May 2018
Beginner to intermediate
384 pages
10h 19m
English
book
Artificial Intelligence for Big Data
by Anand Deshpande, Manish Kumar, Albenzo Coletta, Giancarlo Zaccone
Content preview from Artificial Intelligence for Big Data
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Implementing SVD with Spark ML
It is very easy to implement the SVD algorithm explained earlier with Spark ML. The code for it is given for your reference:
import org.apache.spark.mllib.linalg.Matriximport org.apache.spark.mllib.linalg.Vectorsimport org.apache.spark.mllib.linalg.distributed.RowMatrixval data = Array(Vectors.dense(2.0, 1.0, 75.0, 18.0, 1.0,2),Vectors.dense(0.0, 1.0, 21.0, 28.0, 2.0,4),Vectors.dense(0.0, 1.0, 32.0, 61.0, 5.0,10),Vectors.dense(0.0, 1.0, 56.0, 39.0, 2.0,4),Vectors.dense(1.0, 1.0, 73.0, 81.0, 3.0,6),Vectors.dense(0.0, 1.0, 97.0, 59.0, 7.0,14))>val rows = sc.parallelize(data)val mat: RowMatrix = new RowMatrix(rows)// Principal components are stored in a local dense matrix.val pc: Matrix = mat.computePrincipalComponents(2) ...
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