diff --git a/parmorceauxMax-git/Dynamics_Model.m b/parmorceauxMax-git/Dynamics_Model.m
index 9ba2a4d16eda2a7e0846af657202756b2758300b..57a8d2403918e226eab2fff17f50e4b172c8e2b7 100644
--- a/parmorceauxMax-git/Dynamics_Model.m
+++ b/parmorceauxMax-git/Dynamics_Model.m
@@ -54,97 +54,9 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
obj.sigma_estimation = sigma_estimation;
obj.sigma_test = sigma_test;
end
- function process_new_frame(obj, Yt, Tt)
- obj.T = [obj.T; Tt];
- obj.Y = [obj.Y; Yt(:,1:obj.num_dim_Y)];
-
- % estimate alpha and adjust the window size
- alpha = obj.estimate_alpha();
- sprintf('estimated alpha_variable: %f', alpha);
- obj.current_alpha = alpha;
-
- % 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
- end
- function alpha = estimate_alpha(obj)
- N = size(obj.X,1);
-
- if N < 3
- alpha = obj.alpha_initial;
- return
- end
-
- first_index = 1;
- nb_points = N;
-
- if nb_points < 3 % we need at least 2 intervals to compute the mean
- alpha = obj.alpha_initial;
- else
- X_2_to_N = obj.X(first_index+1:N);
- X_1_to_Nminus1 = obj.X(first_index:N-1);
- T_2_to_N = obj.T(first_index+1:N);
- T_1_to_Nminus1 = obj.T(first_index:N-1);
-
- delta_X = X_2_to_N - X_1_to_Nminus1;
- delta_T = T_2_to_N - T_1_to_Nminus1;
-
- factors = delta_X ./ delta_T;
-
- alpha = mean(factors);
-
- if alpha <= 0.85 * obj.alpha_initial; %0.75 %0.015 0.75%0.85%0.9
- alpha = 0.85 * obj.alpha_initial; %0.75%0.015;%0.85%0.9
- end
- 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
- end
- end
- function estimate_X(obj)
- %function to be minimised over new_X, with extra parameters Y, alpha and T
- f = @(variable_X)(-obj.logP_x_knowing_y_paquet(variable_X,obj.current_alpha,obj.sigma_estimation));
-
- N = size(obj.X,1);
-
- if N == 0
- X_ini = obj.initial_estimate_X;
- else
- X_ini = [obj.X; obj.X(N) + obj.current_alpha * (obj.T(N+1) - obj.T(N))];
- end
-
- lb = repmat(0,1,length(X_ini));
- ub = repmat(1,1,length(X_ini));
- A = [];
- b = [];
- Aeq = [];
- beq = [];
- [new_X_variable,loglikelihood] = fmincon(f,X_ini,A,b,Aeq,beq,lb,ub);
-
- obj.X = new_X_variable;
- end
function spread_X(obj)
N = 25;
-% if N ==1
-% obj.X = 0;
-% obj.current_alpha = obj.alpha_initial;
-%
-% obj.plot_estimated_X()
-% obj.compute_loglikelihood_dynamics(1:N)
-% return
-% elseif N == 2
-% obj.X = [0 1];
-% obj.current_alpha = 1;
-%
-% obj.plot_estimated_X()
-% obj.compute_loglikelihood_dynamics(1:N)
-% return
-% end
-
%%% estimation initiale de X
X_tmp = 0:1/(N-1):1;
@@ -197,33 +109,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
if obj.current_alpha >= 1.15 * obj.alpha_initial; %1.25%0.015 1.25%1.15%1.1
obj.current_alpha = 1.15 * obj.alpha_initial; %1.25%0.015;%1.15%1.1
end
-
-
- obj.plot_estimated_X()
- obj.compute_loglikelihood_dynamics(1:N)
- end
- function loglikelihood = logP_x_knowing_y_paquet(obj, variable_X, alpha, sigma)
- N_variable = size(variable_X,1);
-
- % if N == 1, we maximise P_cond_y(Y(1),X(1)) * P_x_Markov_simple(X(1),X(0)) avec X(0) virtuel
- % if N == 2, we maximise P_cond_y(Y(2),X(2)) * P_x_Markov_simple(X(2),X(1)) * P_cond_y(Y(1),X(1)) with P_x_Markov_simple(X(2),X(1)) = 0 if delta_X is negative, and uniform proba otherwise
-
- loglikelihood = obj.logP_cond_y(obj.Y(1,:),variable_X(1));
- loglikelihood = loglikelihood + obj.logP_x_Markov(variable_X(1), variable_X(1)-alpha, 2, 1, alpha, sigma);
-
- if N_variable > 1
- Y_2_to_N = obj.Y(2:N_variable,:);
- X_2_to_N = variable_X(2:N_variable);
- X_1_to_Nminus1 = variable_X(1:N_variable-1);
- T_2_to_N = obj.T(2:N_variable);
- T_1_to_Nminus1 = obj.T(1:N_variable-1);
-
- ll_proba_cond_y = obj.logP_cond_y(Y_2_to_N, X_2_to_N);
- ll_proba_x_Markov = obj.logP_x_Markov(X_2_to_N,X_1_to_Nminus1,T_2_to_N,T_1_to_Nminus1,alpha,sigma);
- vect_tmp = ll_proba_cond_y + ll_proba_x_Markov;
-
- loglikelihood = loglikelihood + sum(vect_tmp);
- end
end
function loglikelihood = logP_x_knowing_y_spread(obj, variable_X, alpha, sigma)
N_variable = size(variable_X,1);
@@ -281,7 +166,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
loglikelihood(index2) = -100;%a été supprimé
end
end
-
function llh_dynamics = compute_loglikelihood_dynamics(obj, frames)
%llh_dynamics(frames <= 1) = nan;
@@ -323,7 +207,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
end
end
-
function [im]=final_plot_estimated_X(obj)
if obj.display_screen ~= -1
N = size(obj.X,1);
@@ -346,7 +229,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
%saveas(obj.display_screen, strcat('estimatedX_frame', int2str(N), '.png'))
end
end
-
function X = normalise_X(obj, X)
X(X<0) = 0;
X(X>1) = 1;