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derivative_tests.jl
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using Quadrature, Cuba, Cubature, Zygote, FiniteDiff, ForwardDiff
using Test
### One Dimensional
f(x,p) = sum(sin.(p[1] * x))
lb = 1.0
ub = 3.0
p = 2.0
prob = QuadratureProblem(f,lb,ub,p)
sol = solve(prob,QuadGKJL(),reltol=1e-3,abstol=1e-3)
function testf(lb,ub,p)
prob = QuadratureProblem(f,lb,ub,p)
sin(solve(prob,QuadGKJL(),reltol=1e-3,abstol=1e-3)[1])
end
dlb1,dub1,dp1 = Zygote.gradient(testf,lb,ub,p)
dlb2 = FiniteDiff.finite_difference_derivative(lb->testf(lb,ub,p),lb)
dub2 = FiniteDiff.finite_difference_derivative(ub->testf(lb,ub,p),ub)
dp2 = FiniteDiff.finite_difference_derivative(p->testf(lb,ub,p),p)
# dlb3 = ForwardDiff.derivative(lb->testf(lb,ub,p),lb)
# dub3 = ForwardDiff.derivative(ub->testf(lb,ub,p),ub)
dp3 = ForwardDiff.derivative(p->testf(lb,ub,p),p)
#@test dlb1 ≈ dlb3
#@test dub1 ≈ dub3
@test dp1 ≈ dp3
### N-dimensional
f(x,p) = sum(sin.(x .* p))
lb = ones(2)
ub = 3ones(2)
p = [1.5,2.0]
prob = QuadratureProblem(f,lb,ub,p)
sol = solve(prob,CubaCuhre(),reltol=1e-3,abstol=1e-3)
function testf(lb,ub,p)
prob = QuadratureProblem(f,lb,ub,p)
sin(solve(prob,CubaCuhre(),reltol=1e-6,abstol=1e-6)[1])
end
function testf2(lb,ub,p)
prob = QuadratureProblem(f,lb,ub,p)
sin(solve(prob,HCubatureJL(),reltol=1e-6,abstol=1e-6)[1])
end
dlb1,dub1,dp1 = Zygote.gradient(testf,lb,ub,p)
dlb2 = FiniteDiff.finite_difference_gradient(lb->testf(lb,ub,p),lb)
dub2 = FiniteDiff.finite_difference_gradient(ub->testf(lb,ub,p),ub)
dp2 = FiniteDiff.finite_difference_gradient(p->testf(lb,ub,p),p)
@test_broken dlb1 ≈ dlb2
@test_broken dub1 ≈ dub2
@test dp1 ≈ dp2
# dlb3 = ForwardDiff.gradient(lb->testf(lb,ub,p),lb)
# dub3 = ForwardDiff.gradient(ub->testf(lb,ub,p),ub)
dp3 = ForwardDiff.gradient(p->testf2(lb,ub,p),p)
#@test dlb1 ≈ dlb3
#@test dub1 ≈ dub3
@test dp1 ≈ dp3
# dlb4 = ForwardDiff.gradient(lb->testf(lb,ub,p),lb)
# dub4 = ForwardDiff.gradient(ub->testf(lb,ub,p),ub)
dp4 = ForwardDiff.gradient(p->testf(lb,ub,p),p)
#@test dlb1 ≈ dlb4
#@test dub1 ≈ dub4
@test dp1 ≈ dp4
### N-dimensional N-out
f(x,p) = sin.(x .* p)
lb = ones(2)
ub = 3ones(2)
p = [1.5,2.0]
prob = QuadratureProblem(f,lb,ub,p,nout=2)
sol = solve(prob,CubaCuhre(),reltol=1e-3,abstol=1e-3)
function testf(lb,ub,p)
prob = QuadratureProblem(f,lb,ub,p,nout=2)
sin(sum(solve(prob,CubaCuhre(),reltol=1e-6,abstol=1e-6)))
end
function testf2(lb,ub,p)
prob = QuadratureProblem(f,lb,ub,p,nout=2)
sin(sum(solve(prob,HCubatureJL(),reltol=1e-6,abstol=1e-6)))
end
dlb1,dub1,dp1 = Zygote.gradient(testf,lb,ub,p)
dlb2 = FiniteDiff.finite_difference_gradient(lb->testf(lb,ub,p),lb)
dub2 = FiniteDiff.finite_difference_gradient(ub->testf(lb,ub,p),ub)
dp2 = FiniteDiff.finite_difference_gradient(p->testf(lb,ub,p),p)
@test_broken dlb1 ≈ dlb2
@test_broken dub1 ≈ dub2
@test dp1 ≈ dp2
# dlb3 = ForwardDiff.gradient(lb->testf(lb,ub,p),lb)
# dub3 = ForwardDiff.gradient(ub->testf(lb,ub,p),ub)
dp3 = ForwardDiff.gradient(p->testf2(lb,ub,p),p)
#@test dlb1 ≈ dlb3
#@test dub1 ≈ dub3
@test dp1 ≈ dp3
# dlb4 = ForwardDiff.gradient(lb->testf(lb,ub,p),lb)
# dub4 = ForwardDiff.gradient(ub->testf(lb,ub,p),ub)
dp4 = ForwardDiff.gradient(p->testf(lb,ub,p),p)
#@test dlb1 ≈ dlb4
#@test dub1 ≈ dub4
@test dp1 ≈ dp4
### Batch Single dim
f(x,p) = x*p[1].+p[2]*p[3]
lb =1.0
ub = 3.0
p = [2.0, 3.0, 4.0]
prob = QuadratureProblem(f,lb,ub,p)
function testf3(lb,ub,p; f=f)
prob = QuadratureProblem(f,lb,ub,p, batch = 10, nout=1)
solve(prob, CubatureJLh(); reltol=1e-3,abstol=1e-3)[1]
end
dp1 = ForwardDiff.gradient(p->testf3(lb,ub,p),p)
dp2 = Zygote.gradient(p->testf3(lb,ub,p),p)[1]
dp3 = FiniteDiff.finite_difference_gradient(p->testf3(lb,ub,p),p)
@test dp1 ≈ dp3 #passes
@test dp2 ≈ dp3 #passes
### Batch single dim, nout
f(x,p) = (x*p[1].+p[2]*p[3]).*[1;2]
lb =1.0
ub = 3.0
p = [2.0, 3.0, 4.0]
prob = QuadratureProblem(f,lb,ub,p)
function testf3(lb,ub,p; f=f)
prob = QuadratureProblem(f,lb,ub,p, batch = 10, nout=2)
sum(solve(prob, CubatureJLh(); reltol=1e-3,abstol=1e-3))
end
dp1 = ForwardDiff.gradient(p->testf3(lb,ub,p),p)
# dp2 = Zygote.gradient(p->testf3(lb,ub,p),p)[1]
dp3 = FiniteDiff.finite_difference_gradient(p->testf3(lb,ub,p),p)
@test dp1 ≈ dp3 #passes
# @test dp2 ≈ dp3 #passes
### Batch multi dim
f(x,p) = x[1,:]*p[1].+p[2]*p[3]
lb =[1.0,1.0]
ub = [3.0,3.0]
p = [2.0, 3.0, 4.0]
prob = QuadratureProblem(f,lb,ub,p)
function testf3(lb,ub,p; f=f)
prob = QuadratureProblem(f,lb,ub,p, batch = 10, nout=1)
solve(prob, CubatureJLh(); reltol=1e-3,abstol=1e-3)[1]
end
dp1 = ForwardDiff.gradient(p->testf3(lb,ub,p),p)
dp2 = Zygote.gradient(p->testf3(lb,ub,p),p)[1]
dp3 = FiniteDiff.finite_difference_gradient(p->testf3(lb,ub,p),p)
@test dp1 ≈ dp3
@test dp2 ≈ dp3
### Batch multi dim nout
f(x,p) = x*p[1].+p[2]*p[3]
lb =[1.0,1.0]
ub = [3.0,3.0]
p = [2.0, 3.0, 4.0]
prob = QuadratureProblem(f,lb,ub,p)
function testf3(lb,ub,p; f=f)
prob = QuadratureProblem(f,lb,ub,p, batch = 10, nout=2)
sum(solve(prob, CubatureJLh(); reltol=1e-3,abstol=1e-3))
end
dp1 = ForwardDiff.gradient(p->testf3(lb,ub,p),p)
# dp2 = Zygote.gradient(p->testf3(lb,ub,p),p)[1]
dp3 = FiniteDiff.finite_difference_gradient(p->testf3(lb,ub,p),p)
@test dp1 ≈ dp3
# @test dp2 ≈ dp3
## iip Batch mulit dim
function g(dx, x,p)
dx.= sum(x*p[1].+p[2]*p[3], dims=1)
end
lb =[1.0,1.0]
ub = [3.0,3.0]
p = [2.0, 3.0, 4.0]
prob = QuadratureProblem(g,lb,ub,p)
function testf3(lb,ub,p; f=g)
prob = QuadratureProblem(f,lb,ub,p, batch = 10, nout=1)
solve(prob, CubatureJLh(); reltol=1e-3,abstol=1e-3)[1]
end
testf3(lb,ub,p)
dp1 = ForwardDiff.gradient(p->testf3(lb,ub,p),p)
dp2 = Zygote.gradient(p->testf3(lb,ub,p),p)[1]
dp3 = FiniteDiff.finite_difference_gradient(p->testf3(lb,ub,p),p)
@test dp1 ≈ dp3
@test dp2 ≈ dp3
## iip Batch mulit dim nout
function g(dx, x,p)
dx.= x*p[1].+p[2]*p[3]
end
lb =[1.0,1.0]
ub = [3.0,3.0]
p = [2.0, 3.0, 4.0]
prob = QuadratureProblem(g,lb,ub,p)
function testf3(lb,ub,p; f=g)
prob = QuadratureProblem(f,lb,ub,p, batch = 10, nout=2)
sum(solve(prob, CubatureJLh(); reltol=1e-3,abstol=1e-3))
end
dp1 = ForwardDiff.gradient(p->testf3(lb,ub,p),p)
# dp2 = Zygote.gradient(p->testf3(lb,ub,p),p)[1]
dp3 = FiniteDiff.finite_difference_gradient(p->testf3(lb,ub,p),p)
@test dp1 ≈ dp3
# @test dp2 ≈ dp3