python/12.FrequentPattemTree/apriori.py diff --git a/docs/12.使用FP-growth算法来高效发现频繁项集.md b/docs/12.使用FP-growth算法来高效发现频繁项集.md index adcb7cb3..437fc99a 100644 --- a/docs/12.使用FP-growth算法来高效发现频繁项集.md +++ b/docs/12.使用FP-growth算法来高效发现频繁项集.md @@ -1,7 +1,7 @@ -# 12.使用FP-growth算法来高效发现频繁项集 # +# 12) 使用FP-growth算法来高效发现频繁项集 -**- 基本过程** +## 基本过程 - 构建FP树 * 对原始数据集扫描两遍 @@ -9,13 +9,21 @@ * 第二遍只扫描频繁元素。 - 从FP树种挖掘频繁项集 -**FP树介绍** - - 是一种紧凑的数据结构，FP代表频繁模式（Frequent Pattem）每个项集以路径的方式存储在树中。 - 包含：项集【集合中的单个元素+出现次数+父节点】 +## FP树介绍 +* FP-growth算法是将数据存储在一种称为FP树的紧凑的数据结构中，FP代表频繁模式（Frequent Pattem）每个项集以路径的方式存储在树中。 +* 包含：项集【集合中的单个元素+出现次数+父节点】 * 与其他树结构相比 * 它通过链接(link)来连接相似元素，被连起来的元素项可以看成一个链表。 * 一个元素项可以出现多次 + * 相似项之间的链接即`节点链接`(node link), 用于快速发现相似项的位置。 +## FP-growth算法 特点 +* 优点： 一般要快于Apriori。(通常性能要好两个数量级以上) +* 缺点： 实现比较困难，在某些数据集上性能会下降。 +* 适用数据类型：标称型数据(离散型数据)。 + +## 项目实战 +* 1.从Twitter文本流中挖掘常用词。 +* 2.从网民页面浏览行为中挖掘常见模式。 diff --git a/src/python/03.DecisionTree/DecisionTree.py b/src/python/03.DecisionTree/DecisionTree.py index 2425803d..dce6eb25 100644 --- a/src/python/03.DecisionTree/DecisionTree.py +++ b/src/python/03.DecisionTree/DecisionTree.py @@ -20,8 +20,6 @@ def createDataSet(): 无需传入参数 Returns: 返回数据集和对应的label标签 - Raises: - """ dataSet = [[1, 1, 'yes'], [1, 1, 'yes'], @@ -44,9 +42,7 @@ def calcShannonEnt(dataSet): Args: dataSet 数据集 Returns: - 返回香农熵的计算值 - Raises: - + 返回 每一组feature下的某个分类下，香农熵的信息期望 """ # 求list的长度，表示计算参与训练的数据量 numEntries = len(dataSet) @@ -81,8 +77,6 @@ def splitDataSet(dataSet, axis, value): value 表示axis列对应的value值 Returns: axis列为value的数据集【该数据集需要排除axis列】 - Raises: - """ retDataSet = [] for featVec in dataSet: @@ -106,10 +100,8 @@ def chooseBestFeatureToSplit(dataSet): dataSet 数据集 Returns: bestFeature 最优的特征列 - Raises: - """ - # 求第一行有多少列的 Feature + # 求第一行有多少列的 Feature, 最后一列是label列嘛 numFeatures = len(dataSet[0]) - 1 # label的信息熵 baseEntropy = calcShannonEnt(dataSet) @@ -147,8 +139,6 @@ def majorityCnt(classList): classList label列的集合 Returns: bestFeature 最优的特征列 - Raises: - """ classCount = {} for vote in classList: @@ -172,6 +162,7 @@ def createTree(dataSet, labels): # 选择最优的列，得到最有列对应的label含义 bestFeat = chooseBestFeatureToSplit(dataSet) + # 获取label的名称 bestFeatLabel = labels[bestFeat] # 初始化myTree myTree = {bestFeatLabel: {}} @@ -190,16 +181,26 @@ def createTree(dataSet, labels): def classify(inputTree, featLabels, testVec): - # 获取tree的第一个节点对应的key值 + """classify(给输入的节点，进行分类) + + Args: + inputTree 决策树模型 + featLabels label标签对应的名称 + testVec 测试输入的数据 + Returns: + classLabel 分类的结果值，需要映射label才能知道名称 + """ + # 获取tree的根节点对于的key值 firstStr = inputTree.keys()[0] - # 获取第一个节点对应的value值 + # 通过key得到根节点对应的value secondDict = inputTree[firstStr] - # 判断根节点的索引值，然后根据testVec来获取对应的树分枝位置 + # 判断根节点名称获取根节点在label中的先后顺序，这样就知道输入的testVec怎么开始对照树来做分类 featIndex = featLabels.index(firstStr) + # 测试数据，找到根节点对应的label位置，也就知道从输入的数据的第几位来开始分类 key = testVec[featIndex] valueOfFeat = secondDict[key] print '+++', firstStr, 'xxx', secondDict, '---', key, '>>>', valueOfFeat - # 判断分枝是否结束 + # 判断分枝是否结束: 判断valueOfFeat是否是dict类型 if isinstance(valueOfFeat, dict): classLabel = classify(valueOfFeat, featLabels, testVec) else: @@ -240,7 +241,7 @@ if __name__ == "__main__": myTree = createTree(myDat, copy.deepcopy(labels)) print myTree # [1, 1]表示要取的分支上的节点位置，对应的结果值 - # print classify(myTree, labels, [1, 1]) + print classify(myTree, labels, [1, 1]) # 画图可视化展现 dtPlot.createPlot(myTree) diff --git a/src/python/03.DecisionTree/DecisionTreePlot.py b/src/python/03.DecisionTree/DecisionTreePlot.py index 3c5c4d31..59b97751 100644 --- a/src/python/03.DecisionTree/DecisionTreePlot.py +++ b/src/python/03.DecisionTree/DecisionTreePlot.py @@ -128,5 +128,5 @@ def retrieveTree(i): return listOfTrees[i] -myTree = retrieveTree(1) -createPlot(myTree) +# myTree = retrieveTree(1) +# createPlot(myTree) diff --git a/src/python/07.AdaBoost/adaboost.py b/src/python/07.AdaBoost/adaboost.py index bf144378..b256f07a 100644 --- a/src/python/07.AdaBoost/adaboost.py +++ b/src/python/07.AdaBoost/adaboost.py @@ -201,7 +201,7 @@ def plotROC(predStrengths, classLabels): ySum += cur[1] # draw line from cur to (cur[0]-delX, cur[1]-delY) # 画点连线 (x1, x2, y1, y2) - print cur[0], cur[0]-delX, cur[1], cur[1]-delY + # print cur[0], cur[0]-delX, cur[1], cur[1]-delY ax.plot([cur[0], cur[0]-delX], [cur[1], cur[1]-delY], c='b') cur = (cur[0]-delX, cur[1]-delY) # 画对角的虚线线 diff --git a/src/python/12.FrequentPattemTree/apriori.py b/src/python/12.FrequentPattemTree/apriori.py deleted file mode 100644 index e172b575..00000000 --- a/src/python/12.FrequentPattemTree/apriori.py +++ /dev/null @@ -1,12 +0,0 @@ -def loadDataSet(): - return [[1,3,4],[2,3,5],[1,2,3,5],[2,5]] -def createC1(dataSet): - c1=[] - for transaction in dataSet: - for item in transaction: - if not [item] in c1: - c1.append([item]) - c1.sort() - return map(frozenset,c1) -def scanD(D,ck,minSupport): - ssCnt = {} \ No newline at end of file diff --git a/src/python/12.FrequentPattemTree/fpGrowth.py b/src/python/12.FrequentPattemTree/fpGrowth.py index 5e3898cc..8f992385 100644 --- a/src/python/12.FrequentPattemTree/fpGrowth.py +++ b/src/python/12.FrequentPattemTree/fpGrowth.py @@ -1,19 +1,178 @@ +''' +Created on Jun 14, 2011 +FP-Growth FP means frequent pattern +the FP-Growth algorithm needs: +1. FP-tree (class treeNode) +2. header table (use dict) + +This finds frequent itemsets similar to apriori but does not find association rules. + +@author: Peter/片刻 +''' +print(__doc__) + + class treeNode: - def __init__(self,nameValue,numOccur,parentNode): + def __init__(self, nameValue, numOccur, parentNode): self.name = nameValue self.count = numOccur self.nodeLink = None + # needs to be updated self.parent = parentNode self.children = {} - def inc(self,numOccur): + + def inc(self, numOccur): self.count += numOccur - def disp(self,ind=1): - print(' '*ind,self.name,' ',self.count) + + def disp(self, ind=1): + print ' '*ind, self.name, ' ', self.count for child in self.children.values(): child.disp(ind+1) - if __name__ == "__main__": - import fpGrowth - rootNode = fpGrowth.treeNode('pyramid',9,None) - rootNode.children['eye']=fpGrowth.treeNode('eye',13,None) - rootNode.disp() \ No newline at end of file + +def createTree(dataSet, minSup=1): #create FP-tree from dataset but don't mine + headerTable = {} + #go over dataSet twice + for trans in dataSet:#first pass counts frequency of occurance + for item in trans: + headerTable[item] = headerTable.get(item, 0) + dataSet[trans] + for k in headerTable.keys(): #remove items not meeting minSup + if headerTable[k] < minSup: + del(headerTable[k]) + freqItemSet = set(headerTable.keys()) + #print 'freqItemSet: ',freqItemSet + if len(freqItemSet) == 0: return None, None #if no items meet min support -->get out + for k in headerTable: + headerTable[k] = [headerTable[k], None] #reformat headerTable to use Node link + #print 'headerTable: ',headerTable + retTree = treeNode('Null Set', 1, None) #create tree + for tranSet, count in dataSet.items(): #go through dataset 2nd time + localD = {} + for item in tranSet: #put transaction items in order + if item in freqItemSet: + localD[item] = headerTable[item][0] + if len(localD) > 0: + orderedItems = [v[0] for v in sorted(localD.items(), key=lambda p: p[1], reverse=True)] + updateTree(orderedItems, retTree, headerTable, count)#populate tree with ordered freq itemset + return retTree, headerTable #return tree and header table + + +def updateTree(items, inTree, headerTable, count): + if items[0] in inTree.children:#check if orderedItems[0] in retTree.children + inTree.children[items[0]].inc(count) #incrament count + else: #add items[0] to inTree.children + inTree.children[items[0]] = treeNode(items[0], count, inTree) + if headerTable[items[0]][1] == None: #update header table + headerTable[items[0]][1] = inTree.children[items[0]] + else: + updateHeader(headerTable[items[0]][1], inTree.children[items[0]]) + if len(items) > 1:#call updateTree() with remaining ordered items + updateTree(items[1::], inTree.children[items[0]], headerTable, count) + + +def updateHeader(nodeToTest, targetNode): #this version does not use recursion + while (nodeToTest.nodeLink != None): #Do not use recursion to traverse a linked list! + nodeToTest = nodeToTest.nodeLink + nodeToTest.nodeLink = targetNode + + +def ascendTree(leafNode, prefixPath): #ascends from leaf node to root + if leafNode.parent != None: + prefixPath.append(leafNode.name) + ascendTree(leafNode.parent, prefixPath) + + +def findPrefixPath(basePat, treeNode): #treeNode comes from header table + condPats = {} + while treeNode != None: + prefixPath = [] + ascendTree(treeNode, prefixPath) + if len(prefixPath) > 1: + condPats[frozenset(prefixPath[1:])] = treeNode.count + treeNode = treeNode.nodeLink + return condPats + + +def mineTree(inTree, headerTable, minSup, preFix, freqItemList): + bigL = [v[0] for v in sorted(headerTable.items(), key=lambda p: p[1])]#(sort header table) + for basePat in bigL: #start from bottom of header table + newFreqSet = preFix.copy() + newFreqSet.add(basePat) + #print 'finalFrequent Item: ',newFreqSet #append to set + freqItemList.append(newFreqSet) + condPattBases = findPrefixPath(basePat, headerTable[basePat][1]) + #print 'condPattBases :',basePat, condPattBases + #2. construct cond FP-tree from cond. pattern base + myCondTree, myHead = createTree(condPattBases, minSup) + #print 'head from conditional tree: ', myHead + if myHead != None: #3. mine cond. FP-tree + #print 'conditional tree for: ',newFreqSet + #myCondTree.disp(1) + mineTree(myCondTree, myHead, minSup, newFreqSet, freqItemList) + + +def loadSimpDat(): + simpDat = [['r', 'z', 'h', 'j', 'p'], + ['z', 'y', 'x', 'w', 'v', 'u', 't', 's'], + ['z'], + ['r', 'x', 'n', 'o', 's'], + ['y', 'r', 'x', 'z', 'q', 't', 'p'], + ['y', 'z', 'x', 'e', 'q', 's', 't', 'm']] + return simpDat + + +def createInitSet(dataSet): + retDict = {} + for trans in dataSet: + retDict[frozenset(trans)] = 1 + return retDict + + +import twitter +from time import sleep +import re + + +def textParse(bigString): + urlsRemoved = re.sub('(http:[/][/]|www.)([a-z]|[A-Z]|[0-9]|[/.]|[~])*', '', bigString) + listOfTokens = re.split(r'\W*', urlsRemoved) + return [tok.lower() for tok in listOfTokens if len(tok) > 2] + + +def getLotsOfTweets(searchStr): + CONSUMER_KEY = '' + CONSUMER_SECRET = '' + ACCESS_TOKEN_KEY = '' + ACCESS_TOKEN_SECRET = '' + api = twitter.Api(consumer_key=CONSUMER_KEY, consumer_secret=CONSUMER_SECRET, + access_token_key=ACCESS_TOKEN_KEY, + access_token_secret=ACCESS_TOKEN_SECRET) + #you can get 1500 results 15 pages * 100 per page + resultsPages = [] + for i in range(1,15): + print "fetching page %d" % i + searchResults = api.GetSearch(searchStr, per_page=100, page=i) + resultsPages.append(searchResults) + sleep(6) + return resultsPages + + +def mineTweets(tweetArr, minSup=5): + parsedList = [] + for i in range(14): + for j in range(100): + parsedList.append(textParse(tweetArr[i][j].text)) + initSet = createInitSet(parsedList) + myFPtree, myHeaderTab = createTree(initSet, minSup) + myFreqList = [] + mineTree(myFPtree, myHeaderTab, minSup, set([]), myFreqList) + return myFreqList + + +#minSup = 3 +#simpDat = loadSimpDat() +#initSet = createInitSet(simpDat) +#myFPtree, myHeaderTab = createTree(initSet, minSup) +#myFPtree.disp() +#myFreqList = [] +#mineTree(myFPtree, myHeaderTab, minSup, set([]), myFreqList) diff --git a/testResult/tree.pdf 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testData/testDigits/0_2.txt | 32 + testData/testDigits/0_20.txt | 32 + testData/testDigits/0_21.txt | 32 + testData/testDigits/0_22.txt | 32 + testData/testDigits/0_23.txt | 32 + testData/testDigits/0_24.txt | 32 + testData/testDigits/0_25.txt | 32 + testData/testDigits/0_26.txt | 32 + testData/testDigits/0_27.txt | 32 + testData/testDigits/0_28.txt | 32 + testData/testDigits/0_29.txt | 32 + testData/testDigits/0_3.txt | 32 + testData/testDigits/0_30.txt | 32 + testData/testDigits/0_31.txt | 32 + testData/testDigits/0_32.txt | 32 + testData/testDigits/0_33.txt | 32 + testData/testDigits/0_34.txt | 32 + testData/testDigits/0_35.txt | 32 + testData/testDigits/0_36.txt | 32 + testData/testDigits/0_37.txt | 32 + testData/testDigits/0_38.txt | 32 + testData/testDigits/0_39.txt | 32 + testData/testDigits/0_4.txt | 32 + testData/testDigits/0_40.txt | 32 + testData/testDigits/0_41.txt | 32 + testData/testDigits/0_42.txt | 32 + testData/testDigits/0_43.txt | 32 + testData/testDigits/0_44.txt | 32 + testData/testDigits/0_45.txt | 32 + testData/testDigits/0_46.txt | 32 + testData/testDigits/0_47.txt | 32 + testData/testDigits/0_48.txt | 32 + testData/testDigits/0_49.txt | 32 + testData/testDigits/0_5.txt | 32 + testData/testDigits/0_50.txt | 32 + testData/testDigits/0_51.txt | 32 + testData/testDigits/0_52.txt | 32 + testData/testDigits/0_53.txt | 32 + testData/testDigits/0_54.txt | 32 + testData/testDigits/0_55.txt | 32 + testData/testDigits/0_56.txt | 32 + testData/testDigits/0_57.txt | 32 + testData/testDigits/0_58.txt | 32 + testData/testDigits/0_59.txt | 32 + testData/testDigits/0_6.txt | 32 + testData/testDigits/0_60.txt | 32 + testData/testDigits/0_61.txt | 32 + testData/testDigits/0_62.txt | 32 + testData/testDigits/0_63.txt | 32 + testData/testDigits/0_64.txt | 32 + testData/testDigits/0_65.txt | 32 + testData/testDigits/0_66.txt | 32 + testData/testDigits/0_67.txt | 32 + testData/testDigits/0_68.txt | 32 + testData/testDigits/0_69.txt | 32 + 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+ testData/testDigits/1_16.txt | 32 + testData/testDigits/1_17.txt | 32 + testData/testDigits/1_18.txt | 32 + testData/testDigits/1_19.txt | 32 + testData/testDigits/1_2.txt | 32 + testData/testDigits/1_20.txt | 32 + testData/testDigits/1_21.txt | 32 + testData/testDigits/1_22.txt | 32 + testData/testDigits/1_23.txt | 32 + testData/testDigits/1_24.txt | 32 + testData/testDigits/1_25.txt | 32 + testData/testDigits/1_26.txt | 32 + testData/testDigits/1_27.txt | 32 + testData/testDigits/1_28.txt | 32 + testData/testDigits/1_29.txt | 32 + testData/testDigits/1_3.txt | 32 + testData/testDigits/1_30.txt | 32 + testData/testDigits/1_31.txt | 32 + testData/testDigits/1_32.txt | 32 + testData/testDigits/1_33.txt | 32 + testData/testDigits/1_34.txt | 32 + testData/testDigits/1_35.txt | 32 + testData/testDigits/1_36.txt | 32 + testData/testDigits/1_37.txt | 32 + testData/testDigits/1_38.txt | 32 + testData/testDigits/1_39.txt | 32 + testData/testDigits/1_4.txt | 32 + testData/testDigits/1_40.txt | 32 + testData/testDigits/1_41.txt | 32 + testData/testDigits/1_42.txt | 32 + testData/testDigits/1_43.txt | 32 + testData/testDigits/1_44.txt | 32 + testData/testDigits/1_45.txt | 32 + testData/testDigits/1_46.txt | 32 + testData/testDigits/1_47.txt | 32 + testData/testDigits/1_48.txt | 32 + testData/testDigits/1_49.txt | 32 + testData/testDigits/1_5.txt | 32 + testData/testDigits/1_50.txt | 32 + testData/testDigits/1_51.txt | 32 + testData/testDigits/1_52.txt | 32 + testData/testDigits/1_53.txt | 32 + testData/testDigits/1_54.txt | 32 + testData/testDigits/1_55.txt | 32 + testData/testDigits/1_56.txt | 32 + testData/testDigits/1_57.txt | 32 + testData/testDigits/1_58.txt | 32 + testData/testDigits/1_59.txt | 32 + testData/testDigits/1_6.txt | 32 + testData/testDigits/1_60.txt | 32 + testData/testDigits/1_61.txt | 32 + testData/testDigits/1_62.txt | 32 + testData/testDigits/1_63.txt | 32 + testData/testDigits/1_64.txt | 32 + testData/testDigits/1_65.txt | 32 + testData/testDigits/1_66.txt | 32 + testData/testDigits/1_67.txt | 32 + testData/testDigits/1_68.txt | 32 + testData/testDigits/1_69.txt | 32 + testData/testDigits/1_7.txt | 32 + testData/testDigits/1_70.txt | 32 + testData/testDigits/1_71.txt | 32 + testData/testDigits/1_72.txt | 32 + testData/testDigits/1_73.txt | 32 + testData/testDigits/1_74.txt | 32 + testData/testDigits/1_75.txt | 32 + testData/testDigits/1_76.txt | 32 + testData/testDigits/1_77.txt | 32 + testData/testDigits/1_78.txt | 32 + testData/testDigits/1_79.txt | 32 + testData/testDigits/1_8.txt | 32 + testData/testDigits/1_80.txt | 32 + testData/testDigits/1_81.txt | 32 + testData/testDigits/1_82.txt | 32 + testData/testDigits/1_83.txt | 32 + testData/testDigits/1_84.txt | 32 + testData/testDigits/1_85.txt | 32 + testData/testDigits/1_86.txt | 32 + testData/testDigits/1_87.txt | 32 + testData/testDigits/1_88.txt | 32 + testData/testDigits/1_89.txt | 32 + testData/testDigits/1_9.txt | 32 + testData/testDigits/1_90.txt | 32 + testData/testDigits/1_91.txt | 32 + testData/testDigits/1_92.txt | 32 + testData/testDigits/1_93.txt | 32 + testData/testDigits/1_94.txt | 32 + testData/testDigits/1_95.txt | 32 + testData/testDigits/1_96.txt | 32 + testData/testDigits/2_0.txt | 32 + testData/testDigits/2_1.txt | 32 + testData/testDigits/2_10.txt | 32 + testData/testDigits/2_11.txt | 32 + testData/testDigits/2_12.txt | 32 + testData/testDigits/2_13.txt | 32 + testData/testDigits/2_14.txt | 32 + testData/testDigits/2_15.txt | 32 + testData/testDigits/2_16.txt | 32 + testData/testDigits/2_17.txt | 32 + testData/testDigits/2_18.txt | 32 + testData/testDigits/2_19.txt | 32 + testData/testDigits/2_2.txt | 32 + testData/testDigits/2_20.txt | 32 + testData/testDigits/2_21.txt | 32 + testData/testDigits/2_22.txt | 32 + testData/testDigits/2_23.txt | 32 + testData/testDigits/2_24.txt | 32 + testData/testDigits/2_25.txt | 32 + testData/testDigits/2_26.txt | 32 + testData/testDigits/2_27.txt | 32 + testData/testDigits/2_28.txt | 32 + testData/testDigits/2_29.txt | 32 + testData/testDigits/2_3.txt | 32 + testData/testDigits/2_30.txt | 32 + testData/testDigits/2_31.txt | 32 + testData/testDigits/2_32.txt | 32 + testData/testDigits/2_33.txt | 32 + testData/testDigits/2_34.txt | 32 + testData/testDigits/2_35.txt | 32 + testData/testDigits/2_36.txt | 32 + testData/testDigits/2_37.txt | 32 + testData/testDigits/2_38.txt | 32 + testData/testDigits/2_39.txt | 32 + testData/testDigits/2_4.txt | 32 + testData/testDigits/2_40.txt | 32 + testData/testDigits/2_41.txt | 32 + testData/testDigits/2_42.txt | 32 + testData/testDigits/2_43.txt | 32 + testData/testDigits/2_44.txt | 32 + testData/testDigits/2_45.txt | 32 + testData/testDigits/2_46.txt | 32 + testData/testDigits/2_47.txt | 32 + testData/testDigits/2_48.txt | 32 + testData/testDigits/2_49.txt | 32 + testData/testDigits/2_5.txt | 32 + testData/testDigits/2_50.txt | 32 + testData/testDigits/2_51.txt | 32 + testData/testDigits/2_52.txt | 32 + testData/testDigits/2_53.txt | 32 + testData/testDigits/2_54.txt | 32 + testData/testDigits/2_55.txt | 32 + testData/testDigits/2_56.txt | 32 + testData/testDigits/2_57.txt | 32 + testData/testDigits/2_58.txt | 32 + testData/testDigits/2_59.txt | 32 + testData/testDigits/2_6.txt | 32 + testData/testDigits/2_60.txt | 32 + testData/testDigits/2_61.txt | 32 + testData/testDigits/2_62.txt | 32 + testData/testDigits/2_63.txt | 32 + testData/testDigits/2_64.txt | 32 + testData/testDigits/2_65.txt | 32 + testData/testDigits/2_66.txt | 32 + testData/testDigits/2_67.txt | 32 + testData/testDigits/2_68.txt | 32 + testData/testDigits/2_69.txt | 32 + testData/testDigits/2_7.txt | 32 + testData/testDigits/2_70.txt | 32 + testData/testDigits/2_71.txt | 32 + testData/testDigits/2_72.txt | 32 + testData/testDigits/2_73.txt | 32 + testData/testDigits/2_74.txt | 32 + testData/testDigits/2_75.txt | 32 + testData/testDigits/2_76.txt | 32 + testData/testDigits/2_77.txt | 32 + testData/testDigits/2_78.txt | 32 + testData/testDigits/2_79.txt | 32 + testData/testDigits/2_8.txt | 32 + testData/testDigits/2_80.txt | 32 + testData/testDigits/2_81.txt | 32 + testData/testDigits/2_82.txt | 32 + testData/testDigits/2_83.txt | 32 + testData/testDigits/2_84.txt | 32 + testData/testDigits/2_85.txt | 32 + testData/testDigits/2_86.txt | 32 + testData/testDigits/2_87.txt | 32 + testData/testDigits/2_88.txt | 32 + testData/testDigits/2_89.txt | 32 + testData/testDigits/2_9.txt | 32 + testData/testDigits/2_90.txt | 32 + testData/testDigits/2_91.txt | 32 + testData/testDigits/3_0.txt | 32 + testData/testDigits/3_1.txt | 32 + testData/testDigits/3_10.txt | 32 + testData/testDigits/3_11.txt | 32 + testData/testDigits/3_12.txt | 32 + testData/testDigits/3_13.txt | 32 + testData/testDigits/3_14.txt | 32 + testData/testDigits/3_15.txt | 32 + testData/testDigits/3_16.txt | 32 + testData/testDigits/3_17.txt | 32 + testData/testDigits/3_18.txt | 32 + testData/testDigits/3_19.txt | 32 + testData/testDigits/3_2.txt | 32 + testData/testDigits/3_20.txt | 32 + testData/testDigits/3_21.txt | 32 + testData/testDigits/3_22.txt | 32 + testData/testDigits/3_23.txt | 32 + testData/testDigits/3_24.txt | 32 + testData/testDigits/3_25.txt | 32 + testData/testDigits/3_26.txt | 32 + testData/testDigits/3_27.txt | 32 + testData/testDigits/3_28.txt | 32 + testData/testDigits/3_29.txt | 32 + testData/testDigits/3_3.txt | 32 + testData/testDigits/3_30.txt | 32 + testData/testDigits/3_31.txt | 32 + testData/testDigits/3_32.txt | 32 + testData/testDigits/3_33.txt | 32 + testData/testDigits/3_34.txt | 32 + testData/testDigits/3_35.txt | 32 + testData/testDigits/3_36.txt | 32 + testData/testDigits/3_37.txt | 32 + testData/testDigits/3_38.txt | 32 + testData/testDigits/3_39.txt | 32 + testData/testDigits/3_4.txt | 32 + testData/testDigits/3_40.txt | 32 + testData/testDigits/3_41.txt | 32 + testData/testDigits/3_42.txt | 32 + testData/testDigits/3_43.txt | 32 + testData/testDigits/3_44.txt | 32 + testData/testDigits/3_45.txt | 32 + testData/testDigits/3_46.txt | 32 + testData/testDigits/3_47.txt | 32 + testData/testDigits/3_48.txt | 32 + testData/testDigits/3_49.txt | 32 + testData/testDigits/3_5.txt | 32 + testData/testDigits/3_50.txt | 32 + testData/testDigits/3_51.txt | 32 + testData/testDigits/3_52.txt | 32 + testData/testDigits/3_53.txt | 32 + testData/testDigits/3_54.txt | 32 + testData/testDigits/3_55.txt | 32 + testData/testDigits/3_56.txt | 32 + testData/testDigits/3_57.txt | 32 + testData/testDigits/3_58.txt | 32 + testData/testDigits/3_59.txt | 32 + testData/testDigits/3_6.txt | 32 + testData/testDigits/3_60.txt | 32 + testData/testDigits/3_61.txt | 32 + testData/testDigits/3_62.txt | 32 + testData/testDigits/3_63.txt | 32 + testData/testDigits/3_64.txt | 32 + testData/testDigits/3_65.txt | 32 + testData/testDigits/3_66.txt | 32 + testData/testDigits/3_67.txt | 32 + testData/testDigits/3_68.txt | 32 + testData/testDigits/3_69.txt | 32 + testData/testDigits/3_7.txt | 32 + testData/testDigits/3_70.txt | 32 + testData/testDigits/3_71.txt | 32 + testData/testDigits/3_72.txt | 32 + testData/testDigits/3_73.txt | 32 + testData/testDigits/3_74.txt | 32 + testData/testDigits/3_75.txt | 32 + testData/testDigits/3_76.txt | 32 + testData/testDigits/3_77.txt | 32 + testData/testDigits/3_78.txt | 32 + testData/testDigits/3_79.txt | 32 + testData/testDigits/3_8.txt | 32 + testData/testDigits/3_80.txt | 32 + testData/testDigits/3_81.txt | 32 + testData/testDigits/3_82.txt | 32 + testData/testDigits/3_83.txt | 32 + testData/testDigits/3_84.txt | 32 + testData/testDigits/3_9.txt | 32 + testData/testDigits/4_0.txt | 32 + testData/testDigits/4_1.txt | 32 + testData/testDigits/4_10.txt | 32 + testData/testDigits/4_100.txt | 32 + testData/testDigits/4_101.txt | 32 + testData/testDigits/4_102.txt | 32 + testData/testDigits/4_103.txt | 32 + testData/testDigits/4_104.txt | 32 + testData/testDigits/4_105.txt | 32 + testData/testDigits/4_106.txt | 32 + testData/testDigits/4_107.txt | 32 + testData/testDigits/4_108.txt | 32 + testData/testDigits/4_109.txt | 32 + testData/testDigits/4_11.txt | 32 + testData/testDigits/4_110.txt | 32 + testData/testDigits/4_111.txt | 32 + testData/testDigits/4_112.txt | 32 + testData/testDigits/4_113.txt | 32 + testData/testDigits/4_12.txt | 32 + testData/testDigits/4_13.txt | 32 + testData/testDigits/4_14.txt | 32 + testData/testDigits/4_15.txt | 32 + testData/testDigits/4_16.txt | 32 + testData/testDigits/4_17.txt | 32 + testData/testDigits/4_18.txt | 32 + testData/testDigits/4_19.txt | 32 + testData/testDigits/4_2.txt | 32 + testData/testDigits/4_20.txt | 32 + testData/testDigits/4_21.txt | 32 + testData/testDigits/4_22.txt | 32 + testData/testDigits/4_23.txt | 32 + testData/testDigits/4_24.txt | 32 + testData/testDigits/4_25.txt | 32 + testData/testDigits/4_26.txt | 32 + testData/testDigits/4_27.txt | 32 + testData/testDigits/4_28.txt | 32 + 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32 + testData/testDigits/4_54.txt | 32 + testData/testDigits/4_55.txt | 32 + testData/testDigits/4_56.txt | 32 + testData/testDigits/4_57.txt | 32 + testData/testDigits/4_58.txt | 32 + testData/testDigits/4_59.txt | 32 + testData/testDigits/4_6.txt | 32 + testData/testDigits/4_60.txt | 32 + testData/testDigits/4_61.txt | 32 + testData/testDigits/4_62.txt | 32 + testData/testDigits/4_63.txt | 32 + testData/testDigits/4_64.txt | 32 + testData/testDigits/4_65.txt | 32 + testData/testDigits/4_66.txt | 32 + testData/testDigits/4_67.txt | 32 + testData/testDigits/4_68.txt | 32 + testData/testDigits/4_69.txt | 32 + testData/testDigits/4_7.txt | 32 + testData/testDigits/4_70.txt | 32 + testData/testDigits/4_71.txt | 32 + testData/testDigits/4_72.txt | 32 + testData/testDigits/4_73.txt | 32 + testData/testDigits/4_74.txt | 32 + testData/testDigits/4_75.txt | 32 + testData/testDigits/4_76.txt | 32 + testData/testDigits/4_77.txt | 32 + testData/testDigits/4_78.txt | 32 + testData/testDigits/4_79.txt | 32 + testData/testDigits/4_8.txt | 32 + testData/testDigits/4_80.txt | 32 + testData/testDigits/4_81.txt | 32 + testData/testDigits/4_82.txt | 32 + testData/testDigits/4_83.txt | 32 + testData/testDigits/4_84.txt | 32 + testData/testDigits/4_85.txt | 32 + testData/testDigits/4_86.txt | 32 + testData/testDigits/4_87.txt | 32 + testData/testDigits/4_88.txt | 32 + testData/testDigits/4_89.txt | 32 + testData/testDigits/4_9.txt | 32 + testData/testDigits/4_90.txt | 32 + testData/testDigits/4_91.txt | 32 + testData/testDigits/4_92.txt | 32 + testData/testDigits/4_93.txt | 32 + testData/testDigits/4_94.txt | 32 + testData/testDigits/4_95.txt | 32 + testData/testDigits/4_96.txt | 32 + testData/testDigits/4_97.txt | 32 + testData/testDigits/4_98.txt | 32 + testData/testDigits/4_99.txt | 32 + testData/testDigits/5_0.txt | 32 + testData/testDigits/5_1.txt | 32 + testData/testDigits/5_10.txt | 32 + testData/testDigits/5_100.txt | 32 + testData/testDigits/5_101.txt | 32 + testData/testDigits/5_102.txt | 32 + testData/testDigits/5_103.txt | 32 + testData/testDigits/5_104.txt | 32 + testData/testDigits/5_105.txt | 32 + testData/testDigits/5_106.txt | 32 + testData/testDigits/5_107.txt | 32 + testData/testDigits/5_11.txt | 32 + testData/testDigits/5_12.txt | 32 + testData/testDigits/5_13.txt | 32 + testData/testDigits/5_14.txt | 32 + testData/testDigits/5_15.txt | 32 + testData/testDigits/5_16.txt | 32 + testData/testDigits/5_17.txt | 32 + testData/testDigits/5_18.txt | 32 + testData/testDigits/5_19.txt | 32 + testData/testDigits/5_2.txt | 32 + testData/testDigits/5_20.txt | 32 + testData/testDigits/5_21.txt | 32 + testData/testDigits/5_22.txt | 32 + testData/testDigits/5_23.txt | 32 + testData/testDigits/5_24.txt | 32 + testData/testDigits/5_25.txt | 32 + testData/testDigits/5_26.txt | 32 + testData/testDigits/5_27.txt | 32 + testData/testDigits/5_28.txt | 32 + testData/testDigits/5_29.txt | 32 + testData/testDigits/5_3.txt | 32 + 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32 + testData/testDigits/5_56.txt | 32 + testData/testDigits/5_57.txt | 32 + testData/testDigits/5_58.txt | 32 + testData/testDigits/5_59.txt | 32 + testData/testDigits/5_6.txt | 32 + testData/testDigits/5_60.txt | 32 + testData/testDigits/5_61.txt | 32 + testData/testDigits/5_62.txt | 32 + testData/testDigits/5_63.txt | 32 + testData/testDigits/5_64.txt | 32 + testData/testDigits/5_65.txt | 32 + testData/testDigits/5_66.txt | 32 + testData/testDigits/5_67.txt | 32 + testData/testDigits/5_68.txt | 32 + testData/testDigits/5_69.txt | 32 + testData/testDigits/5_7.txt | 32 + testData/testDigits/5_70.txt | 32 + testData/testDigits/5_71.txt | 32 + testData/testDigits/5_72.txt | 32 + testData/testDigits/5_73.txt | 32 + testData/testDigits/5_74.txt | 32 + testData/testDigits/5_75.txt | 32 + testData/testDigits/5_76.txt | 32 + testData/testDigits/5_77.txt | 32 + testData/testDigits/5_78.txt | 32 + testData/testDigits/5_79.txt | 32 + testData/testDigits/5_8.txt | 32 + 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testData/trainingDigits/9_97.txt create mode 100644 testData/trainingDigits/9_98.txt create mode 100644 testData/trainingDigits/9_99.txt diff --git a/.gitignore b/.gitignore index 6564ff4a..3729f6a5 100644 --- a/.gitignore +++ b/.gitignore @@ -88,3 +88,4 @@ ENV/ # Rope project settings .ropeproject .vscode +.idea \ No newline at end of file diff --git a/src/python/02.kNN/kNN.py b/src/python/02.kNN/kNN.py index cd75ebca..6cdd9695 100644 --- a/src/python/02.kNN/kNN.py +++ b/src/python/02.kNN/kNN.py @@ -1,24 +1,28 @@ +#!/usr/bin/env python +# encoding: utf-8 ''' 导入科学计算包numpy和运算符模块operator ''' from numpy import * import operator +from os import listdir -''' - 创建数据集和标签 - 调用方式 - import kNN - group, labels = kNN.createDataSet() -''' def createDataSet(): - group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]]) - labels = ['A','A','B','B'] + """ + 创建数据集和标签 + + 调用方式 + import kNN + group, labels = kNN.createDataSet() + """ + group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]]) + labels = ['A', 'A', 'B', 'B'] return group, labels def classify0(inX, dataSet, labels, k): - ''' + """ inX: 用于分类的输入向量 dataSet: 输入的训练样本集 labels: 标签向量 @@ -27,19 +31,155 @@ def classify0(inX, dataSet, labels, k): 预测数据所在分类可在输入下列命令 kNN.classify0([0,0], group, labels, 3) - ''' + """ # 1. 距离计算 dataSetSize = dataSet.shape[0] - diffMat = tile(inX, (dataSetSize,1)) - dataSet - sqDiffMat = diffMat**2 + # tile生成和训练样本对应的矩阵，并与训练样本求差 + diffMat = tile(inX, (dataSetSize, 1)) - dataSet + # 取平方 + sqDiffMat = diffMat ** 2 + # 将矩阵的每一行相加 sqDistances = sqDiffMat.sum(axis=1) - distances = sqDistances**0.5 + # 开方 + distances = sqDistances ** 0.5 + # 距离排序 sortedDistIndicies = distances.argsort() + # 2. 选择距离最小的k个点 classCount = {} for i in range(k): + # 找到该样本的类型 voteIlabel = labels[sortedDistIndicies[i]] - classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1 - # 3. 排序 + # 在字典中将该类型加一 + classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1 + # 3. 排序并返回出现最多的那个类型 sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True) - return sortedClassCount[0][0] \ No newline at end of file + return sortedClassCount[0][0] + + +def test1(): + """ + 第一个例子演示 + """ + group, labels = createDataSet() + print str(group) + print str(labels) + print classify0([0, 0], group, labels, 3) + + +# ---------------------------------------------------------------------------------------- +def file2matrix(filename): + """ + 导入训练数据 + :param filename: 数据文件路径 + :return: 数据矩阵returnMat和对应的类别classLabelVector + """ + fr = open(filename) + numberOfLines = len(fr.readlines()) # get the number of lines in the file + # 生成对应的空矩阵 + returnMat = zeros((numberOfLines, 3)) # prepare matrix to return + classLabelVector = [] # prepare labels return + fr = open(filename) + index = 0 + for line in fr.readlines(): + line = line.strip() + listFromLine = line.split('\t') + # 每列的属性数据 + returnMat[index, :] = listFromLine[0:3] + # 每列的类别数据 + classLabelVector.append(int(listFromLine[-1])) + index += 1 + # 返回数据矩阵returnMat和对应的类别classLabelVector + return returnMat, classLabelVector + + +def autoNorm(dataSet): + """ + 归一化特征值，消除属性之间量级不同导致的影响 + :param dataSet: 数据集 + :return: 归一化后的数据集normDataSet,ranges和minVals即最小值与范围，并没有用到 + """ + # 计算每种属性的最大值、最小值、范围 + minVals = dataSet.min(0) + maxVals = dataSet.max(0) + ranges = maxVals - minVals + normDataSet = zeros(shape(dataSet)) + m = dataSet.shape[0] + # 生成与最小值之差组成的矩阵 + normDataSet = dataSet - tile(minVals, (m, 1)) + # 将最小值之差除以范围组成矩阵 + normDataSet = normDataSet / tile(ranges, (m, 1)) # element wise divide + return normDataSet, ranges, minVals + + +def datingClassTest(): + """ + 对约会网站的测试方法 + :return: 错误数 + """ + hoRatio = 0.9 # 测试范围,一部分测试一部分作为样本 + # 从文件中加载数据 + datingDataMat, datingLabels = file2matrix('../../../testData/datingTestSet2.txt') # load data setfrom file + # 归一化数据 + normMat, ranges, minVals = autoNorm(datingDataMat) + m = normMat.shape[0] + # 测试的数据 + numTestVecs = int(m * hoRatio) + errorCount = 0.0 + for i in range(numTestVecs): + # 对数据测试， + classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :], datingLabels[numTestVecs:m], 3) + print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, datingLabels[i]) + if (classifierResult != datingLabels[i]): errorCount += 1.0 + print "the total error rate is: %f" % (errorCount / float(numTestVecs)) + print errorCount + + +def img2vector(filename): + """ + 将图像数据转换为向量 + :param filename: 图片文件 + :return: 一纬矩阵 + """ + returnVect = zeros((1, 1024)) + fr = open(filename) + for i in range(32): + lineStr = fr.readline() + for j in range(32): + returnVect[0, 32 * i + j] = int(lineStr[j]) + return returnVect + + +def handwritingClassTest(): + # 1. 导入数据 + hwLabels = [] + trainingFileList = listdir('../../../testData/trainingDigits') # load the training set + m = len(trainingFileList) + trainingMat = zeros((m, 1024)) + for i in range(m): + fileNameStr = trainingFileList[i] + fileStr = fileNameStr.split('.')[0] # take off .txt + classNumStr = int(fileStr.split('_')[0]) + hwLabels.append(classNumStr) + trainingMat[i, :] = img2vector('../../../testData/trainingDigits/%s' % fileNameStr) + + # 2. 导入测试数据 + testFileList = listdir('../../../testData/testDigits') # iterate through the test set + errorCount = 0.0 + mTest = len(testFileList) + for i in range(mTest): + fileNameStr = testFileList[i] + fileStr = fileNameStr.split('.')[0] # take off .txt + classNumStr = int(fileStr.split('_')[0]) + vectorUnderTest = img2vector('../../../testData/testDigits/%s' % fileNameStr) + classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3) + print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr) + if (classifierResult != classNumStr): errorCount += 1.0 + print "\nthe total number of errors is: %d" % errorCount + print "\nthe total error rate is: %f" % (errorCount / float(mTest)) + + +if __name__ == '__main__': + # test1() + # datingClassTest() + handwritingClassTest() diff --git a/src/python/08.Predictive numerical data regression/regression.py b/src/python/08.Predictive numerical data regression/regression.py index be857aa7..ec0a376c 100644 --- a/src/python/08.Predictive numerical data regression/regression.py +++ b/src/python/08.Predictive numerical data regression/regression.py @@ -223,7 +223,7 @@ def crossValidation(xArr,yArr,numVal=10): #test for xianxinghuigui - def regression1(): +def regression1(): xArr, yArr = loadDataSet("ex0.txt") xMat = mat(xArr) yMat = mat(yArr) @@ -242,7 +242,7 @@ if __name__ == "__main__": #test for jiaquanhuigui - def regression1(): +def regression1(): xArr, yArr = loadDataSet("ex0.txt") yHat = lwlrTest(xArr, xArr, yArr, 0.003) xMat = mat(xArr) diff --git a/testData/datingTestSet2.txt b/testData/datingTestSet2.txt new file mode 100755 index 00000000..d2f074e6 --- /dev/null +++ b/testData/datingTestSet2.txt @@ -0,0 +1,1000 @@ +40920 8.326976 0.953952 3 +14488 7.153469 1.673904 2 +26052 1.441871 0.805124 1 +75136 13.147394 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+00000000000000111111110000000000 diff --git a/testData/trainingDigits/9_99.txt b/testData/trainingDigits/9_99.txt new file mode 100644 index 00000000..cd6f21fb --- /dev/null +++ b/testData/trainingDigits/9_99.txt @@ -0,0 +1,32 @@ +00000000000000111111000000000000 +00000000000001111111100000000000 +00000000000111111111110000000000 +00000000000111111111111100000000 +00000000000111111111111100000000 +00000000001111111111111110000000 +00000000001111111011111110000000 +00000000001111100001111100000000 +00000000000111110001111111000000 +00000000000111110011111111000000 +00000000000111111111111111000000 +00000000000111111111111111000000 +00000000000011111111111111000000 +00000000000001111111111111000000 +00000000000000011111101111100000 +00000000000000001111001111100000 +00000000000000000000001111100000 +00000001000000000000001111100000 +00000011100000000000001111100000 +00000011111000000000000111110000 +00000001111000000000001111100000 +00000001111000000000001111100000 +00000001111000000000001111100000 +00000001111100000000011111000000 +00000001111110000000011111000000 +00000000011111000000011111100000 +00000000011111100000011111100000 +00000000001111111111111111000000 +00000000000111111111111111000000 +00000000000111111111111110000000 +00000000000001111111111110000000 +00000000000000011111111100000000 From 624ada37f92df7ac1033b191fc89b5c791739591 Mon Sep 17 00:00:00 2001 From: sheepmen Date: Sat, 18 Mar 2017 00:18:47 +0800 Subject: [PATCH 08/11] DEV: ADD NaiveBayes CODE --- src/python/04.NaiveBayes/bayes.py | 166 ++++++++++++++++++++++++++++++ 1 file changed, 166 insertions(+) create mode 100755 src/python/04.NaiveBayes/bayes.py diff --git a/src/python/04.NaiveBayes/bayes.py b/src/python/04.NaiveBayes/bayes.py new file mode 100755 index 00000000..a4d10d95 --- /dev/null +++ b/src/python/04.NaiveBayes/bayes.py @@ -0,0 +1,166 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +from numpy import * + + +def loadDataSet(): + """ + 创建数据集 + :return: 单词列表postingList, 所属类别classVec + """ + postingList = [['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'], + ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'], + ['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'], + ['stop', 'posting', 'stupid', 'worthless', 'garbage'], + ['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'], + ['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']] + classVec = [0, 1, 0, 1, 0, 1] # 1 is abusive, 0 not + return postingList, classVec + + +def createVocabList(dataSet): + """ + 获取所有单词的集合 + :param dataSet: 数据集 + :return: 所有单词的集合(即不含重复元素的单词列表) + """ + vocabSet = set([]) # create empty set + for document in dataSet: + vocabSet = vocabSet | set(document) # union of the two sets + return list(vocabSet) + + +def setOfWords2Vec(vocabList, inputSet): + """ + 遍历查看该单词属否出现，出现该单词则将该单词置1 + :param vocabList: 所有单词集合列表 + :param inputSet: 输入数据集 + :return: 匹配列表[0,1,0,1...] + """ + returnVec = [0] * len(vocabList) + for word in inputSet: + if word in vocabList: + returnVec[vocabList.index(word)] = 1 + else: + print "the word: %s is not in my Vocabulary!" % word + return returnVec + + +def _trainNB0(trainMatrix, trainCategory): + """ + 训练数据原版 + :param trainMatrix: 文件单词矩阵 + :param trainCategory: 文件对应的类别 + :return: + """ + # 文件数 + numTrainDocs = len(trainMatrix) + # 单词数 + numWords = len(trainMatrix[0]) + # 侮辱性文件的出现概率 + pAbusive = sum(trainCategory) / float(numTrainDocs) + # 构造单词出现次数列表 + p0Num = zeros(numWords) + p1Num = zeros(numWords) + + # 整个数据集单词出现总数 + p0Denom = 0.0 + p1Denom = 0.0 + for i in range(numTrainDocs): + if trainCategory[i] == 1: + p1Num += trainMatrix[i] + p1Denom += sum(trainMatrix[i]) + else: + p0Num += trainMatrix[i] + p0Denom += sum(trainMatrix[i]) + # 类别1，即侮辱性文档的[P(F1|C1),P(F2|C1),P(F3|C1),P(F4|C1),P(F5|C1)....]列表 + p1Vect = p1Num / p1Denom + # 类别0，即正常文档的[P(F1|C0),P(F2|C0),P(F3|C0),P(F4|C0),P(F5|C0)....]列表 + p0Vect = p0Num / p0Denom + return p0Vect, p1Vect, pAbusive + + +def trainNB0(trainMatrix, trainCategory): + """ + 训练数据优化版本 + :param trainMatrix: 文件单词矩阵 + :param trainCategory: 文件对应的类别 + :return: + """ + # 文件数 + numTrainDocs = len(trainMatrix) + # 单词数 + numWords = len(trainMatrix[0]) + # 侮辱性文件的出现概率 + pAbusive = sum(trainCategory) / float(numTrainDocs) + # 构造单词出现次数列表 + p0Num = ones(numWords) + p1Num = ones(numWords) + + # 整个数据集单词出现总数，2.0根据样本/实际调查结果调整分母的值 + p0Denom = 2.0 + p1Denom = 2.0 + for i in range(numTrainDocs): + if trainCategory[i] == 1: + p1Num += trainMatrix[i] + p1Denom += sum(trainMatrix[i]) + else: + p0Num += trainMatrix[i] + p0Denom += sum(trainMatrix[i]) + # 类别1，即侮辱性文档的[log(P(F1|C1)),log(P(F2|C1)),log(P(F3|C1)),log(P(F4|C1)),log(P(F5|C1))....]列表 + p1Vect = log(p1Num / p1Denom) + # 类别0，即正常文档的[log(P(F1|C0)),log(P(F2|C0)),log(P(F3|C0)),log(P(F4|C0)),log(P(F5|C0))....]列表 + p0Vect = log(p0Num / p0Denom) + return p0Vect, p1Vect, pAbusive + + +def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1): + """ + 使用算法 + :param vec2Classify: 待测数据 + :param p0Vec: 类别1，即侮辱性文档的[log(P(F1|C1)),log(P(F2|C1)),log(P(F3|C1)),log(P(F4|C1)),log(P(F5|C1))....]列表 + :param p1Vec: 类别0，即正常文档的[log(P(F1|C0)),log(P(F2|C0)),log(P(F3|C0)),log(P(F4|C0)),log(P(F5|C0))....]列表 + :param pClass1: 类别1，侮辱性文件的出现概率 + :return: 类别1 or 0 + """ + # 计算公式 log(P(F1|C))+log(P(F2|C))+....+log(P(Fn|C))+log(P(C)) + p1 = sum(vec2Classify * p1Vec) + log(pClass1) + p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1) + if p1 > p0: + return 1 + else: + return 0 + + +def bagOfWords2VecMN(vocabList, inputSet): + returnVec = [0] * len(vocabList) + for word in inputSet: + if word in vocabList: + returnVec[vocabList.index(word)] += 1 + return returnVec + + +def testingNB(): + """ + 测试朴素贝叶斯算法 + """ + # 1. 加载数据集 + listOPosts, listClasses = loadDataSet() + # 2. 创建单词集合 + myVocabList = createVocabList(listOPosts) + # 3. 计算单词是否出现并创建数据矩阵 + trainMat = [] + for postinDoc in listOPosts: + trainMat.append(setOfWords2Vec(myVocabList, postinDoc)) + # 4. 训练数据 + p0V, p1V, pAb = trainNB0(array(trainMat), array(listClasses)) + # 5. 测试数据 + testEntry = ['love', 'my', 'dalmation'] + thisDoc = array(setOfWords2Vec(myVocabList, testEntry)) + print testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb) + testEntry = ['stupid', 'garbage'] + thisDoc = array(setOfWords2Vec(myVocabList, testEntry)) + print testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb) + + +testingNB() From fc481871c4571a2e5142e203487bbc44d91daf80 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Sat, 18 Mar 2017 20:55:22 +0800 Subject: [PATCH 09/11] =?UTF-8?q?2017-03-18=5F=E6=B7=BB=E5=8A=A0=E4=BA=A4?= =?UTF-8?q?=E6=B5=81=E7=9A=84=E8=AF=BE=E7=A8=8B=E6=B3=A8=E9=87=8A?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/python/02.kNN/kNN.py | 18 ++++-- src/python/03.DecisionTree/DecisionTree.py | 8 +-- src/python/04.NaiveBayes/bayes.py | 24 ++++---- src/python/12.FrequentPattemTree/fpGrowth.py | 59 +++++++++++++++----- 4 files changed, 74 insertions(+), 35 deletions(-) diff --git a/src/python/02.kNN/kNN.py b/src/python/02.kNN/kNN.py index 6cdd9695..271aa621 100644 --- a/src/python/02.kNN/kNN.py +++ b/src/python/02.kNN/kNN.py @@ -23,6 +23,8 @@ def createDataSet(): def classify0(inX, dataSet, labels, k): """ + inx[1,2,3] + DS=[[1,2,3],[1,2,0]] inX: 用于分类的输入向量 dataSet: 输入的训练样本集 labels: 标签向量 @@ -36,6 +38,10 @@ def classify0(inX, dataSet, labels, k): dataSetSize = dataSet.shape[0] # tile生成和训练样本对应的矩阵，并与训练样本求差 diffMat = tile(inX, (dataSetSize, 1)) - dataSet + """ + [[1,2,3],[1,2,3]]-[[1,2,3],[1,2,0]] + (A1-A2)^2+(B1-B2)^2+(c1-c2)^2 + """ # 取平方 sqDiffMat = diffMat ** 2 # 将矩阵的每一行相加 @@ -64,7 +70,7 @@ def test1(): group, labels = createDataSet() print str(group) print str(labels) - print classify0([0, 0], group, labels, 3) + print classify0([0.1, 0.1], group, labels, 3) # ---------------------------------------------------------------------------------------- @@ -119,7 +125,7 @@ def datingClassTest(): """ hoRatio = 0.9 # 测试范围,一部分测试一部分作为样本 # 从文件中加载数据 - datingDataMat, datingLabels = file2matrix('../../../testData/datingTestSet2.txt') # load data setfrom file + datingDataMat, datingLabels = file2matrix('testData/datingTestSet2.txt') # load data setfrom file # 归一化数据 normMat, ranges, minVals = autoNorm(datingDataMat) m = normMat.shape[0] @@ -153,7 +159,7 @@ def img2vector(filename): def handwritingClassTest(): # 1. 导入数据 hwLabels = [] - trainingFileList = listdir('../../../testData/trainingDigits') # load the training set + trainingFileList = listdir('testData/trainingDigits') # load the training set m = len(trainingFileList) trainingMat = zeros((m, 1024)) for i in range(m): @@ -161,17 +167,17 @@ def handwritingClassTest(): fileStr = fileNameStr.split('.')[0] # take off .txt classNumStr = int(fileStr.split('_')[0]) hwLabels.append(classNumStr) - trainingMat[i, :] = img2vector('../../../testData/trainingDigits/%s' % fileNameStr) + trainingMat[i, :] = img2vector('testData/trainingDigits/%s' % fileNameStr) # 2. 导入测试数据 - testFileList = listdir('../../../testData/testDigits') # iterate through the test set + testFileList = listdir('testData/testDigits') # iterate through the test set errorCount = 0.0 mTest = len(testFileList) for i in range(mTest): fileNameStr = testFileList[i] fileStr = fileNameStr.split('.')[0] # take off .txt classNumStr = int(fileStr.split('_')[0]) - vectorUnderTest = img2vector('../../../testData/testDigits/%s' % fileNameStr) + vectorUnderTest = img2vector('testData/testDigits/%s' % fileNameStr) classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3) print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr) if (classifierResult != classNumStr): errorCount += 1.0 diff --git a/src/python/03.DecisionTree/DecisionTree.py b/src/python/03.DecisionTree/DecisionTree.py index dce6eb25..ed266377 100644 --- a/src/python/03.DecisionTree/DecisionTree.py +++ b/src/python/03.DecisionTree/DecisionTree.py @@ -31,6 +31,7 @@ def createDataSet(): # ['no'], # ['no'], # ['no']] + # labels 露出水面 脚蹼 labels = ['no surfacing', 'flippers'] # change to discrete values return dataSet, labels @@ -122,10 +123,9 @@ def chooseBestFeatureToSplit(dataSet): subDataSet = splitDataSet(dataSet, i, value) prob = len(subDataSet)/float(len(dataSet)) newEntropy += prob * calcShannonEnt(subDataSet) - # gain[信息增益] 值越大，意味着该分类提供的信息量越大，该特征对分类的不确定程度越小 - # 也就说： 列进行group分组后，对应的类别越多，信息量越大，那么香农熵越小，那么信息增益就越大，所以gain越大 + # gain[信息增益]: 划分数据集前后的信息变化， 获取信息熵最大的值 infoGain = baseEntropy - newEntropy - # print 'infoGain=', infoGain, 'bestFeature=', i + print 'infoGain=', infoGain, 'bestFeature=', i, baseEntropy, newEntropy if (infoGain > bestInfoGain): bestInfoGain = infoGain bestFeature = i @@ -133,7 +133,7 @@ def chooseBestFeatureToSplit(dataSet): def majorityCnt(classList): - """majorityCnt(选择出线次数最多的一个结果) + """majorityCnt(选择出现次数最多的一个结果) Args: classList label列的集合 diff --git a/src/python/04.NaiveBayes/bayes.py b/src/python/04.NaiveBayes/bayes.py index a4d10d95..138e7bb1 100755 --- a/src/python/04.NaiveBayes/bayes.py +++ b/src/python/04.NaiveBayes/bayes.py @@ -1,6 +1,10 @@ #!/usr/bin/env python # -*- coding:utf-8 -*- from numpy import * +""" +p(xy)=p(x|y)p(y)=p(y|x)p(x) +p(x|y)=p(y|x)p(x)/p(y) +""" def loadDataSet(): @@ -8,7 +12,7 @@ def loadDataSet(): 创建数据集 :return: 单词列表postingList, 所属类别classVec """ - postingList = [['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'], + postingList = [['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'], #[0,0,1,1,1......] ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'], ['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'], ['stop', 'posting', 'stupid', 'worthless', 'garbage'], @@ -37,7 +41,7 @@ def setOfWords2Vec(vocabList, inputSet): :param inputSet: 输入数据集 :return: 匹配列表[0,1,0,1...] """ - returnVec = [0] * len(vocabList) + returnVec = [0] * len(vocabList)# [0,0......] for word in inputSet: if word in vocabList: returnVec[vocabList.index(word)] = 1 @@ -49,8 +53,8 @@ def setOfWords2Vec(vocabList, inputSet): def _trainNB0(trainMatrix, trainCategory): """ 训练数据原版 - :param trainMatrix: 文件单词矩阵 - :param trainCategory: 文件对应的类别 + :param trainMatrix: 文件单词矩阵 [[1,0,1,1,1....],[],[]...] + :param trainCategory: 文件对应的类别[0,1,1,0....] :return: """ # 文件数 @@ -60,21 +64,21 @@ def _trainNB0(trainMatrix, trainCategory): # 侮辱性文件的出现概率 pAbusive = sum(trainCategory) / float(numTrainDocs) # 构造单词出现次数列表 - p0Num = zeros(numWords) - p1Num = zeros(numWords) + p0Num = zeros(numWords)[0,0,0,.....] + p1Num = zeros(numWords)[0,0,0,.....] # 整个数据集单词出现总数 p0Denom = 0.0 p1Denom = 0.0 for i in range(numTrainDocs): if trainCategory[i] == 1: - p1Num += trainMatrix[i] + p1Num += trainMatrix[i] #[0,1,1,....]->[0,1,1,...] p1Denom += sum(trainMatrix[i]) else: p0Num += trainMatrix[i] p0Denom += sum(trainMatrix[i]) # 类别1，即侮辱性文档的[P(F1|C1),P(F2|C1),P(F3|C1),P(F4|C1),P(F5|C1)....]列表 - p1Vect = p1Num / p1Denom + p1Vect = p1Num / p1Denom# [1,2,3,5]/90->[1/90,...] # 类别0，即正常文档的[P(F1|C0),P(F2|C0),P(F3|C0),P(F4|C0),P(F5|C0)....]列表 p0Vect = p0Num / p0Denom return p0Vect, p1Vect, pAbusive @@ -94,7 +98,7 @@ def trainNB0(trainMatrix, trainCategory): # 侮辱性文件的出现概率 pAbusive = sum(trainCategory) / float(numTrainDocs) # 构造单词出现次数列表 - p0Num = ones(numWords) + p0Num = ones(numWords)#[0,0......]->[1,1,1,1,1.....] p1Num = ones(numWords) # 整个数据集单词出现总数，2.0根据样本/实际调查结果调整分母的值 @@ -117,7 +121,7 @@ def trainNB0(trainMatrix, trainCategory): def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1): """ 使用算法 - :param vec2Classify: 待测数据 + :param vec2Classify: 待测数据[0,1,1,1,1...] :param p0Vec: 类别1，即侮辱性文档的[log(P(F1|C1)),log(P(F2|C1)),log(P(F3|C1)),log(P(F4|C1)),log(P(F5|C1))....]列表 :param p1Vec: 类别0，即正常文档的[log(P(F1|C0)),log(P(F2|C0)),log(P(F3|C0)),log(P(F4|C0)),log(P(F5|C0))....]列表 :param pClass1: 类别1，侮辱性文件的出现概率 diff --git a/src/python/12.FrequentPattemTree/fpGrowth.py b/src/python/12.FrequentPattemTree/fpGrowth.py index 8f992385..75f07aea 100644 --- a/src/python/12.FrequentPattemTree/fpGrowth.py +++ b/src/python/12.FrequentPattemTree/fpGrowth.py @@ -1,3 +1,6 @@ +#!/usr/bin/python +# coding:utf8 + ''' Created on Jun 14, 2011 FP-Growth FP means frequent pattern @@ -22,15 +25,41 @@ class treeNode: self.children = {} def inc(self, numOccur): + """inc(对count变量增加给定值) + + """ self.count += numOccur def disp(self, ind=1): + """disp(用于将树以文本形式显示) + + """ print ' '*ind, self.name, ' ', self.count for child in self.children.values(): child.disp(ind+1) +def loadSimpDat(): + simpDat = [['r', 'z', 'h', 'j', 'p'], + ['z', 'y', 'x', 'w', 'v', 'u', 't', 's'], + ['z'], + ['r', 'x', 'n', 'o', 's'], + ['y', 'r', 'x', 'z', 'q', 't', 'p'], + ['y', 'z', 'x', 'e', 'q', 's', 't', 'm']] + return simpDat + + +def createInitSet(dataSet): + retDict = {} + for trans in dataSet: + retDict[frozenset(trans)] = 1 + return retDict + + def createTree(dataSet, minSup=1): #create FP-tree from dataset but don't mine + """ + + """ headerTable = {} #go over dataSet twice for trans in dataSet:#first pass counts frequency of occurance @@ -76,6 +105,21 @@ def updateHeader(nodeToTest, targetNode): #this version does not use recursion nodeToTest.nodeLink = targetNode +if __name__ == "__main__": + rootNode = treeNode('pyramid', 9, None) + rootNode.children['eye'] = treeNode('eye', 13, None) + rootNode.children['phoenix'] = treeNode('phoenix', 3, None) + # 将树以文本形式显示 + # print rootNode.disp() + + # load样本数据 + simpDat = loadSimpDat() + print simpDat + # 重新装载 frozen set 格式化样本数据，用dist存储数据和对应的次数 + initSet = createInitSet(simpDat) + print initSet + + def ascendTree(leafNode, prefixPath): #ascends from leaf node to root if leafNode.parent != None: prefixPath.append(leafNode.name) @@ -111,21 +155,6 @@ def mineTree(inTree, headerTable, minSup, preFix, freqItemList): mineTree(myCondTree, myHead, minSup, newFreqSet, freqItemList) -def loadSimpDat(): - simpDat = [['r', 'z', 'h', 'j', 'p'], - ['z', 'y', 'x', 'w', 'v', 'u', 't', 's'], - ['z'], - ['r', 'x', 'n', 'o', 's'], - ['y', 'r', 'x', 'z', 'q', 't', 'p'], - ['y', 'z', 'x', 'e', 'q', 's', 't', 'm']] - return simpDat - - -def createInitSet(dataSet): - retDict = {} - for trans in dataSet: - retDict[frozenset(trans)] = 1 - return retDict import twitter From 7c41123de9ae998368f2bf484edbdd5d179417f3 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Sat, 18 Mar 2017 21:55:28 +0800 Subject: [PATCH 10/11] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E8=A7=86=E9=A2=91?= =?UTF-8?q?=E6=B3=A8=E8=A7=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/python/03.DecisionTree/DecisionTree.py | 4 ++-- testResult/tree.pdf | Bin 16281 -> 15775 bytes 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/python/03.DecisionTree/DecisionTree.py b/src/python/03.DecisionTree/DecisionTree.py index ed266377..f6f4bf1f 100644 --- a/src/python/03.DecisionTree/DecisionTree.py +++ b/src/python/03.DecisionTree/DecisionTree.py @@ -185,7 +185,7 @@ def classify(inputTree, featLabels, testVec): Args: inputTree 决策树模型 - featLabels label标签对应的名称 + featLabels Feature标签对应的名称 testVec 测试输入的数据 Returns: classLabel 分类的结果值，需要映射label才能知道名称 @@ -241,7 +241,7 @@ if __name__ == "__main__": myTree = createTree(myDat, copy.deepcopy(labels)) print myTree # [1, 1]表示要取的分支上的节点位置，对应的结果值 - print classify(myTree, labels, [1, 1]) + # print classify(myTree, labels, [1, 1]) # 画图可视化展现 dtPlot.createPlot(myTree) diff --git a/testResult/tree.pdf b/testResult/tree.pdf index 2a7e0a8dbe808859469dd86053c86c5fc4a3dc78..6ab09f258bd4bff86a1766b1949ef2417a34775f 100644 GIT binary patch delta 11640 zcmaia1xzK(vL<$Lcb8#scXxM(!JWZ5ug{z+sUZv9L8C-LSpMFWt+F#(DUb+*LNsEFd|(wYFIUD-47AmyS#% z`mM>Sq!P&^%6?S@fVh}YT!Sp_q6gcf>J6d?TWzI%_#hlWCJ13b`P0tH`Kk%ezp$#D zh9{{OhKKjD)pO^mSH~ZK2nAzB_jw->iUQYL7Y*_sgtc@u{S}M9RhFn9#)@{=S3(^q zOhUPzGXTca?GJI{plaO1StAZ?)z}N#P|w8=+RG09%F7Qc9P%? z<5|ntijFYwAmW9Po?o1fCj}x=k=`lsgx{jBk7WUZEnJ{&0E?CF%-y$TfU2I5=tC;t z0SSOTy(rgTfZU9!(J$go-AE^!i0-GBtb~AU*h_6IA%h1*R(9y3?L}Bgg+-v(fr~~Z 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zRiz1U#pAyRjq|x-f3EQiq0Nli#D7;{gVQ<0|C7xI4XJ)*6}L&2BEZl5eH3S8&rAc}COD=)v_cM!BT-#)iayTlFPAbCdjv`W#>% zGsBr93=Yw|J7mY{mA@@+-lNInW)cC49&^Lndry=6OMI+1FL6PD8qC4_uQhfMJI8m#U%Y>tVdvud|Aha4U4;J} z(LW#`cOs;au(3Ih2`7)Ksfh(UCl{AFCod1TDWACoC&-e^gp&_s$}0>M{C`r&49J9q cfv#>QE^b~f7M4JEE*=mkkeXUjSqk{S0F5QJTL1t6 From 0ddfeab7a614dae544afd1e5ccff41606985f4a7 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Mon, 20 Mar 2017 18:20:28 +0800 Subject: [PATCH 11/11] =?UTF-8?q?=E6=9B=B4=E6=96=B0Knn=E6=B3=A8=E9=87=8A?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/python/02.kNN/kNN.py | 32 ++++++++++++++++++++++++++++++-- 1 file changed, 30 insertions(+), 2 deletions(-) diff --git a/src/python/02.kNN/kNN.py b/src/python/02.kNN/kNN.py index 271aa621..f2f8616a 100644 --- a/src/python/02.kNN/kNN.py +++ b/src/python/02.kNN/kNN.py @@ -37,8 +37,29 @@ def classify0(inX, dataSet, labels, k): # 1. 距离计算 dataSetSize = dataSet.shape[0] # tile生成和训练样本对应的矩阵，并与训练样本求差 + """ + tile: 列-3表示复制的行树， 行-1／2表示对inx的重复的次数 + + In [8]: tile(inx, (3, 1)) + Out[8]: + array([[1, 2], + [1, 2], + [1, 2]]) + + In [9]: tile(inx, (3, 2)) + Out[9]: + array([[1, 2, 1, 2], + [1, 2, 1, 2], + [1, 2, 1, 2]]) + """ diffMat = tile(inX, (dataSetSize, 1)) - dataSet """ + 欧氏距离： 点到点之间的距离 + 第一行： 同一个点 到 dataSet的第一个点的距离。 + 第二行： 同一个点 到 dataSet的第二个点的距离。 + ... + 第N行： 同一个点 到 dataSet的第N个点的距离。 + [[1,2,3],[1,2,3]]-[[1,2,3],[1,2,0]] (A1-A2)^2+(B1-B2)^2+(c1-c2)^2 """ @@ -104,10 +125,14 @@ def autoNorm(dataSet): 归一化特征值，消除属性之间量级不同导致的影响 :param dataSet: 数据集 :return: 归一化后的数据集normDataSet,ranges和minVals即最小值与范围，并没有用到 + + 归一化公式： + Y = (X-Xmin)-(Xmax-Xmin) """ # 计算每种属性的最大值、最小值、范围 minVals = dataSet.min(0) maxVals = dataSet.max(0) + # 极差 ranges = maxVals - minVals normDataSet = zeros(shape(dataSet)) m = dataSet.shape[0] @@ -123,14 +148,16 @@ def datingClassTest(): 对约会网站的测试方法 :return: 错误数 """ - hoRatio = 0.9 # 测试范围,一部分测试一部分作为样本 + # 设置测试数据的的一个比例（训练数据集比例=1-hoRatio） + hoRatio = 0.1 # 测试范围,一部分测试一部分作为样本 # 从文件中加载数据 datingDataMat, datingLabels = file2matrix('testData/datingTestSet2.txt') # load data setfrom file # 归一化数据 normMat, ranges, minVals = autoNorm(datingDataMat) m = normMat.shape[0] - # 测试的数据 + # 设置测试的样本数量， numTestVecs:m表示训练样本的数量 numTestVecs = int(m * hoRatio) + print 'numTestVecs=', numTestVecs errorCount = 0.0 for i in range(numTestVecs): # 对数据测试， @@ -162,6 +189,7 @@ def handwritingClassTest(): trainingFileList = listdir('testData/trainingDigits') # load the training set m = len(trainingFileList) trainingMat = zeros((m, 1024)) + # hwLabels存储0～9对应的index位置， trainingMat存放的每个位置对应的图片向量 for i in range(m): fileNameStr = trainingFileList[i] fileStr = fileNameStr.split('.')[0] # take off .txt

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zX=Pybyh^R!9Un5LV_ahl_I8v={O~B|{O9Pb$!v6)20C4|Awk-MUsQ;CNnGcuFnc^ewCMX~A`*5>GoCO*{ zf2Vg7*9q;6pEI|Wv(?zZ8y-&mK0dCoe2qOkXh7ZZkU9F#ctwjx{vA}G>34eaE4e2R zJl>O@wwZBmu4$SxQ}?WIenj6&uhXPWJb5(VDA1oiI`;MD!s$D>{{RsQh4m!2x48fS N002ovPDHLkV1gL3MkN3M literal 0 HcmV?d00001 From a0427c0812f0c0eec6da210c644f3cbce9d92bd2 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Thu, 16 Mar 2017 17:50:10 +0800 Subject: [PATCH 02/11] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E5=AE=8Capriori?= =?UTF-8?q?=E7=9A=84=E9=A2=91=E7=B9=81=E5=AD=90=E9=A1=B9=E7=9A=84=E4=BB=A3?= =?UTF-8?q?=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/python/03.DecisionTree/DecisionTree.py | 3 +- src/python/09.RegTrees/regTrees.py | 3 +- src/python/{ => 11.Apriori}/apriori.py | 75 ++++++++++++++++------ 3 files changed, 61 insertions(+), 20 deletions(-) rename src/python/{ => 11.Apriori}/apriori.py (72%) diff --git a/src/python/03.DecisionTree/DecisionTree.py b/src/python/03.DecisionTree/DecisionTree.py index e5f3d41b..2425803d 100644 --- a/src/python/03.DecisionTree/DecisionTree.py +++ b/src/python/03.DecisionTree/DecisionTree.py @@ -5,8 +5,9 @@ Created on Oct 12, 2010 Update on 2017-02-27 Decision Tree Source Code for Machine Learning in Action Ch. 3 -@author: Peter Harrington/jiangzhonglian +@author: Peter Harrington/片刻 ''' +print(__doc__) import operator from math import log import decisionTreePlot as dtPlot diff --git a/src/python/09.RegTrees/regTrees.py b/src/python/09.RegTrees/regTrees.py index 11a8be85..c95dcf54 100644 --- a/src/python/09.RegTrees/regTrees.py +++ b/src/python/09.RegTrees/regTrees.py @@ -5,8 +5,9 @@ Created on Feb 4, 2011 Update on 2017-03-02 Tree-Based Regression Methods Source Code for Machine Learning in Action Ch. 9 -@author: Peter Harrington/jiangzhonglian +@author: Peter Harrington/片刻 ''' +print(__doc__) from numpy import * diff --git a/src/python/apriori.py b/src/python/11.Apriori/apriori.py similarity index 72% rename from src/python/apriori.py rename to src/python/11.Apriori/apriori.py index 17d847d3..7f3c28f2 100644 --- a/src/python/apriori.py +++ b/src/python/11.Apriori/apriori.py @@ -3,86 +3,125 @@ ''' Created on Mar 24, 2011 +Update on 2017-03-16 Ch 11 code -@author: Peter +@author: Peter/片刻 ''' +print(__doc__) from numpy import * + def loadDataSet(): return [[1, 3, 4], [2, 3, 5], [1, 2, 3, 5], [2, 5]] + def createC1(dataSet): C1 = [] for transaction in dataSet: for item in transaction: if not [item] in C1: + # 遍历所有的元素，然后append到C1中 C1.append([item]) - + # 对数组进行 从小到大 的排序 C1.sort() - return map(frozenset, C1) # use frozen set so we - # can use it as a key in a dict + # frozenset表示冻结的set集合，元素无可改变；可以把它当字典的key来使用 + return map(frozenset, C1) + def scanD(D, Ck, minSupport): + # 临时存放，查看Ck每个元素 并 计算元素出现的次数 生成相应的字典 + # D用来判断，CK中的元素，是否存在于原数据D中 ssCnt = {} for tid in D: for can in Ck: # s.issubset(t) 测试是否 s 中的每一个元素都在 t 中 if can.issubset(tid): - if not ssCnt.has_key(can): ssCnt[can]=1 - else: ssCnt[can] += 1 + if not ssCnt.has_key(can): + ssCnt[can] = 1 + else: + ssCnt[can] += 1 + # 元素有多少行 numItems = float(len(D)) retList = [] supportData = {} for key in ssCnt: + # 计算支持度 support = ssCnt[key]/numItems if support >= minSupport: + # 在retList的首位插入元素，只存储支持度满足频繁项集的值 retList.insert(0, key) + # 存储所有的key和对应的support值 supportData[key] = support return retList, supportData -def aprioriGen(Lk, k): #creates Ck + +# creates Ck +def aprioriGen(Lk, k): + """aprioriGen(循环数据集，然后进行两两合并) + + Args: + Lk 频繁项集 + k 元素的前k-2相同，就进行合并 + Returns: + retList 元素两两合并的数据集 + """ retList = [] lenLk = len(Lk) + # 循环Lk这个数组 for i in range(lenLk): for j in range(i+1, lenLk): - L1 = list(Lk[i])[:k-2]; L2 = list(Lk[j])[:k-2] - L1.sort(); L2.sort() - if L1==L2: #if first k-2 elements are equal - retList.append(Lk[i] | Lk[j]) #set union + L1 = list(Lk[i])[: k-2] + L2 = list(Lk[j])[: k-2] + # print '-----', Lk, Lk[i], L1 + L1.sort() + L2.sort() + # 第一次L1,L2为空，元素直接进行合并，返回元素两两合并的数据集 + # if first k-2 elements are equal + if L1 == L2: + # set union + retList.append(Lk[i] | Lk[j]) return retList -def apriori(dataSet, minSupport = 0.5): + +def apriori(dataSet, minSupport=0.5): # 冻结每一行数据 C1 = createC1(dataSet) D = map(set, dataSet) - # 计算支持support + # 计算支持support， L1表示满足support的key, supportData表示全集的集合 L1, supportData = scanD(D, C1, minSupport) - print("outcome: ", supportData) + # print "L1=", L1, "\n", "outcome: ", supportData L = [L1] k = 2 while (len(L[k-2]) > 0): + # print 'L[k-2]=', L[k-2], k Ck = aprioriGen(L[k-2], k) - Lk, supK = scanD(D, Ck, minSupport)#scan DB to get Lk + # print 'Ck=', Ck + # can DB to get Lk + Lk, supK = scanD(D, Ck, minSupport) supportData.update(supK) + # L元素在增加 L.append(Lk) k += 1 + # print 'k=', k, len(L[k-2]) return L, supportData + def main(): # project_dir = os.path.dirname(os.path.dirname(os.getcwd())) # 1.收集并准备数据 # dataMat, labelMat = loadDataSet("%s/resources/Apriori_testdata.txt" % project_dir) - # 1. 加载数据 dataSet = loadDataSet() print(dataSet) # 调用 apriori 做购物篮分析 - apriori(dataSet, minSupport = 0.7) + L, supportData = apriori(dataSet, minSupport=0.7) + print L, supportData -if __name__=="__main__": + +if __name__ == "__main__": main() From 8f25688d54b35d8511653b5f6ae2076aaff8d9cc Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Thu, 16 Mar 2017 18:25:00 +0800 Subject: [PATCH 03/11] =?UTF-8?q?=E9=A2=91=E7=B9=81=E9=A1=B9=E9=9B=86?= =?UTF-8?q?=E7=9A=84=E6=B3=A8=E9=87=8A=E6=9B=B4=E6=96=B0=E5=AE=8C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/python/11.Apriori/apriori.py | 34 ++++++++++++++++++++++++++------ 1 file changed, 28 insertions(+), 6 deletions(-) diff --git a/src/python/11.Apriori/apriori.py b/src/python/11.Apriori/apriori.py index 7f3c28f2..68fc7d08 100644 --- a/src/python/11.Apriori/apriori.py +++ b/src/python/11.Apriori/apriori.py @@ -29,8 +29,16 @@ def createC1(dataSet): def scanD(D, Ck, minSupport): - # 临时存放，查看Ck每个元素 并 计算元素出现的次数 生成相应的字典 - # D用来判断，CK中的元素，是否存在于原数据D中 + """scanD + + Args: + D 原始数据集, D用来判断，CK中的元素，是否存在于原数据D中 + Ck 合并后的数据集 + Returns: + retList 支持度大于阈值的集合 + supportData 全量key的字典集合 + """ + # ssCnt 临时存放Ck的元素集合，查看Ck每个元素 并 计算元素出现的次数 生成相应的字典 ssCnt = {} for tid in D: for can in Ck: @@ -84,6 +92,15 @@ def aprioriGen(Lk, k): def apriori(dataSet, minSupport=0.5): + """aprioriGen(循环数据集，然后进行两两合并) + + Args: + dataSet 原始数据集 + minSupport 支持度的阈值 + Returns: + L 频繁项集的全集 + supportData 所有元素的支持度全集 + """ # 冻结每一行数据 C1 = createC1(dataSet) D = map(set, dataSet) @@ -95,13 +112,17 @@ def apriori(dataSet, minSupport=0.5): L = [L1] k = 2 while (len(L[k-2]) > 0): - # print 'L[k-2]=', L[k-2], k + # 合并k-2相同的数据集 Ck = aprioriGen(L[k-2], k) - # print 'Ck=', Ck - # can DB to get Lk + # print '-----------', D, Ck + # 计算合并后的数据集的支持度 + # Lk满足支持度的key的list， supK表示key全集 Lk, supK = scanD(D, Ck, minSupport) + # 如果字典没有，就追加元素，如果有，就更新元素 supportData.update(supK) - # L元素在增加 + if len(Lk) == 0: + break + # Lk表示满足频繁子项的集合，L元素在增加 L.append(Lk) k += 1 # print 'k=', k, len(L[k-2]) @@ -117,6 +138,7 @@ def main(): dataSet = loadDataSet() print(dataSet) # 调用 apriori 做购物篮分析 + # 支持度满足阈值的key集合L，和所有key的全集suppoerData L, supportData = apriori(dataSet, minSupport=0.7) print L, supportData From cade394e0e08959dd18885cd0a54266ac7bf0f7a Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 17 Mar 2017 00:08:16 +0800 Subject: [PATCH 04/11] =?UTF-8?q?=E9=A2=91=E7=B9=81=E9=A1=B9=E9=9B=86->?= =?UTF-8?q?=E5=85=B3=E8=81=94=E8=A7=84=E5=88=99=E7=9A=84=E4=BB=A3=E7=A0=81?= =?UTF-8?q?=E6=B5=8B=E8=AF=95=E5=AE=8C=E6=AF=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/11.使用Apriori算法进行关联分析.md | 7 ++ docs/11.所有可能的项集组合.png | Bin 0 -> 129031 bytes src/python/11.Apriori/apriori.py | 122 +++++++++++++++++-------- 3 files changed, 92 insertions(+), 37 deletions(-) create mode 100644 docs/11.所有可能的项集组合.png diff --git a/docs/11.使用Apriori算法进行关联分析.md b/docs/11.使用Apriori算法进行关联分析.md index 2e6cb07b..feee8369 100644 --- a/docs/11.使用Apriori算法进行关联分析.md +++ b/docs/11.使用Apriori算法进行关联分析.md @@ -28,3 +28,10 @@ * 例如： 我们假设知道{2, 3}是非频繁项，那么{0, 2, 3}, {1, 2, 3}, {0, 1, 2, 3}都是非频繁项。 * 如下图： * ![非频繁项集](./11.非频繁项集.png) +* 分级法： 频繁项集->关联规则 + * 1.首先从一个频繁项集开始，接着创建一个规则列表，其中规则右部分只包含一个元素，然后对这个规则进行测试。 + * 2.接下来合并所有剩余规则来创建一个新的规则列表，其中规则右部包含两个元素。 + * 如下图： + * ![所有可能的项集组合](./11.所有可能的项集组合.png) + + diff --git a/docs/11.所有可能的项集组合.png b/docs/11.所有可能的项集组合.png new file mode 100644 index 0000000000000000000000000000000000000000..78b797fabbb8cc1247afe8a697dd574a46112689 GIT binary patch literal 129031 zcmaf*byU-D`1c9FU=X6xF(pKD3P{6Hx|LKK=@{r}*a(9T=@?y;7##v*peQNbqeMVr zbLn$gXzT3HchG)nUcqHN(DcYvzXavAk?=99{WH3`uNe~3~`pnuf#UQq|=H`QbO;^ zt}`1kIg~o3Jd&nRmNfn*18UB1;BC}U|0WK6t*`#Aw{wK7nC_m_?II9$n6A@3xrEyT z*1erJWxqN*=Re%m&g$;0fzeZb_#yByqhFqiD%g!G9K3$>PRyNUI;V^7=aJUgj_%iN z52c?xu30?4x~gZRq&(-P3h%gkb#>Lgadp)Z6Lw6wvaLi%#_Q+uJJ#(EJsFu2nWoA! zBY*Pk9O?`Q?JR7qidp{}08z?s(*`sIuC zM8VdY^rs_~;=^O}wmXwLU6eIG^|O_)>0e>~!L_rzf=IqqlH) 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