Book Image

Learning Apache Mahout

Book Image

Learning Apache Mahout

Overview of this book

Table of Contents (17 chapters)
Learning Apache Mahout
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
1
Introduction to Mahout
9
Case Study – Churn Analytics and Customer Segmentation
Index

In-core types


Vector and Matrices are of type in-core or in-memory. We will try out some basic commands to get a feel of the linear algebra operations possible.

Vector

We will first discuss vectors and then cover matrices. We will see some examples of operations that can be performed on vectors.

Initializing a vector inline

Dense vector: The dense vector is a vector with relatively fewer zero elements. On the Mahout command line, please type the following command to initialize a dense vector:

mahout>val denseVec1: Vector = (1.0, 1.1, 1.2)

Each element is prefixed by its index, which starts with 0. The output of the command executed is given as follows:

denseVec1: org.apache.mahout.math.Vector = {0:1.0,1:1.1,2:1.2}

Sparse vector: Sparse vector is a vector with a relatively large number of zero elements. On the Mahout command line, please type the following command to initialize a sparse vector:

mahout>val sparseVec = svec((5 -> 1) :: (10 -> 2.0) :: Nil)

The output of the command...