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Http://product.dangdang.com/23829918.htmlSpark has attracted wide attention as the emerging, most widely used open source framework for big data processing, attracting a lot of programming and developers to learn and develop relevant content, Mllib is the core of the spark framework. This book is a detailed introduction to the Spark mllib program design book, the introduction of simple, rich examples.This b
[Spark] [Hive] [Python] [SQL] A small example of Spark reading a hive table$ cat Customers.txt1Alius2Bsbca3Carlsmx$ hiveHive>> CREATE TABLE IF not EXISTS customers (> cust_id String,> Name string,> Country String>)> ROW FORMAT delimited fields TERMINATED by ' \ t ';hive> Load Data local inpath '/home/training/customers
[TOC]This article refers to the Spark rapid Big data analysis, which summarizes the use of the RDD and mllib of the spark technology core and several of its key libraries. Initialize Operation
Spark Shell:bin/pysparkEach spark application consists of a drive program (driver programs) that initiates various parallel ope
, Hadoop, Scala, Docker videos released in 51CTO:1, "Scala Beginner's introductory classic video course" http://edu.51cto.com/lesson/id-66538.html2, "Scala Advanced Advanced Classic Video Course" http://edu.51cto.com/lesson/id-67139.html3, "Akka-in-depth practical classic video Course" http://edu.51cto.com/lesson/id-77672.html4, "Spark Asia-Pacific Research Institute wins big Data Times Public Welfare lecture" http://edu.51cto.com/lesson/id-30815.html
node.Right-click the node, tap Excute, then right-click the decision Tree model to view the results.9 test the model with a test data set and spark Predictor node.Copy the CSV reader,missing value and table to spark node and refer to 3,4,6 step to configure the read test data set and process and convert the data. Add the Spark Predictor node, configure the
vectors:def cosineSimilarity(vec1: DoubleMatrix, vec2: DoubleMatrix): Double = { vec1.dot(vec2) / (vec1.norm2() * vec2.norm2()) }Now to check if it's right, pick a movie. See if it is 1 with its own similarity:val567val itemFactor = model.productFeatures.lookup(itemId).headvalnew DoubleMatrix(itemFactor)println(cosineSimilarity(itemVector, itemVector))Can see the result is 1!Next we calculate the similarity of other movies to it:valcase (id, factor) => valnew DoubleMatrix(factor)
) / (vec1.norm2() * vec2.norm2()) }Now to detect whether it is correct, choose a movie and see if it is 1 with its own similarity:val567val itemFactor = model.productFeatures.lookup(itemId).headvalnew DoubleMatrix(itemFactor)println(cosineSimilarity(itemVector, itemVector))You can see that the result is 1!Next we calculate the similarity of the other movies to it:valcase (id, factor) => valnew DoubleMatrix(factor) val sim = cosineSimilarity(factorVector, itemVector) (id,sim)
Part of the theoretical principle can be seen in this article: http://www.cnblogs.com/charlesblc/p/6109551.htmlThis is the actual combat section. Reference to the Http://www.cnblogs.com/shishanyuan/p/4747778.htmlThe algorithm of clustering, regression and collaborative filtering is used in three cases.I feel good and need to try each one in the actual system.More API Introduction can refer to http://spark.apache.org/docs/2.0.1/ml-guide.html"Todo" Spark
Discovering and exploring data using advanced analytic algorithms such as large-scale machine learning, graphical analysis, statistical modelling, and so on is a popular idea, and in the IDF16 technology class, Intel software Development Engineer Wang Yiheng shares the course on machine learning and neural network algo
(1))) Val Indexrowmatrix = new Indexedrowmatrix (RDD1)//convert Indexedrowmatrix to Blockmatrix, specify the number of rows per block Val Blockmatrix:bloc Kmatrix=indexrowmatrix. Toblockmatrix(2,2)//After the execution of the printed content://index: (0,0) Matrixcontent:2 x 2Cscmatrix//(1,0)20.0//(1,1)30.0Index: (1,1) Matrixcontent:2 x 1Cscmatrix//(0,0)70.0//(1,0)100.0Index: (1,0) Matrixcontent:2 x 2Cscmatrix//(0,0)50.0//(1,0)80.0//(0,1)60.0//(1,1)90.0Index: (0,1) Matrixcontent:2 x 1Cscmatrix//(
Recently in the study "Spark machine learning this book", the book used Ipython, the machine is Redhat version, with the Python2.6.6, installation needs to upgrade more than 2.7, or will report
IPython requires Python version 2.7 or 3.3 or above. This is a mistake. The follo
1. Alternating Least SquareALS (Alternating Least Square), alternating least squares. In machine learning, a collaborative recommendation algorithm using least squares method is specified. As shown, u represents the user, v denotes the product, the user scores the item, but not every user will rate each item. For example, user U6 did not give the product V3 scori
Transformer: is an abstract class containing a feature converter, and the final learning model, the need to implement the Transformer method typically Transformer add several columns to an RDD, eventually converting to another RDD, 1. A feature converter typically processes a dataset, converting one column of data into a new set of data. and add a new data column behind the dataset, resulting in a new dataset output. 2. A
Since Scala is just beginning to learn, or more familiar with Python, it's a good way to document your learning process, mainly from the official help documentation for Spark, which is addressed in the following sections:Http://spark.apache.org/docs/latest/quick-start.htmlThe article mainly translated the contents of the document, but also in the inside to add so
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