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Refactor parameter setup to return structured configs #2

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840e0a7
Refactor parameter setup to return structured configs
yexf308 Nov 6, 2025
b16b56e
Fix Halfmoon and Peanut parameter setup calls
yexf308 Nov 6, 2025
4d07117
Merge branch 'main' into codex/refactor-set_parameter-function-to-ret…
yexf308 Nov 6, 2025
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62 changes: 31 additions & 31 deletions ATLAS/Learning_ini_chart.m
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Original file line number Diff line number Diff line change
@@ -1,46 +1,46 @@
% Find the chart for randomly sampled initial points on the manifold.
%These initial points are evenly sampled from a long trajectory
chart = cell(1,K_int);
% Find the chart for randomly sampled initial points on the manifold.
% These initial points are evenly sampled from a long trajectory.

chart = cell(1, params.learning.K_int);
Sim_parameter = struct( ...
'T_max', T_one, ...
'dt', dt , ...
'gap', 1, ...
'X_int', X_int ...
'T_max', params.learning.T_one, ...
'dt', params.RHS.dt , ...
'gap', 1, ...
'X_int', params.learning.X_int ...
);


disp(['Initial point: ', num2str(X_int), ' T_max: ', num2str(Sim_parameter.T_max)])

% Simulate a long trajctory starting from this initial condition.
tic

[output] = simulator_one(Sim_parameter);
t1=toc;
disp(['one long trajctory is simulated. The time spent is ', num2str(t1/60), ' mins.' ])



% evenly sample N initial points from the trajctory
disp(['Initial point: ', num2str(params.learning.X_int), ' T_max: ', num2str(Sim_parameter.T_max)])

% Simulate a long trajectory starting from this initial condition.
tic

[output] = params.simulator.one(Sim_parameter);
t1=toc;
disp(['one long trajectory is simulated. The time spent is ', num2str(t1/60), ' mins.' ])



% evenly sample N initial points from the trajectory
output = output(10000:end,:);
[L, ~] = size(output);
X_int_sample = output(1: round(L/K_int):end, :);
[L, ~] = size(output);
X_int_sample = output(1: round(L/params.learning.K_int):end, :);

tic
disp(['Parallelly run chart simulator starting from ', num2str(K_int) ,' initial points. '])
for i = 1 : K_int
disp(['Parallelly run chart simulator starting from ', num2str(params.learning.K_int) ,' initial points. '])
for i = 1 : params.learning.K_int
disp(['Landmark',num2str(i), ' is learned.'])

Sim_parameter = struct( ...
'T_max', T_max, ...
'dt', dt , ...
'N', N, ...
'T_max', params.RHS.T_max, ...
'dt', params.RHS.dt , ...
'N', params.RHS.N, ...
'X_int', X_int_sample(i,:) ...
);
[data, Cov_store, Mean_store] = simulator_par(Sim_parameter);
[chart{i}] = Learning_Slow_Manifold( data, Cov_store, Mean_store, D, d,modify );
[data, Cov_store, Mean_store] = params.simulator.parallel(Sim_parameter);
[chart{i}] = Learning_Slow_Manifold( data, Cov_store, Mean_store, params.RHS.D, params.RHS.d, params.RHS.modify );


end
t2 =toc;
disp(['Initial learning stage is completed. The time spent is ', num2str(t2/60), ' mins.' ])
Expand Down
10 changes: 5 additions & 5 deletions ATLAS/MSM_learning.m
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Original file line number Diff line number Diff line change
@@ -1,9 +1,9 @@

disp('Starting MSM learning stage')

[TranM] = MSM(chart, connectivity, MSM_parameter, weighted_dd2, d);
step = MSM_parameter.step;
N_state = MSM_parameter.N_state;
dt_s = MSM_parameter.dt_s;
save( TranM_fileName ,'TranM','step','N_state','dt_s')
[TranM] = MSM(chart, connectivity, params.MSM, weighted_dd2, params.RHS.d);
step = params.MSM.step;
N_state = params.MSM.N_state;
dt_s = params.MSM.dt_s;
save( params.paths.TranM ,'TranM','step','N_state','dt_s')
disp('MSM learning stage is completed')
27 changes: 13 additions & 14 deletions ATLAS/exploration_learning.m
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Original file line number Diff line number Diff line change
@@ -1,33 +1,32 @@

disp('Starting exploration learning stage')

% set to exploration mode
mode = 1;
weighted_dd2 = @(X0, chart,neigh,nearest, connectivity_indices) weighted_drift_diffusion2( X0, chart, neigh, nearest, connectivity_indices, t0, chi_p, D,d, threshold, option,mode );
weighted_dd2 = @(X0, chart,neigh,nearest, connectivity_indices) weighted_drift_diffusion2( X0, chart, neigh,nearest, connectivity_indices, params.RHS.t0, params.RHS.chi_p, params.RHS.D,params.RHS.d, params.RHS.threshold, params.learning.option,mode );


%% Use the exploration mode to further Learn
%% Use the exploration mode to further Learn
explore_round = 1;
explore_round_max = 100;
explore_round_max = 100;
K = length(chart);
random_start;
random_start;
while bin_N~=1 && explore_round<explore_round_max
disp(['Round ',num2str(explore_round),' of exploration'])
[~, ~, chart] = ATLAS_simulator2(weighted_dd2, chart_sim_parameter,RHS_parameter, simulator_par, chart);
[chart, ~, ~, ~, ~] = landmark(chart, t0, chi_p,threshold(1), connectivity_threshold);
[~, ~, chart] = ATLAS_simulator2(weighted_dd2, params.chart_sim_parameter,params.RHS, params.simulator.parallel, chart);
[chart, ~, ~, ~, ~] = landmark(chart, params.RHS.t0, params.RHS.chi_p,params.RHS.threshold(1), params.RHS.connectivity_threshold);
disp(['No. of landmarks after exploration is ', num2str(length(chart)) ])

if relearn_option == 1
if params.relearn.option == 1
K_next = length(chart);
index_to_learn = K+1: K_next;
[ chart ] = relearn_chart(chart, relearn_parameter, index_to_learn);
[chart, connectivity, P, bin_N, bins] = landmark(chart, t0, chi_p,threshold(1), connectivity_threshold);
[ chart ] = relearn_chart(chart, params.relearn.settings, index_to_learn);
[chart, connectivity, P, bin_N, bins] = landmark(chart, params.RHS.t0, params.RHS.chi_p,params.RHS.threshold(1), params.RHS.connectivity_threshold);
end
K = length(chart);
random_start;
random_start;
explore_round = explore_round + 1;
end

save( chart_fileName ,'chart','connectivity','P')
disp('exploration learning stage is completed')
save( params.paths.chart ,'chart','connectivity','P')

disp('exploration learning stage is completed')
12 changes: 6 additions & 6 deletions ATLAS/initial_learning.m
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Original file line number Diff line number Diff line change
Expand Up @@ -2,15 +2,15 @@

disp('Starting initial learning stage')
Learning_ini_chart;
[chart, ~, ~, ~] = landmark(chart, t0, chi_p,threshold(1), connectivity_threshold);
if relearn_option == 1
[chart, ~, ~, ~] = landmark(chart, params.RHS.t0, params.RHS.chi_p,params.RHS.threshold(1), params.RHS.connectivity_threshold);

if params.relearn.option == 1
K = length(chart);
index_to_learn = 1:K;
[ chart ] = relearn_chart(chart, relearn_parameter, index_to_learn);
[chart, connectivity, P, bin_N, bins] = landmark(chart, t0, chi_p,threshold(1), connectivity_threshold);
[ chart ] = relearn_chart(chart, params.relearn.settings, index_to_learn);
[chart, connectivity, P, bin_N, bins] = landmark(chart, params.RHS.t0, params.RHS.chi_p,params.RHS.threshold(1), params.RHS.connectivity_threshold);
end
save(chart_part_fileName , 'chart','connectivity','P');
save(params.paths.chart_part , 'chart','connectivity','P');
disp(['No. of landmarks after initial learning stage is ', num2str(length(chart)) ])

disp('Initial learning stage is completed')
6 changes: 3 additions & 3 deletions ATLAS/random_start.m
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Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,8 @@
[N_min, index_min] = min(bins_hist);
minimum_block = find(bins==bins_unique(index_min));
nearest = minimum_block(ceil(rand * N_min));
chart_sim_parameter.nearest = nearest;
chart_sim_parameter.connectivity = connectivity;
chart_sim_parameter.X_int = chart{nearest}.X_int;
params.chart_sim_parameter.nearest = nearest;
params.chart_sim_parameter.connectivity = connectivity;
params.chart_sim_parameter.X_int = chart{nearest}.X_int;


60 changes: 28 additions & 32 deletions Butane.m
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Original file line number Diff line number Diff line change
@@ -1,10 +1,9 @@

clear all
clear all
close all
clc


system_type = 'Butane';
system_type = 'Butane';
disp(['Starting ',system_type,' model.'])
addpath_settings; % setup path

Expand All @@ -16,68 +15,65 @@
for k =1:10

%% Set up the parameter
rng(k)
set_parameter; % setup parameter
disp('Parameter is set')

%set path
butane_paths = build_butane_paths(datapath, k);
chart_fileName = butane_paths.chart;
chart_part_fileName = butane_paths.chart_part;
TranM_fileName = butane_paths.TranM;
diary_fileName = butane_paths.diary;
rng(k)
params = set_parameter(); % setup parameter
disp('Parameter is set')

% Update paths for this iteration
params.paths.chart = [params.paths.datapath,'chart',num2str(k),'.mat'];
params.paths.chart_part = [params.paths.datapath,'chart_part',num2str(k),'.mat'];
params.paths.TranM = [params.paths.datapath,'TranM',num2str(k),'.mat'];
diary_fileName = [params.paths.datapath,'diary',num2str(k),'.txt'];
diary(diary_fileName)

tstart=tic;
tstart=tic;

%% Generate the chart for randomly sampled initial points.

initial_learning;


%% Use the exploration mode to further Learn
%% Use the exploration mode to further Learn

exploration_learning;

%% Using ATLAS simulator to run lots of trajs to further Learn

butane_simulation_learning;


%% Relearn ATLAS
%% Relearn ATLAS

butane_relearning;

%% Build up Markov state model

MSM_learning;
%%


%%
t_final = toc(tstart);
disp(['Learning stage is finished. The time spent is ',num2str(t_final/3600), ' hours']);
diary off

end

elseif x==2

set_parameter;
disp('Parameter is set')
butane_paths = build_butane_paths(datapath);
diary_FPT_fileName = butane_paths.diary_FPT;
Butane_analysis_fileName = butane_paths.Butane_analysis;
params = set_parameter();
disp('Parameter is set')
diary_FPT_fileName = [params.paths.datapath,'diary_FPT.txt'];
diary(diary_FPT_fileName)


%% Simulate long trajectory with the original simulator

butane_ori_simulation;

%% Calculate MFPT

butane_MFPT;

diary off

end
end
58 changes: 28 additions & 30 deletions Halfmoon.m
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@

clear all
close all
clc
Expand All @@ -11,68 +10,67 @@
prompt = '1-Learning ATLAS, 2-Simulating MRT';
x = input(prompt);

if x==1
if x==1

for k =1:10
%% Set up the parameter
rng(k)
set_parameter;
disp('Parameter is set')

chart_fileName = [datapath,'chart',num2str(k),'.mat'];
chart_part_fileName = [datapath,'chart_part',num2str(k),'.mat'];
TranM_fileName = [datapath,'TranM',num2str(k),'.mat'];
diary_fileName = [datapath,'diary',num2str(k),'.txt'];
rng(k)
params = set_parameter();
disp('Parameter is set')

params.paths.chart = [params.paths.datapath,'chart',num2str(k),'.mat'];
params.paths.chart_part = [params.paths.datapath,'chart_part',num2str(k),'.mat'];
params.paths.TranM = [params.paths.datapath,'TranM',num2str(k),'.mat'];
diary_fileName = [params.paths.datapath,'diary',num2str(k),'.txt'];
diary(diary_fileName)
tstart=tic;
tstart=tic;


%% Generate the chart for randomly sampled initial points.

initial_learning;
initial_learning;


%% Use the exploration mode to further Learn
%% Use the exploration mode to further Learn

exploration_learning;

%% Using ATLAS simulator to run lots of trajs to further learn.

halfmoon_simulation_learning;
%% Relearn ATLAS

%% Relearn ATLAS

halfmoon_relearning;

%% Build up Markov state model

MSM_learning;

%%
%%
t_final = toc(tstart);
disp(['Learning stage is finished. The time spent is ',num2str(t_final/3600), ' hours']);
diary off

end

elseif x==2

set_parameter;
disp('Parameter is set')
diary_FPT_fileName = [datapath,'diary_FPT.txt'];
Halfmoon_analysis_fileName = [datapath,'Halfmoon_analysis.mat'];

params = set_parameter();
disp('Parameter is set')
diary_FPT_fileName = [params.paths.datapath,'diary_FPT.txt'];
diary(diary_FPT_fileName)


%% Simulate long trajectory with the original simulator

halfmoon_ori_simulation;
%% Calculate MFPT

%% Calculate MFPT

halfmoon_MFPT;
diary off

diary off


end
end
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