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Functions414 in github.com/aimi-cn/AILearners

⨍ Functions 414◇ Types & classes 15

↓ 11 callersFunctionisTree

(obj)

data/ml/jqxxsz/sourceData/Ch09/regTrees.py:86

↓ 11 callersFunctionisTree

Desc: 测试输入变量是否是一棵树,即是否是一个字典 Args: obj -- 输入变量 Returns: 返回布尔类型的结果。如果 obj 是一个字典，返回true，否则返回 false

src/py2.x/ml/jqxxsz/9.RegTrees/demo.py:180

↓ 9 callersFunctionmap

(key, value)

data/ml/jqxxsz/sourceData/Ch15/proximalSVM.py:8

↓ 8 callersFunctioncalcEk

(oS, k)

data/ml/jqxxsz/sourceData/Ch06/svmMLiA.py:99

↓ 8 callersFunctionisTree

(obj)

src/py2.x/ml/jqxxsz/9.RegTrees/treePruning.py:137

↓ 8 callersFunctionrssError

(yArr, yHatArr)

src/py2.x/ml/jqxxsz/8.Regression/abalone.py:99

↓ 6 callersFunctionlwlrTest

(testArr, xArr, yArr, k=1.0)

src/py2.x/ml/jqxxsz/8.Regression/abalone.py:70

↓ 6 callersFunctionscrapePage

(inFile,outFile,yr,numPce,origPrc)

data/ml/jqxxsz/sourceData/Ch08/Old_regression.py:117

↓ 6 callersFunctionscrapePage

(retX, retY, inFile, yr, numPce, origPrc)

src/py2.x/ml/jqxxsz/8.Regression/lego/Sklearn_lego.py:29

↓ 6 callersFunctionscrapePage

(retX, retY, inFile, yr, numPce, origPrc)

src/py2.x/ml/jqxxsz/8.Regression/lego/lego.py:28

↓ 6 callersFunctionsearchForSet

(retX, retY, setNum, yr, numPce, origPrc)

data/ml/jqxxsz/sourceData/Ch08/regression.py:149

↓ 5 callersFunctionbinSplitDataSet

(dataSet, feature, value)

data/ml/jqxxsz/sourceData/Ch09/regTrees.py:17

↓ 5 callersFunctionbinSplitDataSet

(dataSet, feature, value)

src/py2.x/ml/jqxxsz/9.RegTrees/treePruning.py:39

↓ 5 callersFunctionbinSplitDataSet

binSplitDataSet(将数据集，按照feature列的value进行二元切分) Description：在给定特征和特征值的情况下，该函数通过数组过滤方式将上述数据集合切分得到两个子集并返回。 Args: dataMat 数据集 f

src/py2.x/ml/jqxxsz/9.RegTrees/demo.py:45

↓ 5 callersFunctionkernelTrans

(X, A, kTup)

data/ml/jqxxsz/sourceData/Ch06/svmMLiA.py:72

↓ 4 callersFunctionbagOfWords2VecMN

(vocabList, inputSet)

data/ml/jqxxsz/sourceData/Ch04/bayes.py:57

↓ 4 callersFunctioncalcEk

计算误差 Parameters： oS - 数据结构 k - 标号为k的数据 Returns: Ek - 标号为k的数据误差

src/py2.x/ml/jqxxsz/6.SVM/svm_demo02.py:81

↓ 4 callersFunctioncalcEk

计算误差 Parameters： oS - 数据结构 k - 标号为k的数据 Returns: Ek - 标号为k的数据误差

src/py2.x/ml/jqxxsz/6.SVM/svm_demo01.py:55

↓ 4 callersFunctioncalcEk

计算误差 Parameters： oS - 数据结构 k - 标号为k的数据 Returns: Ek - 标号为k的数据误差

src/py2.x/ml/jqxxsz/6.SVM/svm2.py:103

↓ 4 callersFunctionclassifyNB

(vec2Classify, p0Vec, p1Vec, pClass1)

data/ml/jqxxsz/sourceData/Ch04/bayes.py:49

↓ 4 callersFunctioninnerL

(i, oS)

data/ml/jqxxsz/sourceData/Ch06/svmMLiA.py:125

↓ 4 callersFunctionsigmoid

(inX)

data/ml/jqxxsz/sourceData/Ch05/logRegres.py:17

↓ 4 callersFunctionsigmoid

(inX)

src/py2.x/ml/jqxxsz/5.Logistic/Logistic.py:101

↓ 4 callersFunctiontextParse

(bigString)

data/ml/jqxxsz/sourceData/Ch04/bayes.py:78

↓ 4 callersFunctionupdateEk

(oS, k)

data/ml/jqxxsz/sourceData/Ch06/svmMLiA.py:121

↓ 3 callersFunctionbinSplitDataSet

(dataSet, feature, value)

src/py2.x/ml/jqxxsz/9.RegTrees/regTrees.py:25

↓ 3 callersFunctionbinSplitDataSet

(dataSet, feature, value)

src/py2.x/ml/jqxxsz/9.RegTrees/modelTree.py:26

↓ 3 callersFunctionclipAlpha

(aj,H,L)

data/ml/jqxxsz/sourceData/Ch06/svmMLiA.py:24

↓ 3 callersFunctioncreateVocabList

(dataSet)

data/ml/jqxxsz/sourceData/Ch04/bayes.py:18

↓ 3 callersFunctionfile2matrix

(filename)

src/py2.x/ml/jqxxsz/2.KNN/KNN_demo01.py:30

↓ 3 callersFunctionkernelTrans

通过核函数将数据转换更高维的空间 Parameters： X - 数据矩阵 A - 单个数据的向量 kTup - 包含核函数信息的元组 Returns: K - 计算的核K

src/py2.x/ml/jqxxsz/6.SVM/svm_demo02.py:43

↓ 3 callersFunctionkernelTrans

通过核函数将数据转换更高维的空间 Parameters： X - 数据矩阵 A - 单个数据的向量 kTup - 包含核函数信息的元组 Returns: K - 计算的核K

src/py2.x/ml/jqxxsz/6.SVM/svm2.py:82

↓ 3 callersFunctionlinearSolve

Desc: 将数据集格式化成目标变量Y和自变量X，执行简单的线性回归，得到ws Args: dataSet -- 输入数据 Returns: ws -- 执行线性回归的回归系数 X -- 格式化自变量X

src/py2.x/ml/jqxxsz/9.RegTrees/demo.py:291

↓ 3 callersFunctionloadDataSet

()

src/py2.x/ml/jqxxsz/5.Logistic/Logistic.py:49

↓ 3 callersFunctionloadDataSet

(fileName)

src/py2.x/ml/jqxxsz/8.Regression/regression.py:24

↓ 3 callersFunctionloadDataSet

(fileName)

src/py2.x/ml/jqxxsz/9.RegTrees/regTrees.py:35

↓ 3 callersFunctionloadDataSet

(fileName)

src/py2.x/ml/jqxxsz/9.RegTrees/modelTree.py:36

↓ 3 callersFunctionlwlrTest

(testArr, xArr, yArr, k=1.0)

src/py2.x/ml/jqxxsz/8.Regression/regression.py:117

↓ 3 callersFunctionselectJrand

(i,m)

data/ml/jqxxsz/sourceData/Ch06/svmMLiA.py:18

↓ 3 callersFunctionsetOfWords2Vec

(vocabList, inputSet)

data/ml/jqxxsz/sourceData/Ch04/bayes.py:24

↓ 3 callersFunctionsetOfWords2Vec

(vocabList, inputSet)

src/py2.x/ml/jqxxsz/4.NaiveBayes/NaiveBayesDemo01.py:50

↓ 3 callersFunctionsigmoid

(inX)

src/py2.x/ml/jqxxsz/5.Logistic/Logistic_demo01.py:22

↓ 3 callersFunctiontrainNB0

(trainMatrix,trainCategory)

data/ml/jqxxsz/sourceData/Ch04/bayes.py:32

↓ 2 callersFunctionadaClassify

(datToClass,classifierArr)

src/py2.x/ml/jqxxsz/7.AdaBoost/horse_adaboost.py:135

↓ 2 callersFunctionaprioriGen

(Lk, k)

data/ml/jqxxsz/sourceData/Ch11/apriori.py:39

↓ 2 callersFunctionautoNorm

(dataSet)

src/py2.x/ml/jqxxsz/2.KNN/KNN_demo01.py:136

↓ 2 callersFunctioncalcConf

(freqSet, H, supportData, brl, minConf=0.7)

data/ml/jqxxsz/sourceData/Ch11/apriori.py:75

↓ 2 callersFunctioncalcShannonEnt

(dataSet)

data/ml/jqxxsz/sourceData/Ch03/trees.py:19

↓ 2 callersFunctioncalcShannonEnt

(dataSet)

src/py2.x/ml/jqxxsz/3.DecisionTree/DecisionTree_demo01.py:53

↓ 2 callersFunctionclassify0

(inX, dataSet, labels, k)

data/ml/jqxxsz/sourceData/Ch02/kNN.py:18

↓ 2 callersFunctionclassifyNB

(vec2Classify, p0Vec, p1Vec, pClass1)

src/py2.x/ml/jqxxsz/4.NaiveBayes/NaiveBayesDemo01.py:143

↓ 2 callersFunctionclassifyVector

(inX, weights)

src/py2.x/ml/jqxxsz/5.Logistic/Logistic_demo01.py:137

↓ 2 callersFunctioncreateForeCast

Desc: 调用 treeForeCast ，对特定模型的树进行预测，可以是回归树也可以是模型树 Args: tree -- 已经训练好的树的模型 testData -- 输入的测试数据 modelEval --

src/py2.x/ml/jqxxsz/9.RegTrees/demo.py:389

↓ 2 callersFunctioncreateTree

(dataSet, minSup=1)

data/ml/jqxxsz/sourceData/Ch12/fpGrowth.py:29

↓ 2 callersFunctioncreateTree

createTree(获取回归树) Description：递归函数：如果构建的是回归树，该模型是一个常数，如果是模型树，其模型师一个线性方程。 Args: dataSet 加载的原始数据集 leafType 建立叶子点的函数

src/py2.x/ml/jqxxsz/9.RegTrees/demo.py:147

↓ 2 callersFunctiongetNumLeafs

(myTree)

data/ml/jqxxsz/sourceData/Ch03/treePlotter.py:12

↓ 2 callersFunctiongetNumLeafs

(myTree)

src/py2.x/ml/jqxxsz/3.DecisionTree/decisionTreePlot.py:25

↓ 2 callersFunctiongetTreeDepth

(myTree)

data/ml/jqxxsz/sourceData/Ch03/treePlotter.py:22

↓ 2 callersFunctionimg2vector

(filename)

data/ml/jqxxsz/sourceData/Ch02/kNN.py:76

↓ 2 callersFunctionimg2vector

将图像数据转换为向量 :param filename: 图片文件因为我们的输入数据的图片格式是 32 * 32的 :return: 一维矩阵该函数将图像转换为向量：该函数创建 1 * 1024 的NumPy数组，然后打开给定的文件，循环读出文件的前32

src/py2.x/ml/jqxxsz/2.KNN/KNN_demo03.py:19

↓ 2 callersFunctionimg2vector

src/py2.x/ml/jqxxsz/2.KNN/KNN_demo02.py:22

↓ 2 callersFunctionimg2vector

将32x32的二进制图像转换为1x1024向量。 Parameters: filename - 文件名 Returns: returnVect - 返回的二进制图像的1x1024向量

src/py2.x/ml/jqxxsz/6.SVM/svm_svc.py:20

↓ 2 callersFunctioninnerL

优化的SMO算法 Parameters： i - 标号为i的数据的索引值 oS - 数据结构 Returns: 1 - 有任意一对alpha值发生变化 0 - 没有任意一对alpha值发生变化或变化太小

src/py2.x/ml/jqxxsz/6.SVM/svm_demo02.py:165

↓ 2 callersFunctioninnerL

优化的SMO算法 Parameters： i - 标号为i的数据的索引值 oS - 数据结构 Returns: 1 - 有任意一对alpha值发生变化 0 - 没有任意一对alpha值发生变化或变化太小

src/py2.x/ml/jqxxsz/6.SVM/svm_demo01.py:139

↓ 2 callersFunctioninnerL

优化的SMO算法 Parameters： i - 标号为i的数据的索引值 oS - 数据结构 Returns: 1 - 有任意一对alpha值发生变化 0 - 没有任意一对alpha值发生变化或变化太小

src/py2.x/ml/jqxxsz/6.SVM/svm2.py:186

↓ 2 callersFunctionlinearSolve

(dataSet)

data/ml/jqxxsz/sourceData/Ch09/regTrees.py:28

↓ 2 callersFunctionlinearSolve

Desc: 将数据集格式化成目标变量Y和自变量X，执行简单的线性回归，得到ws Args: dataSet -- 输入数据 Returns: ws -- 执行线性回归的回归系数 X -- 格式化自变量X

src/py2.x/ml/jqxxsz/9.RegTrees/modelTree.py:213

↓ 2 callersFunctionloadDataSet

(fileName)

data/ml/jqxxsz/sourceData/Ch06/svmMLiA.py:9

↓ 2 callersFunctionloadDataSet

(fileName)

src/py2.x/ml/jqxxsz/7.AdaBoost/horse_adaboost.py:17

↓ 2 callersFunctionloadDataSet

(fileName)

src/py2.x/ml/jqxxsz/7.AdaBoost/sklearn_adaboost.py:18

↓ 2 callersFunctionloadDataSet

(fileName)

src/py2.x/ml/jqxxsz/9.RegTrees/treePruning.py:22

↓ 2 callersFunctionloadDataSet

loadDataSet(解析每一行，并转化为float类型) Desc：该函数读取一个以 tab 键为分隔符的文件，然后将每行的内容保存成一组浮点数 Args: fileName 文件名 Returns: dataMat 每一行的数据集

src/py2.x/ml/jqxxsz/9.RegTrees/demo.py:22

↓ 2 callersFunctionloadDataSet

(filename)

src/py2.x/ml/jqxxsz/6.SVM/svm2.py:24

↓ 2 callersFunctionloadImages

(dirName)

data/ml/jqxxsz/sourceData/Ch06/svmMLiA.py:217

↓ 2 callersFunctionloadImages

加载图片 Parameters: dirName - 文件夹的名字 Returns: trainingMat - 数据矩阵 hwLabels - 数据标签

src/py2.x/ml/jqxxsz/6.SVM/svm_demo02.py:276

↓ 2 callersFunctionlwlr

(testPoint,xArr,yArr,k=1.0)

data/ml/jqxxsz/sourceData/Ch08/Old_regression.py:30

↓ 2 callersFunctionlwlr

(testPoint,xArr,yArr,k=1.0)

data/ml/jqxxsz/sourceData/Ch08/regression.py:30

↓ 2 callersFunctionplotMidText

(cntrPt, parentPt, txtString)

data/ml/jqxxsz/sourceData/Ch03/treePlotter.py:38

↓ 2 callersFunctionplotMidText

(cntrPt, parentPt, txtString)

src/py2.x/ml/jqxxsz/3.DecisionTree/decisionTreePlot.py:61

↓ 2 callersFunctionplotNode

(nodeTxt, centerPt, parentPt, nodeType)

data/ml/jqxxsz/sourceData/Ch03/treePlotter.py:33

↓ 2 callersFunctionplotNode

(nodeTxt, centerPt, parentPt, nodeType)

src/py2.x/ml/jqxxsz/3.DecisionTree/decisionTreePlot.py:56

↓ 2 callersFunctionpredict

(w, x)

data/ml/jqxxsz/sourceData/Ch15/pegasos.py:32

↓ 2 callersFunctionprintMat

(inMat, thresh=0.8)

data/ml/jqxxsz/sourceData/Ch14/svdRec.py:86

↓ 2 callersFunctionreDraw

(tolS,tolN)

data/ml/jqxxsz/sourceData/Ch09/treeExplore.py:11

↓ 2 callersFunctionridgeTest

(xArr, yArr)

src/py2.x/ml/jqxxsz/8.Regression/lego/lego.py:198

↓ 2 callersFunctionrssError

(yArr,yHatArr)

data/ml/jqxxsz/sourceData/Ch08/Old_regression.py:59

↓ 2 callersFunctionrssError

(yArr,yHatArr)

data/ml/jqxxsz/sourceData/Ch08/regression.py:59

↓ 2 callersFunctionscanD

(D, Ck, minSupport)

data/ml/jqxxsz/sourceData/Ch11/apriori.py:22

↓ 2 callersFunctionselectJ

(i, oS, Ei)

data/ml/jqxxsz/sourceData/Ch06/svmMLiA.py:104

↓ 2 callersFunctionsetDataCollect

(retX, retY)

src/py2.x/ml/jqxxsz/8.Regression/lego/lego.py:71

↓ 2 callersFunctionsmoP

(dataMatIn, classLabels, C, toler, maxIter,kTup=('lin', 0))

data/ml/jqxxsz/sourceData/Ch06/svmMLiA.py:153

↓ 2 callersFunctionsplitDataSet

(dataSet, axis, value)

data/ml/jqxxsz/sourceData/Ch03/trees.py:32

↓ 2 callersFunctionsplitDataSet

(dataSet, index, value)

src/py2.x/ml/jqxxsz/3.DecisionTree/DecisionTree_demo01.py:96

↓ 2 callersFunctionstumpClassify

(dataMatrix,dimen,threshVal,threshIneq)

data/ml/jqxxsz/sourceData/Ch07/adaboost.py:30

↓ 2 callersFunctionstumpClassify

(dataMatrix,dimen,threshVal,threshIneq)

src/py2.x/ml/jqxxsz/7.AdaBoost/horse_adaboost.py:39

↓ 2 callersFunctionstumpClassify

(dataMatrix,dimen,threshVal,threshIneq)

src/py2.x/ml/jqxxsz/7.AdaBoost/AdaBoost01.py:62

↓ 2 callersFunctiontreeForeCast

Desc: 对特定模型的树进行预测，可以是回归树也可以是模型树 Args: tree -- 已经训练好的树的模型 inData -- 输入的测试数据，只有一行 modelEval -- 预测的树的模型类型，可选值

src/py2.x/ml/jqxxsz/9.RegTrees/demo.py:360

↓ 2 callersFunctionupdateEk

计算Ek,并更新误差缓存 Parameters： oS - 数据结构 k - 标号为k的数据的索引值 Returns: 无

src/py2.x/ml/jqxxsz/6.SVM/svm_demo02.py:136

↓ 2 callersFunctionupdateEk

计算Ek,并更新误差缓存 Parameters： oS - 数据结构 k - 标号为k的数据的索引值 Returns: 无

src/py2.x/ml/jqxxsz/6.SVM/svm_demo01.py:110

↓ 2 callersFunctionupdateEk

计算Ek,并更新误差缓存 Parameters： oS - 数据结构 k - 标号为k的数据的索引值 Returns: 无

src/py2.x/ml/jqxxsz/6.SVM/svm2.py:158

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