Small optimization
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8
EC.jl
8
EC.jl
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@ -66,7 +66,8 @@ end
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"Extrapolate using a trained EC model for a given range of c values
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If a list is provided for ref_eval, they are used as reference values for picking the closest eigenvalues at each point.
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If a single number is provided for ref_eval, it is used as a reference for the first point, and the previous eigenvalue is used as the reference for each successive point."
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If a single number is provided for ref_eval, it is used as a reference for the first point, and the previous eigenvalue is used as the reference for each successive point.
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If precalculated_exact_E is provided, ref_eval is ignored."
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function extrapolate!(EC::affine_EC, c_vals; ref_eval=EC.training_E[end], verbose=true, tol=1e-5, precalculated_exact_E=nothing)
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@assert EC.trained "EC model must be trained using train() before extrapolation"
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@ -74,6 +75,8 @@ function extrapolate!(EC::affine_EC, c_vals; ref_eval=EC.training_E[end], verbos
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verbose && println("Extact solution for c = $c")
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global current_E
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if isnothing(precalculated_exact_E)
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if ref_eval isa Number
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current_E = ref_eval
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ref_eval = nothing
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@ -81,8 +84,8 @@ function extrapolate!(EC::affine_EC, c_vals; ref_eval=EC.training_E[end], verbos
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current_E = popfirst!(ref_eval)
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end
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if isnothing(precalculated_exact_E)
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verbose && println("Extact solution for c = $c")
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H = EC.H0 + c .* EC.H1
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evals, _ = eigs(H, sigma=current_E, maxiter=5000, tol=tol, ritzvec=false, check=1)
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current_E = nearest(evals, current_E)
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@ -95,7 +98,6 @@ function extrapolate!(EC::affine_EC, c_vals; ref_eval=EC.training_E[end], verbos
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verbose && println("Extrapolating for c = $c")
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H_EC = EC.H0_EC + c .* EC.H1_EC
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evals = isnothing(EC.N_EC) ? eigvals(H_EC) : eigvals(H_EC, EC.N_EC)
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push!(EC.extrapolated_E, nearest(evals, current_E))
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end
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