mydat

[bestFeatLabel] [value] = createTree (splitDataSet (dataSet, bestFeat, value), subLabels) return myTree def classify (inputTree, featLabels, testVec): firstStr = list (inputTree. keys () [0] secondDict = inputTree [firstStr] featIndex = featLabels. index (firstStr) for key in secondDict. keys (): if testVec [featIndex] = key: if type (secondDict [key]). _ name __= = 'dict ': classLabel = classify (secondDict [key], featLabels, testVec) else: classLabel = secondDict [key] return classLabel

if Classlist.count (classlist[0]) = = Len (cla Sslist): #类别完全相同不需要再划分 return classlist[0] If Len (dataset[0]) = = 1: #这里为什么是1呢? That is, when the feature number is 1, return majoritycnt (classlist) #就返回这个特征就行了, because this one feature bestfeat = Choosebestfeaturetosplit (DataSet) Print (' The Bestfeatue in creating is: ') print (bestfeat) bEstfeatlabel = labels[bestfeat] #运行结果 ' no surfacing ' mytree = {bestfeatlabel:{}} #嵌套字典, currently value is an empty dictionary del (LABELS[BESTF Eat])

("SELECT * from Product", sqlcnt);D ataset mydataset = new DataSet ();//Create Datasetmydataadapter.fill (myDataSet, "product");//fill in the dataset with the returned data set as "table", the table name can be different from the real table name of the database, and does not affect the subsequent operations such as add, delete, change, etc.① accessing data in a datasetSqlDataAdapter mydataadapter = new SqlDataAdapter ("SELECT * from Product", sqlcnt);D ataset mydataset = new DataSet ();

mydataadapter = new SqlDataAdapter ("SELECT * from Product", sqlcnt);D ataset mydataset = new DataSet ();//Create Datasetmydataadapter.fill (myDataSet, "product");//fill in the dataset with the returned data set as "table", the table name can be different from the real table name of the database, and does not affect the subsequent operations such as add, delete, change, etc.① accessing data in a datasetSqlDataAdapter mydataadapter = new SqlDataAdapter ("SELECT * from Product", sqlcnt);D ataset

entropy def calcshannonent (Dataset): numentries = Len (Dataset) labelcounts ={} for VEC in Dataset: currentlabel = VEC [-1] If currentlabel not in labelcounts. keys (): # create a dictionary labelcounts [currentlabel] = 0 labelcounts [currentlabel] + = 1 shannonent = 0.0 for key in labelcounts for all possible categories: prob = float (labelcounts [Key])/numentries shannonent-= prob * log (prob, 2) return shannonent # simple test mydat, labels = cre

support, join Retlist ifSupport >=minSupport:retList.insert (0, key)#summarize support level datasupportdata[key] = Supportreturnretlist, Supportdata#######################################if __name__=='__main__': #Import data setsMydat =Loaddataset ()#build the first list of candidate Itemsets C1C1 =createC1 (Mydat)#Building a DataSet representation of a data set DD =map (set, Mydat)#Select a set

numentries = Len ( DataSet) labelcounts = {} for Featvec in DataSet: currentlabel=featvec[-1] if CurrentLabel not in Labelcounts.keys (): Labelcounts[currentlabel] = 0 Labelcounts[currentlabel] +=1 shannonent = 0.00000 For key in labelcounts: prob = float (Labelcounts[key])/numentries shannonent-= prob * log (prob,2) #log b ASE 2 return shannonent Execution

. An error occurred while executing the following commands:(1) mov ah, the source operand of BX is different from the destination operand.(2) mov [BX], [Si]: Two memory units are transmitting data, which is not allowed in assembly languages.(3) mov ax, [Si] [di] cannot be used with two address changes, one of which should be the base address(4) mov mydat [BX] [Si], ES: Same as ax (2)(5) mov byte PTR [BX], 1000 must have a register(6) mov BX, offset

return sum (Power (y-yhat,2))main.py# coding:utf-8#!/usr/bin/env pythonimport regtreesimport matplotlib.pyplot as pltfrom numpy import *if __name__ = = ' __mai N__ ': Mydat = Regtrees.loaddataset (' exp2.txt ') Mymat = Mat (mydat) mytree = Regtrees.createtree (mymat,regtrees.modelleaf , Regtrees.modelerr, (1,10)) Print mytreeregtrees.plotbestfit (' exp2.txt ')Get two-segment functions, dividing by 0.28Y

This article does not involve a number of conceptual things, please forgive us a lotThis is the simple frame of Android SQLite.Using SQLite is about 3 stepsThe first step: Create your own Sqliteopenhelper classStep two: Create a DAO layer in the database with the operations of the databaseStep three: Use the DAO layer operation in activity (multi-threaded form, prevent card interface)First step: Create Sqliteopenhelper/** * @author Skyfin * @time 2015/6/4 */public class MyDatabase extends Sqlite

entropy increases. Here, three types of changed datasets are 'yes', 'no', and 'maybe'. That is to say, the more chaotic the data is, the greater the entropy. Classification Algorithms calculate information entropy and divide datasets. In the decision tree algorithm, we calculate the entropy of a dataset based on each feature, and then determine which feature is the best classification method. def splitDataSet(dataSet,axis,value): retDataSet = [] for featVec in dataSet: if featVec[axis] == val

control the export of shared directories after the NFS service is started. Usage: exportfs [-aruv]-A: All content in mount or unmount/etc/exports-R: Re-mount the directory shared in/etc/exports-U: umount directory-V: when the export is used, the detailed information is output to the screen.Example:# Exportfs-au Uninstall all shared directories# Exportfs-rv share all directories again and output details5. Use of the Client First, use showmont-e SER_NAME to find the shared directory on the server

, read through the Getting Started with Windows Azure PowerShell Cmdlets first.Uploading a VHD Import-module azureselect-azuresubscription "mysubscriptionname" $sourceVHD = "D:\StorageDemos\myvhd.vhd" $ Destinationvhd = "Https://mwwestus1.blob.core.windows.net/uploads/myvhd.vhd" Add-azurevhd-localfilepath $sourceVHD -destination $destinationVHD ' -numberofuploaderthreads 5 As you can see the code to upload a VHDs to your Windows Azure Storage account are pr

target type */public class MyDat Econverter implements ConverterEffect:Return form page When form data is filled in errorWhen the data type conversion exception, you need to return to the form page, let the user rewrite fill, but the actual situation is a type conversion exception, the system will automatically jump to 400 pages. So, to jump to the specified page after a type conversion exception occurs, you need to catch the exception and jump to th

(); dataSet. append (data); return dataSetmyDat, labels = createTrainData () myTree = createTree (myDat, labels) print myTreebootList = ['outlook ', 'temperature', 'humidity ', 'windy ']; testList = createTestData (); for testData in testList: dic = classify (myTree, bootList, testData) print dic V. Differences between C4.5 and ID3 Codes For example, lines 52nd and 53 of C4.5 code are different from ID3 (information gain is obtained by ID3, and info

vectorization, the simplest way is to follow a certain separator cut into a word vector, the sample code is as follows: Mydat=[]with open ("Xss-train.txt") as f: for Line in f: tokens=re.split (' \=||\?| \%3e|\%3c|\%3e|\%3c|\%20|\%22|code Implementation of AprioriThere is a lot of code implementation on the Web Apriori, here is one of the implementations.def createC1 (dataSet): C1 = [] for transaction in Dataset:for item on TRANSACTION:IF [item]

vectorization, the simplest way is to follow a certain separator cut into a word vector, the sample code is as follows: Mydat=[]with open ("Xss-train.txt") as f: for Line in f: tokens=re.split (' \=||\?| \%3e|\%3c|\%3e|\%3c|\%20|\%22|code Implementation of AprioriThere is a lot of code implementation on the Web Apriori, here is one of the implementations.def createC1 (dataSet): C1 = [] for transaction in Dataset:for item on TRANSACTION:IF [item]

=basicdatasourcefactory.createdatasource (prop); } catch (Exception e) {e.printstacktrace ();}} public static Connection GetConn () throws SQLException {return mydatasource.getconnection ();} public static DataSource Getdatasource () {return mydat Asource; } public static PreparedStatement Getps () {return PS; /** execute an updated SQL statement with parameters * @param SQL * @param parameters * @return */public static Boolean exec Ut

[[email protected] mydata]# mkdir data[[email protected] mydata]# chown-r Mysql:mysql/mydat A/data[[email protected] mydata]# ls-ld/mydata/datadrwxr-xr-x. 2 MySQL mysql 4096 6 20:10/mydata/dataTwo, install MySQL:http://dev.mysql.com/downloads/mysql/Download mysql-5.5.33-linux2.6-x86_64.tar.gz[[emailprotected] ~]# lsanaconda-ks.cfg install.log.syslog linux-3.10.tar.xzinstall.log linux-2.6.32.60.t AR.XZ mysql-5.5.33-linux2.6-x86_64.tar.gz[[emailprotect

>/back/dbs+%F-%H-%M-% S.sqlIncremental backup: Back up the binary log of the day10. Backup strategy: Weekly full + daily incrementFull backup: mysqldump msyqldump-uroot-p--master-data=2--flush-logs--all-databases--lock-all-tables>/back/ Alldatabases.sqlMysql>purge binary logs to ' msyql-bin-000011 '; Delete the binary log before ' msyql-bin-000011 ';Msyql>show binary logs;Incremental backup: Backing up binary log files (flush logs)1, Mysql>flush logs; First log in MySQL refresh the log;2. cd/my