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src/ComponentArrayInterpreter.jl

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@@ -138,7 +138,7 @@ function ComponentArrayInterpreter(
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end
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# ambuiguity with two empty Tuples (edge case that does not make sense)
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# Empty ComponentVector with no other array dimenstions -> empty componentVector
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# Empty ComponentVector with no other array dimensions -> empty componentVector
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function ComponentArrayInterpreter(n_dims1::Tuple{}, n_dims2::Tuple{})
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ComponentArrayInterpreter(CA.ComponentVector())
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end

src/gencovar.jl

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@@ -3,7 +3,7 @@ Generate correlated covariates and synthetic true parameters that
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are a linear combination of the uncorrelated underlying principal
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factors and their binary combinations.
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In addtion provide a SimpleChains model of adequate complexity to
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In addition provide a SimpleChains model of adequate complexity to
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fit this realationship θMs_true = f(x_o)
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"""
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function gen_cov_pred(rng::AbstractRNG, T::DataType,

src/hybrid_case.jl

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@@ -9,7 +9,7 @@ For a specific case, provide functions that specify details
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- gen_hybridcase_PBmodel
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optionally
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- gen_hybridcase_synthetic
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- get_hybridcase_FloatType (if it shoudl differ from Float32)
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- get_hybridcase_FloatType (if it should differ from Float32)
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"""
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abstract type AbstractHybridCase end;
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src/util_opt.jl

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"""
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create a funciton (state, l) -> false that prints iter and loss each moditer
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create a function (state, l) -> false that prints iter and loss each moditer
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"""
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callback_loss = (moditer) -> let iter = 1, moditer = moditer
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function (state, l)

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