From e59c520ca7ea5a7f544b8e6b9f62bd91ba686c7e Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 7 Apr 2017 14:53:49 +0800 Subject: [PATCH 01/16] =?UTF-8?q?=E6=9B=B4=E6=96=B0=2016.=E6=8E=A8?= =?UTF-8?q?=E8=8D=90=E7=B3=BB=E7=BB=9Fmd=E5=86=85=E5=AE=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/16.推荐系统.md | 25 ++++++++++++++++++ .../16.RecommendedSystem/基于知识的推荐.jpg | Bin 0 -> 131952 bytes images/16.RecommendedSystem/推荐方法对比.png | Bin 0 -> 39691 bytes 3 files changed, 25 insertions(+) create mode 100644 images/16.RecommendedSystem/基于知识的推荐.jpg create mode 100644 images/16.RecommendedSystem/推荐方法对比.png diff --git a/docs/16.推荐系统.md b/docs/16.推荐系统.md index e69de29b..43b92447 100644 --- a/docs/16.推荐系统.md +++ b/docs/16.推荐系统.md @@ -0,0 +1,25 @@ +# 第16章 推荐系统 + +| 推荐方法 | 描述 | +| --- | --- | +| 基于内容的推荐 | | +| 协同过滤的推荐 | | +| 基于关联规则推荐 | | +| 基于效用推荐 | | +| 基于知识推荐 | | +| 组合推荐 | | + +![推荐方法对比](/images/16.RecommendedSystem/推荐方法对比.png) + +## 基于知识推荐 + +基于知识的推荐（Knowledge-based Recommendation）在某种程度是可以看成是一种推理（Inference）技术，它不是建立在用户需要和偏好基础上推荐的。基于知识的方法因它们所用的功能知识不同而有明显区别。效用知识（Functional Knowledge）是一种关于一个项目如何满足某一特定用户的知识，因此能解释需要和推荐的关系，所以用户资料可以是任何能支持推理的知识结构，它可以是用户已经规范化的查询，也可以是一个更详细的用户需要的表示。 + +![基于知识的推荐](/images/16.RecommendedSystem/基于知识的推荐.jpg) + +* * * + +> 摘录的原文地址： + +* [推荐系统中常用算法 以及优点缺点对比](http://www.36dsj.com/archives/9519) +* [推荐算法的基于知识推荐](https://zhidao.baidu.com/question/2013524494179442228.html) \ No newline at end of file diff --git a/images/16.RecommendedSystem/基于知识的推荐.jpg b/images/16.RecommendedSystem/基于知识的推荐.jpg new file mode 100644 index 0000000000000000000000000000000000000000..a39a2967fd7137f734f93eda95e9e840e8e49b02 GIT binary patch literal 131952 zcmce-2|QH)|35k{k|f!;G8GbHDx{G4mJmXeeVHU#CQXG*3{%OzeacqEd=RpXDGX&B z%-~a!?2H&QQ+7G0GM$(i_k8c~-rw)>xR2lCKK}pvzxU2~kC}(#%z3?+*ZcKc&gS^$ z3}Ua1rL`qu%a$#OtMC)CIf*d;_ox4*$G=bVzjT6s|MO-$LTcw$%`MB@wj4rimD;jR zYRe`AfkYs-i2TR75&y?y%hqk%MRthp6x$^ZA3)uU*t%ugwyoQ@iHL094j&y2{~fVi 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zs@519-I&;choo!nPpu}}UUYjquXhT-x_oYUkqJ4l^kO0y_vsL9^GWLs1~MV1&mZ)V zPo%WCe<~ Date: Fri, 7 Apr 2017 14:58:19 +0800 Subject: [PATCH 02/16] =?UTF-8?q?=E6=9B=B4=E6=96=B0=2016.=E6=8E=A8?= =?UTF-8?q?=E8=8D=90=E7=B3=BB=E7=BB=9Fmd=E5=86=85=E5=AE=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/16.推荐系统.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/16.推荐系统.md b/docs/16.推荐系统.md index 43b92447..d3f9f06f 100644 --- a/docs/16.推荐系统.md +++ b/docs/16.推荐系统.md @@ -2,8 +2,8 @@ | 推荐方法 | 描述 | | --- | --- | -| 基于内容的推荐 | | -| 协同过滤的推荐 | | +| 基于内容推荐 | | +| 协同过滤推荐 | | | 基于关联规则推荐 | | | 基于效用推荐 | | | 基于知识推荐 | | @@ -22,4 +22,4 @@ > 摘录的原文地址： * [推荐系统中常用算法 以及优点缺点对比](http://www.36dsj.com/archives/9519) -* [推荐算法的基于知识推荐](https://zhidao.baidu.com/question/2013524494179442228.html) \ No newline at end of file +* [推荐算法的基于知识推荐](https://zhidao.baidu.com/question/2013524494179442228.html) From 63a96478618057bb0efd0dde354541db8ec37e6e Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 7 Apr 2017 14:59:40 +0800 Subject: [PATCH 03/16] =?UTF-8?q?=E6=9B=B4=E6=96=B0=2016.=E6=8E=A8?= =?UTF-8?q?=E8=8D=90=E7=B3=BB=E7=BB=9Fmd=E5=86=85=E5=AE=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/16.推荐系统.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/16.推荐系统.md b/docs/16.推荐系统.md index d3f9f06f..75fc7857 100644 --- a/docs/16.推荐系统.md +++ b/docs/16.推荐系统.md @@ -2,12 +2,12 @@ | 推荐方法 | 描述 | | --- | --- | -| 基于内容推荐 | | -| 协同过滤推荐 | | -| 基于关联规则推荐 | | -| 基于效用推荐 | | -| 基于知识推荐 | | -| 组合推荐 | | +| 基于内容推荐 | | +| 协同过滤推荐 | | +| 基于规则推荐 | | +| 基于效用推荐 | | +| 基于知识推荐 | | +| 组合推荐 | | ![推荐方法对比](/images/16.RecommendedSystem/推荐方法对比.png) From b05c79819d6dff2bac7ef2ef611b20f0b964937a Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 7 Apr 2017 15:35:07 +0800 Subject: [PATCH 04/16] =?UTF-8?q?=E7=A7=BB=E5=87=BAtools=E5=B7=A5=E5=85=B7?= =?UTF-8?q?=E5=8C=85=E5=88=B0=E6=A0=B9=E7=9B=AE=E5=BD=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- {src/python/tools => tools}/DecisionTree_getInfoGain.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {src/python/tools => tools}/DecisionTree_getInfoGain.py (100%) diff --git a/src/python/tools/DecisionTree_getInfoGain.py b/tools/DecisionTree_getInfoGain.py similarity index 100% rename from src/python/tools/DecisionTree_getInfoGain.py rename to tools/DecisionTree_getInfoGain.py From 0c1cab40243e755c25873f83699326ad24312fb4 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 7 Apr 2017 15:37:03 +0800 Subject: [PATCH 05/16] =?UTF-8?q?=E8=A7=84=E8=8C=83=E5=8C=96=E7=AC=AC?= =?UTF-8?q?=E4=B8=80=E7=AB=A0=E7=9A=84=E6=96=87=E4=BB=B6=E4=BD=8D=E7=BD=AE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/python/{ => 1.MLFoundation}/01.NumPy.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename src/python/{ => 1.MLFoundation}/01.NumPy.py (100%) diff --git a/src/python/01.NumPy.py b/src/python/1.MLFoundation/01.NumPy.py similarity index 100% rename from src/python/01.NumPy.py rename to src/python/1.MLFoundation/01.NumPy.py From 5d7fe9f61c9311c22ae2d79ea757e2710fe0efc0 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 7 Apr 2017 15:38:13 +0800 Subject: [PATCH 06/16] =?UTF-8?q?=E7=A7=BB=E5=8A=A8=E4=B8=AA=E4=BA=BA?= =?UTF-8?q?=E6=B5=8B=E8=AF=95=E7=9A=84py=E6=96=87=E4=BB=B6=E5=88=B0test?= =?UTF-8?q?=E7=9B=AE=E5=BD=95=E4=B8=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/{python/regression.py => test/jiangzhonglian_regression.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename src/{python/regression.py => test/jiangzhonglian_regression.py} (100%) diff --git a/src/python/regression.py b/src/test/jiangzhonglian_regression.py similarity index 100% rename from src/python/regression.py rename to src/test/jiangzhonglian_regression.py From a05ba82e3e0c516271fdf148eb0b50406febe40d Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 7 Apr 2017 15:41:56 +0800 Subject: [PATCH 07/16] =?UTF-8?q?=E6=9B=B4=E6=96=B003=E5=86=B3=E7=AD=96?= =?UTF-8?q?=E6=A0=91=20=E8=BE=93=E5=87=BA=E8=B7=AF=E5=BE=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- {data => input}/13.PCA/testSet.txt | 0 output/03.DecisionTree/tree.pdf | Bin 0 -> 16596 bytes src/python/03.DecisionTree/DTSklearn.py | 2 +- src/python/13.PCA/pca.py | 2 +- testResult/tree.pdf | Bin 15775 -> 0 bytes 5 files changed, 2 insertions(+), 2 deletions(-) rename {data => input}/13.PCA/testSet.txt (100%) create mode 100644 output/03.DecisionTree/tree.pdf delete mode 100644 testResult/tree.pdf diff --git a/data/13.PCA/testSet.txt b/input/13.PCA/testSet.txt similarity index 100% rename from data/13.PCA/testSet.txt rename to input/13.PCA/testSet.txt diff --git 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s%xcET%)`OX!38wqNBDn>{KesQb^$%oe;x!D4pufU1PTf<1#yJ`4|F}A`~Uy| From d923016b3360b3c44db99fa018b15f7d26d840c5 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 7 Apr 2017 15:48:02 +0800 Subject: [PATCH 08/16] =?UTF-8?q?=E6=9B=B4=E6=96=B0=2011.Apriori=20?= =?UTF-8?q?=E8=BE=93=E5=85=A5=E6=96=87=E4=BB=B6=E4=BD=8D=E7=BD=AE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../Apriori_mushroom.dat => input/11.Apriori/mushroom.dat | 0 .../11.Apriori/recent20bills.txt | 0 src/python/11.Apriori/apriori.py | 6 +++--- 3 files changed, 3 insertions(+), 3 deletions(-) rename testData/Apriori_mushroom.dat => input/11.Apriori/mushroom.dat (100%) rename testData/Apriori_recent20bills.txt => input/11.Apriori/recent20bills.txt (100%) diff --git a/testData/Apriori_mushroom.dat b/input/11.Apriori/mushroom.dat similarity index 100% rename from testData/Apriori_mushroom.dat rename to input/11.Apriori/mushroom.dat diff --git a/testData/Apriori_recent20bills.txt b/input/11.Apriori/recent20bills.txt similarity index 100% rename from testData/Apriori_recent20bills.txt rename to input/11.Apriori/recent20bills.txt diff --git a/src/python/11.Apriori/apriori.py b/src/python/11.Apriori/apriori.py index b4daef2b..1f8e648d 100644 --- a/src/python/11.Apriori/apriori.py +++ b/src/python/11.Apriori/apriori.py @@ -239,7 +239,7 @@ def getActionIds(): votesmart.apikey = 'a7fa40adec6f4a77178799fae4441030' actionIdList = [] billTitleList = [] - fr = open('testData/Apriori_recent20bills.txt') + fr = open('input/11.Apriori/recent20bills.txt') for line in fr.readlines(): billNum = int(line.split('\t')[0]) try: @@ -306,7 +306,7 @@ def main(): # 现在的的测试 # 1. 加载数据 dataSet = loadDataSet() - print(dataSet) + print dataSet # 调用 apriori 做购物篮分析 # 支持度满足阈值的key集合L，和所有元素和支持度的全集suppoerData L, supportData = apriori(dataSet, minSupport=0.5) @@ -331,7 +331,7 @@ def main(): # # 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()] + # dataSet = [line.split() for line in open("input/11.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 From 1eecf70e5c7ae503104fe0c186c1c721f64f3ec9 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 7 Apr 2017 15:51:49 +0800 Subject: [PATCH 09/16] =?UTF-8?q?=E6=9B=B4=E6=96=B0=2012.FPGrowth=E6=96=87?= =?UTF-8?q?=E4=BB=B6=E8=B7=AF=E5=BE=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- testData/FPGrowth_kosarak.dat => input/12.FPGrowth/kosarak.dat | 0 src/python/12.FrequentPattemTree/fpGrowth.py | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) rename testData/FPGrowth_kosarak.dat => input/12.FPGrowth/kosarak.dat (100%) diff --git a/testData/FPGrowth_kosarak.dat b/input/12.FPGrowth/kosarak.dat similarity index 100% rename from testData/FPGrowth_kosarak.dat rename to input/12.FPGrowth/kosarak.dat diff --git a/src/python/12.FrequentPattemTree/fpGrowth.py b/src/python/12.FrequentPattemTree/fpGrowth.py index 42a9f3f7..4a299d74 100644 --- a/src/python/12.FrequentPattemTree/fpGrowth.py +++ b/src/python/12.FrequentPattemTree/fpGrowth.py @@ -332,7 +332,7 @@ if __name__ == "__main__": # print t # # 2.新闻网站点击流中挖掘，例如：文章1阅读过的人，还阅读过什么？ - # parsedDat = [line.split() for line in open('testData/FPGrowth_kosarak.dat').readlines()] + # parsedDat = [line.split() for line in open('input/12.FPGrowth/kosarak.dat').readlines()] # initSet = createInitSet(parsedDat) # myFPtree, myHeaderTab = createTree(initSet, 100000) From 4df7935b5de0ff9609c24c085887a2a89c10f5db Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 7 Apr 2017 16:01:04 +0800 Subject: [PATCH 10/16] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E6=96=87=E4=BB=B6?= =?UTF-8?q?=E8=B7=AF=E5=BE=84=E5=88=B0input=E7=9B=AE=E5=BD=95=E4=B8=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../03.DecisionTree/data.txt | 0 .../07.AdaBoost/horseColicTest2.txt | 0 .../07.AdaBoost/horseColicTraining2.txt | 0 .../09.RegTrees/bikeSpeedVsIq_test.txt | 0 .../09.RegTrees/bikeSpeedVsIq_train.txt | 0 .../09.RegTrees/data1.txt | 0 .../09.RegTrees/data2.txt | 0 .../09.RegTrees/data3.txt | 0 .../09.RegTrees/data3test.txt | 0 .../09.RegTrees/data4.txt | 0 .../RT_sine.txt => input/09.RegTrees/sine.txt | 0 output/03.DecisionTree/tree.pdf | Bin 16596 -> 16550 bytes src/python/03.DecisionTree/DTSklearn.py | 2 +- src/python/07.AdaBoost/adaboost.py | 74 +++++++++--------- src/python/09.RegTrees/regTrees.py | 14 ++-- 15 files changed, 45 insertions(+), 45 deletions(-) rename testData/DT_data.txt => input/03.DecisionTree/data.txt (100%) rename testData/AB_horseColicTest2.txt => input/07.AdaBoost/horseColicTest2.txt (100%) rename testData/AB_horseColicTraining2.txt => input/07.AdaBoost/horseColicTraining2.txt (100%) rename testData/RT_bikeSpeedVsIq_test.txt => input/09.RegTrees/bikeSpeedVsIq_test.txt (100%) rename testData/RT_bikeSpeedVsIq_train.txt => input/09.RegTrees/bikeSpeedVsIq_train.txt (100%) rename testData/RT_data1.txt => input/09.RegTrees/data1.txt (100%) rename testData/RT_data2.txt => input/09.RegTrees/data2.txt (100%) rename testData/RT_data3.txt => input/09.RegTrees/data3.txt (100%) rename testData/RT_data3test.txt => input/09.RegTrees/data3test.txt (100%) rename testData/RT_data4.txt => input/09.RegTrees/data4.txt (100%) rename testData/RT_sine.txt => input/09.RegTrees/sine.txt (100%) diff --git a/testData/DT_data.txt b/input/03.DecisionTree/data.txt similarity index 100% rename from testData/DT_data.txt rename to input/03.DecisionTree/data.txt diff --git a/testData/AB_horseColicTest2.txt b/input/07.AdaBoost/horseColicTest2.txt similarity index 100% rename from testData/AB_horseColicTest2.txt rename to input/07.AdaBoost/horseColicTest2.txt diff --git a/testData/AB_horseColicTraining2.txt b/input/07.AdaBoost/horseColicTraining2.txt similarity index 100% rename from testData/AB_horseColicTraining2.txt rename to input/07.AdaBoost/horseColicTraining2.txt diff --git a/testData/RT_bikeSpeedVsIq_test.txt b/input/09.RegTrees/bikeSpeedVsIq_test.txt similarity index 100% rename from testData/RT_bikeSpeedVsIq_test.txt rename to input/09.RegTrees/bikeSpeedVsIq_test.txt diff --git a/testData/RT_bikeSpeedVsIq_train.txt b/input/09.RegTrees/bikeSpeedVsIq_train.txt similarity index 100% rename from testData/RT_bikeSpeedVsIq_train.txt rename to input/09.RegTrees/bikeSpeedVsIq_train.txt diff --git a/testData/RT_data1.txt b/input/09.RegTrees/data1.txt similarity index 100% rename from testData/RT_data1.txt rename to input/09.RegTrees/data1.txt diff --git a/testData/RT_data2.txt b/input/09.RegTrees/data2.txt similarity index 100% rename from testData/RT_data2.txt rename to input/09.RegTrees/data2.txt diff --git a/testData/RT_data3.txt b/input/09.RegTrees/data3.txt similarity index 100% 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+258,47 @@ def plotROC(predStrengths, classLabels): if __name__ == "__main__": - # 我们要将5个点进行分类 - dataArr, labelArr = loadSimpData() - print 'dataArr', dataArr, 'labelArr', labelArr + # # 我们要将5个点进行分类 + # dataArr, labelArr = loadSimpData() + # print 'dataArr', dataArr, 'labelArr', labelArr - # D表示最初值，对1进行均分为5份，平均每一个初始的概率都为0.2 - # D的目的是为了计算错误概率： weightedError = D.T*errArr - D = mat(ones((5, 1))/5) - print 'D=', D.T + # # D表示最初值，对1进行均分为5份，平均每一个初始的概率都为0.2 + # # D的目的是为了计算错误概率： weightedError = D.T*errArr + # D = mat(ones((5, 1))/5) + # print 'D=', D.T - # bestStump, minError, bestClasEst = buildStump(dataArr, labelArr, D) - # print 'bestStump=', bestStump - # print 'minError=', minError - # print 'bestClasEst=', bestClasEst.T + # # bestStump, minError, bestClasEst = buildStump(dataArr, labelArr, D) + # # print 'bestStump=', bestStump + # # print 'minError=', minError + # # print 'bestClasEst=', bestClasEst.T - # 分类器：weakClassArr - # 历史累计的分类结果集 - weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 9) - print '\nweakClassArr=', weakClassArr, '\naggClassEst=', aggClassEst.T + # # 分类器：weakClassArr + # # 历史累计的分类结果集 + # weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 9) + # print '\nweakClassArr=', weakClassArr, '\naggClassEst=', aggClassEst.T - """ - 发现: - 分类的权重值：最大的值，为alpha的加和，最小值为-最大值 - 特征的权重值：如果一个值误判的几率越小，那么D的特征权重越少 - """ + # """ + # 发现: + # 分类的权重值：最大的值，为alpha的加和，最小值为-最大值 + # 特征的权重值：如果一个值误判的几率越小，那么D的特征权重越少 + # """ - # 测试数据的分类结果, 观测：aggClassEst分类的最终权重 - print adaClassify([0, 0], weakClassArr).T - print adaClassify([[5, 5], [0, 0]], weakClassArr).T + # # 测试数据的分类结果, 观测：aggClassEst分类的最终权重 + # print adaClassify([0, 0], weakClassArr).T + # print adaClassify([[5, 5], [0, 0]], weakClassArr).T - # # 马疝病数据集 - # # 训练集合 - # dataArr, labelArr = loadDataSet("testData/AB_horseColicTraining2.txt") - # weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 40) - # print weakClassArr, '\n-----\n', aggClassEst.T - # # 计算ROC下面的AUC的面积大小 - # plotROC(aggClassEst.T, labelArr) - # # 测试集合 - # dataArrTest, labelArrTest = loadDataSet("testData/AB_horseColicTest2.txt") - # m = shape(dataArrTest)[0] - # predicting10 = adaClassify(dataArrTest, weakClassArr) - # errArr = mat(ones((m, 1))) - # # 测试：计算总样本数，错误样本数，错误率 - # print m, errArr[predicting10 != mat(labelArrTest).T].sum(), errArr[predicting10 != mat(labelArrTest).T].sum()/m + # 马疝病数据集 + # 训练集合 + dataArr, labelArr = loadDataSet("input/07.AdaBoost/horseColicTraining2.txt") + weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 40) + print weakClassArr, '\n-----\n', aggClassEst.T + # 计算ROC下面的AUC的面积大小 + plotROC(aggClassEst.T, labelArr) + # 测试集合 + dataArrTest, labelArrTest = loadDataSet("input/07.AdaBoost/horseColicTest2.txt") + m = shape(dataArrTest)[0] + predicting10 = adaClassify(dataArrTest, weakClassArr) + errArr = mat(ones((m, 1))) + # 测试：计算总样本数，错误样本数，错误率 + print m, errArr[predicting10 != mat(labelArrTest).T].sum(), errArr[predicting10 != mat(labelArrTest).T].sum()/m diff --git a/src/python/09.RegTrees/regTrees.py b/src/python/09.RegTrees/regTrees.py index b621c1cb..fec88808 100644 --- a/src/python/09.RegTrees/regTrees.py +++ b/src/python/09.RegTrees/regTrees.py @@ -290,8 +290,8 @@ if __name__ == "__main__": # print mat0, '\n-----------\n', mat1 # # 回归树 - # myDat = loadDataSet('testData/RT_data1.txt') - # # myDat = loadDataSet('testData/RT_data2.txt') + # myDat = loadDataSet('input/09.RegTrees/data1.txt') + # # myDat = loadDataSet('input/09.RegTrees/data2.txt') # # print 'myDat=', myDat # myMat = mat(myDat) # # print 'myMat=', myMat @@ -299,13 +299,13 @@ if __name__ == "__main__": # print myTree # # 1. 预剪枝就是：提起设置最大误差数和最少元素数 - # myDat = loadDataSet('testData/RT_data3.txt') + # myDat = loadDataSet('input/09.RegTrees/data3.txt') # myMat = mat(myDat) # myTree = createTree(myMat, ops=(0, 1)) # print myTree # # 2. 后剪枝就是：通过测试数据，对预测模型进行合并判断 - # myDatTest = loadDataSet('testData/RT_data3test.txt') + # myDatTest = loadDataSet('input/09.RegTrees/data3test.txt') # myMat2Test = mat(myDatTest) # myFinalTree = prune(myTree, myMat2Test) # print '\n\n\n-------------------' @@ -313,14 +313,14 @@ if __name__ == "__main__": # # -------- # # 模型树求解 - # myDat = loadDataSet('testData/RT_data4.txt') + # myDat = loadDataSet('input/09.RegTrees/data4.txt') # myMat = mat(myDat) # myTree = createTree(myMat, modelLeaf, modelErr) # print myTree # 回归树 VS 模型树 VS 线性回归 - trainMat = mat(loadDataSet('testData/RT_bikeSpeedVsIq_train.txt')) - testMat = mat(loadDataSet('testData/RT_bikeSpeedVsIq_test.txt')) + trainMat = mat(loadDataSet('input/09.RegTrees/bikeSpeedVsIq_train.txt')) + testMat = mat(loadDataSet('input/09.RegTrees/bikeSpeedVsIq_test.txt')) # 回归树 myTree1 = createTree(trainMat, ops=(1, 20)) print myTree1 From 5857502a77d1873a29829988300b8dd6c5d926e8 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 7 Apr 2017 16:01:30 +0800 Subject: [PATCH 11/16] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E5=86=B3=E7=AD=96?= 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input/02.KNN/trainingDigits/9_93.txt diff --git a/testData/trainingDigits/9_94.txt b/input/02.KNN/trainingDigits/9_94.txt similarity index 100% rename from testData/trainingDigits/9_94.txt rename to input/02.KNN/trainingDigits/9_94.txt diff --git a/testData/trainingDigits/9_95.txt b/input/02.KNN/trainingDigits/9_95.txt similarity index 100% rename from testData/trainingDigits/9_95.txt rename to input/02.KNN/trainingDigits/9_95.txt diff --git a/testData/trainingDigits/9_96.txt b/input/02.KNN/trainingDigits/9_96.txt similarity index 100% rename from testData/trainingDigits/9_96.txt rename to input/02.KNN/trainingDigits/9_96.txt diff --git a/testData/trainingDigits/9_97.txt b/input/02.KNN/trainingDigits/9_97.txt similarity index 100% rename from testData/trainingDigits/9_97.txt rename to input/02.KNN/trainingDigits/9_97.txt diff --git a/testData/trainingDigits/9_98.txt b/input/02.KNN/trainingDigits/9_98.txt similarity index 100% rename from testData/trainingDigits/9_98.txt rename to input/02.KNN/trainingDigits/9_98.txt diff --git a/testData/trainingDigits/9_99.txt b/input/02.KNN/trainingDigits/9_99.txt similarity index 100% rename from testData/trainingDigits/9_99.txt rename to input/02.KNN/trainingDigits/9_99.txt diff --git a/src/python/02.kNN/kNN.py b/src/python/02.kNN/kNN.py index f2f8616a..f10e2021 100644 --- a/src/python/02.kNN/kNN.py +++ b/src/python/02.kNN/kNN.py @@ -151,7 +151,7 @@ def datingClassTest(): # 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 + datingDataMat, datingLabels = file2matrix('input/02.KNN/datingTestSet2.txt') # load data setfrom file # 归一化数据 normMat, ranges, minVals = autoNorm(datingDataMat) m = normMat.shape[0] @@ -186,7 +186,7 @@ def img2vector(filename): def handwritingClassTest(): # 1. 导入数据 hwLabels = [] - trainingFileList = listdir('testData/trainingDigits') # load the training set + trainingFileList = listdir('input/02.KNN/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 @@ -195,17 +195,17 @@ 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('input/02.KNN/trainingDigits/%s' % fileNameStr) # 2. 导入测试数据 - testFileList = listdir('testData/testDigits') # iterate through the test set + testFileList = listdir('input/02.KNN/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('input/02.KNN/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 From 46f9aa97c0bea00491e5c9a0b234526e7dfe4e53 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 7 Apr 2017 16:13:54 +0800 Subject: [PATCH 13/16] =?UTF-8?q?=E6=9B=B4=E6=96=B005.Logistic=E7=9A=84?= =?UTF-8?q?=E6=96=87=E4=BB=B6=E8=B7=AF=E5=BE=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../05.Logistic/HorseColicTest.txt | 0 .../05.Logistic/HorseColicTraining.txt | 0 .../05.Logistic/TestSet.txt | 0 src/python/05.Logistic/logistic.py | 4 +- testData/Logistic_testdata.txt | 100 ------------------ 5 files changed, 1 insertion(+), 103 deletions(-) rename testData/LogisticHorseColicTest.txt => input/05.Logistic/HorseColicTest.txt (100%) rename testData/LogisticHorseColicTraining.txt => input/05.Logistic/HorseColicTraining.txt (100%) rename testData/LogisticTestSet.txt => input/05.Logistic/TestSet.txt (100%) delete mode 100644 testData/Logistic_testdata.txt diff --git a/testData/LogisticHorseColicTest.txt b/input/05.Logistic/HorseColicTest.txt similarity index 100% rename from testData/LogisticHorseColicTest.txt rename to input/05.Logistic/HorseColicTest.txt diff --git a/testData/LogisticHorseColicTraining.txt b/input/05.Logistic/HorseColicTraining.txt similarity index 100% rename from testData/LogisticHorseColicTraining.txt rename to input/05.Logistic/HorseColicTraining.txt diff --git a/testData/LogisticTestSet.txt b/input/05.Logistic/TestSet.txt similarity index 100% rename from testData/LogisticTestSet.txt rename to input/05.Logistic/TestSet.txt diff --git a/src/python/05.Logistic/logistic.py b/src/python/05.Logistic/logistic.py index 7a727dc2..3c1b9b8a 100644 --- a/src/python/05.Logistic/logistic.py +++ b/src/python/05.Logistic/logistic.py @@ -128,10 +128,8 @@ def plotBestFit(dataArr, labelMat, weights): def main(): - # project_dir = os.path.dirname(os.path.dirname(os.getcwd())) # 1.收集并准备数据 - # dataMat, labelMat = loadDataSet("%s/testData/Logistic_testdata.txt" % project_dir) - dataMat, labelMat = loadDataSet("testData/Logistic_testdata.txt") + dataMat, labelMat = loadDataSet("input/05.Logistic/TestSet.txt") # print dataMat, '---\n', labelMat # 2.训练模型， f(x)=a1*x1+b2*x2+..+nn*xn中 (a1,b2, .., nn).T的矩阵值 diff --git a/testData/Logistic_testdata.txt b/testData/Logistic_testdata.txt deleted file mode 100644 index 2356ac54..00000000 --- a/testData/Logistic_testdata.txt +++ /dev/null @@ -1,100 +0,0 @@ --0.017612 14.053064 0 --1.395634 4.662541 1 --0.752157 6.538620 0 --1.322371 7.152853 0 -0.423363 11.054677 0 -0.406704 7.067335 1 -0.667394 12.741452 0 --2.460150 6.866805 1 -0.569411 9.548755 0 --0.026632 10.427743 0 -0.850433 6.920334 1 -1.347183 13.175500 0 -1.176813 3.167020 1 --1.781871 9.097953 0 --0.566606 5.749003 1 -0.931635 1.589505 1 --0.024205 6.151823 1 --0.036453 2.690988 1 --0.196949 0.444165 1 -1.014459 5.754399 1 -1.985298 3.230619 1 --1.693453 -0.557540 1 --0.576525 11.778922 0 --0.346811 -1.678730 1 --2.124484 2.672471 1 -1.217916 9.597015 0 --0.733928 9.098687 0 --3.642001 -1.618087 1 -0.315985 3.523953 1 -1.416614 9.619232 0 --0.386323 3.989286 1 -0.556921 8.294984 1 -1.224863 11.587360 0 --1.347803 -2.406051 1 -1.196604 4.951851 1 -0.275221 9.543647 0 -0.470575 9.332488 0 --1.889567 9.542662 0 --1.527893 12.150579 0 --1.185247 11.309318 0 --0.445678 3.297303 1 -1.042222 6.105155 1 --0.618787 10.320986 0 -1.152083 0.548467 1 -0.828534 2.676045 1 --1.237728 10.549033 0 --0.683565 -2.166125 1 -0.229456 5.921938 1 --0.959885 11.555336 0 -0.492911 10.993324 0 -0.184992 8.721488 0 --0.355715 10.325976 0 --0.397822 8.058397 0 -0.824839 13.730343 0 -1.507278 5.027866 1 -0.099671 6.835839 1 --0.344008 10.717485 0 -1.785928 7.718645 1 --0.918801 11.560217 0 --0.364009 4.747300 1 --0.841722 4.119083 1 -0.490426 1.960539 1 --0.007194 9.075792 0 -0.356107 12.447863 0 -0.342578 12.281162 0 --0.810823 -1.466018 1 -2.530777 6.476801 1 -1.296683 11.607559 0 -0.475487 12.040035 0 --0.783277 11.009725 0 -0.074798 11.023650 0 --1.337472 0.468339 1 --0.102781 13.763651 0 --0.147324 2.874846 1 -0.518389 9.887035 0 -1.015399 7.571882 0 --1.658086 -0.027255 1 -1.319944 2.171228 1 -2.056216 5.019981 1 --0.851633 4.375691 1 --1.510047 6.061992 0 --1.076637 -3.181888 1 -1.821096 10.283990 0 -3.010150 8.401766 1 --1.099458 1.688274 1 --0.834872 -1.733869 1 --0.846637 3.849075 1 -1.400102 12.628781 0 -1.752842 5.468166 1 -0.078557 0.059736 1 -0.089392 -0.715300 1 -1.825662 12.693808 0 -0.197445 9.744638 0 -0.126117 0.922311 1 --0.679797 1.220530 1 -0.677983 2.556666 1 -0.761349 10.693862 0 --2.168791 0.143632 1 -1.388610 9.341997 0 -0.317029 14.739025 0 \ No newline at end of file From 5240c5c0c95d5a0d01e11388a30a97c25926b462 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 7 Apr 2017 16:17:52 +0800 Subject: [PATCH 14/16] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E6=96=87=E4=BB=B6?= =?UTF-8?q?=E7=9A=84=E8=B7=AF=E5=BE=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../08.Regression/abalone.txt | 0 .../Regression_data.txt => input/08.Regression/data.txt | 0 .../15.BigData_MapReduce/inputFile.txt | 0 .../08.Predictive numerical data regression/regression.py | 8 ++++---- 4 files changed, 4 insertions(+), 4 deletions(-) rename testData/Regression_abalone.txt => input/08.Regression/abalone.txt (100%) rename testData/Regression_data.txt => input/08.Regression/data.txt (100%) rename testData/MR_inputFile.txt => input/15.BigData_MapReduce/inputFile.txt (100%) diff --git a/testData/Regression_abalone.txt b/input/08.Regression/abalone.txt similarity index 100% rename from testData/Regression_abalone.txt rename to input/08.Regression/abalone.txt diff --git a/testData/Regression_data.txt b/input/08.Regression/data.txt similarity index 100% rename from testData/Regression_data.txt rename to input/08.Regression/data.txt diff --git a/testData/MR_inputFile.txt b/input/15.BigData_MapReduce/inputFile.txt similarity index 100% rename from testData/MR_inputFile.txt rename to input/15.BigData_MapReduce/inputFile.txt diff --git a/src/python/08.Predictive numerical data regression/regression.py b/src/python/08.Predictive numerical data regression/regression.py index e2ef8aa1..480cffba 100644 --- a/src/python/08.Predictive numerical data regression/regression.py +++ b/src/python/08.Predictive numerical data regression/regression.py @@ -251,7 +251,7 @@ def regression1(): #test for LWLR def regression2(): - xArr, yArr = loadDataSet("testData/Regression_data.txt") + xArr, yArr = loadDataSet("input/08.Regression/data.txt") yHat = lwlrTest(xArr, xArr, yArr, 0.003) xMat = mat(xArr) srtInd = xMat[:,1].argsort(0) #argsort()函数是将x中的元素从小到大排列，提取其对应的index(索引)，然后输出 @@ -265,7 +265,7 @@ def regression2(): #test for ridgeRegression def regression3(): - abX,abY = loadDataSet("testData/Regression_abalone.txt") + abX,abY = loadDataSet("input/08.Regression/abalone.txt") ridgeWeights = ridgeTest(abX, abY) fig = plt.figure() ax = fig.add_subplot(111) @@ -275,7 +275,7 @@ def regression3(): #test for stageWise def regression4(): - xArr,yArr=loadDataSet("testData/Regression_abalone.txt") + xArr,yArr=loadDataSet("input/08.Regression/abalone.txt") stageWise(xArr,yArr,0.01,200) xMat = mat(xArr) yMat = mat(yArr).T @@ -289,4 +289,4 @@ if __name__ == "__main__": # regression1() regression2() # regression3() - # regression4() \ No newline at end of file + # regression4() From 11856b6238d3ae87851a732ca889d38c387cbddd Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 7 Apr 2017 16:24:39 +0800 Subject: [PATCH 15/16] =?UTF-8?q?=E6=9B=B4=E6=96=B07.adaboost=E6=B3=A8?= =?UTF-8?q?=E9=87=8A?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/python/07.AdaBoost/adaboost.py | 74 +++++++++++++++--------------- 1 file changed, 37 insertions(+), 37 deletions(-) diff --git a/src/python/07.AdaBoost/adaboost.py b/src/python/07.AdaBoost/adaboost.py index bc8cd7db..427559d4 100644 --- a/src/python/07.AdaBoost/adaboost.py +++ b/src/python/07.AdaBoost/adaboost.py @@ -258,47 +258,47 @@ def plotROC(predStrengths, classLabels): if __name__ == "__main__": - # # 我们要将5个点进行分类 - # dataArr, labelArr = loadSimpData() - # print 'dataArr', dataArr, 'labelArr', labelArr + # 我们要将5个点进行分类 + dataArr, labelArr = loadSimpData() + print 'dataArr', dataArr, 'labelArr', labelArr - # # D表示最初值，对1进行均分为5份，平均每一个初始的概率都为0.2 - # # D的目的是为了计算错误概率： weightedError = D.T*errArr - # D = mat(ones((5, 1))/5) - # print 'D=', D.T + # D表示最初值，对1进行均分为5份，平均每一个初始的概率都为0.2 + # D的目的是为了计算错误概率： weightedError = D.T*errArr + D = mat(ones((5, 1))/5) + print 'D=', D.T - # # bestStump, minError, bestClasEst = buildStump(dataArr, labelArr, D) - # # print 'bestStump=', bestStump - # # print 'minError=', minError - # # print 'bestClasEst=', bestClasEst.T + # bestStump, minError, bestClasEst = buildStump(dataArr, labelArr, D) + # print 'bestStump=', bestStump + # print 'minError=', minError + # print 'bestClasEst=', bestClasEst.T - # # 分类器：weakClassArr - # # 历史累计的分类结果集 - # weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 9) - # print '\nweakClassArr=', weakClassArr, '\naggClassEst=', aggClassEst.T + # 分类器：weakClassArr + # 历史累计的分类结果集 + weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 9) + print '\nweakClassArr=', weakClassArr, '\naggClassEst=', aggClassEst.T - # """ - # 发现: - # 分类的权重值：最大的值，为alpha的加和，最小值为-最大值 - # 特征的权重值：如果一个值误判的几率越小，那么D的特征权重越少 - # """ + """ + 发现: + 分类的权重值：最大的值，为alpha的加和，最小值为-最大值 + 特征的权重值：如果一个值误判的几率越小，那么D的特征权重越少 + """ - # # 测试数据的分类结果, 观测：aggClassEst分类的最终权重 - # print adaClassify([0, 0], weakClassArr).T - # print adaClassify([[5, 5], [0, 0]], weakClassArr).T + # 测试数据的分类结果, 观测：aggClassEst分类的最终权重 + print adaClassify([0, 0], weakClassArr).T + print adaClassify([[5, 5], [0, 0]], weakClassArr).T - # 马疝病数据集 - # 训练集合 - dataArr, labelArr = loadDataSet("input/07.AdaBoost/horseColicTraining2.txt") - weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 40) - print weakClassArr, '\n-----\n', aggClassEst.T - # 计算ROC下面的AUC的面积大小 - plotROC(aggClassEst.T, labelArr) - # 测试集合 - dataArrTest, labelArrTest = loadDataSet("input/07.AdaBoost/horseColicTest2.txt") - m = shape(dataArrTest)[0] - predicting10 = adaClassify(dataArrTest, weakClassArr) - errArr = mat(ones((m, 1))) - # 测试：计算总样本数，错误样本数，错误率 - print m, errArr[predicting10 != mat(labelArrTest).T].sum(), errArr[predicting10 != mat(labelArrTest).T].sum()/m + # # 马疝病数据集 + # # 训练集合 + # dataArr, labelArr = loadDataSet("input/07.AdaBoost/horseColicTraining2.txt") + # weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 40) + # print weakClassArr, '\n-----\n', aggClassEst.T + # # 计算ROC下面的AUC的面积大小 + # plotROC(aggClassEst.T, labelArr) + # # 测试集合 + # dataArrTest, labelArrTest = loadDataSet("input/07.AdaBoost/horseColicTest2.txt") + # m = shape(dataArrTest)[0] + # predicting10 = adaClassify(dataArrTest, weakClassArr) + # errArr = mat(ones((m, 1))) + # # 测试：计算总样本数，错误样本数，错误率 + # print m, errArr[predicting10 != mat(labelArrTest).T].sum(), errArr[predicting10 != mat(labelArrTest).T].sum()/m From ebc6bbad782822f5101edabeadd0f05031c61feb Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 7 Apr 2017 16:36:11 +0800 Subject: [PATCH 16/16] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E6=96=87=E4=BB=B6?= =?UTF-8?q?=E5=90=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/python/{1.MLFoundation => 01.MLFoundation}/01.NumPy.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename src/python/{1.MLFoundation => 01.MLFoundation}/01.NumPy.py (100%) diff --git a/src/python/1.MLFoundation/01.NumPy.py b/src/python/01.MLFoundation/01.NumPy.py similarity index 100% rename from src/python/1.MLFoundation/01.NumPy.py rename to src/python/01.MLFoundation/01.NumPy.py