Implement InfRandMatrix

src/infrand.jl

@@ -46,9 +46,9 @@ function InfRandVector(rng=default_rng(), dist=Float64)
     _rng = copy(rng)
     return InfRandVector{T,typeof(dist),typeof(_rng)}(_rng, dist, T[], 0)
 end
-Base.size(::InfRandVector) = (ℵ₀,)
-Base.axes(::InfRandVector) = (1:ℵ₀,)
-Base.length(::InfRandVector) = ℵ₀
+size(::InfRandVector) = (ℵ₀,)
+axes(::InfRandVector) = (1:ℵ₀,)
+length(::InfRandVector) = ℵ₀
 function resizedata!(seq::InfRandVector, inds)
     newlen = maximum(inds)
     curlen = length(seq.data)
@@ -61,4 +61,26 @@ function resizedata!(seq::InfRandVector, inds)
     end
     seq.datasize = newlen
     return seq
-end
+end
+
+"""
+    InfRandMatrix([rng=default_rng()], n; dist=Float64])
+
+Represents a random infinite matrix with `n` rows. The random number generator 
+can be specified by the first argument `rng`, which defaults to `Random.default_rng()`, and the number of 
+rows is given by the `n` argument. The `dist` keyword argument (default `Float64`)
+can be used to specify the distribution to sample from. 
+"""
+struct InfRandMatrix{T, S <: InfRandVector{T}} <: LazyMatrix{T} 
+    seq::S 
+    n::Int
+end 
+InfRandMatrix(n::Int; dist=Float64) = InfRandMatrix(default_rng(), n; dist)
+InfRandMatrix(rng, n::Int; dist=Float64) = InfRandMatrix(InfRandVector(rng, dist), n)
+function Base.getindex(A::InfRandMatrix, i::Int, j::Int)
+    ((i < 1) || (i > A.n) || (j < 0)) && throw(BoundsError(A, (i, j)))
+    lin = (j - 1) * A.n + i 
+    return A.seq[lin]
+end
+size(A::InfRandMatrix) = (A.n, ∞)
+

test/test_infrand.jl

@@ -20,13 +20,47 @@
         seq = InfiniteArrays.InfRandVector(rng, Float16)
         rng2 = MersenneTwister(123)
         @test seq[1:10000] == [rand(rng2, Float16) for _ in 1:10000]
-    end 
+    end
 
-    @testset "Distributions.jl" begin 
+    @testset "Distributions.jl" begin
         dist = Normal(0.3, 1.7) # do Normal{Float32} for example if you want that number type
         rng = Xoshiro(5)
         seq = InfiniteArrays.InfRandVector(rng, dist)
         rng2 = Xoshiro(5)
         @test seq[1:100] == [0.3 + 1.7randn(rng2) for _ in 1:100]
     end
+end
+
+@testset "InfRandMatrix" begin
+    @testset "Default constructor" begin
+        Random.seed!(123)
+        A = InfiniteArrays.InfRandMatrix(5)
+        @test size(A) == (5, ∞)
+        @test axes(A) == (1:5, 1:∞)
+        val = A[1, 1]
+        @inferred A[5, 5]
+        @test A[1:3, 1:100] == A[1:3, 1:100]
+        @test_throws BoundsError A[0, 1]
+        Random.seed!(123)
+        _A = [rand() for _ in 1:5, _ in 1:1000]
+        @test _A == A[1:5, 1:1000]
+        @test (A+A)[1:5, 1:1000] ≈ 2_A
+        @test (A'+A')[1:1000, 1:5] ≈ 2_A'
+        @test A[11] ≈ A.seq[11] ≈ A[1, 3]
+    end
+
+    @testset "Providing an RNG and a distribution" begin
+        rng = MersenneTwister(123)
+        seq = InfiniteArrays.InfRandMatrix(rng, 10; dist=Float16)
+        rng2 = MersenneTwister(123)
+        @test seq[1:10000] == [rand(rng2, Float16) for _ in 1:10000]
+    end
+
+    @testset "Distributions.jl" begin
+        dist = Normal(0.3, 1.7)
+        rng = Xoshiro(5)
+        seq = InfiniteArrays.InfRandMatrix(rng, 2; dist)
+        rng2 = Xoshiro(5)
+        @test seq[1:1000] == [0.3 + 1.7randn(rng2) for _ in 1:1000]
+    end
 end