UncorEncounterModel class support for unconventional and due regard models

CHANGELOG.md

@@ -23,6 +23,11 @@ and this project should adhere to [Semantic Versioning](https://semver.org/spec/
 
 ### Fixed
 
+- Updates `dbn_sample` to use previous implementation if a dynamic variable depends on another dynamic variable. In release [1.4.0] `dbn_sample` was updated to calculate the index, `j`, upfront because `asub2ind` can introduce unwanted overhead and also preallocated events as a NaN array. In this previous release, the `for ii = order_transition` loop was added to identify the relationship between dynamic variables and its parents. Notably in the for `ii = order_transition` loop, the variable `x` was not updated. Now this is where the bug was introduced. If a dynamic variable was dependent on another dynamic variable (see unconventional glider model), `xj = x(parents)` would be equal for the element with the dynamic variable dependence. This would results in `asub2ind(rj, xj)` returning a negative value, which would create an error when indexing `N_transition{ii}(:, j(ii))`. Since the uncorrelated conventional models transition networks do not have any dynamic variables not dependent on another dynamic variable, this bug was not identified in release [1.4.0]. For this release, the bug was addressed by determining if any of the dynamic variables depend on another dynamic variable. This determines if we can calculate the index, `j`, upfront or via each iterate of `t`. If there is a dependence, it will sample the model similar to Release [1.3.0] where the events matrix was also preallocated as an empty array
+- Update `UncorEncounterModel/getDynamicLimits` to check that variable indices (i.e. `idx_G`, `idx_A`, etc.) are not empty. Currently this check will only pass for model structures as the uncorrelated conventional aircraft models; the unconventional models currently all lack geographic domain (G) and will not pass this check. Without this check an error would throw when trying to use a logical operator on an empty variable.
+- Update how `UncorEncounterModel/sample` calculates the order of variables when reorganizing the controls matrix. The model's temporal matrix is used explicitly instead of trying to infer the order from the controls matrix
+- Update `UncorEncounterModel/sample` to ensure that the altitude minimum (`min_alt_ft`) and maximum (`max_alt_ft`) are not empty. They can be empty if the model structure does not have boundaries defined for altitude layer, L
+
 ## [2.1.0] - 2021-10-01
 
 ### Added

code/matlab/@UncorEncounterModel/UncorEncounterModel.m

@@ -278,7 +278,8 @@
 
                 % Reorder to [t dh dpsi dv] to order that EncounterModelEvents (EME) expects
                 % As of July 2021, for the uncorrelated models, idxEME = [3 4 2]
-                idxEME = [idxDH, idxDPsi, idxDV] - size(controls, 2) + 1;
+                % idxEME = [idxDH, idxDPsi, idxDV] - size(controls, 2) + 1;
+                idxEME = [find(s.temporal_map(:, 1) == idxDH), find(s.temporal_map(:, 1) == idxDPsi), find(s.temporal_map(:, 1) == idxDV)] + 1;
                 controls = controls(:, [1 idxEME]);
 
                 % Convert to units used by EncounterModelEvents
@@ -383,6 +384,13 @@
             min_alt_ft = min(self.boundaries{idx_L}); % ft
             max_alt_ft = max(self.boundaries{idx_L}); % ft
 
+            if isempty(min_alt_ft)
+                min_alt_ft = 0;
+            end
+            if isempty(max_alt_ft)
+                max_alt_ft = inf;
+            end
+
             % Other dynamic thresholds
             min_DH_ft_s = min(self.boundaries{idx_DH}) / 60; % hdot: ft/min -> ft/s
             max_DH_ft_s = max(self.boundaries{idx_DH}) / 60; % hdot: ft/min -> ft/s

code/matlab/@UncorEncounterModel/getDynamicLimits.m

@@ -10,8 +10,11 @@
     prct_low = 1;
     prct_high = 99;
 
+    % Logical if model has all expected variable indices
+    is_idx = ~any(isempty(idx_G) | isempty(idx_A) | isempty(idx_L) | isempty(idx_V) | isempty(idx_DH));
+
     %% Do something if variables are in expected order
-    if idx_G == 1 && idx_A == 2 && idx_L == 3 && idx_V == 4 && idx_DH == 6
+    if is_idx && (idx_G == 1 && idx_A == 2 && idx_L == 3 && idx_V == 4 && idx_DH == 6)
 
         % Geographic domain
         if is_discretized(idx_G)

code/matlab/dbn_sample.m

@@ -39,10 +39,6 @@
     dynamic_variables = temporal_map(:, 2);
     x = [initial zeros(1, numel(dynamic_variables))];
 
-    % events
-    events = nan(n_initial * t_max, numel(dynamic_variables));
-    counter = 0;
-
     % Time between different events
     delta_t = 0;
 
@@ -53,69 +49,118 @@
         ia = ia';
     end
 
-    %% Calculate index for variables
-    % We calculate index, j, upfront because asub2ind() can
-    % introduce unwanted overhead
-    j = nan(size(order_transition));
-    % N_trans_dyn = cell(size(order_transition));
-    % dirichlet_trans_dyn = cell(size(order_transition));
-
-    s = cell(size(order_transition));
-    sthres = zeros(t_max, numel(order_transition));
-
-    for ii = order_transition
-        if any(ii == dynamic_variables)
-            % only dynamic variables change
-            parents = G_transition(:, ii);
-            if any(parents)
-                rj = r_transition(parents);
-                xj = x(parents);
-                j(ii) = asub2ind(rj, xj);
-            else
-                j(ii) = 1;
-            end
+    % Determine if any of the dynamic variables depend on another dynamic
+    % variable. This determines if we can calculate the index, j, upfront
+    % or via each iterate of t
+    is_dynvar_depend = any(G_transition(dynamic_variables, dynamic_variables), 'all');
 
-            % Parse N_transition and dirichlet_transition for each dyn variable
-            N_trans_dyn = N_transition{ii}(:, j(ii));
-            dirichlet_trans_dyn = dirichlet_transition{ii}(:, j(ii));
+    % events
+    if is_dynvar_depend
+        events = [];
+    else
+        events = nan(n_initial * t_max, numel(dynamic_variables));
+    end
 
-            % Combine transition and dirichlet for weights
-            weights = N_trans_dyn + dirichlet_trans_dyn;
+    %% Iterate
+    if is_dynvar_depend
+        for t = 2:t_max
+            delta_t = delta_t + 1;
+            x_old = x;
+            for i = order_transition
+                if any(i == dynamic_variables)
+                    % only dynamic variables change
+                    parents = G_transition(:, i);
+                    j = 1;
+                    if any(parents)
+                        j = asub2ind(r_transition(parents), x(parents));
+                    end
+                    x(i) = select_random(N_transition{i}(:, j) + dirichlet_transition{i}(:, j));
+                end
+            end
 
-            % Calculate cumsum of weights
-            s{ii} = cumsum(weights);
+            % map back
+            x(temporal_map(:, 1)) = x(temporal_map(:, 2));
 
-            % Calculate random threshold
-            sthres(:, ii) = s{ii}(end) * rand(t_max, 1);
+            if any(x(1:n_initial) ~= x_old(1:n_initial))
+                % change (i.e. new event)
+                for i = 1:n_initial
+                    if x(i) ~= x_old(i)
+                        events = [events; delta_t i x(i)];
+                        delta_t = 0;
+                    end
+                end
+            end
         end
-    end
 
-    %% Iterate
-    for t = 2:t_max
-        delta_t = delta_t + 1;
-        x_old = x;
-
-        % Iterate over dynamic variables to select random index
-        for ii = ia
-            x(ii) = find(s{ii} >= sthres(t, ii), 1, 'first');
-            % x(i) = select_random(weights);
+    else
+
+        % Calculate index for variables
+        % We calculate index, j, upfront because asub2ind() can
+        % introduce unwanted overhead
+        j = nan(size(order_transition));
+        % N_trans_dyn = cell(size(order_transition));
+        % dirichlet_trans_dyn = cell(size(order_transition));
+
+        s = cell(size(order_transition));
+        sthres = zeros(t_max, numel(order_transition));
+
+        % Counter for events matrix
+        counter = 0;
+
+        for ii = order_transition
+            if any(ii == dynamic_variables)
+                % only dynamic variables change
+                parents = G_transition(:, ii);
+                if any(parents)
+                    rj = r_transition(parents);
+                    xj = x(parents);
+                    j(ii) = asub2ind(rj, xj);
+                else
+                    j(ii) = 1;
+                end
+
+                % Parse N_transition and dirichlet_transition for each dyn variable
+                N_trans_dyn = N_transition{ii}(:, j(ii));
+                dirichlet_trans_dyn = dirichlet_transition{ii}(:, j(ii));
+
+                % Combine transition and dirichlet for weights
+                weights = N_trans_dyn + dirichlet_trans_dyn;
+
+                % Calculate cumsum of weights
+                s{ii} = cumsum(weights);
+
+                % Calculate random threshold
+                sthres(:, ii) = s{ii}(end) * rand(t_max, 1);
+            end
         end
 
-        % map back
-        x(temporal_map(:, 1)) = x(temporal_map(:, 2));
-
-        if any(x(1:n_initial) ~= x_old(1:n_initial))
-            % change (i.e. new event)
-            for ii = 1:n_initial
-                if x(ii) ~= x_old(ii)
-                    counter = counter + 1;
-                    events(counter, :) = [delta_t, ii, x(ii)];
-                    % events = [events; delta_t, i, x(i)]; % Deprecated
-                    delta_t = 0;
+        % Iterate
+        for t = 2:t_max
+            delta_t = delta_t + 1;
+            x_old = x;
+
+            % Iterate over dynamic variables to select random index
+            for ii = ia
+                x(ii) = find(s{ii} >= sthres(t, ii), 1, 'first');
+                % x(i) = select_random(weights);
+            end
+
+            % map back
+            x(temporal_map(:, 1)) = x(temporal_map(:, 2));
+
+            if any(x(1:n_initial) ~= x_old(1:n_initial))
+                % change (i.e. new event)
+                for ii = 1:n_initial
+                    if x(ii) ~= x_old(ii)
+                        counter = counter + 1;
+                        events(counter, :) = [delta_t, ii, x(ii)];
+                        % events = [events; delta_t, i, x(i)]; % Deprecated
+                        delta_t = 0;
+                    end
                 end
             end
         end
-    end
 
-    % Remove unused prelloacate rows
-    events = events(1:counter, :);
+        % Remove unused prelloacate rows
+        events = events(1:counter, :);
+    end