Overdetermined fit (decreased Pade order)
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@ -27,7 +27,7 @@ training_E = EC.training_E
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quick_extrapolate(c) = argmin(real, get_extrapolated_evals(EC.H0_EC, EC.H1_EC, EC.N_EC, c, 0))
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quick_extrapolate(c) = argmin(real, get_extrapolated_evals(EC.H0_EC, EC.H1_EC, EC.N_EC, c, 0))
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n_EC = 10
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n_EC = 30
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EC_c = (1.8 .+ rand(n_EC)) .+ 0.001im .* (-2 .+ 4 * rand(n_EC))
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EC_c = (1.8 .+ rand(n_EC)) .+ 0.001im .* (-2 .+ 4 * rand(n_EC))
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target_c = 0.0 : 0.2 : 1.2
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target_c = 0.0 : 0.2 : 1.2
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@ -47,7 +47,7 @@ println("Estimated c0 = ", c0)
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EC_k = alt_sqrt.(EC_E)
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EC_k = alt_sqrt.(EC_E)
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order::Int = ceil((length(EC_c) - 1) / 2) # order of the Pade approximant
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order::Int = 5 # order of the Pade approximant
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# Solve coefficients as a linear system
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# Solve coefficients as a linear system
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M_left_element(c, i) = alt_sqrt(c - c0)^i
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M_left_element(c, i) = alt_sqrt(c - c0)^i
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