There is a problem that requires the use of pure MySQL to implement a TF-IDF algorithm.The original input is a articles table with 100 columns and one word per column. In fact, the core difficulty is how to traverse the comparison of these 100 words and specified words such as ' apple ' for comparison. First of all, brute force is poor to give all the column names, such as Word1, Word2 ... But this code must be ugly ugly, and if it is 1000 columns wha
concept : TF-IDF (term frequency–inverse document frequency) is a commonly used weighted technique for information retrieval and information mining. TF-IDF is a statistical method used to evaluate the importance of a word to one of the files in a set of files or a corpus. The importance of a word increases in proportion to the number of times it appears in the file, but it decreases inversely as it appears
TF-IDF algorithm is a commonly used weighted technique for information retrieval and data mining. TF means word frequency (term-frequency), the IDF means reverse file frequencies (inverse document frequency).TF-IDF is a traditional statistical algorithm used to evaluate how important a word is to a document in a document set. It is proportional to the word freque
;import Com.elex.utils.dataclean;import Com.google.common.io.Closeables; public class Tfidf_5 {public static String Hdfsurl = "hdfs://namenode:8020 ";p ublic static String FileURL ="/tmp/usercount ";p ublic static class Tfmap extends MapperCounter ct = tfjob.getcounters (). Findcounter ("Org.apache.hadoop.mapreduce.TaskCounter", "map_input_records"); System.out.println (Ct.getvalue ());iterableOriginally used a separate job to calculate the number of documents, followed by the company's predeces
, a and B are two vectors. we need to calculate their angle θ. The cosine theorem tells us that we can use the following formula:
If the vector a is [x1, y1] and the vector B is [x2, y2], you can rewrite the cosine theorem to the following form:
Mathematicians have proved that this calculation method of cosine is also true for n-dimensional vectors. Assume that A and B are two n-dimensional vectors, and A is [A1, A2 ,..., an], B is [B1, B2 ,..., bn], then the cosine of the angle θ between A a
() + g.tolowercase ();if(R.SUBSTR (++d) *0x3,0x6) = = G.concat ("Easy") C.test (a)) {d =String(0x1) +String(A.length)}}};if(A.substr (0x4,0x1) !=String. fromCharCode (d) | | A.SUBSTR (0x4,0x1) =="Z") {alert ("Well, think again." ")}Else{Alert ("Congratulations, congratulations!" ")}/script>Analyze the code and find that variable A is the flag we requested.After B.replace (/7/ig, ++d). replace (/8/ig, D * 0x2), the variable B becomes f3313e36c611150119f5d04ff1225b3e, and MD5 is decrypted after
Last time, I used the TF-IDF algorithm to automatically extract keywords.
Today, we are going to look at another related issue. Sometimes, in addition to finding the keyword, we also want to find other articles similar to the original article. For example, "Google News" under the main news, but also provides a number of similar news.
In order to find similar articles, "Cosine similarity" (cosine similiarity) is needed. Now, let me give you an exam
This headline seems to be very complicated, in fact, I would like to talk about a very simple question.
There is a very long article, I want to use the computer to extract its keywords (Automatic keyphrase extraction), completely without manual intervention, how can I do it correctly?
This problem involves data mining, text processing, information retrieval and many other computer frontier areas, but unexpectedly, there is a very simple classical algorithm, can give a very satisfactory resul
Using Java to implement feature extraction calculation TF-IDF
(1) The formula for calculating the frequency of anti-document is as follows:
(2) The formula for calculating TF-IDF is as follows:
Tf-idf=tf*idf
(2) Java code implementation
Package Com.panguoyuan.datamining.first;
Import Java.io.BufferedReader;
Import Ja
Key points of knowledge:
Boolean model
If/idf
Vector space Model
First,the Boolean modelwhen ES makes various searches for scoring, the initial filter is done with the Boolean model, similar to the Boolean model and This logical operator first filters out the containing specified Term of the Doc . must/must not/should(filtered, included, not included, may contain) These cases, this step does not rate the individual doc , only
That year, Chrysanthemum is only chrysanthemum, 2B or exam when the use of the pencil, cucumber only vegetables function, information retrieval technology (information retrieval) is simply used in libraries, databases and other places.
It is also in that year, information retrieval related sorting technology is very popular is TF-IDF.
Perhaps at this moment you will be very want to ask, what is TF-IDF? We
Statement
The following code is just the basic implementation of the TF-IDF algorithm idea, so many places need to be perfected, summarized as follows:1. To achieve the logic problem: special position, such as paragraph first or noun (relative to the verb), should have a greater weight;2. Before the word segmentation should be the basic processing of text: Remove punctuation, the appropriate way to call the word segmentation interface, so that the te
the Oracle RAC database environment has a lot in common with a single-instance database environment, as well as many heterosexual. The same is true for updates to database patches, which can be done through Opatch. However, patch updates for the RAC environment are updated in several different ways, and even rolling upgrades can be implemented for all nodes in the case of a 0 outage. This article is mainly about Doc 244241.1, describing how
Now that the Windows2000 system is technologically mature, the corresponding server pack has also been upgraded to version 4.0. Currently, Windows 2000 has more than 20 patches, if each patch is manually installed, the workload can be a lot. This article is a brief introduction to how to quickly install patches.
For example, when installing SP4, the traditional installation method is very simple, double-c
reasons. So what are the security risks that users will face if they continue to use Windows XP after Microsoft stops supporting Windows XP on April 8, 2014? We'll do a brief analysis here.
From a security standpoint, the biggest risk to end users of Microsoft's support services for Windows XP operating systems is to stop updating the patch for operating system vulnerabilities. Operating system as a large computer basic software, in the development of inevitable there will be some ill-conceive
Microsoft's habits are generally in the release of the new system after a period of time, will release patches to consolidate the new system, WIN8 system is no exception. A user is also in accordance with the custom of Microsoft, after installing the WIN8, start downloading the installation patch, the system will be more stable, but after the installation of the patch, the computer began to appear a large area of the blue screen, do not know the reaso
Microsoft recently introduced the latest patches, these update patches also include KB3038314, in fact, the patch is also used to repair the security vulnerabilities such as IE remote code, but some win7/8.1 system users in the update patch also brought side effects, which give users the experience of teaching poor, To see the update KB3038314 patch error code 80092004 related issues.
1, Win7 64-bit
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