use juliaformatter

Author: zsteve

src/OptimalTransport.jl

@@ -115,8 +115,9 @@ function emd2(μ, ν, C, optimizer; map=nothing)
         # check dimensions
         size(C) == (length(μ), length(ν)) ||
             error("cost matrix `C` must be of size `(length(μ), length(ν))`")
-        size(map) == size(C) ||
-            error("optimal transport map `map` and cost matrix `C` must be of the same size")
+        size(map) == size(C) || error(
+            "optimal transport map `map` and cost matrix `C` must be of the same size",
+        )
         map
     end
     return dot(γ, C)
@@ -225,8 +226,9 @@ function sinkhorn2(μ, ν, C, ε; map=nothing, kwargs...)
         # check dimensions
         size(C) == (length(μ), length(ν)) ||
             error("cost matrix `C` must be of size `(length(μ), length(ν))`")
-        size(map) == size(C) ||
-            error("optimal transport map `map` and cost matrix `C` must be of the same size")
+        size(map) == size(C) || error(
+            "optimal transport map `map` and cost matrix `C` must be of the same size",
+        )
         map
     end
     return dot(γ, C)
@@ -328,8 +330,9 @@ function sinkhorn_unbalanced2(μ, ν, C, λ1, λ2, ε; map=nothing, kwargs...)
         # check dimensions
         size(C) == (length(μ), length(ν)) ||
             error("cost matrix `C` must be of size `(length(μ), length(ν))`")
-        size(map) == size(C) ||
-            error("optimal transport map `map` and cost matrix `C` must be of the same size")
+        size(map) == size(C) || error(
+            "optimal transport map `map` and cost matrix `C` must be of the same size",
+        )
         map
     end
     return dot(γ, C)

src/pot.jl

@@ -155,9 +155,18 @@ function sinkhorn_unbalanced2(
 end
 
 function smooth_ot_dual(
-    mu, nu, C, eps, reg_type = "l2", method = "L-BFGS-B", tol = 1e-9, max_iter = 500, verbose = false
+    mu, nu, C, eps, reg_type="l2", method="L-BFGS-B", tol=1e-9, max_iter=500, verbose=false
 )
-    return pot.smooth.smooth_ot_dual(nu, mu, PyReverseDims(C), eps, reg_type = reg_type, method = method, stopThr = tol, numItermax = max_iter)'
+    return pot.smooth.smooth_ot_dual(
+        nu,
+        mu,
+        PyReverseDims(C),
+        eps;
+        reg_type=reg_type,
+        method=method,
+        stopThr=tol,
+        numItermax=max_iter,
+    )'
 end
 
 end

test/runtests.jl

@@ -189,6 +189,6 @@ end
         γ = quadreg(μ, ν, C, eps)
         γ_pot = sparse(POT.smooth_ot_dual(μ, ν, C, eps))
         # need to use a larger tolerance here because of a quirk with the POT solver 
-        @test norm(γ - γ_pot, Inf) < 0.5e-4 
+        @test norm(γ - γ_pot, Inf) < 0.5e-4
     end
 end