sbplib_julia: test/LazyTensors/lazy_tensor_operations

comparison test/LazyTensors/lazy_tensor_operations_test.jl @ 1023:52f07c77299d refactor/sbpoperators/inflation

Merge refactor/lazy_tensors

author	Jonatan Werpers <jonatan@werpers.com>
date	Mon, 21 Mar 2022 09:51:07 +0100
parents	bbbc31953367 56fe037641ef
children	5be17f647018

comparison

equal deleted inserted replaced

-:bbbc31953367
+:52f07c77299d
 using Sbplib.LazyTensors
 using Sbplib.RegionIndices
 using Tullio
+struct DummyMapping{T,R,D} <: LazyTensor{T,R,D} end
+LazyTensors.apply(m::DummyMapping{T,R}, v, I::Vararg{Any,R}) where {T,R} = :apply
+LazyTensors.apply_transpose(m::DummyMapping{T,R,D}, v, I::Vararg{Any,D}) where {T,R,D} = :apply_transpose
+LazyTensors.range_size(m::DummyMapping) = :range_size
+LazyTensors.domain_size(m::DummyMapping) = :domain_size
+struct SizeDoublingMapping{T,R,D} <: LazyTensor{T,R,D}
+domain_size::NTuple{D,Int}
+end
+LazyTensors.apply(m::SizeDoublingMapping{T,R}, v, i::Vararg{Any,R}) where {T,R} = (:apply,v,i)
+LazyTensors.range_size(m::SizeDoublingMapping) = 2 .* m.domain_size
+LazyTensors.domain_size(m::SizeDoublingMapping) = m.domain_size
 @testset "Mapping transpose" begin
-struct DummyMapping{T,R,D} <: TensorMapping{T,R,D} end
-LazyTensors.apply(m::DummyMapping{T,R}, v, I::Vararg{Any,R}) where {T,R} = :apply
-LazyTensors.apply_transpose(m::DummyMapping{T,R,D}, v, I::Vararg{Any,D}) where {T,R,D} = :apply_transpose
-LazyTensors.range_size(m::DummyMapping) = :range_size
-LazyTensors.domain_size(m::DummyMapping) = :domain_size
 m = DummyMapping{Float64,2,3}()
-@test m' isa TensorMapping{Float64, 3,2}
+@test m' isa LazyTensor{Float64, 3,2}
 @test m'' == m
 @test apply(m',zeros(Float64,(0,0)), 0, 0, 0) == :apply_transpose
 @test apply(m'',zeros(Float64,(0,0,0)), 0, 0) == :apply
 @test apply_transpose(m', zeros(Float64,(0,0,0)), 0, 0) == :apply
 @test range_size(m') == :domain_size
 @test domain_size(m') == :range_size
 end
-@testset "TensorApplication" begin
-struct SizeDoublingMapping{T,R,D} <: TensorMapping{T,R,D}
+@testset "LazyTensorApplication" begin
-domain_size::NTuple{D,Int}
-end
-LazyTensors.apply(m::SizeDoublingMapping{T,R}, v, i::Vararg{Any,R}) where {T,R} = (:apply,v,i)
-LazyTensors.range_size(m::SizeDoublingMapping) = 2 .* m.domain_size
-LazyTensors.domain_size(m::SizeDoublingMapping) = m.domain_size
 m = SizeDoublingMapping{Int, 1, 1}((3,))
+mm = SizeDoublingMapping{Int, 1, 1}((6,))
 v = [0,1,2]
 @test size(m*v) == 2 .*size(v)
-@test (m*v)[0] == (:apply,v,(0,))
+@test (m*v)[1] == (:apply,v,(1,))
-@test (m*m*v)[1] == (:apply,m*v,(1,))
+@test (mm*m*v)[1] == (:apply,m*v,(1,))
-@test (m*m*v)[3] == (:apply,m*v,(3,))
+@test (mm*m*v)[3] == (:apply,m*v,(3,))
-@test (m*m*v)[6] == (:apply,m*v,(6,))
+@test (mm*m*v)[6] == (:apply,m*v,(6,))
-@test_broken BoundsError == (m*m*v)[0]
-@test_broken BoundsError == (m*m*v)[7]
 @test_throws MethodError m*m
 @test (m*v)[CartesianIndex(2)] == (:apply,v,(2,))
-@test (m*m*v)[CartesianIndex(2)] == (:apply,m*v,(2,))
+@test (mm*m*v)[CartesianIndex(2)] == (:apply,m*v,(2,))
-m = SizeDoublingMapping{Int, 2, 1}((3,))
-@test_throws MethodError m*ones(Int,2,2)
-@test_throws MethodError m*m*v
 m = SizeDoublingMapping{Float64, 2, 2}((3,3))
+mm = SizeDoublingMapping{Float64, 2, 2}((6,6))
 v = ones(3,3)
 @test size(m*v) == 2 .*size(v)
 @test (m*v)[1,2] == (:apply,v,(1,2))
 @test (m*v)[CartesianIndex(2,3)] == (:apply,v,(2,3))
-@test (m*m*v)[CartesianIndex(4,3)] == (:apply,m*v,(4,3))
+@test (mm*m*v)[CartesianIndex(4,3)] == (:apply,m*v,(4,3))
-struct ScalingOperator{T,D} <: TensorMapping{T,D,D}
+m = ScalingTensor(2,(3,))
-λ::T
-size::NTuple{D,Int}
-end
-LazyTensors.apply(m::ScalingOperator{T,D}, v, I::Vararg{Any,D}) where {T,D} = m.λ*v[I...]
-LazyTensors.range_size(m::ScalingOperator) = m.size
-LazyTensors.domain_size(m::ScalingOperator) = m.size
-m = ScalingOperator{Int,1}(2,(3,))
 v = [1,2,3]
 @test m*v isa AbstractVector
 @test m*v == [2,4,6]
-m = ScalingOperator{Int,2}(2,(2,2))
+m = ScalingTensor(2,(2,2))
 v = [[1 2];[3 4]]
 @test m*v == [[2 4];[6 8]]
 @test (m*v)[2,1] == 6
+@testset "Error on index out of bounds" begin
+m = SizeDoublingMapping{Int, 1, 1}((3,))
+v = [0,1,2]
+@test_throws BoundsError (m*v)[0]
+@test_throws BoundsError (m*v)[7]
+end
+@testset "Error on unmatched dimensions" begin
+v = [0,1,2]
+m = SizeDoublingMapping{Int, 2, 1}((3,))
+@test_throws MethodError m*ones(Int,2,2)
+@test_throws MethodError m*m*v
+end
+@testset "Error on unmatched sizes" begin
+@test_throws DomainSizeMismatch ScalingTensor(2,(2,))*ones(3)
+@test_throws DomainSizeMismatch ScalingTensor(2,(2,))*ScalingTensor(2,(3,))*ones(3)
+end
 @testset "Type calculation" begin
-m = ScalingOperator{Int,1}(2,(3,))
+m = ScalingTensor(2,(3,))
 v = [1.,2.,3.]
 @test m*v isa AbstractVector{Float64}
 @test m*v == [2.,4.,6.]
 @inferred m*v
 @inferred (m*v)[1]
-m = ScalingOperator{Int,2}(2,(2,2))
+m = ScalingTensor(2,(2,2))
 v = [[1. 2.];[3. 4.]]
 @test m*v == [[2. 4.];[6. 8.]]
 @test (m*v)[2,1] == 6.
 @inferred m*v
 @inferred (m*v)[1]
-m = ScalingOperator{ComplexF64,1}(2. +2. *im,(3,))
+m = ScalingTensor(2. +2. *im,(3,))
 v = [1.,2.,3.]
 @test m*v isa AbstractVector{ComplexF64}
 @test m*v == [2. + 2. *im, 4. + 4. *im, 6. + 6. *im]
 @inferred m*v
 @inferred (m*v)[1]
-m = ScalingOperator{ComplexF64,1}(1,(3,))
+m = ScalingTensor(1,(3,))
 v = [2. + 2. *im, 4. + 4. *im, 6. + 6. *im]
 @test m*v isa AbstractVector{ComplexF64}
 @test m*v == [2. + 2. *im, 4. + 4. *im, 6. + 6. *im]
 @inferred m*v
 @inferred (m*v)[1]
-m = ScalingOperator{Float64,1}(2., (3,))
+m = ScalingTensor(2., (3,))
 v = [[1,2,3], [3,2,1],[1,3,1]]
 @test m*v isa AbstractVector{Vector{Float64}}
 @test m*v == [[2.,4.,6.], [6.,4.,2.],[2.,6.,2.]]
 @inferred m*v
 @inferred (m*v)[1]
 end
 end
-@testset "TensorMapping binary operations" begin
-struct ScalarMapping{T,R,D} <: TensorMapping{T,R,D}
+@testset "LazyTensor binary operations" begin
-λ::T
+A = ScalingTensor(2.0, (3,))
-range_size::NTuple{R,Int}
+B = ScalingTensor(3.0, (3,))
-domain_size::NTuple{D,Int}
-end
-LazyTensors.apply(m::ScalarMapping{T,R}, v, I::Vararg{Any,R}) where {T,R} = m.λ*v[I...]
-LazyTensors.range_size(m::ScalarMapping) = m.domain_size
-LazyTensors.domain_size(m::ScalarMapping) = m.range_size
-A = ScalarMapping{Float64,1,1}(2.0, (3,), (3,))
-B = ScalarMapping{Float64,1,1}(3.0, (3,), (3,))
 v = [1.1,1.2,1.3]
 for i ∈ eachindex(v)
 @test ((A+B)*v)[i] == 2*v[i] + 3*v[i]
 end
 for i ∈ eachindex(v)
 @test ((A-B)*v)[i] == 2*v[i] - 3*v[i]
 end
 @test range_size(A+B) == range_size(A) == range_size(B)
 @test domain_size(A+B) == domain_size(A) == domain_size(B)
 @test ((A+B)*ComplexF64[1.1,1.2,1.3])[3] isa ComplexF64
-end
+@testset "Error on unmatched sizes" begin
+@test_throws Union{DomainSizeMismatch, RangeSizeMismatch} ScalingTensor(2.0, (3,)) + ScalingTensor(2.0, (4,))
-@testset "TensorMappingComposition" begin
+@test_throws DomainSizeMismatch ScalingTensor(2.0, (4,)) + SizeDoublingMapping{Float64,1,1}((2,))
+@test_throws DomainSizeMismatch SizeDoublingMapping{Float64,1,1}((2,)) + ScalingTensor(2.0, (4,))
+@test_throws RangeSizeMismatch ScalingTensor(2.0, (2,)) + SizeDoublingMapping{Float64,1,1}((2,))
+@test_throws RangeSizeMismatch SizeDoublingMapping{Float64,1,1}((2,)) + ScalingTensor(2.0, (2,))
+end
+end
+@testset "LazyTensorComposition" begin
 A = rand(2,3)
 B = rand(3,4)
 Ã = LazyLinearMap(A, (1,), (2,))
 B̃ = LazyLinearMap(B, (1,), (2,))
-@test Ã∘B̃ isa TensorMappingComposition
+@test Ã∘B̃ isa LazyTensorComposition
 @test range_size(Ã∘B̃) == (2,)
 @test domain_size(Ã∘B̃) == (4,)
-@test_throws SizeMismatch B̃∘Ã
+@test_throws DomainSizeMismatch B̃∘Ã
 # @test @inbounds B̃∘Ã # Should not error even though dimensions don't match. (Since ]test runs with forced boundschecking this is currently not testable 2020-10-16)
 v = rand(4)
 @test Ã∘B̃*v ≈ A*B*v rtol=1e-14
 @test (Ã∘B̃*ComplexF64[1.,2.,3.,4.])[1] isa ComplexF64
 @test ((Ã∘B̃)'*ComplexF64[1.,2.])[1] isa ComplexF64
 end
-@testset "LazyLinearMap" begin
-# Test a standard matrix-vector product
+@testset "InflatedLazyTensor" begin
-# mapping vectors of size 4 to vectors of size 3.
+I(sz...) = IdentityTensor(sz...)
-A = rand(3,4)
-Ã = LazyLinearMap(A, (1,), (2,))
-v = rand(4)
-w = rand(3)
-@test Ã isa LazyLinearMap{T,1,1} where T
-@test Ã isa TensorMapping{T,1,1} where T
-@test range_size(Ã) == (3,)
-@test domain_size(Ã) == (4,)
-@test Ã*ones(4) ≈ A*ones(4) atol=5e-13
-@test Ã*v ≈ A*v atol=5e-13
-@test Ã'*w ≈ A'*w
-A = rand(2,3,4)
-@test_throws DomainError LazyLinearMap(A, (3,1), (2,))
-# Test more exotic mappings
-B = rand(3,4,2)
-# Map vectors of size 2 to matrices of size (3,4)
-B̃ = LazyLinearMap(B, (1,2), (3,))
-v = rand(2)
-@test range_size(B̃) == (3,4)
-@test domain_size(B̃) == (2,)
-@test B̃ isa TensorMapping{T,2,1} where T
-@test B̃*ones(2) ≈ B[:,:,1] + B[:,:,2] atol=5e-13
-@test B̃*v ≈ B[:,:,1]*v[1] + B[:,:,2]*v[2] atol=5e-13
-# Map matrices of size (3,2) to vectors of size 4
-B̃ = LazyLinearMap(B, (2,), (1,3))
-v = rand(3,2)
-@test range_size(B̃) == (4,)
-@test domain_size(B̃) == (3,2)
-@test B̃ isa TensorMapping{T,1,2} where T
-@test B̃*ones(3,2) ≈ B[1,:,1] + B[2,:,1] + B[3,:,1] +
-B[1,:,2] + B[2,:,2] + B[3,:,2] atol=5e-13
-@test B̃*v ≈ B[1,:,1]*v[1,1] + B[2,:,1]*v[2,1] + B[3,:,1]*v[3,1] +
-B[1,:,2]v[1,2] + B[2,:,2]*v[2,2] + B[3,:,2]*v[3,2] atol=5e-13
-# TODO:
-# @inferred (B̃*v)[2]
-end
-@testset "IdentityMapping" begin
-@test IdentityMapping{Float64}((4,5)) isa IdentityMapping{T,2} where T
-@test IdentityMapping{Float64}((4,5)) isa TensorMapping{T,2,2} where T
-@test IdentityMapping{Float64}((4,5)) == IdentityMapping{Float64}(4,5)
-@test IdentityMapping(3,2) isa IdentityMapping{Float64,2}
-for sz ∈ [(4,5),(3,),(5,6,4)]
-I = IdentityMapping{Float64}(sz)
-v = rand(sz...)
-@test I*v == v
-@test I'*v == v
-v = rand(ComplexF64,sz...)
-@test I*v == v
-@test I'*v == v
-@test range_size(I) == sz
-@test domain_size(I) == sz
-end
-I = IdentityMapping{Float64}((4,5))
-v = rand(4,5)
-@inferred (I*v)[3,2]
-@inferred (I'*v)[3,2]
-@inferred range_size(I)
-@inferred range_dim(I)
-@inferred domain_dim(I)
-Ã = rand(4,2)
-A = LazyLinearMap(Ã,(1,),(2,))
-I1 = IdentityMapping{Float64}(2)
-I2 = IdentityMapping{Float64}(4)
-@test A∘I1 == A
-@test I2∘A == A
-@test I1∘I1 == I1
-@test_throws SizeMismatch I1∘A
-@test_throws SizeMismatch A∘I2
-@test_throws SizeMismatch I1∘I2
-end
-@testset "InflatedTensorMapping" begin
-I(sz...) = IdentityMapping(sz...)
 Ã = rand(4,2)
 B̃ = rand(4,2,3)
 C̃ = rand(4,2,3)
 A = LazyLinearMap(Ã,(1,),(2,))
 B = LazyLinearMap(B̃,(1,2),(3,))
 C = LazyLinearMap(C̃,(1,),(2,3))
 @testset "Constructors" begin
-@test InflatedTensorMapping(I(3,2), A, I(4)) isa TensorMapping{Float64, 4, 4}
+@test InflatedLazyTensor(I(3,2), A, I(4)) isa LazyTensor{Float64, 4, 4}
-@test InflatedTensorMapping(I(3,2), B, I(4)) isa TensorMapping{Float64, 5, 4}
+@test InflatedLazyTensor(I(3,2), B, I(4)) isa LazyTensor{Float64, 5, 4}
-@test InflatedTensorMapping(I(3), C, I(2,3)) isa TensorMapping{Float64, 4, 5}
+@test InflatedLazyTensor(I(3), C, I(2,3)) isa LazyTensor{Float64, 4, 5}
-@test InflatedTensorMapping(C, I(2,3)) isa TensorMapping{Float64, 3, 4}
+@test InflatedLazyTensor(C, I(2,3)) isa LazyTensor{Float64, 3, 4}
-@test InflatedTensorMapping(I(3), C) isa TensorMapping{Float64, 2, 3}
+@test InflatedLazyTensor(I(3), C) isa LazyTensor{Float64, 2, 3}
-@test InflatedTensorMapping(I(3), I(2,3)) isa TensorMapping{Float64, 3, 3}
+@test InflatedLazyTensor(I(3), I(2,3)) isa LazyTensor{Float64, 3, 3}
 end
 @testset "Range and domain size" begin
-@test range_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,4,4)
+@test range_size(InflatedLazyTensor(I(3,2), A, I(4))) == (3,2,4,4)
-@test domain_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,2,4)
+@test domain_size(InflatedLazyTensor(I(3,2), A, I(4))) == (3,2,2,4)
-@test range_size(InflatedTensorMapping(I(3,2), B, I(4))) == (3,2,4,2,4)
+@test range_size(InflatedLazyTensor(I(3,2), B, I(4))) == (3,2,4,2,4)
-@test domain_size(InflatedTensorMapping(I(3,2), B, I(4))) == (3,2,3,4)
+@test domain_size(InflatedLazyTensor(I(3,2), B, I(4))) == (3,2,3,4)
-@test range_size(InflatedTensorMapping(I(3), C, I(2,3))) == (3,4,2,3)
+@test range_size(InflatedLazyTensor(I(3), C, I(2,3))) == (3,4,2,3)
-@test domain_size(InflatedTensorMapping(I(3), C, I(2,3))) == (3,2,3,2,3)
+@test domain_size(InflatedLazyTensor(I(3), C, I(2,3))) == (3,2,3,2,3)
-@inferred range_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,4,4)
+@inferred range_size(InflatedLazyTensor(I(3,2), A, I(4))) == (3,2,4,4)
-@inferred domain_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,2,4)
+@inferred domain_size(InflatedLazyTensor(I(3,2), A, I(4))) == (3,2,2,4)
 end
 @testset "Application" begin
 # Testing regular application and transposed application with inflation "before", "after" and "before and after".
 # The inflated tensor mappings are chosen to preserve, reduce and increase the dimension of the result compared to the input.
-tests = [
+cases = [
 (
-InflatedTensorMapping(I(3,2), A, I(4)),
+InflatedLazyTensor(I(3,2), A, I(4)),
 (v-> @tullio res[a,b,c,d] := Ã[c,i]*v[a,b,i,d]), # Expected result of apply
 (v-> @tullio res[a,b,c,d] := Ã[i,c]*v[a,b,i,d]), # Expected result of apply_transpose
 ),
 (
-InflatedTensorMapping(I(3,2), B, I(4)),
+InflatedLazyTensor(I(3,2), B, I(4)),
 (v-> @tullio res[a,b,c,d,e] := B̃[c,d,i]*v[a,b,i,e]),
 (v-> @tullio res[a,b,c,d] := B̃[i,j,c]*v[a,b,i,j,d]),
 ),
 (
-InflatedTensorMapping(I(3,2), C, I(4)),
+InflatedLazyTensor(I(3,2), C, I(4)),
 (v-> @tullio res[a,b,c,d] := C̃[c,i,j]*v[a,b,i,j,d]),
 (v-> @tullio res[a,b,c,d,e] := C̃[i,c,d]*v[a,b,i,e]),
 ),
 (
-InflatedTensorMapping(I(3,2), A),
+InflatedLazyTensor(I(3,2), A),
 (v-> @tullio res[a,b,c] := Ã[c,i]*v[a,b,i]),
 (v-> @tullio res[a,b,c] := Ã[i,c]*v[a,b,i]),
 ),
 (
-InflatedTensorMapping(I(3,2), B),
+InflatedLazyTensor(I(3,2), B),
 (v-> @tullio res[a,b,c,d] := B̃[c,d,i]*v[a,b,i]),
 (v-> @tullio res[a,b,c] := B̃[i,j,c]*v[a,b,i,j]),
 ),
 (
-InflatedTensorMapping(I(3,2), C),
+InflatedLazyTensor(I(3,2), C),
 (v-> @tullio res[a,b,c] := C̃[c,i,j]*v[a,b,i,j]),
 (v-> @tullio res[a,b,c,d] := C̃[i,c,d]*v[a,b,i]),
 ),
 (
-InflatedTensorMapping(A,I(4)),
+InflatedLazyTensor(A,I(4)),
 (v-> @tullio res[a,b] := Ã[a,i]*v[i,b]),
 (v-> @tullio res[a,b] := Ã[i,a]*v[i,b]),
 ),
 (
-InflatedTensorMapping(B,I(4)),
+InflatedLazyTensor(B,I(4)),
 (v-> @tullio res[a,b,c] := B̃[a,b,i]*v[i,c]),
 (v-> @tullio res[a,b] := B̃[i,j,a]*v[i,j,b]),
 ),
 (
-InflatedTensorMapping(C,I(4)),
+InflatedLazyTensor(C,I(4)),
 (v-> @tullio res[a,b] := C̃[a,i,j]*v[i,j,b]),
 (v-> @tullio res[a,b,c] := C̃[i,a,b]*v[i,c]),
 ),
 ]
-@testset "apply" begin
+@testset "$tm" for (tm, true_apply, true_apply_transpose) ∈ cases
-for i ∈ 1:length(tests)
+v = rand(domain_size(tm)...)
-tm = tests[i][1]
+@test tm*v ≈ true_apply(v) rtol=1e-14
-v = rand(domain_size(tm)...)
-true_value = tests[i][2](v)
+v = rand(range_size(tm)...)
-@test tm*v ≈ true_value rtol=1e-14
+@test tm'*v ≈ true_apply_transpose(v) rtol=1e-14
-end
 end
-@testset "apply_transpose" begin
-for i ∈ 1:length(tests)
-tm = tests[i][1]
-v = rand(range_size(tm)...)
-true_value = tests[i][3](v)
-@test tm'*v ≈ true_value rtol=1e-14
-end
-end
 @testset "application to other type" begin
-tm = InflatedTensorMapping(I(3,2), A, I(4))
+tm = InflatedLazyTensor(I(3,2), A, I(4))
 v = rand(ComplexF64, domain_size(tm)...)
 @test (tm*v)[1,2,3,1] isa ComplexF64
 v = rand(ComplexF64, domain_size(tm')...)
 @test (tm'*v)[1,2,2,1] isa ComplexF64
 end
 @testset "Inference of application" begin
-struct ScalingOperator{T,D} <: TensorMapping{T,D,D}
+tm = InflatedLazyTensor(I(2,3),ScalingTensor(2.0, (3,2)),I(3,4))
-λ::T
-size::NTuple{D,Int}
-end
-LazyTensors.apply(m::ScalingOperator{T,D}, v, I::Vararg{Any,D}) where {T,D} = m.λ*v[I...]
-LazyTensors.range_size(m::ScalingOperator) = m.size
-LazyTensors.domain_size(m::ScalingOperator) = m.size
-tm = InflatedTensorMapping(I(2,3),ScalingOperator(2.0, (3,2)),I(3,4))
 v = rand(domain_size(tm)...)
 @inferred apply(tm,v,1,2,3,2,2,4)
 @inferred (tm*v)[1,2,3,2,2,4]
 end
 end
-@testset "InflatedTensorMapping of InflatedTensorMapping" begin
+@testset "InflatedLazyTensor of InflatedLazyTensor" begin
-A = ScalingOperator(2.0,(2,3))
+A = ScalingTensor(2.0,(2,3))
-itm = InflatedTensorMapping(I(3,2), A, I(4))
+itm = InflatedLazyTensor(I(3,2), A, I(4))
-@test  InflatedTensorMapping(I(4), itm, I(2)) == InflatedTensorMapping(I(4,3,2), A, I(4,2))
+@test  InflatedLazyTensor(I(4), itm, I(2)) == InflatedLazyTensor(I(4,3,2), A, I(4,2))
-@test  InflatedTensorMapping(itm, I(2)) == InflatedTensorMapping(I(3,2), A, I(4,2))
+@test  InflatedLazyTensor(itm, I(2)) == InflatedLazyTensor(I(3,2), A, I(4,2))
-@test  InflatedTensorMapping(I(4), itm) == InflatedTensorMapping(I(4,3,2), A, I(4))
+@test  InflatedLazyTensor(I(4), itm) == InflatedLazyTensor(I(4,3,2), A, I(4))
-@test InflatedTensorMapping(I(2), I(2), I(2)) isa InflatedTensorMapping # The constructor should always return its type.
+@test InflatedLazyTensor(I(2), I(2), I(2)) isa InflatedLazyTensor # The constructor should always return its type.
 end
 end
-@testset "split_index" begin
-@test LazyTensors.split_index(Val(2),Val(1),Val(2),Val(2),1,2,3,4,5,6) == ((1,2,:,5,6),(3,4))
-@test LazyTensors.split_index(Val(2),Val(3),Val(2),Val(2),1,2,3,4,5,6) == ((1,2,:,:,:,5,6),(3,4))
-@test LazyTensors.split_index(Val(3),Val(1),Val(1),Val(2),1,2,3,4,5,6) == ((1,2,3,:,5,6),(4,))
-@test LazyTensors.split_index(Val(3),Val(2),Val(1),Val(2),1,2,3,4,5,6) == ((1,2,3,:,:,5,6),(4,))
-@test LazyTensors.split_index(Val(1),Val(1),Val(2),Val(3),1,2,3,4,5,6) == ((1,:,4,5,6),(2,3))
-@test LazyTensors.split_index(Val(1),Val(2),Val(2),Val(3),1,2,3,4,5,6) == ((1,:,:,4,5,6),(2,3))
-@test LazyTensors.split_index(Val(0),Val(1),Val(3),Val(3),1,2,3,4,5,6) == ((:,4,5,6),(1,2,3))
-@test LazyTensors.split_index(Val(3),Val(1),Val(3),Val(0),1,2,3,4,5,6) == ((1,2,3,:),(4,5,6))
-@inferred LazyTensors.split_index(Val(2),Val(3),Val(2),Val(2),1,2,3,2,2,4)
-end
-@testset "slice_tuple" begin
-@test LazyTensors.slice_tuple((1,2,3),Val(1), Val(3)) == (1,2,3)
-@test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(2), Val(5)) == (2,3,4,5)
-@test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(1), Val(3)) == (1,2,3)
-@test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(4), Val(6)) == (4,5,6)
-end
-@testset "split_tuple" begin
-@testset "2 parts" begin
-@test LazyTensors.split_tuple((),Val(0)) == ((),())
-@test LazyTensors.split_tuple((1,),Val(0)) == ((),(1,))
-@test LazyTensors.split_tuple((1,),Val(1)) == ((1,),())
-@test LazyTensors.split_tuple((1,2,3,4),Val(0)) == ((),(1,2,3,4))
-@test LazyTensors.split_tuple((1,2,3,4),Val(1)) == ((1,),(2,3,4))
-@test LazyTensors.split_tuple((1,2,3,4),Val(2)) == ((1,2),(3,4))
-@test LazyTensors.split_tuple((1,2,3,4),Val(3)) == ((1,2,3),(4,))
-@test LazyTensors.split_tuple((1,2,3,4),Val(4)) == ((1,2,3,4),())
-@test LazyTensors.split_tuple((1,2,true,4),Val(3)) == ((1,2,true),(4,))
-@inferred LazyTensors.split_tuple((1,2,3,4),Val(3))
-@inferred LazyTensors.split_tuple((1,2,true,4),Val(3))
-end
-@testset "3 parts" begin
-@test LazyTensors.split_tuple((),Val(0),Val(0)) == ((),(),())
-@test LazyTensors.split_tuple((1,2,3),Val(1), Val(1)) == ((1,),(2,),(3,))
-@test LazyTensors.split_tuple((1,true,3),Val(1), Val(1)) == ((1,),(true,),(3,))
-@test LazyTensors.split_tuple((1,2,3,4,5,6),Val(1),Val(2)) == ((1,),(2,3),(4,5,6))
-@test LazyTensors.split_tuple((1,2,3,4,5,6),Val(3),Val(2)) == ((1,2,3),(4,5),(6,))
-@inferred LazyTensors.split_tuple((1,2,3,4,5,6),Val(3),Val(2))
-@inferred LazyTensors.split_tuple((1,true,3),Val(1), Val(1))
-end
-end
-@testset "flatten_tuple" begin
-@test LazyTensors.flatten_tuple((1,)) == (1,)
-@test LazyTensors.flatten_tuple((1,2,3,4,5,6)) == (1,2,3,4,5,6)
-@test LazyTensors.flatten_tuple((1,2,(3,4),5,6)) == (1,2,3,4,5,6)
-@test LazyTensors.flatten_tuple((1,2,(3,(4,5)),6)) == (1,2,3,4,5,6)
-@test LazyTensors.flatten_tuple(((1,2),(3,4),(5,),6)) == (1,2,3,4,5,6)
-end
 @testset "LazyOuterProduct" begin
-struct ScalingOperator{T,D} <: TensorMapping{T,D,D}
+A = ScalingTensor(2.0, (5,))
-λ::T
+B = ScalingTensor(3.0, (3,))
-size::NTuple{D,Int}
+C = ScalingTensor(5.0, (3,2))
-end
-LazyTensors.apply(m::ScalingOperator{T,D}, v, I::Vararg{Any,D}) where {T,D} = m.λ*v[I...]
-LazyTensors.range_size(m::ScalingOperator) = m.size
-LazyTensors.domain_size(m::ScalingOperator) = m.size
-A = ScalingOperator(2.0, (5,))
-B = ScalingOperator(3.0, (3,))
-C = ScalingOperator(5.0, (3,2))
 AB = LazyOuterProduct(A,B)
-@test AB isa TensorMapping{T,2,2} where T
+@test AB isa LazyTensor{T,2,2} where T
 @test range_size(AB) == (5,3)
 @test domain_size(AB) == (5,3)
 v = rand(range_size(AB)...)
 @test AB*v == 6*v
 ABC = LazyOuterProduct(A,B,C)
-@test ABC isa TensorMapping{T,4,4} where T
+@test ABC isa LazyTensor{T,4,4} where T
 @test range_size(ABC) == (5,3,3,2)
 @test domain_size(ABC) == (5,3,3,2)
 @test A⊗B == AB
 @test A⊗B⊗C == ABC
 B̃Ã = LazyOuterProduct(B̃,Ã)
 @tullio BAv[k,i] := A[i,j]*B[k,l,m]*v₂[l,m,j]
 @test B̃Ã*v₂ ≈ BAv
 @testset "Indentity mapping arguments" begin
-@test LazyOuterProduct(IdentityMapping(3,2), IdentityMapping(1,2)) == IdentityMapping(3,2,1,2)
+@test LazyOuterProduct(IdentityTensor(3,2), IdentityTensor(1,2)) == IdentityTensor(3,2,1,2)
 Ã = LazyLinearMap(A,(1,),(2,))
-@test LazyOuterProduct(IdentityMapping(3,2), Ã) == InflatedTensorMapping(IdentityMapping(3,2),Ã)
+@test LazyOuterProduct(IdentityTensor(3,2), Ã) == InflatedLazyTensor(IdentityTensor(3,2),Ã)
-@test LazyOuterProduct(Ã, IdentityMapping(3,2)) == InflatedTensorMapping(Ã,IdentityMapping(3,2))
+@test LazyOuterProduct(Ã, IdentityTensor(3,2)) == InflatedLazyTensor(Ã,IdentityTensor(3,2))
-I1 = IdentityMapping(3,2)
+I1 = IdentityTensor(3,2)
-I2 = IdentityMapping(4)
+I2 = IdentityTensor(4)
-@test I1⊗Ã⊗I2 == InflatedTensorMapping(I1, Ã, I2)
+@test I1⊗Ã⊗I2 == InflatedLazyTensor(I1, Ã, I2)
 end
 end
 @testset "inflate" begin
-struct ScalingOperator{T,D} <: TensorMapping{T,D,D}
+I = LazyTensors.inflate(IdentityTensor(),(3,4,5,6), 2)
-λ::T
+@test I isa LazyTensor{Float64, 3,3}
-size::NTuple{D,Int}
-end
-LazyTensors.apply(m::ScalingOperator{T,D}, v, I::Vararg{Any,D}) where {T,D} = m.λ*v[I...]
-LazyTensors.range_size(m::ScalingOperator) = m.size
-LazyTensors.domain_size(m::ScalingOperator) = m.size
-I = LazyTensors.inflate(IdentityMapping(),(3,4,5,6), 2)
-@test I isa TensorMapping{Float64, 3,3}
 @test range_size(I) == (3,5,6)
 @test domain_size(I) == (3,5,6)
 # TODO: More tests

Mercurial > repos > public > sbplib_julia

comparison test/LazyTensors/lazy_tensor_operations_test.jl @ 1023:52f07c77299d refactor/sbpoperators/inflation