yapanuwan
/
BergEC-jl
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

CI estimation

This commit is contained in:
Nuwan Yapa 2025-01-27 20:17:41 -05:00
parent ad25531571
commit e54dd66cda
4 changed files with 65 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

65
EC.jl
View File

@ -1,4 +1,4 @@
using SparseArrays, LinearAlgebra, Arpack, Plots
using Statistics, SparseArrays, LinearAlgebra, Arpack, Plots
include("common.jl")
"EC model for a Hamiltonian family H(c) = H0 + c * H1"
@ -12,11 +12,18 @@ mutable struct affine_EC
H1_EC
N_EC
ensemble_size::Int
H0_EC_ensemble
H1_EC_ensemble
N_EC_ensemble
training_E::Vector{ComplexF64}
exact_E::Vector{ComplexF64}
extrapolated_E::Vector{ComplexF64}
extrapolated_CI::Vector{ComplexF64}
affine_EC(H0::AbstractMatrix{ComplexF64}, H1::AbstractMatrix{ComplexF64}, weights::Vector{ComplexF64}=ones(ComplexF64, size(H0, 1))) = new(H0, H1, weights, false, nothing, nothing, nothing, ComplexF64[], ComplexF64[], ComplexF64[])
affine_EC(H0::AbstractMatrix{ComplexF64}, H1::AbstractMatrix{ComplexF64}, weights::Vector{ComplexF64}=ones(ComplexF64, size(H0, 1)); ensemble_size=0) =
new(H0, H1, weights, false, nothing, nothing, nothing, ensemble_size, Matrix[], Matrix[], Matrix[], ComplexF64[], ComplexF64[], ComplexF64[], ComplexF64[])
end
"Train an EC model for a given range of c values.
@ -51,18 +58,20 @@ function train!(EC::affine_EC, c_vals; ref_eval=-10.0, pseudo_inv_rtol=1e-6, gra
end
CAEC && append!(training_vecs, conj.(training_vecs))
weights_mat = spdiagm(EC.weights)
if gram_schmidt_threshold ≠ 0
if gram_schmidt_threshold > 0
EC.ensemble_size > 0 && generate_resampled_EC_matrices(EC, training_vecs, weights_mat, gram_schmidt_threshold)
training_vecs = gram_schmidt!(training_vecs, EC.weights, gram_schmidt_threshold; verbose=verbose)
end
EC_basis = hcat(training_vecs...)
weights_mat = spdiagm(EC.weights)
EC.H0_EC = transpose(EC_basis) * weights_mat * EC.H0 * EC_basis
EC.H1_EC = transpose(EC_basis) * weights_mat * EC.H1 * EC_basis
EC.N_EC = transpose(EC_basis) * weights_mat * EC_basis
if pseudo_inv_rtol > 0
EC.ensemble_size > 0 && generate_resampled_EC_matrices(EC, pseudo_inv_rtol)
inv_N_EC = pinv(EC.N_EC; rtol=pseudo_inv_rtol)
EC.H0_EC = inv_N_EC * EC.H0_EC
EC.H1_EC = inv_N_EC * EC.H1_EC
@ -72,6 +81,38 @@ function train!(EC::affine_EC, c_vals; ref_eval=-10.0, pseudo_inv_rtol=1e-6, gra
EC.trained = true
end
resample(n::Int) = rand(1:n, n) |> unique |> sort
"Generate an resampled ensemble of reduced matrices performing Gram-Schmidt orthonormalization for each."
function generate_resampled_EC_matrices(EC::affine_EC, training_vecs, weights_mat, gram_schmidt_threshold)
for _ in 1:EC.ensemble_size
sampled_vecs = deepcopy(training_vecs[resample(length(training_vecs))])
orth_vecs = gram_schmidt!(sampled_vecs, EC.weights, gram_schmidt_threshold; verbose=false)
EC_basis = hcat(orth_vecs...)
H0_EC = transpose(EC_basis) * weights_mat * EC.H0 * EC_basis
H1_EC = transpose(EC_basis) * weights_mat * EC.H1 * EC_basis
N_EC = transpose(EC_basis) * weights_mat * EC_basis
push!(EC.H0_EC_ensemble, H0_EC)
push!(EC.H1_EC_ensemble, H1_EC)
push!(EC.N_EC_ensemble, N_EC)
end
end
"Generate an resampled ensemble of reduced matrices performing Moore-Penrose psuedoinverse for each."
function generate_resampled_EC_matrices(EC::affine_EC, pseudo_inv_rtol)
for _ in 1:EC.ensemble_size
sample = resample(size(EC.N_EC, 1))
new_N_EC = EC.N_EC[sample, sample]
new_H0_EC = EC.H0_EC[sample, sample]
new_H1_EC = EC.H1_EC[sample, sample]
inv_N_EC = pinv(new_N_EC; rtol=pseudo_inv_rtol)
push!(EC.H0_EC_ensemble, inv_N_EC * new_H0_EC)
push!(EC.H1_EC_ensemble, inv_N_EC * new_H1_EC)
end
end
"Extrapolate using a trained EC model for a given range of c values
If a list is provided for ref_eval, they are used as reference values for picking the closest eigenvalues at each point.
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.
@ -106,6 +147,18 @@ function extrapolate!(EC::affine_EC, c_vals; ref_eval=EC.training_E[end], verbos
H_EC = EC.H0_EC + c .* EC.H1_EC
evals = isnothing(EC.N_EC) ? eigvals(H_EC) : eigvals(H_EC, EC.N_EC)
push!(EC.extrapolated_E, nearest(evals, current_E))
if EC.ensemble_size > 0
E_ensemble = ComplexF64[]
for i in 1:EC.ensemble_size
H_EC = EC.H0_EC_ensemble[i] + c .* EC.H1_EC_ensemble[i]
evals = isempty(EC.N_EC_ensemble) ? eigvals(H_EC) : eigvals(H_EC, EC.N_EC_ensemble[i])
push!(E_ensemble, nearest(evals, current_E))
end
re_CI = std(real.(E_ensemble))
im_CI = std(imag.(E_ensemble))
push!(EC.extrapolated_CI, complex(re_CI, im_CI))
end
end
end
@ -116,7 +169,11 @@ exportCSV(EC::affine_EC, filename) = exportCSV(filename, (EC.training_E, EC.exac
function plot(EC::affine_EC, save_fig_filename=nothing; basis_points=nothing, basis_contour=nothing, xlims=nothing, ylims=nothing)
scatter(real.(EC.training_E), imag.(EC.training_E), label="training")
scatter!(real.(EC.exact_E), imag.(EC.exact_E), label="exact")
if EC.ensemble_size > 0
scatter!(real.(EC.extrapolated_E), imag.(EC.extrapolated_E), xerror=real.(EC.extrapolated_CI), yerror=imag.(EC.extrapolated_CI), label="extrapolated")
else
scatter!(real.(EC.extrapolated_E), imag.(EC.extrapolated_E), label="extrapolated")
end
isnothing(basis_points) || scatter!(real.(basis_points), imag.(basis_points), m=:x, label="basis")
isnothing(basis_contour) || plot!(real.(basis_contour), imag.(basis_contour), label="contour")

2
calculations/3body_Berggren_B2R_EC.jl
View File

@ -32,7 +32,7 @@ extrapolating_ref = [4.076662025307587-0.012709842443350328im,
1.7164583929199813-0.0005455212208182736im,
1.233088227541505-0.0003070320106485624im]
EC = affine_EC(H0, Vp, weights)
EC = affine_EC(H0, Vp, weights; ensemble_size=32)
train!(EC, training_c; ref_eval=training_ref, CAEC=true)
extrapolate!(EC, extrapolating_c; ref_eval=extrapolating_ref)

2
calculations/3body_CSM_B2R_EC.jl
View File

@ -32,7 +32,7 @@ exact_E = [4.076662025307587-0.012709842443350328im,
1.7164583929199813-0.0005455212208182736im,
1.233088227541505-0.0003070320106485624im]
EC = affine_EC(H0, Vp, weights)
EC = affine_EC(H0, Vp, weights; ensemble_size=32)
train!(EC, training_c; ref_eval=training_ref, CAEC=true)
extrapolate!(EC, extrapolating_c; precalculated_exact_E=exact_E)

2
calculations/3body_HO_B2R_EC.jl
View File

@ -28,7 +28,7 @@ extrapolating_ref = [4.076662025307587-0.012709842443350328im,
training_c = [2.6, 2.4, 2.2, 2.0, 1.8]
extrapolating_c = 0.0 : 0.2 : 1.2
EC = affine_EC(H0, Vp)
EC = affine_EC(H0, Vp; ensemble_size=32)
train!(EC, training_c; ref_eval=training_ref, CAEC=true)
extrapolate!(EC, extrapolating_c; ref_eval=extrapolating_ref)