spark machine learning example python

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

[Email protected] ~]$ HDFs dfs-cat People.json{"Name": "Alice", "Pcode": "94304"}{"Name": "Brayden", "age": +, "Pcode": "94304"}{"Name": "Carla", "age": +, "Pcoe": "10036"}{"Name": "Diana", "Age": 46}{"Name": "Etienne", "Pcode": "94104"}[Email protected] ~]$HDFs Dfs-cat Pcodes.json{"Pcode": "10036", "City": "New York", "state": "NY"}{"Pcode:" 87501 "," City ":" Santa Fe "," state ":" NM "}{"Pcode": "94304", "City": "Palo Alto", "state": "CA"}{"Pcode": "94104", "City": "San Francisco", "state": "

Data for mongodb-implementation Repo Interface +mongotemplate+crud operation 00:36:17 min16th Spring data for mongodb-paged query 00:13:32 min17th Section Zookeeper cluster installation 00:13:41 min18th Section Zookeeper Basic introduction -100:22:36 minutes19th Section Zookeeper working principle-election process (Basic Paxos algorithm) -200:24:27 min20th Section Zookeeper working principle-election process (Fast Paxos algorithm) -300:31:16 min21st kafka-Background and architecture introductio

[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