Skip to content
Snippets Groups Projects
Commit a176dd73 authored by Adeline Paiement's avatar Adeline Paiement
Browse files

Clean and simplify Dynamics_Model.m

parent 5d9c5558
Branches
No related tags found
No related merge requests found
Show whitespace changes
Inline Side-by-side
parmorceauxMax-git/Dynamics_Model.m
+ 0
− 118
View file @ a176dd73
...@@ -54,97 +54,9 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle ...@@ -54,97 +54,9 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
obj.sigma_estimation = sigma_estimation; obj.sigma_estimation = sigma_estimation;
obj.sigma_test = sigma_test; obj.sigma_test = sigma_test;
end 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) function spread_X(obj)
N = 25; 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 %%% estimation initiale de X
X_tmp = 0:1/(N-1):1; X_tmp = 0:1/(N-1):1;
...@@ -197,33 +109,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle ...@@ -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 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 obj.current_alpha = 1.15 * obj.alpha_initial; %1.25%0.015;%1.15%1.1
end 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 end
function loglikelihood = logP_x_knowing_y_spread(obj, variable_X, alpha, sigma) function loglikelihood = logP_x_knowing_y_spread(obj, variable_X, alpha, sigma)
N_variable = size(variable_X,1); N_variable = size(variable_X,1);
...@@ -281,7 +166,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle ...@@ -281,7 +166,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
loglikelihood(index2) = -100;%a été supprimé loglikelihood(index2) = -100;%a été supprimé
end end
end end
function llh_dynamics = compute_loglikelihood_dynamics(obj, frames) function llh_dynamics = compute_loglikelihood_dynamics(obj, frames)
%llh_dynamics(frames <= 1) = nan; %llh_dynamics(frames <= 1) = nan;
...@@ -323,7 +207,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle ...@@ -323,7 +207,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
end end
end end
function [im]=final_plot_estimated_X(obj) function [im]=final_plot_estimated_X(obj)
if obj.display_screen ~= -1 if obj.display_screen ~= -1
N = size(obj.X,1); N = size(obj.X,1);
...@@ -346,7 +229,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle ...@@ -346,7 +229,6 @@ classdef Dynamics_Model < matlab.mixin.Copyable %handle
%saveas(obj.display_screen, strcat('estimatedX_frame', int2str(N), '.png')) %saveas(obj.display_screen, strcat('estimatedX_frame', int2str(N), '.png'))
end end
end end
function X = normalise_X(obj, X) function X = normalise_X(obj, X)
X(X<0) = 0; X(X<0) = 0;
X(X>1) = 1; X(X>1) = 1;
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment