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Commit c0b8ba67 authored by Bentriou Mahmoud's avatar Bentriou Mahmoud
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Reimplementation + methods of ParametricModel. It is more clear and more 

efficient.
parent dc15adc5
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Inline Side-by-side
core/common.jl
View file @ c0b8ba67
import Distributions: Distribution, Univariate, Continuous, UnivariateDistribution,
MultivariateDistribution, product_distribution
abstract type Model end
abstract type AbstractTrajectory end
......@@ -32,11 +35,6 @@ struct Trajectory <: AbstractTrajectory
transitions::Vector{Transition}
end
struct ParametricModel
m::Model
map_l_param_dist::Dict{Vector{String},Distribution}
end
struct Edge
transitions::Vector{Transition}
check_constraints::Function
......@@ -76,6 +74,12 @@ struct SynchronizedTrajectory <: AbstractTrajectory
transitions::Vector{Transition}
end
struct ParametricModel
m::Model
l_param::Vector{String}
dist::Distribution
end
# Constructors
function ContinuousTimeModel(d::Int, k::Int, map_var_idx::Dict, map_param_idx::Dict, l_transitions::Vector{String},
p::Vector{Float64}, x0::Vector{Int}, t0::Float64,
......@@ -105,16 +109,19 @@ LHA(A::LHA, map_var::Dict{String,Int}) = LHA(A.l_transitions, A.l_loc, A.Λ,
Base.:*(m::ContinuousTimeModel, A::LHA) = SynchronizedModel(m, A)
Base.:*(A::LHA, m::ContinuousTimeModel) = SynchronizedModel(m, A)
function ParametricModel(m::Model, priors::Tuple{Vector{String},Distribution}...)
map = Dict{Vector{String},Distribution}()
function ParametricModel(am::Model, priors::Tuple{String,UnivariateDistribution}...)
m = get_proba_model(am)
l_param = String[]
l_dist = Distribution{Univariate,Continuous}[]
for prior in priors
check_vars = true
for var in prior[1]
check_vars = check_vars && var in keys(get_proba_model(m).map_param_idx)
end
@assert check_vars
map[prior[1]] = prior[2]
str_p = prior[1]
dist = prior[2]
@assert str_p in keys(m.map_param_idx)
push!(l_param, str_p)
push!(l_dist, dist)
end
return ParametricModel(m, map)
return ParametricModel(m, l_param, product_distribution(l_dist))
end
core/model.jl
View file @ c0b8ba67
import Random: rand, rand!
import Distributions: pdf
load_model(name_model::String) = include(get_module_path() * "/models/" * name_model * ".jl")
function _resize_trajectory!(values::Vector{Vector{Int}}, times::Vector{Float64},
......@@ -29,7 +32,11 @@ end
Simulates a model. If `m::SynchronizedModel`, the simulation is synchronized with a
Linear Hybrid Automaton.
"""
function simulate(m::ContinuousTimeModel)
function simulate(m::ContinuousTimeModel; p::Union{Nothing,Vector{Float64}} = nothing)
p_sim = m.p
if p != nothing
p_sim = p
end
# First alloc
full_values = Vector{Vector{Int}}(undef, m.d)
for i = eachindex(full_values) full_values[i] = zeros(Int, m.estim_min_states) end
......@@ -44,7 +51,7 @@ function simulate(m::ContinuousTimeModel)
xn = m.x0 # View for type stability
tn = m.t0
# at time n+1
isabsorbing::Bool = m.isabsorbing(m.p,xn)
isabsorbing::Bool = m.isabsorbing(p_sim,xn)
# If x0 is absorbing
if isabsorbing
_resize_trajectory!(full_values, times, transitions, 1)
......@@ -60,7 +67,7 @@ function simulate(m::ContinuousTimeModel)
l_tr = Transition[nothing]
# First we fill the allocated array
for i = 2:m.estim_min_states
m.f!(vec_x, l_t, l_tr, xn, tn, m.p)
m.f!(vec_x, l_t, l_tr, xn, tn, p_sim)
tn = l_t[1]
if tn > m.time_bound
break
......@@ -68,7 +75,7 @@ function simulate(m::ContinuousTimeModel)
n += 1
xn = vec_x
_update_values!(full_values, times, transitions, xn, tn, l_tr[1], i)
isabsorbing = m.isabsorbing(m.p,xn)
isabsorbing = m.isabsorbing(p_sim,xn)
if isabsorbing
break
end
......@@ -90,7 +97,7 @@ function simulate(m::ContinuousTimeModel)
i = 0
while i < m.buffer_size
i += 1
m.f!(vec_x, l_t, l_tr, xn, tn, m.p)
m.f!(vec_x, l_t, l_tr, xn, tn, p_sim)
tn = l_t[1]
if tn > m.time_bound
i -= 1
......@@ -99,7 +106,7 @@ function simulate(m::ContinuousTimeModel)
xn = vec_x
_update_values!(full_values, times, transitions,
xn, tn, l_tr[1], m.estim_min_states+size_tmp+i)
isabsorbing = m.isabsorbing(m.p,xn)
isabsorbing = m.isabsorbing(p_sim,xn)
if isabsorbing
break
end
......@@ -119,7 +126,11 @@ function simulate(m::ContinuousTimeModel)
end
return Trajectory(m, values, times, transitions)
end
function simulate(product::SynchronizedModel)
function simulate(product::SynchronizedModel; p::Union{Nothing,Vector{Float64}} = nothing)
p_sim = m.p
if p != nothing
p_sim = p
end
m = product.m
A = product.automaton
# First alloc
......@@ -137,7 +148,7 @@ function simulate(product::SynchronizedModel)
xn = m.x0 # View for type stability
tn = m.t0
Sn = copy(S0)
isabsorbing::Bool = m.isabsorbing(m.p,xn)
isabsorbing::Bool = m.isabsorbing(p_sim,xn)
isacceptedLHA::Bool = isaccepted(Sn)
# If x0 is absorbing
if isabsorbing || isacceptedLHA
......@@ -155,7 +166,7 @@ function simulate(product::SynchronizedModel)
Snplus1 = copy(Sn)
# First we fill the allocated array
for i = 2:m.estim_min_states
m.f!(vec_x, l_t, l_tr, xn, tn, m.p)
m.f!(vec_x, l_t, l_tr, xn, tn, p_sim)
tn = l_t[1]
if tn > m.time_bound
break
......@@ -166,7 +177,7 @@ function simulate(product::SynchronizedModel)
next_state!(Snplus1, A, xn, tn, tr_n, Sn)
_update_values!(full_values, times, transitions, xn, tn, tr_n, i)
Sn = Snplus1
isabsorbing = m.isabsorbing(m.p,xn)
isabsorbing = m.isabsorbing(p_sim,xn)
isacceptedLHA = isaccepted(Snplus1)
if isabsorbing || isacceptedLHA
break
......@@ -189,7 +200,7 @@ function simulate(product::SynchronizedModel)
i = 0
while i < m.buffer_size
i += 1
m.f!(vec_x, l_t, l_tr, xn, tn, m.p)
m.f!(vec_x, l_t, l_tr, xn, tn, p_sim)
tn = l_t[1]
if tn > m.time_bound
i -= 1
......@@ -201,7 +212,7 @@ function simulate(product::SynchronizedModel)
_update_values!(full_values, times, transitions,
xn, tn, tr_n, m.estim_min_states+size_tmp+i)
Sn = Snplus1
isabsorbing = m.isabsorbing(m.p,xn)
isabsorbing = m.isabsorbing(p_sim,xn)
isacceptedLHA = isaccepted(Snplus1)
if isabsorbing || isacceptedLHA
break
......@@ -223,11 +234,24 @@ function simulate(product::SynchronizedModel)
return SynchronizedTrajectory(Sn, product, values, times, transitions)
end
function simulate(m::ContinuousTimeModel, n::Int)
end
function set_observed_var!(m::Model, g::Vector{String})
isbounded(m::ContinuousTimeModel) = m.time_bound < Inf
function check_consistency(m::ContinuousTimeModel)
@assert m.d == length(m.map_var_idx)
@assert m.k == length(m.map_param_idx)
@assert m.k == length(m.p)
@assert length(m.g) <= m.d
@assert length(m._g_idx) == length(m.g)
@assert m.buffer_size >= 0
@assert typeof(m.isabsorbing(m.p, m.x0)) == Bool
return true
end
# Set and get Model fields
function set_observed_var!(am::Model, g::Vector{String})
m = get_proba_model(am)
dobs = length(g)
_map_obs_var_idx = Dict{String}{Int}()
_g_idx = zeros(Int, dobs)
......@@ -239,8 +263,8 @@ function set_observed_var!(m::Model, g::Vector{String})
m._g_idx = _g_idx
m._map_obs_var_idx = _map_obs_var_idx
end
function observe_all!(m::Model)
function observe_all!(am::Model)
m = get_proba_model(am)
g = Vector{String}(undef, m.d)
_g_idx = collect(1:m.d)
for var in keys(m.map_var_idx)
......@@ -250,32 +274,17 @@ function observe_all!(m::Model)
m._g_idx = _g_idx
m._map_obs_var_idx = m.map_var_idx
end
isbounded(m::ContinuousTimeModel) = m.time_bound < Inf
function check_consistency(m::ContinuousTimeModel)
@assert m.d == length(m.map_var_idx)
@assert m.k == length(m.map_param_idx)
@assert m.k == length(m.p)
@assert length(m.g) <= m.d
@assert length(m._g_idx) == length(m.g)
@assert m.buffer_size >= 0
@assert typeof(m.isabsorbing(m.p, m.x0)) == Bool
return true
end
set_param!(m::ContinuousTimeModel, p::Vector{Float64}) = (m.p = p)
set_param!(m::ContinuousTimeModel, name_p::String, p_i::Float64) = (m.p[m.map_param_idx[name_p]] = p_i)
function set_param!(m::ContinuousTimeModel, l_name_p::Vector{String}, p::Vector{Float64})
nb_param = length(l_name_p)
for i = 1:nb_param
for i = eachindex(l_name_p)
set_param!(m, l_name_p[i], p[i])
end
end
get_param(m::ContinuousTimeModel) = m.p
getindex(m::ContinuousTimeModel, name_p::String) = m.p[m.map_param_idx[name_p]]
set_time_bound!(m::ContinuousTimeModel, b::Float64) = (m.time_bound = b)
set_time_bound!(sm::SynchronizedModel, b::Float64) = set_time_bound!(sm.m, b)
set_time_bound!(am::Model, b::Float64) = (get_proba_model(am).time_bound = b)
function getproperty(m::ContinuousTimeModel, sym::Symbol)
if sym == :dobs
......@@ -284,33 +293,37 @@ function getproperty(m::ContinuousTimeModel, sym::Symbol)
return getfield(m, sym)
end
end
function getproperty(pm::ParametricModel, sym::Symbol)
if sym == :df
return length(pm.l_param)
else
return getfield(pm, sym)
end
end
get_proba_model(m::ContinuousTimeModel) = m
get_proba_model(sm::SynchronizedModel) = sm.m
get_observed_var(m::ContinuousTimeModel) = m.g
get_observed_var(sm::SynchronizedModel) = (sm.m).g
get_proba_model(pm::ParametricModel) = get_proba_model(pm.m)
get_observed_var(m::Model) = get_proba_model(am).g
# Prior methods
function draw_model!(pm::ParametricModel)
dict_dist = pm.map_l_param_dist
for l_name in keys(dict_dist)
if length(l_name) == 1
set_param!(get_proba_model(pm.m), l_name[1], rand(dict_dist[l_name]))
else
set_param!(get_proba_model(pm.m), l_name, rand(dict_dist[l_name]))
end
draw_model!(pm::ParametricModel) = set_param!(get_proba_model(pm), pm.l_param, rand(pm.dist))
draw!(vec_p::AbstractVector{Float64}, pm::ParametricModel) = rand!(pm.dist, vec_p)
function draw!(mat_p::Matrix{Float64}, pm::ParametricModel)
for i = 1:size(mat_p)[2]
draw!(view(mat_p, :, i), pm)
end
end
function dim_free_param(pm::ParametricModel)
return 1
end
function draw!(vec_p::Vector{Float64}, pm::ParametricModel)
prior_density(vec_p::AbstractVector{Float64}, pm::ParametricModel) = pdf(pm.dist, vec_p)
function prior_density!(res_density::Vector{Float64}, mat_p::Matrix{Float64}, pm::ParametricModel)
for i = eachindex(res_density)
res_density[i] = prior_density(view(mat_p, :, i), pm)
end
end
function draw!(mat_p::Matrix{Float64}, pm::ParametricModel)
end
function check_bounds() end
function prior_density!(wl::Vector{Float64}, mat_p::Matrix{Float64}, pm::ParametricModel)
end
# to do: simulate(pm), create_res_dir, check_bounds, ajouter un champ _param_idx pour pm.
#
tests/unit/model_prior.jl
View file @ c0b8ba67
......@@ -5,17 +5,17 @@ load_model("ER")
test_all = true
load_model("ER")
prior1 = ParametricModel(ER, (["k2"], Uniform(2.0, 4.0)))
prior1 = ParametricModel(ER, ("k2", Uniform(2.0, 4.0)))
draw_model!(prior1)
test_all = test_all && 2.0 <= ER["k2"] <= 4.0
test_all = test_all && 2.0 <= ER["k2"] <= 4.0 && prior1.df == 1
prior2 = ParametricModel(ER, (["k3","k2"], Product(Uniform.([2.5,6.0], [3.5,7.0]))))
prior2 = ParametricModel(ER, ["k3","k2"], Product(Uniform.([2.5,6.0], [3.5,7.0])))
draw_model!(prior2)
test_all = test_all && 2.5 <= ER["k3"] <= 3.5 && 6.0 <= ER["k2"] <= 7.0
test_all = test_all && 2.5 <= ER["k3"] <= 3.5 && 6.0 <= ER["k2"] <= 7.0 && prior2.df == 2
prior3 = ParametricModel(ER, (["k3"], Uniform(10.0, 11.0)), (["k2"], Uniform(13.0, 14.0)))
prior3 = ParametricModel(ER, ("k3", Uniform(10.0, 11.0)), ("k2", Uniform(13.0, 14.0)))
draw_model!(prior3)
test_all = test_all && 10.0 <= ER["k3"] <= 11.0 && 13.0 <= ER["k2"] <= 14.0
test_all = test_all && 10.0 <= ER["k3"] <= 11.0 && 13.0 <= ER["k2"] <= 14.0 && prior3.df == 2
return test_all
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