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Commit 58be3f0c authored by adeline.paiement's avatar adeline.paiement
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nettoyage code

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......@@ -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
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