Matrix Examples

import vecxt.all.*
import vecxt.BoundsCheck.DoBoundsCheck.yes
import narr.*

val nestedArr = NArray(
  NArray[Double](1.0, 2.0, 3.5),
  NArray[Double](3.0, 4.0, 5.0),
  NArray[Double](6.0, 7.0, 8.0)
)
// nestedArr: Array[Array[Double]] = [[D@6422d5df

val matrix = Matrix.fromRows(nestedArr)
// matrix: Matrix[Double] = ([D@51002361,(3,3))
val matrix2 = Matrix.fromColumns(nestedArr)
// matrix2: Matrix[Double] = ([D@61ef29a,(3,3))

matrix.shape
// res0: Tuple2[Row, Col] = (3,3)

matrix.printMat
// res1: String = 1.0 2.0 3.5
// 3.0 4.0 5.0
// 6.0 7.0 8.0

matrix2.printMat
// res2: String = 1.0 3.0 6.0
// 2.0 4.0 7.0
// 3.5 5.0 8.0

matrix.col(1).printArr
// res3: String = [2.0,4.0,7.0]

matrix.row(2).printArr
// res4: String = [6.0,7.0,8.0]

// Note that indexing is done via a tuple.
matrix((1, 2))
// res5: Double = 5.0

import vecxt.all.*
import vecxt.BoundsCheck.DoBoundsCheck.yes
import narr.*

val mat1 = Matrix(NArray(1.0, 4.0, 2.0, 5.0, 3.0, 6.0), (2, 3))
// mat1: Matrix[Double] = ([D@412add4b,(2,3))
val mat2 = Matrix(NArray(7.0, 9.0, 11.0, 8.0, 10, 12.0), (3, 2))
// mat2: Matrix[Double] = ([D@3d88c9d2,(3,2))
val result = mat1.matmul(mat2)
// result: Matrix[Double] = ([D@68f3287c,(2,2))

result.printMat
// res6: String = 58.0 64.0
// 139.0 154.0

// @ is a reserved character, so we can't just copy numpy syntax... experimental
val result2 = mat1 @@ mat2
// result2: Matrix[Double] = ([D@765ef6e2,(2,2))

result2.printMat
// res7: String = 58.0 64.0
// 139.0 154.0

// opperator precedence...
val result3 = Matrix.eye[Double](2) + mat1 @@ mat2
// result3: Matrix[Double] = ([D@3aebeafd,(2,2))

result3.printMat
// res8: String = 59.0 64.0
// 139.0 155.0

val mat3 = mat2.transpose + mat1
// mat3: Matrix[Double] = ([D@69988a20,(2,3))
mat3.printMat
// res9: String = 8.0 11.0 14.0
// 12.0 15.0 18.0

(mat2.transpose - mat1).printMat
// res10: String = 6.0 7.0 8.0
// 4.0 5.0 6.0


// TODO: Check performance of vectorised version on JVM
mat1.exp.printMat
// res11: String = 2.718281828459045 7.38905609893065 20.085536923187668
// 54.598150033144236 148.4131591025766 403.4287934927351

// TODO: Check performance of vectorised version on JVM
mat1.log.printMat
// res12: String = 0.0 0.6931471805599453 1.0986122886681098
// 1.3862943611198906 1.6094379124341003 1.791759469228055

import vecxt.all.*
import vecxt.BoundsCheck.DoBoundsCheck.yes
import narr.*

val mat = Matrix.fromRows(
  NArray(
    NArray[Double](1.0, 2.0, 3.0),
    NArray[Double](4.0, 5.0, 6.0),
    NArray[Double](7.0, 8.0, 9.0)
  )
)
// mat: Matrix[Double] = ([D@55e68eb0,(3,3))
mat(::, ::).printMat
// res13: String = 1.0 2.0 3.0
// 4.0 5.0 6.0
// 7.0 8.0 9.0
mat(1, ::).printMat
// res14: String = 4.0 5.0 6.0
mat(::, 1).printMat
// res15: String = 2.0
// 5.0
// 8.0
mat(1, 1).printMat
// res16: String = 5.0
mat(0 to 1, 0 to 1).printMat
// res17: String = 1.0 2.0
// 4.0 5.0
mat(NArray.from[Int](Array(0, 2)), 0 to 1).printMat
// res18: String = 1.0 2.0
// 7.0 8.0