idf rack

, you can see right away that we scaled down the vectors in a proportional way so that each of their elements is between 0 and 1 and does not lose much valuable information. You see, a word with a count of 1 is no longer the same as the value in one vector and its value in another. Why do we care about this standardization? Considering this, if you want a document to look more relevant to a particular topic than it actually is, you might increase the likelihood that it will be included in a sub

Address: https://en.wikipedia.org/wiki/Okapi_BM25In information retrieval, okapi BM25 (BM stands for best Matching) is a ranking function used by search engines T o Rank matching documents according to their relevance to a given search query. It is based on the probabilistic retrieval framework developed in the 1970s and 1980s Bystephen E. Robertson, Karen Spärck Jones, and others.The name of the actual ranking function is BM25. To set the right context, however, it usually referred to as "Okapi

Emotional analysis based on social network Iiiby white Shinhuata (http://blog.csdn.net/whiterbear) reprint need to indicate the source, thank you.In front of the micro-Bo data capture, simple processing, this article on the school Micro-blog similarity analysis.Similarity analysis of WeiboThis is an attempt to calculate the similarity between any two schools ' microblog words.Idea: First of all, the school micro-bo participle, traverse to get each school's high-frequency Dictionary of words, set

operations per second for 16-bit integer operations. For comparison purposes, Google claims that in the case of a FP16 floating-point number (TPU2), a floating-point operation of 45 trillion times per second can be achieved. TPU does not have a built-in scheduling function, nor can it be virtualized. It is a simple matrix multiplication coprocessor that is directly connected to the server board. Google's first generation of TPU card: A figure does not have a radiator; b picture has radiator Goo

TF-IDF_MapReduceJava Code Implementation ideas, mapreducetfidf Thursday, February 16, 2017TF-IDF 1. Concept 2. Principles 3. java code implementation ideas Dataset: Three MapReduce First MapReduce: (use the ik tokenizer to split words in a blog post, that is, content in a record)Result of the first MapReduce operation: 1. Obtain the dataset Total number of Weibo posts; 2. Get TF value of each word on the current WeiboMapper end: key: LongWritable (of

high-dimensional space, and the more commonly used mapping function is TF*IDF, which takes into account the occurrence of words in the document and document collections. A basic TF*IDF formula is as follows: Ω=tfi (d) *log (N/DFI) (2-1) (2-2) where n is the number of documents in the document collection, TFI (d) is called the word frequency, the number of occurrences of the

describe information. Tagging is the user's behavior of assigning tags to information. Killer features: From our team's understanding of the current project, the entire site landing, uploading files, translation files and other display interfaces are written by the WPF design, that is, the so-called client, and we want to achieve a comprehensive web site. Peripheral Features: A good UI design Scalability: Enhance functionality without destroying the underlying st

Official document Http://lucene.apache.org/core/4_9_0/core/org/apache/lucene/search/similarities/TFIDFSimilarity.htmlterm: not a simple key. Is Field-key, the key under the specified domainfactors that affect scoringCoord:document hit query in the number of term (not count, is the number of different term) term.tf:term in the corresponding field frequency TERM.IDF: The number of document containing this term Query.boost:query weights (when search is set) the weight of the term in term.boost:quer

The plane layout of data centers is usually in a rectangular structure. To ensure the cooling effect, 10 to 20 cabinets are usually placed back to back and discharged into one row to form a Cabinet group (also known as a POD ). The cabinets in the POD adopt the ventilation mode before and after, and the cold air is sucked from the front panel of the cabinet and discharged from the rear. Thus, a "hot channel" is formed between the pods placed in the back-to-back of the Cabinet ", two Adjacent pod

TF-IDF (term frequency–inverse document frequency) is a commonly used weighted technique for information retrieval and information mining.The main idea of TFIDF is that if a word or phrase appears in an article with a high frequency of TF and is seldom seen in other articles, it is considered to be a good category-distinguishing ability and suitable for classification.TFIDF is actually: TF * IDF,TF Word fre

First of all to ensure that the computer has downloaded the Git client, no self- https://git-scm.com/downloadSTEP1: Get the Build ToolchainWindows does not have a built-in "make" environment, so you will need a GNU-compatible environment to install the toolchain. We use the MSYS2 Environment to provide this. You don't have to use this environment all the time, you can program with front-end software such as Eclipse or Arduio, but the toolchain is actually running in the background. The quick

Directory:Calculates the similarity between two stringsII. application of TF-IDF and cosine similarity (II.): Finding similar articlesCalculates the similarity between two stringsThis article is reproduced from Cscmaker(1) cosine similarityThe similarity between the two vectors is measured by measuring the cosine of the corners between them. The cosine of the 0-degree angle is 1, and the cosine of any other angle is no greater than 1, and its minimum

_3823890314914825 2For data processing, according to "\ T" cutting, and then according to "_" Cut, output Context.write (today, 1)//Note that this will count the total number of files that are included today, so don't pay attention to Weibo IDReducer End: Key:w value:{1,1,1} Data sample: Key= Today, value={1,1,1,1,1} //each 1 means there is a microblog in the data set containing the word today Step one: The data after the shuffle process is consolidated (the same value for key is a group, and

Since the specification and number of information points of large, medium, and small computers are determined by host devices, wiring designers generally only collect the types and quantities of their information points, rather than wiring them. Therefore, the number of information points discussed in cabling planning mainly comes from server cabinets. Before counting the number of information points, it should be noted that the number of information points on each server terminal NIC/network bl

equipment ). The next location where frequent power measurements are performed is the uninterruptible power supply (UPS ). If IT only supplies power to IT devices, this data can be used as the denominator for PUE computing. However, UPS may also provide power for rack-mounted refrigeration devices. The third position of power measurement is the rack itself, which features the Distribution Unit (PDU) of the

at a time (except for append and truncation), and there is a writer at any time. Namenode makes all decisions about block replication. It regularly receives heartbeat and blockreport from each datanode in the cluster. When heartbeat is received, datanode is running properly. Blockreport contains a list of all blocks on datanode. Copy placement: Step 1 The placement of copies is critical to the reliability and performance of HDFS. Optimized copy placement separates HDFS from most other distribut

mongrel--pre Install mongrel instead of Webrick and encounter the following issues (Ruby version 1.9.2 rails version 3.1.3) Error:error Installing Mongrel:Error:failed to build gem native extension. The reason is that mongrel 1.1.5 is incompatible with Ruby 1.9.x. You can install a different version by installing Gem Install mongrel--pre Or Gem install mongrel-v 1.2.0.pre2--pre--sourcehttp://ruby.taobao.org Successfully installed After the installation is complete, run:

1. First. We use the surf algorithm to generate the feature points and descriptive descriptors of each picture in the image library.2. The K-means algorithm is used to train the feature points in the image library to generate the class heart.3. Generate BOF for each image. The detailed method is: Infer each feature point of the image with which class heart is recent. In the near future, a series of frequency tables will be generated. That is, the initial right to BOF.4. Add weights to the freque

. HDCSFTTD, a European-branded German Rosenberg, offers a holistic approach to the profession. Body In the current mainstream building wiring system, the backbone of the network data transmission is composed by the optical cable, no matter what horizontal wiring scheme is used, there is no substantial difference in the trunk, and as the wiring scheme of the FTTD, the main difference lies in the horizontal wiring system that is from the floor distribution between the

the page containing the word "automobile", and the page that actually contains the word "car" may be required by the user.Here is an example of LDA primitive paper[1]:is a term-document matrix, x means that the word appears in the corresponding file, the asterisk indicates that the word appears in the query, and when the user enters the query "IDF in computer-based information look up", The user is looking for pages related to