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examples/train-kernel-parameters/script.jl

@@ -32,7 +32,10 @@ plot!(x_test, sinc; lab="true function")
 # To train the kernel parameters via ForwardDiff.jl
 # we need to create a function creating a kernel from an array
 
-kernelcall(θ) = (exp(θ[1]) * SqExponentialKernel() + exp(θ[2]) * Matern32Kernel()) ∘ ScaleTransform(exp(θ[3]))
+function kernelcall(θ)
+    return (exp(θ[1]) * SqExponentialKernel() + exp(θ[2]) * Matern32Kernel()) ∘
+           ScaleTransform(exp(θ[3]))
+end
 
 # From theory we know the prediction for a test set x given
 # the kernel parameters and normalization constant