Added random seeds to all tests

acfr · Jul 18, 2023 · 153bf96 · 153bf96
1 parent cacbc5a
commit 153bf96
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Showing 9 changed files with 55 additions and 37 deletions.
diff --git a/test/ParameterTypes/contraction.jl b/test/ParameterTypes/contraction.jl
@@ -6,6 +6,8 @@ using Test
 
 # include("../test_utils.jl")
 
+rng = MersenneTwister(42)
+
 """
 Test that the contracting REN actually does contract.
 
@@ -17,15 +19,15 @@ nu, nx, nv, ny = 4, 5, 10, 5
 
 ren_ps = ContractingRENParams{Float64}(
     nu, nx, nv, ny; 
-    init=:cholesky, αbar=ᾱ, polar_param=false, output_map=false
+    init=:cholesky, αbar=ᾱ, polar_param=false, output_map=false, rng
 )
 ren = REN(ren_ps)
 
 # Same inputs. different initial conditions
-u0 = randn(nu, batches)
+u0 = randn(rng, nu, batches)
 
-x0 = randn(nx, batches)
-x1 = randn(nx, batches)
+x0 = randn(rng, nx, batches)
+x1 = randn(rng, nx, batches)
 
 # Simulate
 x0n, y0 = ren(x0, u0)

diff --git a/test/ParameterTypes/dense_lbdn.jl b/test/ParameterTypes/dense_lbdn.jl
@@ -6,6 +6,8 @@ using Test
 
 # include("../test_utils.jl")
 
+rng = MersenneTwister(42)
+
 """
 Test that the model satisfies a specified Lipschitz bound
 """
@@ -14,12 +16,12 @@ nu, ny = 4, 2
 nh = [10, 5, 20, 4]
 γ = 1e-5
 
-lbdn_ps = DenseLBDNParams{Float64}(nu, nh, ny, γ)
+lbdn_ps = DenseLBDNParams{Float64}(nu, nh, ny, γ; rng)
 lbdn = LBDN(lbdn_ps)
 
 # Different inputs with different initial conditions
-u0 = randn(nu, batches)
-u1 = u0 .+ 0.001*rand(nu, batches)
+u0 = randn(rng, nu, batches)
+u1 = u0 .+ 0.001*rand(rng, nu, batches)
 
 # Simulate
 y0 = lbdn(u0)

diff --git a/test/ParameterTypes/general_behavioural_constrains.jl b/test/ParameterTypes/general_behavioural_constrains.jl
@@ -7,29 +7,31 @@ using Test
 
 # include("../test_utils.jl")
 
+rng = MersenneTwister(42)
+
 """
 Test that the behavioural constraints are satisfied
 """
 batches = 42
 nu, nx, nv, ny = 10, 5, 10, 20
 
 # Generate random matrices
-X = randn(ny,ny)
-Y = randn(nu,nu)
-S = rand(nu,ny)
+X = randn(rng, ny,ny)
+Y = randn(rng, nu,nu)
+S = rand(rng, nu,ny)
 
 Q = -X'*X
 R = S * (Q \ S') + Y'*Y
 
-ren_ps = GeneralRENParams{Float64}(nu, nx, nv, ny, Q, S, R)
+ren_ps = GeneralRENParams{Float64}(nu, nx, nv, ny, Q, S, R; rng)
 ren = REN(ren_ps)
 
 # Different inputs with different initial conditions
-u0 = 10*randn(nu, batches)
-u1 = rand(nu, batches)
+u0 = 10*randn(rng, nu, batches)
+u1 = rand(rng, nu, batches)
 
-x0 = randn(nx, batches)
-x1 = randn(nx, batches)
+x0 = randn(rng, nx, batches)
+x1 = randn(rng, nx, batches)
 
 # Simulate
 x0n, y0 = ren(x0, u0)

diff --git a/test/ParameterTypes/lipschitz_bound.jl b/test/ParameterTypes/lipschitz_bound.jl
@@ -6,22 +6,24 @@ using Test
 
 # include("../test_utils.jl")
 
+rng = MersenneTwister(42)
+
 """
 Test that the model satisfies a specified Lipschitz bound
 """
 batches = 42
 nu, nx, nv, ny = 4, 5, 10, 2
 γ = 10
 
-ren_ps = LipschitzRENParams{Float64}(nu, nx, nv, ny, γ)
+ren_ps = LipschitzRENParams{Float64}(nu, nx, nv, ny, γ; rng)
 ren = REN(ren_ps)
 
 # Different inputs with different initial conditions
-u0 = 10*randn(nu, batches)
-u1 = rand(nu, batches)
+u0 = 10*randn(rng, nu, batches)
+u1 = rand(rng, nu, batches)
 
-x0 = randn(nx, batches)
-x1 = randn(nx, batches)
+x0 = randn(rng, nx, batches)
+x1 = randn(rng, nx, batches)
 
 # Simulate
 x0n, y0 = ren(x0, u0)

diff --git a/test/ParameterTypes/passivity.jl b/test/ParameterTypes/passivity.jl
@@ -7,6 +7,8 @@ using Test
 
 # include("../test_utils.jl")
 
+rng = MersenneTwister(42)
+
 """
 Test passivity inequality
 """
@@ -15,15 +17,15 @@ nu, nx, nv, ny = 6, 5, 10, 6
 T = 100
 
 # Test constructors
-ren_ps = PassiveRENParams{Float64}(nu, nx, nv, ny; init=:random, ν= 1.0)
+ren_ps = PassiveRENParams{Float64}(nu, nx, nv, ny; init=:random, ν= 1.0, rng)
 ren = REN(ren_ps)
 
 # Different inputs with different initial conditions
-u0 = 10*randn(nu, batches)
-u1 = rand(nu, batches)
+u0 = 10*randn(rng, nu, batches)
+u1 = rand(rng, nu, batches)
 
-x0 = randn(nx, batches)
-x1 = randn(nx, batches)
+x0 = randn(rng, nx, batches)
+x1 = randn(rng, nx, batches)
 
 # Simulate
 x0n, y0 = ren(x0, u0)

diff --git a/test/Wrappers/diff_lbdn.jl b/test/Wrappers/diff_lbdn.jl
@@ -5,18 +5,20 @@ using Random
 using RobustNeuralNetworks
 using Test
 
+rng = MersenneTwister(42)
+
 """
 Test that backpropagation runs and parameters change
 """
 batches = 10
 nu, ny, γ = 2, 3, 1
 nh = [10,5]
-model_ps = DenseLBDNParams{Float32}(nu, nh, ny, γ; learn_γ=true)
+model_ps = DenseLBDNParams{Float32}(nu, nh, ny, γ; learn_γ=true, rng)
 model = DiffLBDN(model_ps)
 
 # Dummy data
-us = randn(nu, batches)
-ys = randn(ny, batches)
+us = randn(rng, nu, batches)
+ys = randn(rng, ny, batches)
 data = [(us[:,k], ys[:,k]) for k in 1:batches]
 
 # Dummy loss function just for testing

diff --git a/test/Wrappers/diff_ren.jl b/test/Wrappers/diff_ren.jl
@@ -5,18 +5,20 @@ using Random
 using RobustNeuralNetworks
 using Test
 
+rng = MersenneTwister(42)
+
 """
 Test that backpropagation runs and parameters change
 """
 batches = 10
 nu, nx, nv, ny = 4, 5, 10, 2
 γ = 10
-ren_ps = LipschitzRENParams{Float64}(nu, nx, nv, ny, γ)
+ren_ps = LipschitzRENParams{Float64}(nu, nx, nv, ny, γ; rng)
 model = DiffREN(ren_ps)
 
 # Dummy data
-us = randn(nu, batches)
-ys = randn(ny, batches)
+us = randn(rng, nu, batches)
+ys = randn(rng, ny, batches)
 data = [(us[:,k], ys[:,k]) for k in 1:batches]
 
 # Dummy loss function just for testing

diff --git a/test/Wrappers/wrap_ren.jl b/test/Wrappers/wrap_ren.jl
@@ -5,6 +5,8 @@ using Random
 using RobustNeuralNetworks
 using Test
 
+rng = MersenneTwister(42)
+
 """
 Test REN wrapper with General REN params
 """
@@ -15,15 +17,15 @@ Q = Matrix{Float64}(-I(ny))
 R = 0.1^2 * Matrix{Float64}(I(nu))
 S = zeros(Float64, nu, ny)
 
-ren_ps = GeneralRENParams{Float64}(nu, nx, nv, ny, Q, S, R)
+ren_ps = GeneralRENParams{Float64}(nu, nx, nv, ny, Q, S, R; rng)
 ren1 = WrapREN(ren_ps)
 
 x0 = init_states(ren1, batches)
-u0 = randn(nu, batches)
+u0 = randn(rng, nu, batches)
 
 # Update the model after changing a parameter
 old_B2 = deepcopy(ren1.explicit.B2)
-ren1.params.direct.B2 .*= rand(size(ren1.params.direct.B2)...)
+ren1.params.direct.B2 .*= rand(rng, size(ren1.params.direct.B2)...)
 
 x1, y1 = ren1(x0, u0)
 update_explicit!(ren1)

diff --git a/test/Wrappers/zero_dim.jl b/test/Wrappers/zero_dim.jl
@@ -5,17 +5,19 @@ using Random
 using RobustNeuralNetworks
 using Test
 
+rng = MersenneTwister(42)
+
 """
 Test that backpropagation runs when nx = 0 and nv = 0
 """
 batches = 10
 nu, nx, nv, ny = 4, 0, 0, 2
 γ = 10
-model_ps = LipschitzRENParams{Float64}(nu, nx, nv, ny, γ)
+model_ps = LipschitzRENParams{Float64}(nu, nx, nv, ny, γ; rng)
 
 # Dummy data
-us = randn(nu, batches)
-ys = randn(ny, batches)
+us = randn(rng, nu, batches)
+ys = randn(rng, ny, batches)
 data = [(us, ys)]
 
 # Dummy loss function just for testing