- Machine Learning with Spark(Second Edition)
- Rajdeep Dua Manpreet Singh Ghotra Nick Pentreath
- 306字
- 2021-07-09 21:07:49
Types of matrices
In Scala, we will use the Breeze library to represent a matrix. A matrix can be represented as a dense or a CSC matrix.
- Dense matrix: A dense matrix is created with a constructor method call. Its elements can be accessed and updated. It is a column major, and can be transposed to convert to row major.
val a = DenseMatrix((1,2),(3,4))
println("a : n" + a)
val m = DenseMatrix.zeros[Int](5,5)
The columns of a matrix can be accessed as Dense Vectors, and
the rows as Dense Matrices.
println( "m.rows :" + m.rows + " m.cols : " + m.cols)
m(::,1)
println("m : n" + m)
- Transposing a matrix: Transposing a matrix means swapping its rows and columns. The transpose of a P × Q matrix, written MT, is a Q × P matrix such that (MT ) j, I = Mi, j for every I ∈ P, j ∈ Q. Vector transpose to create a matrix row.
m(4,::) := DenseVector(5,5,5,5,5).t
println(m)
The output of the preceding program is as follows:
a :
1 2
3 4
Created a 5x5 matrix
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
m.rows :5 m.cols : 5
First Column of m :
DenseVector(0, 0, 0, 0, 0)
Assigned 5,5,5,5,5 to last row of m.
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
5 5 5 5 5
- CSC matrix: The CSC matrix is known as the Compressed Sparse Columns matrix. Each column within the CSC matrix represents a sparse vector. The CSC matrix supports all matrix operations, and is constructed using Builder.
val builder = new CSCMatrix.Builder[Double](rows=10,
cols=10)
builder.add(3,4, 1.0)
// etc.
val myMatrix = builder.result()