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hello19883
2017-03-20 23:12:15 +08:00
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# Rope project settings
.ropeproject
.vscode
.idea

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# 11.使用Apriori算法进行关联分析 #
**- 概念**
# 11) 使用Apriori算法进行关联分析
1. 关联关系associati analysis 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或者关联规则学习。关系有两种形式:频繁项集(经常一块出现的物品集合);关联规则(暗示两种物品之间可能存在很强的关系)
2. 项集的支持度(support):数据集中包含该项集的记录所占的比例
3. 置信度confidence):置信度({A}->{B}) = 支持度{A,B}/支持度{A}
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。
* 使用场景:
* 目的:商店希望从客户身上获取尽可能多的利润。
* 忠诚度计划: 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)
* 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)
* 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

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# 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。

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@@ -1,24 +1,30 @@
#!/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[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
@@ -27,19 +33,187 @@ 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
"""
tile: 列-3表示复制的行树 行-12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
"""
# 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]
return sortedClassCount[0][0]
def test1():
"""
第一个例子演示
"""
group, labels = createDataSet()
print str(group)
print str(labels)
print classify0([0.1, 0.1], 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
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]
# 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
"""
# 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]
# 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,
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))
# hwLabels存储09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
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()

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@@ -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
@@ -19,8 +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'],
@@ -32,6 +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
@@ -43,9 +43,7 @@ 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)
@@ -80,8 +78,6 @@ 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:
@@ -105,10 +101,8 @@ 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)
@@ -129,10 +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
@@ -140,14 +133,12 @@ def chooseBestFeatureToSplit(dataSet):
def majorityCnt(classList):
"""majorityCnt(选择出线次数最多的一个结果)
"""majorityCnt(选择出次数最多的一个结果)
Args:
classList label列的集合
Returns:
bestFeature 最优的特征列
Raises:
"""
classCount = {}
for vote in classList:
@@ -171,6 +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: {}}
@@ -189,16 +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 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
"""
# 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
# 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:

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@@ -128,5 +128,5 @@ def retrieveTree(i):
return listOfTrees[i]
myTree = retrieveTree(1)
createPlot(myTree)
# myTree = retrieveTree(1)
# createPlot(myTree)

170
src/python/04.NaiveBayes/bayes.py Executable file
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@@ -0,0 +1,170 @@
#!/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():
"""
创建数据集
:return: 单词列表postingList, 所属类别classVec
"""
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'],
['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)# [0,0......]
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 [[1,0,1,1,1....],[],[]...]
:param trainCategory: 文件对应的类别[0,1,1,0....]
: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)[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] #[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# [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
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)#[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
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[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
: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()

View File

@@ -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

View File

@@ -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)

View File

@@ -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 *

View File

@@ -0,0 +1,324 @@
#!/usr/bin/python
# coding: utf8
'''
Created on Mar 24, 2011
Update on 2017-03-16
Ch 11 code
@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()
# 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):
"""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:
# 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
# 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
# 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]
# 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
# print 'union=', Lk[i] | Lk[j], Lk[i], Lk[j]
retList.append(Lk[i] | Lk[j])
return retList
def apriori(dataSet, minSupport=0.5):
"""apriori
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)
# 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
L = [L1]
k = 2
while (len(L[k-2]) > 0):
# 合并k-2相同的数据集
Ck = aprioriGen(L[k-2], k)
# 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
# print 'Ck', Ck
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)
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])
return L, supportData
def calcConf(freqSet, H, supportData, brl, minConf=0.7):
"""calcConf
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
"""
# 记录 可信度大于阈值的集合
prunedH = []
for conseq in H:
# 计算自信度的值,例如元素 H=set(1, 2) 分别求supportData[1] 和 supportData[2]
# print 'confidence=', freqSet, conseq, freqSet-conseq
conf = supportData[freqSet]/supportData[freqSet-conseq]
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):
"""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 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()))
# 收集并准备数据
# dataMat, labelMat = loadDataSet("%s/resources/Apriori_testdata.txt" % project_dir)
# 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()

View File

@@ -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 = {}

View File

@@ -1,19 +1,207 @@
#!/usr/bin/python
# coding:utf8
'''
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):
"""inc(对count变量增加给定值)
"""
self.count += numOccur
def disp(self,ind=1):
print(' '*ind,self.name,' ',self.count)
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)
if __name__ == "__main__":
import fpGrowth
rootNode = fpGrowth.treeNode('pyramid',9,None)
rootNode.children['eye']=fpGrowth.treeNode('eye',13,None)
rootNode.disp()
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
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
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)
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)
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)

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@@ -1,206 +0,0 @@
#!/usr/bin/python
# coding: utf8
'''
Created on Mar 24, 2011
Ch 11 code
@author: Peter
'''
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:
C1.append([item])
C1.sort()
return map(frozenset, C1) # use frozen set so we
# can use it as a key in a dict
def scanD(D, Ck, minSupport):
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
numItems = float(len(D))
retList = []
supportData = {}
for key in ssCnt:
support = ssCnt[key]/numItems
if support >= minSupport:
retList.insert(0, key)
supportData[key] = support
return retList, supportData
def aprioriGen(Lk, k): #creates Ck
retList = []
lenLk = len(Lk)
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
return retList
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
L1, supportData = scanD(D, C1, minSupport)
print("outcome: ", supportData)
L = [L1]
k = 2
while (len(L[k-2]) > 0):
Ck = aprioriGen(L[k-2], k)
Lk, supK = scanD(D, Ck, minSupport)#scan DB to get Lk
supportData.update(supK)
L.append(Lk)
k += 1
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)
if __name__=="__main__":
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]:
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

8124
testData/Apriori_mushroom.dat Executable file

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@@ -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

1000
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00000000111111111111111110000000
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00000000000111111111100000000000
00000000000011111100000000000000

View File

@@ -0,0 +1,32 @@
00000000000011111111111100000000
00000000001111111111111110000000
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00000001111110000000001111000000
00000001111110000000001111000000
00000001111100000000001111000000
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00000001111100000000001111000000
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00000001111100000000011111000000
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00000001111110000000111111000000
00000001111110000001111110000000
00000001111111000001111100000000
00000000111111111111111100000000
00000000111111111111111100000000
00000000111111111111111000000000
00000000011111111111110000000000
00000000011111111111110000000000
00000000001111111111000000000000
00000000011111111100000000000000
00000000000010000000000000000000

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