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Commit 146e2afa authored by Bentriou Mahmoud's avatar Bentriou Mahmoud
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Improvement of the simulation core, less memory allocated.

parent 618d7910
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automata/automaton_F.jl
View file @ 146e2afa
......@@ -32,7 +32,7 @@ function create_automaton_F(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1
# "cc" as check_constraints
tuple = ("l0", "l1")
cc_aut_F_l0l1_1(A::LHA, S::StateLHA) = true
us_aut_F_l0l1_1!(A::LHA, S::StateLHA, x::SubArray{Int,1}) =
us_aut_F_l0l1_1!(A::LHA, S::StateLHA, x::Vector{Int}) =
(S.loc = "l1"; S["d"] = Inf; S["n"] = get_value(A, x, str_obs))
edge1 = Edge([nothing], cc_aut_F_l0l1_1, us_aut_F_l0l1_1!)
map_edges[tuple] = [edge1]
......@@ -41,34 +41,34 @@ function create_automaton_F(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1
tuple = ("l1", "l2")
cc_aut_F_l1l2_1(A::LHA, S::StateLHA) =
(A.l_ctes["x1"] <= S["n"] <= A.l_ctes["x2"]) && (A.l_ctes["t1"] <= S.time <= A.l_ctes["t2"])
us_aut_F_l1l2_1!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S["d"] = 0; S.loc = "l2")
us_aut_F_l1l2_1!(A::LHA, S::StateLHA, x::Vector{Int}) = (S["d"] = 0; S.loc = "l2")
edge1 = Edge([nothing], cc_aut_F_l1l2_1, us_aut_F_l1l2_1!)
cc_aut_F_l1l2_2(A::LHA, S::StateLHA) = (S["d"] > 0 && S.time > A.l_ctes["t2"])
us_aut_F_l1l2_2!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S.loc = "l2")
us_aut_F_l1l2_2!(A::LHA, S::StateLHA, x::Vector{Int}) = (S.loc = "l2")
edge2 = Edge([nothing], cc_aut_F_l1l2_2, us_aut_F_l1l2_2!)
cc_aut_F_l1l2_3(A::LHA, S::StateLHA) = (S["d"] == 0 && S.time >= A.l_ctes["t1"])
us_aut_F_l1l2_3!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S.loc = "l2")
us_aut_F_l1l2_3!(A::LHA, S::StateLHA, x::Vector{Int}) = (S.loc = "l2")
edge3 = Edge([nothing], cc_aut_F_l1l2_3, us_aut_F_l1l2_3!)
map_edges[tuple] = [edge1, edge2, edge3]
tuple = ("l1", "l3")
cc_aut_F_l1l3_1(A::LHA, S::StateLHA) = (A.l_ctes["x1"] <= S["n"] <= A.l_ctes["x2"])
us_aut_F_l1l3_1!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S["d"] = 0; S.loc = "l3")
us_aut_F_l1l3_1!(A::LHA, S::StateLHA, x::Vector{Int}) = (S["d"] = 0; S.loc = "l3")
edge1 = Edge([nothing], cc_aut_F_l1l3_1, us_aut_F_l1l3_1!)
cc_aut_F_l1l3_2(A::LHA, S::StateLHA) =
(A.l_ctes["x1"] > S["n"] || S["n"] > A.l_ctes["x2"]) && (S.time < A.l_ctes["t1"])
us_aut_F_l1l3_2!(A::LHA, S::StateLHA, x::SubArray{Int,1}) =
us_aut_F_l1l3_2!(A::LHA, S::StateLHA, x::Vector{Int}) =
(S["d"] = min(sqrt((S.time - A.l_ctes["t1"])^2 + (S["n"] - A.l_ctes["x2"])^2),
sqrt((S.time - A.l_ctes["t1"])^2 + (S["n"] - A.l_ctes["x1"])^2)); S.loc = "l3")
edge2 = Edge([nothing], cc_aut_F_l1l3_2, us_aut_F_l1l3_2!)
cc_aut_F_l1l3_3(A::LHA, S::StateLHA) =
(A.l_ctes["x1"] > S["n"] || S["n"] > A.l_ctes["x2"]) && (A.l_ctes["t1"] <= S.time <= A.l_ctes["t2"])
us_aut_F_l1l3_3!(A::LHA, S::StateLHA, x::SubArray{Int,1}) =
us_aut_F_l1l3_3!(A::LHA, S::StateLHA, x::Vector{Int}) =
(S["d"] = min(S["d"], min(abs(S["n"] - A.l_ctes["x1"]), abs(S["n"] - A.l_ctes["x2"]))); S.loc = "l3")
edge3 = Edge([nothing], cc_aut_F_l1l3_3, us_aut_F_l1l3_3!)
map_edges[tuple] = [edge1, edge2, edge3]
......@@ -76,11 +76,11 @@ function create_automaton_F(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1
# l3 loc
tuple = ("l3", "l1")
cc_aut_F_l3l1_1(A::LHA, S::StateLHA) = true
us_aut_F_l3l1_1!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S["n"] = get_value(A, x, str_obs); S.loc = "l1")
us_aut_F_l3l1_1!(A::LHA, S::StateLHA, x::Vector{Int}) = (S["n"] = get_value(A, x, str_obs); S.loc = "l1")
edge1 = Edge(["ALL"], cc_aut_F_l3l1_1, us_aut_F_l3l1_1!)
tuple = ("l3", "l2")
cc_aut_F_l3l2_1(A::LHA, S::StateLHA) = (S.time >= A.l_ctes["t2"])
us_aut_F_l3l2_1!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S["n"] = get_value(A, x, str_obs); S.loc = "l2")
us_aut_F_l3l2_1!(A::LHA, S::StateLHA, x::Vector{Int}) = (S["n"] = get_value(A, x, str_obs); S.loc = "l2")
edge2 = Edge([nothing], cc_aut_F_l3l2_1, us_aut_F_l3l2_1!)
map_edges[tuple] = [edge1, edge2]
......
automata/automaton_G.jl
View file @ 146e2afa
......@@ -33,7 +33,7 @@ function create_automaton_G(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1
# l0 loc
tuple = ("l0", "l1")
cc_aut_G_l0l1_1(A::LHA, S::StateLHA) = true
us_aut_G_l0l1_1!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S.loc = "l1"; S["d"] = 0; S["n"] = get_value(A, x, str_obs); S["in"] = true)
us_aut_G_l0l1_1!(A::LHA, S::StateLHA, x::Vector{Int}) = (S.loc = "l1"; S["d"] = 0; S["n"] = get_value(A, x, str_obs); S["in"] = true)
edge1 = Edge([nothing], cc_aut_G_l0l1_1, us_aut_G_l0l1_1!)
map_edges[tuple] = [edge1]
......@@ -41,64 +41,64 @@ function create_automaton_G(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1
tuple = ("l1", "l3")
cc_aut_G_l1l3_1(A::LHA, S::StateLHA) =
(S.time < A.l_ctes["t1"] && (S["n"] < A.l_ctes["x1"] || S["n"] > A.l_ctes["x2"]))
us_aut_G_l1l3_1!(A::LHA, S::StateLHA, x::SubArray{Int,1}) =
us_aut_G_l1l3_1!(A::LHA, S::StateLHA, x::Vector{Int}) =
(S.loc = "l3"; S["d"] = min(abs(A.l_ctes["x1"] - S["n"]), abs(A.l_ctes["x2"] - S["n"])); S["in"] = false)
edge1 = Edge([nothing], cc_aut_G_l1l3_1, us_aut_G_l1l3_1!)
cc_aut_G_l1l3_2(A::LHA, S::StateLHA) =
(S.time < A.l_ctes["t1"] && (A.l_ctes["x1"] <= S["n"] <= A.l_ctes["x2"]))
us_aut_G_l1l3_2!(A::LHA, S::StateLHA, x::SubArray{Int,1}) =
us_aut_G_l1l3_2!(A::LHA, S::StateLHA, x::Vector{Int}) =
(S.loc = "l3"; S["d"] = 0; S["in"] = false)
edge2 = Edge([nothing], cc_aut_G_l1l3_2, us_aut_G_l1l3_2!)
cc_aut_G_l1l3_3(A::LHA, S::StateLHA) =
(!isin(S["in"]) && (A.l_ctes["t1"] <= S.time <= A.l_ctes["t2"]) && (A.l_ctes["x1"] <= S["n"] <= A.l_ctes["x2"]))
us_aut_G_l1l3_3!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S.loc = "l3"; S["d"] = S["d"] * (S.time - A.l_ctes["t1"]); S["t'"] = 0.0)
us_aut_G_l1l3_3!(A::LHA, S::StateLHA, x::Vector{Int}) = (S.loc = "l3"; S["d"] = S["d"] * (S.time - A.l_ctes["t1"]); S["t'"] = 0.0)
edge3 = Edge([nothing], cc_aut_G_l1l3_3, us_aut_G_l1l3_3!)
cc_aut_G_l1l3_4(A::LHA, S::StateLHA) =
(isin(S["in"]) && (A.l_ctes["t1"] <= S.time <= A.l_ctes["t2"]) && (A.l_ctes["x1"] <= S["n"] <= A.l_ctes["x2"]))
us_aut_G_l1l3_4!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S.loc = "l3"; S["t'"] = 0.0)
us_aut_G_l1l3_4!(A::LHA, S::StateLHA, x::Vector{Int}) = (S.loc = "l3"; S["t'"] = 0.0)
edge4 = Edge([nothing], cc_aut_G_l1l3_4, us_aut_G_l1l3_4!)
map_edges[tuple] = [edge1, edge2, edge3, edge4]
tuple = ("l1", "l4")
cc_aut_G_l1l4_1(A::LHA, S::StateLHA) =
(!isin(S["in"]) && (A.l_ctes["t1"] <= S.time <= A.l_ctes["t2"]) && (S["n"] < A.l_ctes["x1"] || S["n"] > A.l_ctes["x2"]))
us_aut_G_l1l4_1!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S.loc = "l4"; S["d"] += S["d"] * (S.time - A.l_ctes["t1"]))
us_aut_G_l1l4_1!(A::LHA, S::StateLHA, x::Vector{Int}) = (S.loc = "l4"; S["d"] += S["d"] * (S.time - A.l_ctes["t1"]))
edge1 = Edge([nothing], cc_aut_G_l1l4_1, us_aut_G_l1l4_1!)
cc_aut_G_l1l4_2(A::LHA, S::StateLHA) =
(isin(S["in"]) && (A.l_ctes["t1"] <= S.time <= A.l_ctes["t2"]) && (S["n"] < A.l_ctes["x1"] || S["n"] > A.l_ctes["x2"]))
us_aut_G_l1l4_2!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S.loc = "l4")
us_aut_G_l1l4_2!(A::LHA, S::StateLHA, x::Vector{Int}) = (S.loc = "l4")
edge2 = Edge([nothing], cc_aut_G_l1l4_2, us_aut_G_l1l4_2!)
map_edges[tuple] = [edge1, edge2]
tuple = ("l1", "l2")
cc_aut_G_l1l2_1(A::LHA, S::StateLHA) = (isin(S["in"]) && S.time >= A.l_ctes["t2"])
us_aut_G_l1l2_1!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S.loc = "l2")
us_aut_G_l1l2_1!(A::LHA, S::StateLHA, x::Vector{Int}) = (S.loc = "l2")
edge1 = Edge([nothing], cc_aut_G_l1l2_1, us_aut_G_l1l2_1!)
cc_aut_G_l1l2_2(A::LHA, S::StateLHA) = (!isin(S["in"]) && S.time >= A.l_ctes["t2"])
us_aut_G_l1l2_2!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S.loc = "l2"; S["d"] = S["d"] * (A.l_ctes["t2"] - A.l_ctes["t1"]))
us_aut_G_l1l2_2!(A::LHA, S::StateLHA, x::Vector{Int}) = (S.loc = "l2"; S["d"] = S["d"] * (A.l_ctes["t2"] - A.l_ctes["t1"]))
edge2 = Edge([nothing], cc_aut_G_l1l2_2, us_aut_G_l1l2_2!)
map_edges[tuple] = [edge1, edge2]
# l3 loc
tuple = ("l3", "l1")
cc_aut_G_l3l1_1(A::LHA, S::StateLHA) = true
us_aut_G_l3l1_1!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S.loc = "l1"; S["n"] = get_value(A, x, str_obs))
us_aut_G_l3l1_1!(A::LHA, S::StateLHA, x::Vector{Int}) = (S.loc = "l1"; S["n"] = get_value(A, x, str_obs))
edge1 = Edge(["ALL"], cc_aut_G_l3l1_1, us_aut_G_l3l1_1!)
map_edges[tuple] = [edge1]
tuple = ("l3", "l2")
cc_aut_G_l3l2_1(A::LHA, S::StateLHA) = (isin(S["in"]) && S.time >= A.l_ctes["t2"])
us_aut_G_l3l2_1!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S.loc = "l2")
us_aut_G_l3l2_1!(A::LHA, S::StateLHA, x::Vector{Int}) = (S.loc = "l2")
edge1 = Edge([nothing], cc_aut_G_l3l2_1, us_aut_G_l3l2_1!)
cc_aut_G_l3l2_2(A::LHA, S::StateLHA) = (!isin(S["in"]) && S.time >= A.l_ctes["t2"])
us_aut_G_l3l2_2!(A::LHA, S::StateLHA, x::SubArray{Int,1}) = (S.loc = "l2"; S["d"] = S["d"] * (A.l_ctes["t2"] - A.l_ctes["t1"]))
us_aut_G_l3l2_2!(A::LHA, S::StateLHA, x::Vector{Int}) = (S.loc = "l2"; S["d"] = S["d"] * (A.l_ctes["t2"] - A.l_ctes["t1"]))
edge2 = Edge([nothing], cc_aut_G_l3l2_2, us_aut_G_l3l2_2!)
map_edges[tuple] = [edge1, edge2]
# l4 loc
tuple = ("l4", "l1")
cc_aut_G_l4l1_1(A::LHA, S::StateLHA) = true
us_aut_G_l4l1_1!(A::LHA, S::StateLHA, x::SubArray{Int,1}) =
us_aut_G_l4l1_1!(A::LHA, S::StateLHA, x::Vector{Int}) =
(S.loc = "l1"; S["d"] += S["t'"] * min(abs(A.l_ctes["x1"] - S["n"]), abs(A.l_ctes["x2"] - S["n"]));
S["t'"] = 0.0; S["n"] = get_value(A, x, str_obs); S["in"] = true)
edge1 = Edge(["ALL"], cc_aut_G_l4l1_1, us_aut_G_l4l1_1!)
......@@ -106,7 +106,7 @@ function create_automaton_G(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1
tuple = ("l4", "l2")
cc_aut_G_l4l2_1(A::LHA, S::StateLHA) = (S.time >= A.l_ctes["t2"])
us_aut_G_l4l2_1!(A::LHA, S::StateLHA, x::SubArray{Int,1}) =
us_aut_G_l4l2_1!(A::LHA, S::StateLHA, x::Vector{Int}) =
(S.loc = "l2"; S["d"] += S["t'"] * min(abs(A.l_ctes["x1"] - S["n"]), abs(A.l_ctes["x2"] - S["n"])); S["t'"] = 0.0)
edge1 = Edge([nothing], cc_aut_G_l4l2_1, us_aut_G_l4l2_1!)
map_edges[tuple] = [edge1]
......
bench/pygmalion/multiple_long_sim_er.jl
View file @ 146e2afa
......@@ -20,29 +20,29 @@ g_all = create_observation_function([ObserverModel(str_oml, tml)])
@timev pygmalion.simulate(f, g_all, x0, u, p_true; on = nothing, full_timeline = true)
b1_pyg = @benchmark for i=1:$(nb_sim) pygmalion.simulate($f, $g_all, $x0, $u, $p_true; on = nothing, full_timeline = true) end
b2_pyg = @benchmark for i=1:$(nb_sim) pygmalion.simulate(f, g_all, x0, u, p_true; on = nothing, full_timeline = true) end
#b2_pyg = @benchmark for i=1:$(nb_sim) pygmalion.simulate(f, g_all, x0, u, p_true; on = nothing, full_timeline = true) end
@show minimum(b1_pyg), mean(b1_pyg), maximum(b1_pyg)
@show minimum(b2_pyg), mean(b2_pyg), maximum(b2_pyg)
#@show minimum(b2_pyg), mean(b2_pyg), maximum(b2_pyg)
println("MarkovProcesses:")
using MarkovProcesses
MarkovProcesses.load_model("ER")
ER.time_bound = 20.0
ER.estim_min_states = 9000
set_param!(ER, "k1", 0.2)
set_param!(ER, "k2", 40.0)
@timev MarkovProcesses.simulate(ER)
println("Default buffer size=10")
b1 = @benchmark for i=1:$(nb_sim) MarkovProcesses.simulate($ER) end
b2 = @benchmark for i=1:$(nb_sim) MarkovProcesses.simulate(ER) end
#b2 = @benchmark for i=1:$(nb_sim) MarkovProcesses.simulate(ER) end
@show minimum(b1), mean(b1), maximum(b1)
@show minimum(b2), mean(b2), maximum(b2)
println("Buffer size 100")
#@show minimum(b2), mean(b2), maximum(b2)
println("Buffer size 100 / min_states 8000")
ER.estim_min_states = 8000
ER.buffer_size = 100
b1_100 = @benchmark for i=1:$(nb_sim) MarkovProcesses.simulate($ER) end
b2_100 = @benchmark for i=1:$(nb_sim) MarkovProcesses.simulate(ER) end
#b2_100 = @benchmark for i=1:$(nb_sim) MarkovProcesses.simulate(ER) end
@show minimum(b1_100), mean(b1_100), maximum(b1_100)
@show minimum(b2_100), mean(b2_100), maximum(b2_100)
#@show minimum(b2_100), mean(b2_100), maximum(b2_100)
core/_tests_simulate.jl
View file @ 146e2afa
......@@ -334,66 +334,3 @@ function _simulate_d7458(m::ContinuousTimeModel)
return Trajectory(m, values, times, transitions)
end
function _simulate_last(product::SynchronizedModel)
# trajectory fields
m = product.m
A = product.automaton
full_values = Vector{Vector{Int}}(undef, m.d)
for i = 1:m.d full_values[i] = Int[m.x0[i]] end
for i = 1:m.d sizehint!(full_values[i], m.estim_min_states) end
times = Float64[m.t0]
transitions = Union{String,Nothing}[nothing]
reshaped_x0 = view(reshape(m.x0, 1, m.d), 1, :) # View for type stability
S0 = init_state(A, m.x0, m.t0)
# values at time n
n = 0
xn = reshaped_x0
tn = m.t0
Sn = copy(S0)
# at time n+1
mat_x = zeros(Int, m.buffer_size, m.d)
l_t = zeros(Float64, m.buffer_size)
l_tr = Vector{String}(undef, m.buffer_size)
isabsorbing::Bool = m.isabsorbing(m.p,xn)
isacceptedLHA::Bool = isaccepted(Sn)
Snplus1 = copy(Sn)
while !isabsorbing && tn <= m.time_bound && !isacceptedLHA
i = 0
while i < m.buffer_size && !isabsorbing && tn <= m.time_bound && !isacceptedLHA
i += 1
m.f!(mat_x, l_t, l_tr, i, xn, tn, m.p)
tn = l_t[i]
if tn > m.time_bound
i -= 1 # 0 is an ok value, 1:0 is allowed
break
end
xn = view(mat_x, i, :)
tr_n = l_tr[i]
next_state!(Snplus1, A, xn, tn, tr_n, Sn)
Sn = Snplus1
isabsorbing = m.isabsorbing(m.p,xn)
isacceptedLHA = isaccepted(Snplus1)
end
for k = 1:m.d
append!(full_values[k], view(mat_x, 1:i, k))
end
append!(times, view(l_t, 1:i))
append!(transitions, view(l_tr, 1:i))
n += i
end
# When the trajectory is accepted, we should not add an end value
if isbounded(m) && !isaccepted(Sn)
@assert times[end] < m.time_bound
for k = 1:m.d
push!(full_values[k], full_values[k][end])
end
push!(times, m.time_bound)
push!(transitions, nothing)
end
values = full_values[m._g_idx]
return SynchronizedTrajectory(Snplus1, product, values, times, transitions)
end
core/lha.jl
View file @ 146e2afa
......@@ -2,7 +2,7 @@
load_automaton(automaton::String) = include(get_module_path() * "/automata/$(automaton).jl")
length_var(A::LHA) = length(A.map_var_automaton_idx)
get_value(A::LHA, x::SubArray{Int,1}, var::String) = x[A.map_var_model_idx[var]]
get_value(A::LHA, x::Vector{Int}, var::String) = x[A.map_var_model_idx[var]]
copy(S::StateLHA) = StateLHA(S.A, S.loc, S.l_var, S.time)
# Not overring getproperty, setproperty to avoid a conversion Symbol => String for the dict key
......@@ -36,7 +36,7 @@ function init_state(A::LHA, x0::Vector{Int}, t0::Float64)
end
function next_state!(Snplus1::StateLHA, A::LHA,
xnplus1::SubArray{Int,1}, tnplus1::Float64, tr_nplus1::Transition,
xnplus1::Vector{Int}, tnplus1::Float64, tr_nplus1::Transition,
Sn::StateLHA; verbose = false)
for i in eachindex(Snplus1.l_var)
Snplus1.l_var[i] += (A.l_flow[Sn.loc])[i]*(tnplus1 - Sn.time)
......@@ -119,17 +119,12 @@ function read_trajectory(A::LHA, σ::Trajectory; verbose = false)
A_new = LHA(A, (σ.m)._map_obs_var_idx)
l_t = times(σ)
l_tr = transitions(σ)
mat_x = zeros(Int, length_states(σ), σ.m.d)
for (i,var) in enumerate(σ.m.g)
mat_x[:,i] = σ[var]
end
Sn = init_state(A_new, σ[1], l_t[1])
Snplus1 = copy(Sn)
if verbose println("Init: ") end
if verbose @show Sn end
for n in 1:length_states(σ)
xn = view(mat_x, n, :)
next_state!(Snplus1, A_new, xn, l_t[n], l_tr[n], Sn; verbose = verbose)
next_state!(Snplus1, A_new, σ[n], l_t[n], l_tr[n], Sn; verbose = verbose)
if Snplus1.loc in A_new.l_loc_final
break
end
......
core/model.jl
View file @ 146e2afa
......@@ -11,12 +11,18 @@ end
function _finish_bounded_trajectory!(values::Vector{Vector{Int}}, times::Vector{Float64},
transitions::Vector{Transition}, time_bound::Float64)
for i = eachindex(values) push!(values[i], values[i][end]) end
push!(times, time_bound)
push!(transitions, nothing)
end
function _update_values!(values::Vector{Vector{Int}}, times::Vector{Float64}, transitions::Vector{Transition},
xn::Vector{Int}, tn::Float64, tr_n::Transition, idx::Int)
for k = eachindex(values) values[k][idx] = xn[k] end
times[idx] = tn
transitions[idx] = tr_n
end
"""
`simulate(m)`
......@@ -35,7 +41,7 @@ function simulate(m::ContinuousTimeModel)
transitions[1] = nothing
# Values at time n
n = 1
xn = view(reshape(m.x0, 1, m.d), 1, :) # View for type stability
xn = m.x0 # View for type stability
tn = m.t0
# at time n+1
isabsorbing::Bool = m.isabsorbing(m.p,xn)
......@@ -48,22 +54,20 @@ function simulate(m::ContinuousTimeModel)
end
return Trajectory(m, values, times, transitions)
end
# First we fill the allocated array
mat_x = zeros(Int, 1, m.d)
# Alloc of vectors where we stock n+1 values
vec_x = zeros(Int, m.d)
l_t = Float64[0.0]
l_tr = Transition[nothing]
# First we fill the allocated array
for i = 2:m.estim_min_states
m.f!(mat_x, l_t, l_tr, 1, xn, tn, m.p)
m.f!(vec_x, l_t, l_tr, xn, tn, m.p)
tn = l_t[1]
if tn > m.time_bound
break
end
n += 1
xn = view(mat_x, 1, :)
# Updating value
for k = eachindex(full_values) full_values[k][n] = xn[k] end
times[n] = tn
transitions[n] = l_tr[1]
xn = vec_x
_update_values!(full_values, times, transitions, xn, tn, l_tr[1], i)
isabsorbing = m.isabsorbing(m.p,xn)
if isabsorbing
break
......@@ -79,48 +83,34 @@ function simulate(m::ContinuousTimeModel)
return Trajectory(m, values, times, transitions)
end
# Otherwise, buffering system
mat_x = zeros(Int, m.buffer_size, m.d)
l_t = zeros(Float64, m.buffer_size)
l_tr = Vector{Transition}(undef, m.buffer_size)
# Alloc buffer values
tmp_full_values = Vector{Vector{Int}}(undef, m.d)
for i = eachindex(tmp_full_values) tmp_full_values[i] = zeros(Int, 0) end
tmp_times = zeros(Float64, 0)
tmp_transitions = Vector{Transition}(undef, 0)
tmp_idx = 0
size_tmp = 0
while !isabsorbing && tn <= m.time_bound
_resize_trajectory!(tmp_full_values, tmp_times, tmp_transitions, tmp_idx+m.buffer_size)
# Alloc buffer
_resize_trajectory!(full_values, times, transitions, m.estim_min_states+size_tmp+m.buffer_size)
i = 0
while i < m.buffer_size
i += 1
m.f!(mat_x, l_t, l_tr, i, xn, tn, m.p)
tn = l_t[i]
m.f!(vec_x, l_t, l_tr, xn, tn, m.p)
tn = l_t[1]
if tn > m.time_bound
i -= 1
break
end
xn = view(mat_x, i, :)
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)
if isabsorbing
break
end
end
# Update values
rng_tmp = (tmp_idx+1):(tmp_idx+m.buffer_size)
for k = eachindex(tmp_full_values) tmp_full_values[k][rng_tmp] = view(mat_x, :, k) end
tmp_times[rng_tmp] = l_t
tmp_transitions[rng_tmp] = l_tr
# If simulation ended before the end of buffer
if i < m.buffer_size
_resize_trajectory!(tmp_full_values, tmp_times, tmp_transitions, tmp_idx+i)
_resize_trajectory!(full_values, times, transitions, m.estim_min_states+size_tmp+i)
end
tmp_idx += i
size_tmp += i
n += i
end
# Push the temporary values
for k = eachindex(full_values) append!(full_values[k], tmp_full_values[k]) end
append!(times, tmp_times)
append!(transitions, tmp_transitions)
values = full_values[m._g_idx]
if isbounded(m)
# Add last value: the convention is the last transition is nothing,
......@@ -144,13 +134,13 @@ function simulate(product::SynchronizedModel)
S0 = init_state(A, m.x0, m.t0)
# Values at time n
n = 1
xn = view(reshape(m.x0, 1, m.d), 1, :) # View for type stability
xn = m.x0 # View for type stability
tn = m.t0
Sn = copy(S0)
# at time n+1
isabsorbing::Bool = m.isabsorbing(m.p,xn)
isacceptedLHA::Bool = isaccepted(Sn)
if isabsorbing || isacceptedLHA
# If x0 is absorbing
if isabsorbing || isacceptedLHA
_resize_trajectory!(full_values, times, transitions, 1)
values = full_values[m._g_idx]
if isbounded(m)
......@@ -158,95 +148,79 @@ function simulate(product::SynchronizedModel)
end
return SynchronizedTrajectory(Sn, product, values, times, transitions)
end
# First we fill the allocated array
mat_x = zeros(Int, 1, m.d)
# Alloc of vectors where we stock n+1 values
vec_x = zeros(Int, m.d)
l_t = Float64[0.0]
l_tr = Transition[nothing]
Snplus1 = copy(Sn)
# First we fill the allocated array
for i = 2:m.estim_min_states
m.f!(mat_x, l_t, l_tr, 1, xn, tn, m.p)
m.f!(vec_x, l_t, l_tr, xn, tn, m.p)
tn = l_t[1]
if tn > m.time_bound
break
end
n += 1
xn = view(mat_x, 1, :)
xn = vec_x
tr_n = l_tr[1]
next_state!(Snplus1, A, xn, tn, tr_n, Sn)
_update_values!(full_values, times, transitions, xn, tn, tr_n, i)
Sn = Snplus1
# Updating value
for k = eachindex(full_values) full_values[k][n] = xn[k] end
times[n] = tn
transitions[n] = l_tr[1]
isabsorbing = m.isabsorbing(m.p,xn)
isacceptedLHA = isaccepted(Snplus1)
if isabsorbing || isacceptedLHA
break
end
end
# If simulation ended
# If simulation ended before the estimation of states
if n < m.estim_min_states
_resize_trajectory!(full_values, times, transitions, n)
values = full_values[m._g_idx]
if isbounded(m)
_finish_bounded_trajectory!(values, times, transitions, m.time_bound)
end
return SynchronizedTrajectory(Snplus1, product, values, times, transitions)
return SynchronizedTrajectory(Sn, product, values, times, transitions)
end
# Otherwise, buffering system
mat_x = zeros(Int, m.buffer_size, m.d)
l_t = zeros(Float64, m.buffer_size)
l_tr = Vector{Transition}(undef, m.buffer_size)
# Alloc buffer values
tmp_full_values = Vector{Vector{Int}}(undef, m.d)
for i = eachindex(tmp_full_values) tmp_full_values[i] = zeros(Int, 0) end
tmp_times = zeros(Float64, 0)
tmp_transitions = Vector{Transition}(undef, 0)
tmp_idx = 0
size_tmp = 0
while !isabsorbing && tn <= m.time_bound && !isacceptedLHA
_resize_trajectory!(tmp_full_values, tmp_times, tmp_transitions, tmp_idx+m.buffer_size)
# Alloc buffer
_resize_trajectory!(full_values, times, transitions, m.estim_min_states+size_tmp+m.buffer_size)
i = 0
while i < m.buffer_size
i += 1
m.f!(mat_x, l_t, l_tr, i, xn, tn, m.p)
tn = l_t[i]
m.f!(vec_x, l_t, l_tr, xn, tn, m.p)
tn = l_t[1]
if tn > m.time_bound
i -= 1
break
end
xn = view(mat_x, i, :)
tr_n = l_tr[i]
xn = vec_x
tr_n = l_tr[1]
next_state!(Snplus1, A, xn, tn, tr_n, Sn)
_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)
isacceptedLHA = isaccepted(Snplus1)
if isabsorbing || isacceptedLHA
if isabsorbing || isacceptedLHA
break
end
end
# Update values
rng_tmp = (tmp_idx+1):(tmp_idx+m.buffer_size)
for k = eachindex(tmp_full_values) tmp_full_values[k][rng_tmp] = view(mat_x, :, k) end
tmp_times[rng_tmp] = l_t
tmp_transitions[rng_tmp] = l_tr
# If simulation ended before the end of buffer
if i < m.buffer_size
_resize_trajectory!(tmp_full_values, tmp_times, tmp_transitions, tmp_idx+i)
_resize_trajectory!(full_values, times, transitions, m.estim_min_states+size_tmp+i)
end
tmp_idx += i
size_tmp += i
n += i
end
# Push the temporary values
for k = eachindex(full_values) append!(full_values[k], tmp_full_values[k]) end
append!(times, tmp_times)
append!(transitions, tmp_transitions)
values = full_values[m._g_idx]
if isbounded(m) && !isaccepted(Sn)
# Add last value: the convention is the last transition is nothing,
# the trajectory is bounded
_finish_bounded_trajectory!(values, times, transitions, m.time_bound)
end
return SynchronizedTrajectory(Snplus1, product, values, times, transitions)
return SynchronizedTrajectory(Sn, product, values, times, transitions)
end
......@@ -274,7 +248,7 @@ function check_consistency(m::ContinuousTimeModel)
@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, view(reshape(m.x0, 1, m.d), 1, :))) == Bool
@assert typeof(m.isabsorbing(m.p, m.x0)) == Bool