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Commit ec345f72 authored by Bentriou Mahmoud's avatar Bentriou Mahmoud
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Small fix in tests

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tests/automata/euclidean_distance_single.jl
+ 2
− 2
View file @ ec345f72
...@@ -10,8 +10,8 @@ set_time_bound!(SIR, 200.0) ...@@ -10,8 +10,8 @@ set_time_bound!(SIR, 200.0)
y_obs = vectorize(simulate(SIR), :I, tml_obs) y_obs = vectorize(simulate(SIR), :I, tml_obs)
sync_SIR = SIR * create_euclidean_distance_automaton(SIR, tml_obs, y_obs, :I) sync_SIR = SIR * create_euclidean_distance_automaton(SIR, tml_obs, y_obs, :I)
σ = simulate(sync_SIR) σ = simulate(sync_SIR)
test = euclidean_distance(σ, tml_obs, y_obs, :I) == σ.state_lha_end[:d] test = euclidean_distance(σ, :I, tml_obs, y_obs) == σ.state_lha_end[:d]
@show test, euclidean_distance(σ, tml_obs, y_obs, :I), σ.state_lha_end[:d] @show test, euclidean_distance(σ, :I, tml_obs, y_obs), σ.state_lha_end[:d]
return test return test
tests/cosmos/distance_F/ER_1D.jl
+ 32
− 30
View file @ ec345f72
...@@ -28,41 +28,43 @@ for exp in l_exp ...@@ -28,41 +28,43 @@ for exp in l_exp
continue continue
end end
val_exp = dict_exp[exp] val_exp = dict_exp[exp]
x1, x2, t1, t2 = val_exp[1], val_exp[2], val_exp[3], val_exp[4]
A_F = create_automaton_F(model, x1, x2, t1, t2, :P)
l_k3 = 0:10:100 l_k3 = 0:10:100
nb_param = length(l_k3) nb_param = length(l_k3)
l_dist_cosmos = zeros(nb_param) l_dist_cosmos = zeros(nb_param)
l_dist_pkg = zeros(nb_param) l_dist_pkg = zeros(nb_param)
for i in 1:nb_param for i in 1:nb_param
# Cosmos estimation let test, test2, nb_sim, sync_ER, k3, x1, x2, t1, t2, A_F
k3 = l_k3[i] x1, x2, t1, t2 = val_exp[1], val_exp[2], val_exp[3], val_exp[4]
command = `Cosmos $(absolute_path * "models/" * str_model * ".gspn") A_F = create_automaton_F(model, x1, x2, t1, t2, :P)
$(absolute_path * "distance_F/dist_F_" * str_model * ".lha") --njob $(nworkers()) # Cosmos estimation
--const k_3=$(k3),x1=$x1,x2=$x2,t1=$t1,t2=$t2 k3 = l_k3[i]
--level $(level) --width $(width) command = `Cosmos $(absolute_path * "models/" * str_model * ".gspn")
--verbose 0` $(absolute_path * "distance_F/dist_F_" * str_model * ".lha") --njob $(nworkers())
#run(command) --const k_3=$(k3),x1=$x1,x2=$x2,t1=$t1,t2=$t2
run(pipeline(command, stderr=devnull)) --level $(level) --width $(width)
dict_values = cosmos_get_values("Result_dist_F_$(str_model).res") --verbose 0`
l_dist_cosmos[i] = dict_values["Estimated value"][1] #run(command)
nb_sim = dict_values["Total paths"][1] run(pipeline(command, stderr=devnull))
nb_accepted = dict_values["Accepted paths"][1] dict_values = cosmos_get_values("Result_dist_F_$(str_model).res")
nb_sim = convert(Int, nb_sim) l_dist_cosmos[i] = dict_values["Estimated value"][1]
# MarkovProcesses estimation nb_sim = dict_values["Total paths"][1]
set_param!(ER, :k3, convert(Float64, k3)) nb_accepted = dict_values["Accepted paths"][1]
sync_ER = ER*A_F nb_sim = convert(Int, nb_sim)
l_dist_pkg[i], nb_accepts_pkg = distribute_mean_value_lha(sync_ER, :d, nb_sim; with_accepts = true) # MarkovProcesses estimation
#@info "About accepts" nb_sim nb_accepted nb_accepts_pkg set_param!(ER, :k3, convert(Float64, k3))
test = isapprox(l_dist_cosmos[i], l_dist_pkg[i]; atol = width*1.01) || sync_ER = ER*A_F
(l_dist_cosmos[i] == 9997999 && l_dist_pkg[i] == Inf) l_dist_pkg[i], nb_accepts_pkg = distribute_mean_value_lha(sync_ER, :d, nb_sim; with_accepts = true)
test2 = nb_accepts_pkg == (nb_sim / nb_accepted) #@info "About accepts" nb_sim nb_accepted nb_accepts_pkg
global test_all = test_all && test && test2 test = isapprox(l_dist_cosmos[i], l_dist_pkg[i]; atol = width*1.01) ||
if !test (l_dist_cosmos[i] == 9997999 && l_dist_pkg[i] == Inf)
@show l_dist_pkg[i], l_dist_cosmos[i] test2 = nb_accepts_pkg == (nb_sim / nb_accepted)
@show exp global test_all = test_all && test && test2
@show k3 if !test
@show nb_sim @show l_dist_pkg[i], l_dist_cosmos[i]
@show exp
@show k3
@show nb_sim
end
end end
end end
@info exp l_dist_pkg l_dist_cosmos @info exp l_dist_pkg l_dist_cosmos
......
tests/cosmos/distance_G/ER_R5.jl
+ 27
− 25
View file @ ec345f72
...@@ -29,32 +29,34 @@ mat_full_k1 = zeros(nb_k1,nb_k2) ...@@ -29,32 +29,34 @@ mat_full_k1 = zeros(nb_k1,nb_k2)
mat_full_k2 = zeros(nb_k1,nb_k2) mat_full_k2 = zeros(nb_k1,nb_k2)
for i = 1:nb_k1 for i = 1:nb_k1
for j = 1:nb_k2 for j = 1:nb_k2
# Cosmos estimation let test, test2, nb_sim, sync_ER, k1, k2
k1 = l_k1[i] # Cosmos estimation
k2 = l_k2[j] k1 = l_k1[i]
command = `Cosmos $(absolute_path * "models/" * str_model * ".gspn") k2 = l_k2[j]
$(absolute_path * "distance_G/dist_G_" * str_model * ".lha") --njob $(nworkers()) command = `Cosmos $(absolute_path * "models/" * str_model * ".gspn")
--const k_1=$(k1),k_2=$(k2),x1=$x1,x2=$x2,t1=$t1,t2=$t2 $(absolute_path * "distance_G/dist_G_" * str_model * ".lha") --njob $(nworkers())
--level $(level) --width $(width) --const k_1=$(k1),k_2=$(k2),x1=$x1,x2=$x2,t1=$t1,t2=$t2
--verbose 0` --level $(level) --width $(width)
run(pipeline(command, stderr=devnull)) --verbose 0`
dict_values = cosmos_get_values("Result_dist_G_$(str_model).res") run(pipeline(command, stderr=devnull))
mat_dist_cosmos[i,j] = dict_values["Estimated value"][1] dict_values = cosmos_get_values("Result_dist_G_$(str_model).res")
nb_sim = dict_values["Total paths"][1] mat_dist_cosmos[i,j] = dict_values["Estimated value"][1]
nb_accepted = dict_values["Accepted paths"][1] nb_sim = dict_values["Total paths"][1]
nb_sim = convert(Int, nb_sim) nb_accepted = dict_values["Accepted paths"][1]
# MarkovProcesses estimation nb_sim = convert(Int, nb_sim)
set_param!(ER, :k1, convert(Float64, k1)) # MarkovProcesses estimation
set_param!(ER, :k2, convert(Float64, k2)) set_param!(ER, :k1, convert(Float64, k1))
sync_ER = ER*A_G set_param!(ER, :k2, convert(Float64, k2))
mat_dist_pkg[i,j], nb_accepts_pkg = distribute_mean_value_lha(sync_ER, :d, nb_sim; with_accepts = true) sync_ER = ER*A_G
#@info "About accepts" nb_sim nb_accepted nb_accepts_pkg mat_dist_pkg[i,j], nb_accepts_pkg = distribute_mean_value_lha(sync_ER, :d, nb_sim; with_accepts = true)
test = isapprox(mat_dist_cosmos[i,j], mat_dist_pkg[i,j]; atol = width*1.01) #@info "About accepts" nb_sim nb_accepted nb_accepts_pkg
test2 = nb_accepts_pkg == (nb_sim / nb_accepted) test = isapprox(mat_dist_cosmos[i,j], mat_dist_pkg[i,j]; atol = width*1.01)
if !test test2 = nb_accepts_pkg == (nb_sim / nb_accepted)
@info "Distances too different" (k1,k2) mat_dist_pkg[i,j] mat_dist_cosmos[i,j] if !test
@info "Distances too different" (k1,k2) mat_dist_pkg[i,j] mat_dist_cosmos[i,j]
end
global test_all = test_all && test && test2
end end
global test_all = test_all && test && test2
end end
end end
......
tests/cosmos/distance_G_F/ER_R6.jl
+ 33
− 31
View file @ ec345f72
...@@ -32,38 +32,40 @@ mat_full_k1 = zeros(nb_k1,nb_k2) ...@@ -32,38 +32,40 @@ mat_full_k1 = zeros(nb_k1,nb_k2)
mat_full_k2 = zeros(nb_k1,nb_k2) mat_full_k2 = zeros(nb_k1,nb_k2)
for i = 1:nb_k1 for i = 1:nb_k1
for j = 1:nb_k2 for j = 1:nb_k2
# Cosmos estimation let test, test2, nb_sim, sync_ER, k1, k2
k1 = l_k1[i] # Cosmos estimation
k2 = l_k2[j] k1 = l_k1[i]
command = `Cosmos $(absolute_path * "models/" * str_model * ".gspn") k2 = l_k2[j]
$(absolute_path * "distance_G_F/dist_G_F_" * str_model * ".lha") --njob $(nworkers()) command = `Cosmos $(absolute_path * "models/" * str_model * ".gspn")
--const k_1=$(k1),k_2=$(k2),x1=$x1,x2=$x2,t1=$t1,t2=$t2 $(absolute_path * "distance_G_F/dist_G_F_" * str_model * ".lha") --njob $(nworkers())
--level $(level) --width $(width) --const k_1=$(k1),k_2=$(k2),x1=$x1,x2=$x2,t1=$t1,t2=$t2
--verbose 0` --level $(level) --width $(width)
run(pipeline(command, stderr=devnull)) --verbose 0`
dict_values = cosmos_get_values("Result_dist_G_F_$(str_model).res") run(pipeline(command, stderr=devnull))
mat_dist_cosmos[i,j] = dict_values["Estimated value"][1] dict_values = cosmos_get_values("Result_dist_G_F_$(str_model).res")
mat_dist_prime_cosmos[i,j] = dict_values["Estimated value"][2] mat_dist_cosmos[i,j] = dict_values["Estimated value"][1]
nb_sim = dict_values["Total paths"][1] mat_dist_prime_cosmos[i,j] = dict_values["Estimated value"][2]
nb_accepted = dict_values["Accepted paths"][1] nb_sim = dict_values["Total paths"][1]
nb_sim = convert(Int, nb_sim) nb_accepted = dict_values["Accepted paths"][1]
# MarkovProcesses estimation nb_sim = convert(Int, nb_sim)
set_param!(ER, :k1, convert(Float64, k1)) # MarkovProcesses estimation
set_param!(ER, :k2, convert(Float64, k2)) set_param!(ER, :k1, convert(Float64, k1))
sync_ER = ER*A_G_F set_param!(ER, :k2, convert(Float64, k2))
mat_dist_pkg[i,j], mat_dist_prime_pkg[i,j], nb_accepts_pkg = distribute_mean_value_lha(sync_ER, [:d,:dprime], nb_sim; with_accepts = true) sync_ER = ER*A_G_F
#@info "Computed distances" mat_dist_pkg[i,j] mat_dist_prime_pkg[i,j] mat_dist_cosmos[i,j] mat_dist_prime_cosmos[i,j] mat_dist_pkg[i,j], mat_dist_prime_pkg[i,j], nb_accepts_pkg = distribute_mean_value_lha(sync_ER, [:d,:dprime], nb_sim; with_accepts = true)
#@info "About accepts" nb_sim nb_accepted nb_accepts_pkg #@info "Computed distances" mat_dist_pkg[i,j] mat_dist_prime_pkg[i,j] mat_dist_cosmos[i,j] mat_dist_prime_cosmos[i,j]
test = (isapprox(mat_dist_cosmos[i,j], mat_dist_pkg[i,j]; atol = width*1.01)) || #@info "About accepts" nb_sim nb_accepted nb_accepts_pkg
(mat_dist_cosmos[i,j] == 9997999 && mat_dist_pkg[i,j] == Inf) test = (isapprox(mat_dist_cosmos[i,j], mat_dist_pkg[i,j]; atol = width*1.01)) ||
test2 = nb_accepts_pkg == (nb_sim / nb_accepted) (mat_dist_cosmos[i,j] == 9997999 && mat_dist_pkg[i,j] == Inf)
if !test test2 = nb_accepts_pkg == (nb_sim / nb_accepted)
@info "Distances too different" (k1,k2) mat_dist_pkg[i,j] mat_dist_prime_pkg[i,j] mat_dist_cosmos[i,j] mat_dist_prime_cosmos[i,j] if !test
@info "Distances too different" (k1,k2) mat_dist_pkg[i,j] mat_dist_prime_pkg[i,j] mat_dist_cosmos[i,j] mat_dist_prime_cosmos[i,j]
end
if !test2
@info "Different proportion of accepted trajectories" nb_sim nb_accepted nb_accepts_pkg
end
global test_all = test_all && test && test2
end end
if !test2
@info "Different proportion of accepted trajectories" nb_sim nb_accepted nb_accepts_pkg
end
global test_all = test_all && test && test2
end end
end end
......
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