spark mllib example

library jblas Because spark MLlib uses the linear algebra library of jlbas, it is helpful for analyzing and learning many MLlib algorithms in spark to learn basic operations in the jlbas library; the following describes basic operations in jlbas using the DoubleMatrix matrix in jlbas: Val matrix1 = DoubleMatrix. ones

3. Spark MLlib Deep Learning convolution neural network (depth learning-convolutional neural network) 3.3Http://blog.csdn.net/sunbow0Chapter III Convolution neural Network (convolutional neural Networks)3 Example3.1 test DataFollow the above example data, or create a new image recognition data.3.2 CNN Example??? //2 te

filtering algorithm in Mllib, please look first:Spark (11) –mllib API Programming Linear regression, Kmeans, collaborative filtering demoNonsense not to say, on the code:To facilitate understanding of the format and meaning of the data, it is specified that the variable/constant name is named as follows:Data name _ Data typeObject Moviesrecommond {def main (args:array[string]) {if(Args.length 2) {System.er

, completed by the program.3, Example val conf = new sparkconf (). Setappname ("Simple Application"). Setmaster ("local") Val sc = new Sparkcontext (conf) val data = Sc.textfile ("Data/mllib/sample_naive_bayes_data.txt") Val parseddata = Data.map {line = val parts = line.split (', ') labeledpoint (parts (0). ToDouble, Vectors.dense (Parts (1). spli T ("). Map (_.todouble))}//Split data into training (60

See The programmer's self-accomplishment –selfup.cn there are k-means clustering algorithms for Spark mllib.But it was the Java language, so I wrote one in Scala as usual and shared it here.As a result of learning spark mllib But such detailed information is really difficult to find here to share.Test data 0.0 0.0 0.0 0.1 0.1 0.10.2 0.2 0.2 9.0 9.0 9.0 9.1 9.1 9.

Catalog Logistic regression principle Logistic regression code (Spark Python) Logistic regression principle See blog: http://www.cnblogs.com/itmorn/p/7890468.htmlBack to Catalog Logistic regression code (Spark Python) code data:https://pan.baidu.com/s/1jHWKG4I Password: acq1#-*-coding=utf-8-*- fromPysparkImportsparkconf, SPARKCONTEXTSC= Sparkcontext ('L

Catalog Gradient Lifting Tree principle gradient lifting Tree code (Spark Python) The principle of gradient lifting tree to be continued ...Back to Catalog Gradient Boost Tree code (Spark Python) 　　Code data: Https://pan.baidu.com/s/1jHWKG4I Password: acq1#-*-coding=utf-8-*- fromPysparkImportsparkconf, SPARKCONTEXTSC= Sparkcontext ('Local') fromPyspark.

Directory Decision tree Principle decision tree Code (Spark Python) Decision Tree Principle See blog: http://www.cnblogs.com/itmorn/p/7918797.htmlBack to Catalog decision Tree Code (Spark Python) 　　Code data: Https://pan.baidu.com/s/1jHWKG4I Password: acq1#-*-coding=utf-8-*- fromPysparkImportsparkconf, SPARKCONTEXTSC= Sparkcontext ('Local') fromPyspark.

Directory support vector machine principle support vector machine code (Spark Python) Principle of support vector machine Cond...Back to Catalog Support Vector Machine code (Spark Python) 　　Code data: Https://pan.baidu.com/s/1jHWKG4I Password: acq1#-*-coding=utf-8-*- fromPysparkImportsparkconf, SPARKCONTEXTSC= Sparkcontext ('Local') fromPyspark.mllib.cla

assess the accuracy of the model, which should be considered a cross-validation: data.map { point => if (nbModel.predict(point.features) == point.label) 1 else 0 }.sum data.count() val nbAccuracy = nbTotalCorrect / numDataAfter running this code, I get the exact rate 0.8261190476190476 .The test data is now identified, and the test data is read first:val unlabeledData = sc.textFile("file://path/test-noheader.csv")It is then preprocessed in the same way as before:val unlabeledRe

Spark ML Model pipelines on distributed Deep neural Nets This notebook describes how to build machine learning pipelines with Spark ML for distributed versions of Keras deep ING models. As data set we use the Otto Product Classification challenge from Kaggle. The reason we chose this data are that it is small and very structured. This is way, we can focus the more on technical components rather than prepcr

Spark ML Model pipelines on distributed deep neural Nets This notebook describes what to build machine learning pipelines with Spark ML for distributed versions of Keras deep learn ING models. As data set we use the Otto Product Classification challenge from Kaggle. The reason we chose this data is, it is small and very structured. This is, we can focus on the technical components rather than prepcrocessin

Directory random forest Principle random Forest code (Spark Python) Random Forest Principles to be continued ...Back to Catalog Random Forest Code (Spark Python) 　　Code data: Https://pan.baidu.com/s/1jHWKG4I Password: acq1#-*-coding=utf-8-*- fromPysparkImportsparkconf, SPARKCONTEXTSC= Sparkcontext ('Local') fromPyspark.mllib.treeImportRandomforest, Rand

users and the number of movies and the number of users who rated the film valnumratings=ratings.count () valnumusers=ratings.map (_._2.user). Distinct (). Count () valnummovies=ratings.map (_._2.product). Distinct (). Count () println ("got" +numRatings+ "ratingsfrom" + numusers+ "users" +numMovies+ "movies") //the sample scoring table with a key value divided into 3 parts, respectively, for training (60%, and adding user ratings), check (20%),and test (20%) //This data is applied multip

Recommendation Model Evaluation In this article, we evaluate the performance of the Spark Machine Learning 1.0: Recommendation engine-Movie recommendation model. Mse/rmse Mean Variance (MSE) is the sum of the values of the POW (forecast score-actual score, 2), divided by the number of items, for each actual existing rating. and the RMS Difference (RMSE) is the MSE open radical. We first use ratings to generate the (user,product) Rdd as a parameter to

Catalog Naive Bayes principle naive Bayesian code (Spark Python) Naive Bayes principle See blog: http://www.cnblogs.com/itmorn/p/7905975.htmlBack to Catalog naive Bayesian code (Spark Python) 　　Code data: Https://pan.baidu.com/s/1jHWKG4I Password: acq1#-*-coding=utf-8-*- fromPysparkImportsparkconf, SPARKCONTEXTSC= Sparkcontext ('Local') fromPyspark.mlli

Words don't say much. Directly on the code slightly. Welcome to the Exchange./*** Created by Whuscalaman on 1/7/16.*/Import Org.apache.spark. {sparkconf, Sparkcontext}Import Org.apache.spark.mllib.classification.SVMWithSGDImport Org.apache.spark.mllib.linalg.VectorsImport Org.apache.spark.mllib.regression.LabeledPointObject Svmpredict {def main (args:array[string]) {Val conf = new sparkconf (). Setmaster ("local[1]"). Setappname ("Svmpredict")Val sc = new Sparkcontext (conf)Val data = Sc.textfil

(0.826) of the last use of naive Bayesian training. Now we start to make predictions for the test data, using the numTree=29,maxDepth=30 following parameters:val predictions = randomForestModel.predict(features).map { p => p.toInt }The results of the training to upload to the kaggle, the accuracy rate is 0.95929 , after my four parameter adjustment, the highest accuracy rate is 0.96586 , set the parameters are: numTree=55,maxDepth=30 , when I change the parameters numTree=70,maxDepth=30 , the a

;=0). Mininfogain: Type: double-precision. Meaning: The minimum information gain required to split a node. Mininstancespernode: Type: integer type. Meaning: The minimum number of instances that are included in a node since splitting. Predictioncol: Type: String type. Meaning: The forecast result column name. Seed Type: Long integral type. Meaning: Random seeds. Subsamplingrate: Type: double-precision. Meaning: Learn a decision tree using the training data scale, range [0,1]. Stepsize: Type: doub

and Parse The data file, converting it to a dataframe. data = Spark.read.format ("LIBSVM"). Load ("Data/mllib/sample_libsvm_data.txt") # Index labels, adding metadata to the Labe L column. # Fit on whole dataset to include all labels in index. Labelindexer = Stringindexer (inputcol= "label", outputcol= "Indexedlabel"). Fit (data) # automatically identify Categorical features, and index them. # Set Maxcategories so features with > 4 distinct values ar