context_start_lineno int64 1 913 | line_no int64 16 984 | repo stringclasses 5
values | id int64 0 416 | target_function_prompt stringlengths 201 13.6k | function_signature stringlengths 201 13.6k | solution_position listlengths 2 2 | raw_solution stringlengths 201 13.6k | focal_code stringlengths 201 13.6k | function_name stringlengths 2 38 | start_line int64 1 913 | end_line int64 16 984 | file_path stringlengths 10 52 | context stringlengths 4.52k 9.85k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
250 | 319 | Turing.jl | 400 | function StatsBase.coeftable(m::ModeResult; level::Real=0.95, numerrors_warnonly::Bool=true)
# Get columns for coeftable.
terms = string.(StatsBase.coefnames(m))
estimates = m.values.array[:, 1]
# If numerrors_warnonly is true, and if either the information matrix is singular or has
# negative entri... | function StatsBase.coeftable(m::ModeResult; level::Real=0.95, numerrors_warnonly::Bool=true)
# Get columns for coeftable.
terms = string.(StatsBase.coefnames(m))
estimates = m.values.array[:, 1]
# If numerrors_warnonly is true, and if either the information matrix is singular or has
# negative entri... | [
250,
319
] | function StatsBase.coeftable(m::ModeResult; level::Real=0.95, numerrors_warnonly::Bool=true)
# Get columns for coeftable.
terms = string.(StatsBase.coefnames(m))
estimates = m.values.array[:, 1]
# If numerrors_warnonly is true, and if either the information matrix is singular or has
# negative entri... | function StatsBase.coeftable(m::ModeResult; level::Real=0.95, numerrors_warnonly::Bool=true)
# Get columns for coeftable.
terms = string.(StatsBase.coefnames(m))
estimates = m.values.array[:, 1]
# If numerrors_warnonly is true, and if either the information matrix is singular or has
# negative entri... | StatsBase.coeftable | 250 | 319 | src/optimisation/Optimisation.jl | #FILE: Turing.jl/test/optimisation/Optimisation.jl
##CHUNK 1
return y ~ MvNormal(a .* x .+ b .* x, 1)
end
model = collinear(xs, ys)
mle_estimate = Turing.Optimisation.estimate_mode(model, MLE())
tab = coeftable(mle_estimate)
@assert isnan(tab.cols[2][1])
@ass... |
321 | 348 | Turing.jl | 401 | function StatsBase.informationmatrix(
m::ModeResult; hessian_function=ForwardDiff.hessian, kwargs...
)
# Calculate Hessian and information matrix.
# Convert the values to their unconstrained states to make sure the
# Hessian is computed with respect to the untransformed parameters.
linked = Dynamic... | function StatsBase.informationmatrix(
m::ModeResult; hessian_function=ForwardDiff.hessian, kwargs...
)
# Calculate Hessian and information matrix.
# Convert the values to their unconstrained states to make sure the
# Hessian is computed with respect to the untransformed parameters.
linked = Dynamic... | [
321,
348
] | function StatsBase.informationmatrix(
m::ModeResult; hessian_function=ForwardDiff.hessian, kwargs...
)
# Calculate Hessian and information matrix.
# Convert the values to their unconstrained states to make sure the
# Hessian is computed with respect to the untransformed parameters.
linked = Dynamic... | function StatsBase.informationmatrix(
m::ModeResult; hessian_function=ForwardDiff.hessian, kwargs...
)
# Calculate Hessian and information matrix.
# Convert the values to their unconstrained states to make sure the
# Hessian is computed with respect to the untransformed parameters.
linked = Dynamic... | StatsBase.informationmatrix | 321 | 348 | src/optimisation/Optimisation.jl | #FILE: Turing.jl/ext/TuringOptimExt.jl
##CHUNK 1
kwargs...,
)
# Convert the initial values, since it is assumed that users provide them
# in the constrained space.
# TODO(penelopeysm): As with in src/optimisation/Optimisation.jl, unclear
# whether initialisation is really necessary at all
vi = D... |
364 | 383 | Turing.jl | 402 | function Base.get(m::ModeResult, var_symbols::AbstractVector{Symbol})
log_density = m.f.ldf
# Get all the variable names in the model. This is the same as the list of keys in
# m.values, but they are more convenient to filter when they are VarNames rather than
# Symbols.
varnames = collect(
... | function Base.get(m::ModeResult, var_symbols::AbstractVector{Symbol})
log_density = m.f.ldf
# Get all the variable names in the model. This is the same as the list of keys in
# m.values, but they are more convenient to filter when they are VarNames rather than
# Symbols.
varnames = collect(
... | [
364,
383
] | function Base.get(m::ModeResult, var_symbols::AbstractVector{Symbol})
log_density = m.f.ldf
# Get all the variable names in the model. This is the same as the list of keys in
# m.values, but they are more convenient to filter when they are VarNames rather than
# Symbols.
varnames = collect(
... | function Base.get(m::ModeResult, var_symbols::AbstractVector{Symbol})
log_density = m.f.ldf
# Get all the variable names in the model. This is the same as the list of keys in
# m.values, but they are more convenient to filter when they are VarNames rather than
# Symbols.
varnames = collect(
... | getsym | 364 | 383 | src/optimisation/Optimisation.jl | #FILE: Turing.jl/src/mcmc/Inference.jl
##CHUNK 1
end
function names_values(xs::AbstractVector{<:NamedTuple})
# Obtain all parameter names.
names_set = Set{Symbol}()
for x in xs
for k in keys(x)
push!(names_set, k)
end
end
names_unique = collect(names_set)
# Extract ... |
450 | 469 | Turing.jl | 403 | function generate_initial_params(model::DynamicPPL.Model, initial_params, constraints)
if initial_params === nothing && has_generic_constraints(constraints)
throw(
ArgumentError(
"You must provide an initial value when using generic constraints."
),
)
end
... | function generate_initial_params(model::DynamicPPL.Model, initial_params, constraints)
if initial_params === nothing && has_generic_constraints(constraints)
throw(
ArgumentError(
"You must provide an initial value when using generic constraints."
),
)
end
... | [
450,
469
] | function generate_initial_params(model::DynamicPPL.Model, initial_params, constraints)
if initial_params === nothing && has_generic_constraints(constraints)
throw(
ArgumentError(
"You must provide an initial value when using generic constraints."
),
)
end
... | function generate_initial_params(model::DynamicPPL.Model, initial_params, constraints)
if initial_params === nothing && has_generic_constraints(constraints)
throw(
ArgumentError(
"You must provide an initial value when using generic constraints."
),
)
end
... | generate_initial_params | 450 | 469 | src/optimisation/Optimisation.jl | #FILE: Turing.jl/src/mcmc/gibbs.jl
##CHUNK 1
# Get the initial values for this component sampler.
initial_params_local = if initial_params === nothing
nothing
else
DynamicPPL.subset(vi, varnames)[:]
end
# Construct the conditioned model.
conditioned_model, context = make_condit... |
487 | 505 | Turing.jl | 404 | function Optimization.OptimizationProblem(log_density::OptimLogDensity, adtype, constraints)
# Note that OptimLogDensity is a callable that evaluates the model with given
# parameters. Hence we can use it in the objective function as below.
f = Optimization.OptimizationFunction(log_density, adtype; cons=con... | function Optimization.OptimizationProblem(log_density::OptimLogDensity, adtype, constraints)
# Note that OptimLogDensity is a callable that evaluates the model with given
# parameters. Hence we can use it in the objective function as below.
f = Optimization.OptimizationFunction(log_density, adtype; cons=con... | [
487,
505
] | function Optimization.OptimizationProblem(log_density::OptimLogDensity, adtype, constraints)
# Note that OptimLogDensity is a callable that evaluates the model with given
# parameters. Hence we can use it in the objective function as below.
f = Optimization.OptimizationFunction(log_density, adtype; cons=con... | function Optimization.OptimizationProblem(log_density::OptimLogDensity, adtype, constraints)
# Note that OptimLogDensity is a callable that evaluates the model with given
# parameters. Hence we can use it in the objective function as below.
f = Optimization.OptimizationFunction(log_density, adtype; cons=con... | Optimization.OptimizationProblem | 487 | 505 | src/optimisation/Optimisation.jl | #FILE: Turing.jl/ext/TuringOptimExt.jl
##CHUNK 1
_optimize(f::OptimLogDensity, optimizer=Optim.LBFGS(), args...; kwargs...)
Estimate a mode, i.e., compute a MLE or MAP estimate.
"""
function _optimize(
f::Optimisation.OptimLogDensity,
init_vals::AbstractArray=DynamicPPL.getparams(f.ldf),
optimizer::Opt... |
539 | 595 | Turing.jl | 405 | function estimate_mode(
model::DynamicPPL.Model,
estimator::ModeEstimator,
solver=nothing;
check_model::Bool=true,
initial_params=nothing,
adtype=ADTypes.AutoForwardDiff(),
cons=nothing,
lcons=nothing,
ucons=nothing,
lb=nothing,
ub=nothing,
kwargs...,
)
check_model &&... | function estimate_mode(
model::DynamicPPL.Model,
estimator::ModeEstimator,
solver=nothing;
check_model::Bool=true,
initial_params=nothing,
adtype=ADTypes.AutoForwardDiff(),
cons=nothing,
lcons=nothing,
ucons=nothing,
lb=nothing,
ub=nothing,
kwargs...,
)
check_model &&... | [
539,
595
] | function estimate_mode(
model::DynamicPPL.Model,
estimator::ModeEstimator,
solver=nothing;
check_model::Bool=true,
initial_params=nothing,
adtype=ADTypes.AutoForwardDiff(),
cons=nothing,
lcons=nothing,
ucons=nothing,
lb=nothing,
ub=nothing,
kwargs...,
)
check_model &&... | function estimate_mode(
model::DynamicPPL.Model,
estimator::ModeEstimator,
solver=nothing;
check_model::Bool=true,
initial_params=nothing,
adtype=ADTypes.AutoForwardDiff(),
cons=nothing,
lcons=nothing,
ucons=nothing,
lb=nothing,
ub=nothing,
kwargs...,
)
check_model &&... | estimate_mode | 539 | 595 | src/optimisation/Optimisation.jl | #FILE: Turing.jl/ext/TuringOptimExt.jl
##CHUNK 1
kwargs...,
)
# Convert the initial values, since it is assumed that users provide them
# in the constrained space.
# TODO(penelopeysm): As with in src/optimisation/Optimisation.jl, unclear
# whether initialisation is really necessary at all
vi = D... |
164 | 176 | Turing.jl | 406 | function unsafe_logpdf_ordered_logistic(η, cutpoints, K, k::Int)
@inbounds begin
logp = if k == 1
-StatsFuns.log1pexp(η - cutpoints[k])
elseif k < K
tmp = StatsFuns.log1pexp(cutpoints[k - 1] - η)
-tmp + StatsFuns.log1mexp(tmp - StatsFuns.log1pexp(cutpoints[k] - η)... | function unsafe_logpdf_ordered_logistic(η, cutpoints, K, k::Int)
@inbounds begin
logp = if k == 1
-StatsFuns.log1pexp(η - cutpoints[k])
elseif k < K
tmp = StatsFuns.log1pexp(cutpoints[k - 1] - η)
-tmp + StatsFuns.log1mexp(tmp - StatsFuns.log1pexp(cutpoints[k] - η)... | [
164,
176
] | function unsafe_logpdf_ordered_logistic(η, cutpoints, K, k::Int)
@inbounds begin
logp = if k == 1
-StatsFuns.log1pexp(η - cutpoints[k])
elseif k < K
tmp = StatsFuns.log1pexp(cutpoints[k - 1] - η)
-tmp + StatsFuns.log1mexp(tmp - StatsFuns.log1pexp(cutpoints[k] - η)... | function unsafe_logpdf_ordered_logistic(η, cutpoints, K, k::Int)
@inbounds begin
logp = if k == 1
-StatsFuns.log1pexp(η - cutpoints[k])
elseif k < K
tmp = StatsFuns.log1pexp(cutpoints[k - 1] - η)
-tmp + StatsFuns.log1mexp(tmp - StatsFuns.log1pexp(cutpoints[k] - η)... | unsafe_logpdf_ordered_logistic | 164 | 176 | src/stdlib/distributions.jl | #FILE: Turing.jl/test/stdlib/distributions.jl
##CHUNK 1
@testset "distributions.jl" begin
rng = StableRNG(12345)
@testset "distributions functions" begin
ns = 10
logitp = randn(rng)
d1 = BinomialLogit(ns, logitp)
d2 = Binomial(ns, logistic(logitp))
k = 3
@test lo... |
133 | 156 | Turing.jl | 407 | function _logpdf_table(d::DirichletProcess{T}, m::AbstractVector{Int}) where {T<:Real}
# construct the table
first_zero = findfirst(iszero, m)
K = first_zero === nothing ? length(m) + 1 : length(m)
table = fill(T(-Inf), K)
# exit if m is empty or contains only zeros
if iszero(m)
table[... | function _logpdf_table(d::DirichletProcess{T}, m::AbstractVector{Int}) where {T<:Real}
# construct the table
first_zero = findfirst(iszero, m)
K = first_zero === nothing ? length(m) + 1 : length(m)
table = fill(T(-Inf), K)
# exit if m is empty or contains only zeros
if iszero(m)
table[... | [
133,
156
] | function _logpdf_table(d::DirichletProcess{T}, m::AbstractVector{Int}) where {T<:Real}
# construct the table
first_zero = findfirst(iszero, m)
K = first_zero === nothing ? length(m) + 1 : length(m)
table = fill(T(-Inf), K)
# exit if m is empty or contains only zeros
if iszero(m)
table[... | function _logpdf_table(d::DirichletProcess{T}, m::AbstractVector{Int}) where {T<:Real}
# construct the table
first_zero = findfirst(iszero, m)
K = first_zero === nothing ? length(m) + 1 : length(m)
table = fill(T(-Inf), K)
# exit if m is empty or contains only zeros
if iszero(m)
table[... | _logpdf_table | 133 | 156 | src/stdlib/RandomMeasures.jl | #FILE: Turing.jl/src/stdlib/distributions.jl
##CHUNK 1
Base.maximum(::FlatPos) = Inf
Base.rand(rng::Random.AbstractRNG, d::FlatPos) = rand(rng) + d.l
function Distributions.logpdf(d::FlatPos, x::Real)
z = float(zero(x))
return x <= d.l ? oftype(z, -Inf) : z
end
# For vec support
function Distributions.loglikel... |
218 | 233 | Turing.jl | 408 | function stickbreak(v)
K = length(v) + 1
cumprod_one_minus_v = cumprod(1 .- v)
eta = [
if k == 1
v[1]
elseif k == K
cumprod_one_minus_v[K - 1]
else
v[k] * cumprod_one_minus_v[k - 1]
end for k in 1:K
]
return eta
end | function stickbreak(v)
K = length(v) + 1
cumprod_one_minus_v = cumprod(1 .- v)
eta = [
if k == 1
v[1]
elseif k == K
cumprod_one_minus_v[K - 1]
else
v[k] * cumprod_one_minus_v[k - 1]
end for k in 1:K
]
return eta
end | [
218,
233
] | function stickbreak(v)
K = length(v) + 1
cumprod_one_minus_v = cumprod(1 .- v)
eta = [
if k == 1
v[1]
elseif k == K
cumprod_one_minus_v[K - 1]
else
v[k] * cumprod_one_minus_v[k - 1]
end for k in 1:K
]
return eta
end | function stickbreak(v)
K = length(v) + 1
cumprod_one_minus_v = cumprod(1 .- v)
eta = [
if k == 1
v[1]
elseif k == K
cumprod_one_minus_v[K - 1]
else
v[k] * cumprod_one_minus_v[k - 1]
end for k in 1:K
]
return eta
end | stickbreak | 218 | 233 | src/stdlib/RandomMeasures.jl | #FILE: Turing.jl/test/stdlib/RandomMeasures.jl
##CHUNK 1
# # Infinite (truncated) collection of breaking points on unit stick.
# v = tzeros(Float64, trunc)
# # Cluster locations.
# x = tzeros(Float64, trunc)
# # Draw weights and locations.
# for... |
242 | 267 | Turing.jl | 409 | function _logpdf_table(d::PitmanYorProcess{T}, m::AbstractVector{Int}) where {T<:Real}
# sanity check
@assert d.t == sum(!iszero, m)
# construct table
first_zero = findfirst(iszero, m)
K = first_zero === nothing ? length(m) + 1 : length(m)
table = fill(T(-Inf), K)
# exit if m is empty or c... | function _logpdf_table(d::PitmanYorProcess{T}, m::AbstractVector{Int}) where {T<:Real}
# sanity check
@assert d.t == sum(!iszero, m)
# construct table
first_zero = findfirst(iszero, m)
K = first_zero === nothing ? length(m) + 1 : length(m)
table = fill(T(-Inf), K)
# exit if m is empty or c... | [
242,
267
] | function _logpdf_table(d::PitmanYorProcess{T}, m::AbstractVector{Int}) where {T<:Real}
# sanity check
@assert d.t == sum(!iszero, m)
# construct table
first_zero = findfirst(iszero, m)
K = first_zero === nothing ? length(m) + 1 : length(m)
table = fill(T(-Inf), K)
# exit if m is empty or c... | function _logpdf_table(d::PitmanYorProcess{T}, m::AbstractVector{Int}) where {T<:Real}
# sanity check
@assert d.t == sum(!iszero, m)
# construct table
first_zero = findfirst(iszero, m)
K = first_zero === nothing ? length(m) + 1 : length(m)
table = fill(T(-Inf), K)
# exit if m is empty or c... | _logpdf_table | 242 | 267 | src/stdlib/RandomMeasures.jl | #FILE: Turing.jl/src/stdlib/distributions.jl
##CHUNK 1
Base.maximum(::FlatPos) = Inf
Base.rand(rng::Random.AbstractRNG, d::FlatPos) = rand(rng) + d.l
function Distributions.logpdf(d::FlatPos, x::Real)
z = float(zero(x))
return x <= d.l ? oftype(z, -Inf) : z
end
# For vec support
function Distributions.loglikel... |
16 | 27 | Turing.jl | 410 | function ADVI(
samples_per_step::Int=1,
max_iters::Int=1000;
adtype::ADTypes.AbstractADType=ADTypes.AutoForwardDiff(),
)
Base.depwarn(
"The type ADVI will be removed in future releases. Please refer to the new interface for `vi`",
:ADVI;
force=true,
)
return ADVI{typeof(a... | function ADVI(
samples_per_step::Int=1,
max_iters::Int=1000;
adtype::ADTypes.AbstractADType=ADTypes.AutoForwardDiff(),
)
Base.depwarn(
"The type ADVI will be removed in future releases. Please refer to the new interface for `vi`",
:ADVI;
force=true,
)
return ADVI{typeof(a... | [
16,
27
] | function ADVI(
samples_per_step::Int=1,
max_iters::Int=1000;
adtype::ADTypes.AbstractADType=ADTypes.AutoForwardDiff(),
)
Base.depwarn(
"The type ADVI will be removed in future releases. Please refer to the new interface for `vi`",
:ADVI;
force=true,
)
return ADVI{typeof(a... | function ADVI(
samples_per_step::Int=1,
max_iters::Int=1000;
adtype::ADTypes.AbstractADType=ADTypes.AutoForwardDiff(),
)
Base.depwarn(
"The type ADVI will be removed in future releases. Please refer to the new interface for `vi`",
:ADVI;
force=true,
)
return ADVI{typeof(a... | ADVI | 16 | 27 | src/variational/deprecated.jl | #FILE: Turing.jl/src/mcmc/external_sampler.jl
##CHUNK 1
return Unconstrained
end
"""
externalsampler(sampler::AbstractSampler; adtype=AutoForwardDiff(), unconstrained=true)
Wrap a sampler so it can be used as an inference algorithm.
# Arguments
- `sampler::AbstractSampler`: The sampler to wrap.
# Keyword Ar... |
29 | 42 | Turing.jl | 411 | function vi(model::DynamicPPL.Model, alg::ADVI; kwargs...)
Base.depwarn(
"This specialization along with the type `ADVI` will be deprecated in future releases. Please refer to the new interface for `vi`.",
:vi;
force=true,
)
q = q_meanfield_gaussian(Random.default_rng(), model)
... | function vi(model::DynamicPPL.Model, alg::ADVI; kwargs...)
Base.depwarn(
"This specialization along with the type `ADVI` will be deprecated in future releases. Please refer to the new interface for `vi`.",
:vi;
force=true,
)
q = q_meanfield_gaussian(Random.default_rng(), model)
... | [
29,
42
] | function vi(model::DynamicPPL.Model, alg::ADVI; kwargs...)
Base.depwarn(
"This specialization along with the type `ADVI` will be deprecated in future releases. Please refer to the new interface for `vi`.",
:vi;
force=true,
)
q = q_meanfield_gaussian(Random.default_rng(), model)
... | function vi(model::DynamicPPL.Model, alg::ADVI; kwargs...)
Base.depwarn(
"This specialization along with the type `ADVI` will be deprecated in future releases. Please refer to the new interface for `vi`.",
:vi;
force=true,
)
q = q_meanfield_gaussian(Random.default_rng(), model)
... | vi | 29 | 42 | src/variational/deprecated.jl | #FILE: Turing.jl/src/variational/VariationalInference.jl
##CHUNK 1
- `q_avg`: Variational distribution formed by the averaged iterates according to `averager`.
- `state`: Collection of states used for optimization. This can be used to resume from a past call to `vi`.
- `info`: Information generated during the optimizat... |
44 | 61 | Turing.jl | 412 | function vi(
model::DynamicPPL.Model,
alg::ADVI,
q::Bijectors.TransformedDistribution{<:DistributionsAD.TuringDiagMvNormal};
kwargs...,
)
Base.depwarn(
"This specialization along with the type `ADVI` will be deprecated in future releases. Please refer to the new interface for `vi`.",
... | function vi(
model::DynamicPPL.Model,
alg::ADVI,
q::Bijectors.TransformedDistribution{<:DistributionsAD.TuringDiagMvNormal};
kwargs...,
)
Base.depwarn(
"This specialization along with the type `ADVI` will be deprecated in future releases. Please refer to the new interface for `vi`.",
... | [
44,
61
] | function vi(
model::DynamicPPL.Model,
alg::ADVI,
q::Bijectors.TransformedDistribution{<:DistributionsAD.TuringDiagMvNormal};
kwargs...,
)
Base.depwarn(
"This specialization along with the type `ADVI` will be deprecated in future releases. Please refer to the new interface for `vi`.",
... | function vi(
model::DynamicPPL.Model,
alg::ADVI,
q::Bijectors.TransformedDistribution{<:DistributionsAD.TuringDiagMvNormal};
kwargs...,
)
Base.depwarn(
"This specialization along with the type `ADVI` will be deprecated in future releases. Please refer to the new interface for `vi`.",
... | vi | 44 | 61 | src/variational/deprecated.jl | #FILE: Turing.jl/src/variational/VariationalInference.jl
##CHUNK 1
- `q_avg`: Variational distribution formed by the averaged iterates according to `averager`.
- `state`: Collection of states used for optimization. This can be used to resume from a past call to `vi`.
- `info`: Information generated during the optimizat... |
130 | 170 | Turing.jl | 413 | function q_locationscale(
rng::Random.AbstractRNG,
model::DynamicPPL.Model;
location::Union{Nothing,<:AbstractVector}=nothing,
scale::Union{Nothing,<:Diagonal,<:LowerTriangular}=nothing,
meanfield::Bool=true,
basedist::Distributions.UnivariateDistribution=Normal(),
kwargs...,
)
varinfo =... | function q_locationscale(
rng::Random.AbstractRNG,
model::DynamicPPL.Model;
location::Union{Nothing,<:AbstractVector}=nothing,
scale::Union{Nothing,<:Diagonal,<:LowerTriangular}=nothing,
meanfield::Bool=true,
basedist::Distributions.UnivariateDistribution=Normal(),
kwargs...,
)
varinfo =... | [
130,
170
] | function q_locationscale(
rng::Random.AbstractRNG,
model::DynamicPPL.Model;
location::Union{Nothing,<:AbstractVector}=nothing,
scale::Union{Nothing,<:Diagonal,<:LowerTriangular}=nothing,
meanfield::Bool=true,
basedist::Distributions.UnivariateDistribution=Normal(),
kwargs...,
)
varinfo =... | function q_locationscale(
rng::Random.AbstractRNG,
model::DynamicPPL.Model;
location::Union{Nothing,<:AbstractVector}=nothing,
scale::Union{Nothing,<:Diagonal,<:LowerTriangular}=nothing,
meanfield::Bool=true,
basedist::Distributions.UnivariateDistribution=Normal(),
kwargs...,
)
varinfo =... | q_locationscale | 130 | 170 | src/variational/VariationalInference.jl | #CURRENT FILE: Turing.jl/src/variational/VariationalInference.jl
##CHUNK 1
function q_initialize_scale(
rng::Random.AbstractRNG,
model::DynamicPPL.Model,
location::AbstractVector,
scale::AbstractMatrix,
basedist::Distributions.UnivariateDistribution;
num_samples::Int=10,
num_max_trials::Int=... |
199 | 209 | Turing.jl | 414 | function q_meanfield_gaussian(
rng::Random.AbstractRNG,
model::DynamicPPL.Model;
location::Union{Nothing,<:AbstractVector}=nothing,
scale::Union{Nothing,<:Diagonal}=nothing,
kwargs...,
)
return q_locationscale(
rng, model; location, scale, meanfield=true, basedist=Normal(), kwargs...
... | function q_meanfield_gaussian(
rng::Random.AbstractRNG,
model::DynamicPPL.Model;
location::Union{Nothing,<:AbstractVector}=nothing,
scale::Union{Nothing,<:Diagonal}=nothing,
kwargs...,
)
return q_locationscale(
rng, model; location, scale, meanfield=true, basedist=Normal(), kwargs...
... | [
199,
209
] | function q_meanfield_gaussian(
rng::Random.AbstractRNG,
model::DynamicPPL.Model;
location::Union{Nothing,<:AbstractVector}=nothing,
scale::Union{Nothing,<:Diagonal}=nothing,
kwargs...,
)
return q_locationscale(
rng, model; location, scale, meanfield=true, basedist=Normal(), kwargs...
... | function q_meanfield_gaussian(
rng::Random.AbstractRNG,
model::DynamicPPL.Model;
location::Union{Nothing,<:AbstractVector}=nothing,
scale::Union{Nothing,<:Diagonal}=nothing,
kwargs...,
)
return q_locationscale(
rng, model; location, scale, meanfield=true, basedist=Normal(), kwargs...
... | q_meanfield_gaussian | 199 | 209 | src/variational/VariationalInference.jl | #CURRENT FILE: Turing.jl/src/variational/VariationalInference.jl
##CHUNK 1
function q_locationscale(model::DynamicPPL.Model; kwargs...)
return q_locationscale(Random.default_rng(), model; kwargs...)
end
"""
q_meanfield_gaussian(
[rng::Random.AbstractRNG,]
model::DynamicPPL.Model;
locat... |
238 | 248 | Turing.jl | 415 | function q_fullrank_gaussian(
rng::Random.AbstractRNG,
model::DynamicPPL.Model;
location::Union{Nothing,<:AbstractVector}=nothing,
scale::Union{Nothing,<:LowerTriangular}=nothing,
kwargs...,
)
return q_locationscale(
rng, model; location, scale, meanfield=false, basedist=Normal(), kwargs... | function q_fullrank_gaussian(
rng::Random.AbstractRNG,
model::DynamicPPL.Model;
location::Union{Nothing,<:AbstractVector}=nothing,
scale::Union{Nothing,<:LowerTriangular}=nothing,
kwargs...,
)
return q_locationscale(
rng, model; location, scale, meanfield=false, basedist=Normal(), kwargs... | [
238,
248
] | function q_fullrank_gaussian(
rng::Random.AbstractRNG,
model::DynamicPPL.Model;
location::Union{Nothing,<:AbstractVector}=nothing,
scale::Union{Nothing,<:LowerTriangular}=nothing,
kwargs...,
)
return q_locationscale(
rng, model; location, scale, meanfield=false, basedist=Normal(), kwargs... | function q_fullrank_gaussian(
rng::Random.AbstractRNG,
model::DynamicPPL.Model;
location::Union{Nothing,<:AbstractVector}=nothing,
scale::Union{Nothing,<:LowerTriangular}=nothing,
kwargs...,
)
return q_locationscale(
rng, model; location, scale, meanfield=false, basedist=Normal(), kwargs... | q_fullrank_gaussian | 238 | 248 | src/variational/VariationalInference.jl | #CURRENT FILE: Turing.jl/src/variational/VariationalInference.jl
##CHUNK 1
model::DynamicPPL.Model;
location::Union{Nothing,<:AbstractVector}=nothing,
scale::Union{Nothing,<:Diagonal}=nothing,
kwargs...,
)
return q_locationscale(
rng, model; location, scale, meanfield=true, basedist=Normal()... |
295 | 323 | Turing.jl | 416 | function vi(
rng::Random.AbstractRNG,
model::DynamicPPL.Model,
q,
n_iterations::Int;
objective=AdvancedVI.RepGradELBO(
10; entropy=AdvancedVI.ClosedFormEntropyZeroGradient()
),
show_progress::Bool=PROGRESS[],
optimizer=AdvancedVI.DoWG(),
averager=AdvancedVI.PolynomialAveragin... | function vi(
rng::Random.AbstractRNG,
model::DynamicPPL.Model,
q,
n_iterations::Int;
objective=AdvancedVI.RepGradELBO(
10; entropy=AdvancedVI.ClosedFormEntropyZeroGradient()
),
show_progress::Bool=PROGRESS[],
optimizer=AdvancedVI.DoWG(),
averager=AdvancedVI.PolynomialAveragin... | [
295,
323
] | function vi(
rng::Random.AbstractRNG,
model::DynamicPPL.Model,
q,
n_iterations::Int;
objective=AdvancedVI.RepGradELBO(
10; entropy=AdvancedVI.ClosedFormEntropyZeroGradient()
),
show_progress::Bool=PROGRESS[],
optimizer=AdvancedVI.DoWG(),
averager=AdvancedVI.PolynomialAveragin... | function vi(
rng::Random.AbstractRNG,
model::DynamicPPL.Model,
q,
n_iterations::Int;
objective=AdvancedVI.RepGradELBO(
10; entropy=AdvancedVI.ClosedFormEntropyZeroGradient()
),
show_progress::Bool=PROGRESS[],
optimizer=AdvancedVI.DoWG(),
averager=AdvancedVI.PolynomialAveragin... | vi | 295 | 323 | src/variational/VariationalInference.jl | #FILE: Turing.jl/test/variational/advi.jl
##CHUNK 1
),
(
"ADVI with STL entropy",
AdvancedVI.RepGradELBO(10; entropy=AdvancedVI.StickingTheLandingEntropy()),
AdvancedVI.ClipScale(),
AdvancedVI.DoG(),
),
]
T = 1000
q, q_avg, _, _... |
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