@@ -8,9 +8,9 @@ const α = 0.75
88
99@testset " Grassmann with space $V " for V in spaces
1010 for T in (Float64,)
11- W, = leftorth (TensorMap ( randn, T, V * V * V, V * V); alg= Polar ())
12- X = TensorMap ( randn, T, space (W))
13- Y = TensorMap ( randn, T, space (W))
11+ W, = leftorth (randn ( T, V * V * V, V * V); alg= Polar ())
12+ X = randn ( T, space (W))
13+ Y = randn ( T, space (W))
1414 Δ = @inferred Grassmann. project (X, W)
1515 Θ = Grassmann. project (Y, W)
1616 γ = randn (T)
@@ -42,7 +42,7 @@ const α = 0.75
4242 @test Grassmann. inner (W2, Δ2, Θ2) ≈ Grassmann. inner (W, Δ, Θ)
4343 @test Grassmann. inner (W2, Ξ2, Θ2) ≈ Grassmann. inner (W, Ξ, Θ)
4444
45- Wend = TensorMap (randhaar, T, codomain (W), domain (W))
45+ Wend = randisometry ( T, codomain (W), domain (W))
4646 Δ3, V = Grassmann. invretract (W, Wend)
4747 @test Wend ≈ retract (W, Δ3, 1 )[1 ] * V
4848 U = Grassmann. relativegauge (W, Wend)
5353
5454@testset " Stiefel with space $V " for V in spaces
5555 for T in (Float64, ComplexF64)
56- W = TensorMap (randhaar, T, V * V * V, V * V)
57- X = TensorMap ( randn, T, space (W))
58- Y = TensorMap ( randn, T, space (W))
56+ W = randisometry ( T, V * V * V, V * V)
57+ X = randn ( T, space (W))
58+ Y = randn ( T, space (W))
5959 Δ = @inferred Stiefel. project_euclidean (X, W)
6060 Θ = Stiefel. project_canonical (Y, W)
6161 γ = rand ()
@@ -116,17 +116,17 @@ end
116116 @test Stiefel. inner_canonical (W2, Δ2, Θ2) ≈ Stiefel. inner_canonical (W, Δ, Θ)
117117 @test Stiefel. inner_canonical (W2, Ξ2, Θ2) ≈ Stiefel. inner_canonical (W, Ξ, Θ)
118118
119- W3 = projectisometric! (W + 1e-1 * TensorMap ( rand, T, codomain (W), domain (W)))
119+ W3 = projectisometric! (W + 1e-1 * rand ( T, codomain (W), domain (W)))
120120 Δ3 = Stiefel. invretract (W, W3)
121121 @test W3 ≈ retract (W, Δ3, 1 )[1 ]
122122 end
123123end
124124
125125@testset " Unitary with space $V " for V in spaces
126126 for T in (Float64, ComplexF64)
127- W, = leftorth (TensorMap ( randn, T, V * V * V, V * V); alg= Polar ())
128- X = TensorMap ( randn, T, space (W))
129- Y = TensorMap ( randn, T, space (W))
127+ W, = leftorth (randn ( T, V * V * V, V * V); alg= Polar ())
128+ X = randn ( T, space (W))
129+ Y = randn ( T, space (W))
130130 Δ = @inferred Unitary. project (X, W)
131131 Θ = Unitary. project (Y, W)
132132 γ = randn ()
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