mllib

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 ('Local') fromPyspark.mllib.classificationImportLogisticregressionwithlbfgs, Logisticregressi

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.mllib.treeImportGradientboostedtrees, Gradientboostedtreesmodel fromPyspark.mllib.utilImpo

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.mllib.treeImportDecisionTree, Decisiontreemodel fromPyspark.mllib.utilImportmlutils#Load a

Org.apache.spark.ml.feature.VectorIndexer import org.apache.spark.ml.regression. {Gbtregressionmodel, gbtregressor}//Load and parse the data file, converting it to a dataframe. Val data = Spark.read.format ("LIBSVM"). Load ("Data/mllib/sample_libsvm_data.txt")//automatically identify Categorical features, and index them. Set maxcategories so features with > 4 distinct values are treated as continuous. Val featureindexer = new Vectorindexer (). Setinp

Previously, a randomized forest algorithm was applied to Titanic survivors ' predictive data sets. In fact, there are a lot of open source algorithms for us to use. Whether the local machine learning algorithm package Sklearn or distributed Spark Mllib, is a very good choice. Spark is a popular distributed computing solution at the same time, which supports both cluster mode and local stand-alone mode. Because of its development through Scala, native

) }.join ( Predictions) Ratingsandpredictions.first () //RES21: ((int, int), (double, double)) = ((291,800), ( 2.0,2.052364223387371)) Using the Mllib evaluation function, we are going to pass in a (actual,predicted) Rdd. Actual and predicted positions can be exchanged: Import Org.apache.spark.mllib.evaluation.RegressionMetrics val predictedandtrue = ratingsandpredictions.map { Case (user, product), (actual, predicted)) = (actual, predicted)} val reg

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.classificationImportSVMWITHSGD, Svmmodel fromPyspark.mllib.regressionImportLabeledpoint#Load

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.mllib.regressionImportLabeledpoint, LINEARREGRESSIONWITHSGD, Linearregressionmodel#Load and pa

Spark mllib is a library dedicated to processing machine learning tasks in Spark, but in the latest Spark 2.0, most machine learning-related tasks have been transferred to the Spark ML package. The difference is that Mllib is based on RDD source data, and ML is a more abstract concept based on dataframe that can create a range of machine learning tasks, from data cleaning to feature engineering to model tra

. Before training we used two data preprocessing methods to transform the features and added metadata to Dataframe. Scala: Import org.apache.spark.ml.Pipeline Import org.apache.spark.ml.classification. {Gbtclassificationmodel, gbtclassifier} import Org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator Import Org.apache.spark.ml.feature. {indextostring, stringindexer, vectorindexer}//Load and parse the data file, converting it to a dataframe. Val data = Spark.read.format ("LIBSVM").

(Input:rdd[labeledpoint], initialweights:vector): M = {if (Numfeatures Optimizer.optimize in the code above, passed in data and initialized Theta, and optimizer was initialized in LOGISTICREGRESSIONWITHSGD: Class LOGISTICREGRESSIONWITHSGD Private[mllib] (private var stepsize:double, private var numiterations:int, PR ivate var regparam:double, private var minibatchfraction:double) extends Generalizedlinearalgorithm[logisticregressi Onmodel] with S

You are welcome to reprint it. Please indicate the source, huichiro.Summary This article briefly describes the implementation of the linear regression algorithm in Spark mllib, involves the theoretical basis of the linear regression algorithm itself and linear regression parallel processing, and then reads the code implementation part.Linear Regression Model The main purpose of the machine learning algorithm is to find the model that best interprets t

You are welcome to reprint it. Please indicate the source, huichiro.Summary This article will give a brief review of the origins of the quasi-Newton method L-BFGS, and then its implementation in Spark mllib for source code reading.Mathematical Principles of the quasi-Newton Method Code Implementation The regularization method used in the L-BFGS algorithm is squaredl2updater. The breezelbfgs function in the breeze library of the scalanlp member

: 0.9997857142857143 //numtree=25,maxdepth=26, accuracy rate: 0.9998333333333334 //numtree=29,maxdepth=30, accuracy rate: 0.9999523809523809It can be found that the accuracy rate numTree=11,maxDepth=12 starts to converge around to 0.999. This time the accuracy is much higher than the accuracy (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.predic

header removed, only the data department, training data is saved in CSV format:val rawData = sc.textFile("file://path/train-noheader.csv")Since the data is in CSV format, then use "," to convert each row of data to an array:val records = rawData.map(lineline.split(","))These are processed into data types that naive Bayes can accept, LabeledPoint this type receives two parameters, the first parameter is label (tag, here is the specific handwritten number), the second parameter is features (eigen

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

= sparksession. Builder. Appname ("naviebayesdemo"). Master ("local "). Config ("spark. SQL. Warehouse. dir", "C: \ study \ sparktest "). Getorcreate ()// Load the data stored in libsvm format as a dataframe.Val dataset = spark. Read. Format ("libsvm"). Load ("Data/mllib/sample_libsvm_data.txt ")// Split the data into training and Test Sets (30% held out for testing)Val array (tranningdata, testdata) = dataset. randomsplit (Array (0.7, 0.3), seed = 1

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 test Data??? Logger.getRootLogger.setLevel (level. WARN)??? Val Data_path="/user/tmp/deeplearn/train_d.txt"??? Val examples=sc. Textfile

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, Randomforestmodel fromPyspark.mllib.utilImportmlutils#Load and parse the data file into an RDD

-precision. Meaning: Learn a decision tree using the training data scale, range [0,1]. Thresholds: Type: double array type. Meaning: Multiple classifications predict the thresholds to adjust the probability of the predicted results in each category. Example: The following example imports the LIBSVM format data and divides it into training data and test data. Use the first part of the data for training, leaving the data to test. Before training we used two data preprocessing methods to transform