nettoyage code

parmorceauxMax-git/Dynamics_Model.m

@@ -12,14 +12,8 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
         X
         T
         Y
-        %previous_N_fixed
-        %N_fixed
-        %nb_times_fixed
         
         current_alpha
-        %alpha_variable
-        %confidence
-        window_size
         
         display_screen
         marginal_X_Yi
@@ -53,12 +47,7 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
             obj.T = [];
             obj.X = [];
             obj.Y = [];
-            %obj.N_fixed = 0;
             obj.current_alpha = alpha_ini;
-            %obj.alpha_variable = [];
-            obj.window_size = [];
-            %obj.confidence = [];
-            %obj.nb_times_fixed = [];
             
             obj.initial_estimate_X = initial_X;
             obj.alpha_initial = alpha_ini;
@@ -68,28 +57,18 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
         function process_new_frame(obj, Yt, Tt)
             obj.T = [obj.T; Tt];
             obj.Y = [obj.Y; Yt(:,1:obj.num_dim_Y)];
-            frame = size(obj.T,1);
             
             % estimate alpha and adjust the window size
             alpha = obj.estimate_alpha();
             sprintf('estimated alpha_variable: %f', alpha);
             obj.current_alpha = alpha;
-            obj.window_size = [obj.window_size, frame];
             
-            % estimate X inside the window (and update N_fixed)
-            %obj.previous_N_fixed = obj.N_fixed;
+            % estimate X inside the window
             obj.estimate_X();
             %sprintf('"normalised" log likelihood of X (or confidence): %f', obj.confidence(frame));
             if obj.display_screen ~= -1
                 obj.plot_estimated_X();
             end
-            
-            % save the value of alpha for the frames that have converged
-            %if obj.previous_N_fixed < obj.N_fixed
-            %    for i=obj.previous_N_fixed+1:obj.N_fixed
-            %        obj.alpha_variable(i) = alpha;
-            %    end
-            %end
         end
         function alpha = estimate_alpha(obj)
             N = size(obj.X,1);
@@ -123,13 +102,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
                 if alpha >= 1.15 * obj.alpha_initial; %1.25%0.015  1.25%1.15%1.1
                     alpha = 1.15 * obj.alpha_initial; %1.25%0.015;%1.15%1.1
                 end
-%                     if alpha <= 0.015
-%                         alpha = 0.015;
-%                     end
-                    
-%                     if alpha > 0.025
-%                         alpha = 0.025;
-%                     end
             end
         end
         function estimate_X(obj)
@@ -151,7 +123,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
             Aeq = [];
             beq = [];
             [new_X_variable,loglikelihood] = fmincon(f,X_ini,A,b,Aeq,beq,lb,ub);
-            %obj.confidence = [obj.confidence, -loglikelihood / size(new_X_variable,1)];
             
             obj.X = new_X_variable;
         end
@@ -160,7 +131,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
             
             if N ==1
                 obj.X = 0;
-                %obj.confidence = 1;
                 obj.current_alpha = obj.alpha_initial;
             
                 obj.plot_estimated_X()
@@ -168,7 +138,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
                 return
             elseif N == 2
                 obj.X = [0 1];
-                %obj.confidence = [0 1];
                 obj.current_alpha = 1;
             
                 obj.plot_estimated_X()
@@ -207,7 +176,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
             
             new_X = [0; obj.normalise_X(new_X); 1];
             obj.X = new_X;
-            %obj.confidence = [1, -loglikelihood / size(new_X,1), 1];
             
             
             %%% calcul du alpha correspondant aux X estimés
@@ -328,7 +296,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
             indexes = frames <= 1
             frames(indexes)
             terme1 = obj.logP_cond_y(obj.Y(frames(indexes),:), obj.X(frames(indexes)));
-            %llh_dynamics(frames <= 1) = terme1 + obj.logP_x_Markov(obj.alpha_initial, 0, 2, 1, obj.alpha_initial, obj.sigma_test);
             llh_dynamics(frames <= 1) = terme1 + obj.logP_x_Markov(obj.X(1), obj.X(1)-alpha, 2, 1, alpha, obj.sigma_test);
         end
         function plot_estimated_X(obj)
@@ -388,9 +355,4 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
             periodic = 0;
         end
     end
-    
-    %methods (Abstract)
-    %    X = normalise_X(obj, X)
-    %    periodic = is_periodic(obj)
-    %end
 end