mahout hadoop

Dataset: The http://grouplens.org/datasets/movielens/ used kb before, now need to download movielens 10 m, use ratings. dat inside Premise: because the file does not conform to the input format of the file in mahout, it needs to be converted. However, in example, grouplensdatamodel is a class for parsing the file, so it is used directly. Package mahout; import Java. io. file; import Org. apache.

Preference object: a single user ID, item ID, and preference value for genericpreference Preferencearray, which is an array of all preference values of a single user, implements genericpreferencearray Sample Code: Package mahout; import Org. apache. mahout. cf. taste. impl. model. genericuserpreferencearray; import Org. apache. mahout. cf. taste. model. preferenc

The bayes implementation in mahout is divided into three parts, 1. Sample Construction; implemented through org. Apache. mahout. classifier. bayesfileformatter, which converts a group of files into label \ t term1 term2 term3... This format is used for the Construction and Classification of the classifier. code analysis is provided in previous blog posts; 2. training; through Org. apache.

) There is no consideration of the effect of the number of scores on the similarity between users (take-to account), and (2) if there is only one common scoring item between two users, the similarity cannot be calculated In the table above, the row represents some of the scoring values for the user (101~103) for the item. Intuitively, User1 and User5 with 3 common scoring items, and the score is not very good, it is supposed that their similarity should be higher than the similarity between Use

In mahout_in_action, a text clustering instance is provided and raw input data is provided. As the main application scenario of clustering algorithms-text classification, text information modeling is also a common problem. There is already a good modeling method in the field of information retrieval, which is the most common vector space model in the field of information retrieval. Term Frequency-inverse Document Frequency (TF-IDF): It is an enhancement to the TF method, and the importance of a

Mahout version: 0.7,hadoop version: 1.0.4,jdk:1.7.0_25 64bit. After the article, Eigen decomposition, the amount, too complex, people too impetuous, static to analyze (say Java to matrix operation support is insufficient, the amount, OK is external reason). 1. Prelude: Eigen decomposition is the tridiag matrix, the matrix, the result of the last article is: [[0.315642761491587, 0.9488780991876485, 0.0],

Software version:hadoop:2.6.0; mahout:1.0 (self-compiled, using only two jar files); Spring:4.0.2;struts:2.3;hibernate:4.3;jquery Easyui : 1.3.6;mysql:5.6; browser: chrome;myeclipse:10.0;Hadoop platform Configuration:Node1:namenode/resourcemanger/datanode/nodemanager memory:2gNode2:nodemanager/datanode/secondarynamenode/jobhistoryserver memory:1.5gNode3:nodemanager/datanode memory:1.5gCode Download: (Tomorr

Mahout version: 0.7,hadoop version: 1.0.4,jdk:1.7.0_25 64bit. After the analysis of the 3 jobs to continue to go down: In fact, there are two functions left: List Look at the Pruneeigens function: Private List See here is actually a screening, three jobs generated three eigenstatus, each eigenstatus has a cosangle and eigenvalue, with these two parameters to determine whether should be retained, the

Mahout version: 0.7,hadoop version: 1.0.4,jdk:1.7.0_25 64bit. 1. Prelude: This chapter continues with the analysis, analysis of lanczossolver: Vector nextvector = issymmetric? Corpus.times (Currentvector): corpus.timessquared (Currentvector); The previous article said this is to establish a job task, and according to a certain algorithm to obtain a nextvector, then next? if (state.getscalefactor () He

The implementation includes three parts: the trainer, the model, and the classifier) 1. Training First, input data must be preprocessed and converted to the format required for reading data by Bayes M/r job. That is, the input data of the trainer is in keyvaluetextinputformat, and the first character is a class label, the remaining is the feature attribute (word ). Taking 20 news as an example, the raw data downloaded from the official website is a category directory, and each folder name below

The Hidden Markov model (Hidden Markov MODEL,HMM) is a statistical model of probability, which is used to describe a Markov process with hidden unknown parameters. The difficulty is to determine the implicit parameters of the procedure from observable parameters. Hmm normal is mainly used to solve three kinds of problems, the corresponding three types of problems are related to the algorithm. Evaluation PROBLEM: Forward algorithm * * decoding PROBLEM: Viterbi algorithm * * Learning problem: Baum

scenario, R and Hadoop each play a very important role. With the idea of a computer developer, all things are done with Hadoop, there is no data to model and prove, "predicted results" must be problematic. The idea of statisticians, all things with R to do, in a sampling way, the "predicted results" must also be problematic. Therefore, the combination of the two is the inevitable direction of the industry,

The classification algorithms implemented by Mahout are:– Random gradient descent (SGD)– Bayesian classification (Bayes)– On-line learning algorithm (online Passive aggressive)– Hidden Markov model (HMM)– Decision Forest (random forest, DF)Example 1: Using a location as a predictor variableUsing a simple example that uses synthetic data, demonstrates how to select predictor variables so that the Mahout mode

The recommended algorithm implemented in Mahout is collaborative filtering, and both USERCF and ITEMCF rely on user similarity or item similarity. This paper is an interpretation of some similarity algorithms in Mahout. Mahout Similarity related class relationships are as follows: A little messy (^.^) As can be seen from the above figure,

Smart applications that can learn from data and user input will become more common when research institutes and companies have access to a dedicated budget. The need for machine learning techniques, such as clustering, collaborative filtering, and classification, has grown ever more, whether it's finding the commonality of a large group of people or automatically tagging mass Web content. The Apache Mahout project is designed to help developers create

The "big data technology series: hadoop Application Development Technology details" consists of 12 chapters. 1st ~ Chapter 2 describes the hadoop ecosystem, key technologies, and installation and configuration in detail. Chapter 2 is an introduction to mapreduce, allowing readers to understand the entire development process ~ Chapter 5 describes in detail the HDFS and h