Disable error estimation and pseudoinverse
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06975b2603
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EC.jl
2
EC.jl
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@ -95,7 +95,7 @@ If a list is provided for ref_eval, they are used as reference values for pickin
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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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If pseudo_inv_tol > 0, the GEVP is avoided using Moore-Penrose psuedoinverse, using this value as the relative tolerance for dropping redundant vectors."
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function extrapolate!(EC::affine_EC, c_vals; ref_eval=EC.training_E[end], pseudo_inv_tol=1e-6, verbose=true, tol=1e-5, precalculated_exact_E=nothing)
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function extrapolate!(EC::affine_EC, c_vals; ref_eval=EC.training_E[end], pseudo_inv_tol=0, 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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for c in c_vals
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@ -32,7 +32,7 @@ extrapolating_ref = [4.076662025307587-0.012709842443350328im,
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1.7164583929199813-0.0005455212208182736im,
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1.233088227541505-0.0003070320106485624im]
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EC = affine_EC(H0, Vp, weights; ensemble_size=32)
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EC = affine_EC(H0, Vp, weights)
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train!(EC, training_c; ref_eval=training_ref, CAEC=true)
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extrapolate!(EC, extrapolating_c; ref_eval=extrapolating_ref)
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@ -32,7 +32,7 @@ exact_E = [4.076662025307587-0.012709842443350328im,
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1.7164583929199813-0.0005455212208182736im,
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1.233088227541505-0.0003070320106485624im]
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EC = affine_EC(H0, Vp, weights; ensemble_size=32)
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EC = affine_EC(H0, Vp, weights)
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train!(EC, training_c; ref_eval=training_ref, CAEC=true)
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extrapolate!(EC, extrapolating_c; precalculated_exact_E=exact_E)
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@ -28,7 +28,7 @@ extrapolating_ref = [4.076662025307587-0.012709842443350328im,
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training_c = [2.6, 2.4, 2.2, 2.0, 1.8]
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extrapolating_c = 0.0 : 0.2 : 1.2
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EC = affine_EC(H0, Vp; ensemble_size=32)
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EC = affine_EC(H0, Vp)
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train!(EC, training_c; ref_eval=training_ref, CAEC=true)
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extrapolate!(EC, extrapolating_c; ref_eval=extrapolating_ref)
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