id3 editor

ID3 algorithmThe ID3 algorithm is J. Ross Quinlan The Classification prediction algorithm presented in 1975. The core of the algorithm is "information entropy".information entropy is a measure of the information contained in a set of data and probabilities. A group of data more orderly information entropy is also lower, extreme if only one group of data is not 0, the other is 0, then the entropy equals 0, b

measure the amount of information The greater the uncertainty of the variable, the greater the Entropy 3.1 decision tree induction algorithm (ID3) 1970-1980, J.ross. Quinlan, ID3 algorithm selects the attribute to determine the node information acquisition (Information Gain): Gain (A) = Info (d)-infor_a (d) using a as a node classification How much information was obtainedSimilarly, Gain (income) = 0.029,

exactly is the problem, I have not yet encountered. There are other ways to solve mp3id3 garbled problems, such as establishing UTF-8 and GB2312/BIG5 encoded images, and so on, but none of this is simple. The existing problems: -can only convert local encoding to UTF-8, that is: Simplified system read Traditional Chinese ID3 will still have garbled, the same traditional system is also-can only convert ID3

This article mainly introduces how to read ID3 information from mp3 using Python. examples of how to use the mutagen package in Python are analyzed, which has some reference value, for more information about how to read ID3 information from mp3 using Python, see the following example. Share it with you for your reference. The specific analysis is as follows: Pyid3 is not easy to use and is often unfamili

General Decision tree Induction Framework See previous posts: http://blog.csdn.net/zhyoulun/article/details/41978381 ID3 Attribute Selection Metric principle ID3 uses information gain as a property selection metric. The measure is based on Shannon's pioneering work in the study of the value of messages or the information theory of "informative content". The node n represents or holds the tuple of partition

We know that ID3 is one of the most basic decision tree algorithms. He was mainly divided by the selection of features according to Infogain, and no pruning was performed. Buildclassifier: public void Buildclassifier (Instances data) throws Exception { //can classifier handle the data? Getcapabilities (). Testwithfail (data); Remove instances with missing class data = new instances (data); Data.deletewithmissingclass ();

How to understand the C4.5 algorithm solves the characteristic problem that the ID3 algorithm is biased to the choice valueConsider an extreme case where the value of a property (feature) is so large that each value corresponds to a category with only one. This is based on \[h (D)-H (d| a) \] to know that the value of the next item is 0. The gain in this information will be very large. The C4.5 algorithm adds a penalty item \[h_a (D) =-\sum_{i=1}^n\df

the image over (1,0) point of the LOG2 function). We define feature A on the data set S information gain Gain (S, A): Gain (S,a) =entropy (s)-S ((| sv|/| s|) *entropy (SV)) S-represents all possible V-values on feature a sv-dataset S with a data subset of the value of V for feature a | Number of SV|-SV | Number of s|-s Example 2 Suppose S is a data set with 14 members, and one of its properties is wind speed. Wind can have Weak and strong. The results of the 14-member classification were 9 yes,

I just wrote about the establishment of the ID3 decision tree, which is predicted by the decision tree. The main use here is the XML traversal parsing, relatively simple.For XML parsing, reference is made to:http://blog.csdn.net/soszou/article/details/8049220http://lavasoft.blog.51cto.com/62575/71669/Ideas:The data to be predicted first, such as "Sunny mild normal TRUE" becomes a map based on the feature table, making it easy to follow up, and the res

results in a binary tree or a multi-fork tree. The inner node (non-leaf node) of a binary tree is generally expressed as a logical judgment, such as the logical judgment of the form A=aj, where a is the attribute, and AJ is the value of the attribute: The edge of the tree is the branching result of the logical judgment. The inner node of a multi-fork tree (ID3) is an attribute, and the edge is all the values of that property, and there are several si

ID3 algorithm#coding =utf-8frommathimportlogimportoperator# This defines a sample set Defcreatedataset ():dataset=[[1,1, ' Yes '],[1 ,1, ' yes '], [1,0, ' No '], [0,1, ' No '], [0,1, ' No ']] labels=[' nosurfacing ', ' flippers '] #change to discretevaluesreturndataSet,labels #这里计算该样本的期望值def calCshannonent (DataSet): numentries=len (DataSet) labelCounts={}forfeatVecin dataset: #the thenumberofuniqueelementsandtheiroccurance currentLabel=featVec[-1]

ID3 (examples, targetattribute, attributes)/*Examples: training sample setTargetattribute: Target attribute to be predictedAttributes: list of attributes other than the target attributes for learning decision trees*/ If the targetattribute values of all examples are the same as a, a single node tree with the node value a is returned.Otherwise, further judgment and analysis are required based on other attributes. If attributes is empty, no attribute

How to obtain id3 from an MP3 file using Python This example describes how Python obtains id3 from an MP3 file. Share it with you for your reference. The details are as follows: ? 1 2 3 4 5 6 7 8 9 10 11 Def getID3 (filename ): Fp = open (filename, 'R ') Fp. seek (-128, 2) Fp. read (3) # TAG iniziale Title = fp. read (30) Artist = fp. read (30) Album = fp. read (30) Anno = fp. rea

MP3 audio music tag ID3 id3v1 ID3v2 tag read information get picture JPEG BMP picture conversion (UP)MP3 file Format (ii)---ID3v2Figure: ID3V1 Tag StructureFigure: ID3V2 Tag StructureFigure: ID3V2 head structureFigure: ID3v2 Frame head structure1. Frame identification A frame with four characters, indicating the meaning of the contents of a frame, the common control is as follows: tit2= title means the title of the song, the same as tpe1= autho

ID3: The numerical data can not be processed directly, but it is possible to quantify the numerical data processing Cheng the data, but it involves too many feature divisions and does not recommendDecision Tree: The biggest advantage is that it can give the intrinsic meaning of data, the data form is very easy to understand;Decision Tree Description: Decision tree classifier is a flowchart with planting, terminating block indicates classification resu

is our slave. Isn't that right? So we don't need to be afraid of our slaves, we just have to know him and conquer him.Two: Dividing data setsIf a demon catches your goddess. The Devil to give you a problem, let you contain black beans white beans red beans three kinds of beans according to different colors, white and white together, black together, red together. This is not very simple, actually partitioning the data set is so simple. Look at a feature item in the data, and then put together th

/** MP3 class** Rel. 0.1** Copyright (c) 2000 Sumatra Solutions srl http://www.sumatrasolutions.com* Ludovico Magnocavallo ludo@sumatrasolutions.com** License type: gnu gpl http://www.gnu.org/copyleft/gpl.html** Heavily converted red* Perl Apache: MP3 module (L. Stein) -- great module to create an apache-based mp3 server* Perl MP3: Info (C. Nandor) -- very complicated, hard stuff but useful* Java class de.vdheide.mp3 (J. Vonderheide) -- great stuff, easy to read, had to debug synchronize () meth

(1:fair; 2:excellent;) Leaf:no () Leaf:yes () Leaf:yes () student (1:no; 2:yes;) Leaf:no () Leaf:yes ()Finally, attach the Java codeDecisiontree.javaPackage Com.zhyoulun.decision;import Java.io.bufferedreader;import Java.io.file;import java.io.FileInputStream; Import Java.io.filenotfoundexception;import Java.io.ioexception;import Java.io.inputstreamreader;import Java.util.arraylist;import java.util.map;/** * Responsible for reading and writing data, and generating decision tree * * @author Zhyo

/* * MP3 Class * * rel. 0.1 * * Copyright (c) Sumatra Solutions srl http://www.sumatrasolutions.com * Ludovico Magnocavallo ludo@sumatrasolutions.com * * License Type:gnu GPL http://www.gnu.org/copyleft/gpl.html * * Heavily inspired by * Perl Apache::mp3 Module (L. Stein)--great module to create a apache-based MP3 server * Perl Mp3::info (C. Nandor)--very complicated hard stuff but useful * Java class De.vdheide.mp3 (J. Vonderheide)--great stuff, easy to read, had to debug synchronize () * * ID3