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Commit 2cb8784d authored by Sylvain Calinon's avatar Sylvain Calinon
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Homogenization of headers in m_fcts folder

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demo_stdPGMM01.m
View file @ 2cb8784d
function demo_stdPGMM01
% Parametric Gaussian mixture model (PGMM) used for task adaptation,
% with DS-GMR employed to retrieve continuous movements.
% Parametric Gaussian mixture model (PGMM) used for task adaptation, with DS-GMR employed
% to retrieve continuous movements. The approach is inspired by Wilson and Bobick (1999),
% with an implementation applied to the special case of Gaussian mixture models (GMM).
%
% Writing code takes time. Polishing it and making it available to others takes longer!
% If some parts of the code were useful for your research of for a better understanding
......@@ -13,6 +14,16 @@ function demo_stdPGMM01
% journal="Intelligent Service Robotics",
% year="2015"
% }
%
% @article{Wilson99,
% author="Wilson, A. D. and Bobick, A. F.",
% title="Parametric Hidden {M}arkov Models for Gesture Recognition",
% journal="{IEEE} Trans. on Pattern Analysis and Machine Intelligence",
% year="1999",
% volume="21",
% number="9",
% pages="884--900"
% }
%
% Copyright (c) 2015 Idiap Research Institute, http://idiap.ch/
% Written by Sylvain Calinon, http://calinon.ch/
......
m_fcts/DTW.m
View file @ 2cb8784d
function [x_new, y_new, p] = DTW(x, y, w)
%Trajectory realignment through dynamic time warping
%Sylvain Calinon, 2015
function [x2, y2, p] = DTW(x, y, w)
% Trajectory realignment through dynamic time warping.
%
% Writing code takes time. Polishing it and making it available to others takes longer!
% If some parts of the code were useful for your research of for a better understanding
% of the algorithms, please reward the authors by citing the related publications,
% and consider making your own research available in this way.
%
% @article{Calinon15,
% author="Calinon, S.",
% title="A Tutorial on Task-Parameterized Movement Learning and Retrieval",
% journal="Intelligent Service Robotics",
% year="2015"
% }
%
% Copyright (c) 2015 Idiap Research Institute, http://idiap.ch/
% Written by Sylvain Calinon, http://calinon.ch/
%
% This file is part of PbDlib, http://www.idiap.ch/software/pbdlib/
%
% PbDlib is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License version 3 as
% published by the Free Software Foundation.
%
% PbDlib is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with PbDlib. If not, see <http://www.gnu.org/licenses/>.
if nargin<3
w = Inf;
end
nx = size(x,2);
ny = size(y,2);
sx = size(x,2);
sy = size(y,2);
w = max(w,abs(nx-ny));
w = max(w,abs(sx-sy));
%Initialization
D = ones(nx+1,ny+1) * Inf;
D = ones(sx+1,sy+1) * Inf;
D(1,1) = 0;
%DP loop
for i=1:nx
for j=max(i-w,1):min(i+w,ny)
for j=max(i-w,1):min(i+w,sy)
D(i+1,j+1) = norm(x(:,i)-y(:,j)) + min([D(i,j+1), D(i+1,j), D(i,j)]);
end
end
i=nx+1; j=ny+1;
i=sx+1; j=sy+1;
p=[];
while i>1 && j>1
[~,id] = min([D(i,j-1), D(i-1,j), D(i-1,j-1)]);
......@@ -39,14 +68,9 @@ end
p = fliplr(p(:,1:end-1)-1);
x_new = x(:,p(1,:));
y_new = y(:,p(2,:));
x2 = x(:,p(1,:));
y2 = y(:,p(2,:));
%Resampling
x_new = spline(1:size(x_new,2), x_new, linspace(1,size(x_new,2),nx));
y_new = spline(1:size(y_new,2), y_new, linspace(1,size(y_new,2),nx));
x2 = spline(1:size(x2,2), x2, linspace(1,size(x2,2),sx));
y2 = spline(1:size(y2,2), y2, linspace(1,size(y2,2),sx));
m_fcts/EM_GMM.m
View file @ 2cb8784d
function [model, GAMMA2, LL] = EM_GMM(Data, model)
% Training of a Gaussian mixture model (GMM) with an expectation-maximization (EM) algorithm.
%
% Author: Sylvain Calinon, 2014
% http://programming-by-demonstration.org/SylvainCalinon
% Writing code takes time. Polishing it and making it available to others takes longer!
% If some parts of the code were useful for your research of for a better understanding
% of the algorithms, please reward the authors by citing the related publications,
% and consider making your own research available in this way.
%
% @article{Calinon15,
% author="Calinon, S.",
% title="A Tutorial on Task-Parameterized Movement Learning and Retrieval",
% journal="Intelligent Service Robotics",
% year="2015"
% }
%
% Copyright (c) 2015 Idiap Research Institute, http://idiap.ch/
% Written by Sylvain Calinon, http://calinon.ch/
%
% This file is part of PbDlib, http://www.idiap.ch/software/pbdlib/
%
% PbDlib is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License version 3 as
% published by the Free Software Foundation.
%
% PbDlib is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with PbDlib. If not, see <http://www.gnu.org/licenses/>.
%Parameters of the EM algorithm
nbMinSteps = 5; %Minimum number of iterations allowed
......@@ -10,8 +37,8 @@ nbMaxSteps = 100; %Maximum number of iterations allowed
maxDiffLL = 1E-4; %Likelihood increase threshold to stop the algorithm
nbData = size(Data,2);
%diagRegularizationFactor = 1E-6; %Regularization term is optional, see Eq. (2.1.2) in doc/TechnicalReport.pdf
diagRegularizationFactor = 1E-4; %Regularization term is optional, see Eq. (2.1.2) in doc/TechnicalReport.pdf
%diagRegularizationFactor = 1E-6; %Regularization term is optional
diagRegularizationFactor = 1E-4; %Regularization term is optional
for nbIter=1:nbMaxSteps
fprintf('.');
......@@ -22,13 +49,13 @@ for nbIter=1:nbMaxSteps
%M-step
for i=1:model.nbStates
%Update Priors, see Eq. (2.0.6) in doc/TechnicalReport.pdf
%Update Priors
model.Priors(i) = sum(GAMMA(i,:)) / nbData;
%Update Mu, see Eq. (2.0.7) in doc/TechnicalReport.pdf
%Update Mu
model.Mu(:,i) = Data * GAMMA2(i,:)';
%Update Sigma, see Eq. (2.0.8) in doc/TechnicalReport.pdf (regularization term is optional, see Eq. (2.1.2))
%Update Sigma
DataTmp = Data - repmat(model.Mu(:,i),1,nbData);
model.Sigma(:,:,i) = DataTmp * diag(GAMMA2(i,:)) * DataTmp' + eye(size(Data,1)) * diagRegularizationFactor;
end
......@@ -46,9 +73,9 @@ end
disp(['The maximum number of ' num2str(nbMaxSteps) ' EM iterations has been reached.']);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [L, GAMMA] = computeGamma(Data, model)
%See Eq. (2.0.5) in doc/TechnicalReport.pdf
L = zeros(model.nbStates,size(Data,2));
for i=1:model.nbStates
L(i,:) = model.Priors(i) * gaussPDF(Data, model.Mu(:,i), model.Sigma(:,:,i));
......@@ -56,5 +83,3 @@ end
GAMMA = L ./ repmat(sum(L,1)+realmin, model.nbStates, 1);
end
m_fcts/EM_HDGMM.m
View file @ 2cb8784d
function [model, GAMMA2] = EM_HDGMM(Data, model)
%EM for High Dimensional Data Clustering (HDDC, HD-GMM) model proposed by Bouveyron (2007)
%Sylvain Calinon, 2015
% EM for High Dimensional Data Clustering (HDDC, HD-GMM) model proposed by Bouveyron (2007).
%
% Writing code takes time. Polishing it and making it available to others takes longer!
% If some parts of the code were useful for your research of for a better understanding
% of the algorithms, please reward the authors by citing the related publications,
% and consider making your own research available in this way.
%
% @article{Calinon15,
% author="Calinon, S.",
% title="A Tutorial on Task-Parameterized Movement Learning and Retrieval",
% journal="Intelligent Service Robotics",
% year="2015"
% }
%
% @article{Bouveyron07,
% author = "Bouveyron, C. and Girard, S. and Schmid, C.",
% title = "High-dimensional data clustering",
% journal = "Computational Statistics and Data Analysis",
% year = "2007",
% volume = "52",
% number = "1",
% pages = "502--519"
% }
%
% Copyright (c) 2015 Idiap Research Institute, http://idiap.ch/
% Written by Sylvain Calinon, http://calinon.ch/
%
% This file is part of PbDlib, http://www.idiap.ch/software/pbdlib/
%
% PbDlib is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License version 3 as
% published by the Free Software Foundation.
%
% PbDlib is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with PbDlib. If not, see <http://www.gnu.org/licenses/>.
%Parameters of the EM iterations
nbMinSteps = 5; %Minimum number of iterations allowed
......@@ -8,7 +47,7 @@ nbMaxSteps = 100; %Maximum number of iterations allowed
maxDiffLL = 1E-4; %Likelihood increase threshold to stop the algorithm
nbData = size(Data,2);
diagRegularizationFactor = 1E-8; %Regularization term is optional, see Eq. (2.1.2) in doc/TechnicalReport.pdf
diagRegularizationFactor = 1E-8; %Regularization term is optional
%EM loop
for nbIter=1:nbMaxSteps
......@@ -19,19 +58,19 @@ for nbIter=1:nbMaxSteps
GAMMA2 = GAMMA ./ repmat(sum(GAMMA,2), 1, nbData);
%M-step
%Update Priors, see Eq. (2.0.6) in doc/TechnicalReport.pdf
%Update Priors
model.Priors = sum(GAMMA,2)/nbData;
%Update Mu, see Eq. (2.0.7) in doc/TechnicalReport.pdf
%Update Mu
model.Mu = Data * GAMMA2';
%Update factor analyser params
%Update factor analyser parameters
for i=1:model.nbStates
%Compute covariance
DataTmp = Data - repmat(model.Mu(:,i),1,nbData);
S(:,:,i) = DataTmp * diag(GAMMA2(i,:)) * DataTmp' + eye(model.nbVar) * diagRegularizationFactor;
%HDGMM update, see Eq. (2.2.2) in doc/TechnicalReport.pdf
%HDGMM update
[V,D] = eig(S(:,:,i));
[~,id] = sort(diag(D),'descend');
% model.D(:,:,i) = D(id(1:model.nbFA), id(1:model.nbFA));
......@@ -40,7 +79,7 @@ for nbIter=1:nbMaxSteps
model.D(:,:,i) = diag([d(id(1:model.nbFA)); repmat(mean(d(id(model.nbFA+1:end))), model.nbVar-model.nbFA, 1)]);
model.V(:,:,i) = V(:,id);
%Reconstruct Sigma, see Eq. (2.2.1) in doc/TechnicalReport.pdf
%Reconstruct Sigma
model.Sigma(:,:,i) = model.V(:,:,i) * model.D(:,:,i) * model.V(:,:,i)' + eye(model.nbVar) * diagRegularizationFactor;
end
......@@ -57,9 +96,9 @@ end
disp(['The maximum number of ' num2str(nbMaxSteps) ' EM iterations has been reached.']);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [Lik, GAMMA] = computeGamma(Data, model)
%See Eq. (2.0.5) in doc/TechnicalReport.pdf
Lik = zeros(model.nbStates,size(Data,2));
for i=1:model.nbStates
Lik(i,:) = model.Priors(i) * gaussPDF(Data, model.Mu(:,i), model.Sigma(:,:,i));
......
m_fcts/EM_MFA.m
View file @ 2cb8784d
function [model, GAMMA2] = EM_MFA(Data, model)
%EM for Mixture of factor analysis
%Implementation inspired by "Parsimonious Gaussian Mixture Models" by McNicholas and Murphy, Appendix 8, p.17 (UUU version)
%Sylvain Calinon, 2015
% EM for Mixture of factor analysis (implementation based on "Parsimonious Gaussian
% Mixture Models" by McNicholas and Murphy, Appendix 8, p.17, UUU version).
%
% Writing code takes time. Polishing it and making it available to others takes longer!
% If some parts of the code were useful for your research of for a better understanding
% of the algorithms, please reward the authors by citing the related publications,
% and consider making your own research available in this way.
%
% @article{Calinon15,
% author="Calinon, S.",
% title="A Tutorial on Task-Parameterized Movement Learning and Retrieval",
% journal="Intelligent Service Robotics",
% year="2015"
% }
%
% Copyright (c) 2015 Idiap Research Institute, http://idiap.ch/
% Written by Sylvain Calinon, http://calinon.ch/
%
% This file is part of PbDlib, http://www.idiap.ch/software/pbdlib/
%
% PbDlib is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License version 3 as
% published by the Free Software Foundation.
%
% PbDlib is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with PbDlib. If not, see <http://www.gnu.org/licenses/>.
%Parameters of the EM iterations
nbMinSteps = 5; %Minimum number of iterations allowed
......@@ -9,7 +38,7 @@ nbMaxSteps = 100; %Maximum number of iterations allowed
maxDiffLL = 1E-4; %Likelihood increase threshold to stop the algorithm
nbData = size(Data,2);
diagRegularizationFactor = 1E-6; %Regularization term is optional, see Eq. (2.1.2) in doc/TechnicalReport.pdf
diagRegularizationFactor = 1E-6; %Optional regularization term
% %Circular initialization of the MFA parameters
% Itmp = eye(model.nbVar)*1E-2;
......@@ -22,7 +51,7 @@ for i=1:model.nbStates
[V,D] = eig(model.Sigma(:,:,i)-model.P(:,:,i));
[~,id] = sort(diag(D),'descend');
V = V(:,id)*D(id,id).^.5;
model.L(:,:,i) = V(:,1:model.nbFA); %->Sigma=LL'+P
model.L(:,:,i) = V(:,1:model.nbFA); %-> Sigma=LL'+P
end
for nbIter=1:nbMaxSteps
for i=1:model.nbStates
......@@ -41,26 +70,26 @@ for nbIter=1:nbMaxSteps
GAMMA2 = GAMMA ./ repmat(sum(GAMMA,2),1,nbData);
%M-step
%Update Priors, see Eq. (2.2.10) in doc/TechnicalReport.pdf
%Update Priors
model.Priors = sum(GAMMA,2) / nbData;
%Update Mu, see Eq. (2.2.11) in doc/TechnicalReport.pdf
%Update Mu
model.Mu = Data * GAMMA2';
%Update factor analysers parameters
for i=1:model.nbStates
%Compute covariance, see Eq. (2.2.15) in doc/TechnicalReport.pdf
%Compute covariance
DataTmp = Data - repmat(model.Mu(:,i),1,nbData);
S(:,:,i) = DataTmp * diag(GAMMA2(i,:)) * DataTmp' + eye(model.nbVar) * diagRegularizationFactor;
%Update B, see Eq. (2.2.16) in doc/TechnicalReport.pdf
%Update B
B(:,:,i) = model.L(:,:,i)' / (model.L(:,:,i) * model.L(:,:,i)' + model.P(:,:,i));
%Update Lambda, see Eq. (2.2.12) in doc/TechnicalReport.pdf
%Update Lambda
model.L(:,:,i) = S(:,:,i) * B(:,:,i)' / (eye(model.nbFA) - B(:,:,i) * model.L(:,:,i) + B(:,:,i) * S(:,:,i) * B(:,:,i)');
%Update Psi, see Eq. (2.2.13) in doc/TechnicalReport.pdf
%Update Psi
model.P(:,:,i) = diag(diag(S(:,:,i) - model.L(:,:,i) * B(:,:,i) * S(:,:,i))) + eye(model.nbVar) * diagRegularizationFactor;
%Reconstruct Sigma, see Eq. (2.2.4) in doc/TechnicalReport.pdf
%Reconstruct Sigma
model.Sigma(:,:,i) = model.L(:,:,i) * model.L(:,:,i)' + model.P(:,:,i);
end
%Compute average log-likelihood
......@@ -76,9 +105,9 @@ end
disp(['The maximum number of ' num2str(nbMaxSteps) ' EM iterations has been reached.']);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [Lik, GAMMA] = computeGamma(Data, model)
%See Eq. (2.2.9) in doc/TechnicalReport.pdf
Lik = zeros(model.nbStates,size(Data,2));
for i=1:model.nbStates
Lik(i,:) = model.Priors(i) * gaussPDF(Data, model.Mu(:,i), model.Sigma(:,:,i));
......
m_fcts/EM_MPPCA.m
View file @ 2cb8784d
function [model, GAMMA2] = EM_MPPCA(Data, model)
%EM for mixture of probabilistic principal component analyzers,
%inspired by "Mixtures of Probabilistic Principal Component Analysers" by Michael E. Tipping and Christopher M. Bishop
%Sylvain Calinon, 2015
% EM for mixture of probabilistic principal component analyzers (implementation based on
% "Mixtures of Probabilistic Principal Component Analysers" by Michael E. Tipping and Christopher M. Bishop)
%
% Writing code takes time. Polishing it and making it available to others takes longer!
% If some parts of the code were useful for your research of for a better understanding
% of the algorithms, please reward the authors by citing the related publications,
% and consider making your own research available in this way.
%
% @article{Calinon15,
% author="Calinon, S.",
% title="A Tutorial on Task-Parameterized Movement Learning and Retrieval",
% journal="Intelligent Service Robotics",
% year="2015"
% }
%
% Copyright (c) 2015 Idiap Research Institute, http://idiap.ch/
% Written by Sylvain Calinon, http://calinon.ch/
%
% This file is part of PbDlib, http://www.idiap.ch/software/pbdlib/
%
% PbDlib is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License version 3 as
% published by the Free Software Foundation.
%
% PbDlib is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with PbDlib. If not, see <http://www.gnu.org/licenses/>.
%Parameters of the EM iterations
nbMinSteps = 5; %Minimum number of iterations allowed
......@@ -29,29 +58,29 @@ for nbIter=1:nbMaxSteps
GAMMA2 = GAMMA ./ repmat(sum(GAMMA,2),1,nbData);
%M-step
%Update Priors, see Eq. (2.2.10) in doc/TechnicalReport.pdf
%Update Priors
model.Priors = sum(GAMMA,2) / nbData;
%Update Mu, see Eq. (2.2.11) in doc/TechnicalReport.pdf
%Update Mu
model.Mu = Data * GAMMA2';
%Update factor analyser params
for i=1:model.nbStates
%Compute covariance, see Eq. (2.2.19) in doc/TechnicalReport.pdf
%Compute covariance
DataTmp = Data - repmat(model.Mu(:,i),1,nbData);
S(:,:,i) = DataTmp * diag(GAMMA2(i,:)) * DataTmp' + eye(model.nbVar) * diagRegularizationFactor;
%Update M, see Eq. (2.2.20) in doc/TechnicalReport.pdf
%Update M
M = eye(model.nbFA)*model.o(i) + model.L(:,:,i)' * model.L(:,:,i);
%Update Lambda, see Eq. (2.2.17) in doc/TechnicalReport.pdf
%Update Lambda
Lnew = S(:,:,i) * model.L(:,:,i) / (eye(model.nbFA)*model.o(i) + M \ model.L(:,:,i)' * S(:,:,i) * model.L(:,:,i));
%Update of sigma^2, see Eq. (2.2.21) in doc/TechnicalReport.pdf
%Update of sigma^2
model.o(i) = trace(S(:,:,i) - S(:,:,i) * model.L(:,:,i) / M * Lnew') / model.nbVar;
model.L(:,:,i) = Lnew;
%Update Psi, see Eq. (2.2.18) in doc/TechnicalReport.pdf
%Update Psi
model.P(:,:,i) = eye(model.nbVar) * model.o(i);
%Reconstruct Sigma, see Eq. (2.2.4) in doc/TechnicalReport.pdf
%Reconstruct Sigma
model.Sigma(:,:,i) = model.L(:,:,i) * model.L(:,:,i)' + model.P(:,:,i);
end
......@@ -68,9 +97,9 @@ end
disp(['The maximum number of ' num2str(nbMaxSteps) ' EM iterations has been reached.']);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [Lik, GAMMA] = computeGamma(Data, model)
%See Eq. (2.0.5) in doc/TechnicalReport.pdf
Lik = zeros(model.nbStates,size(Data,2));
for i=1:model.nbStates
Lik(i,:) = model.Priors(i) * gaussPDF(Data, model.Mu(:,i), model.Sigma(:,:,i));
......
m_fcts/EM_TPGMM.m
View file @ 2cb8784d
......@@ -3,21 +3,35 @@ function model = EM_TPGMM(Data, model)
% The approach allows the modulation of the centers and covariance matrices of the Gaussians with respect to
% external parameters represented in the form of candidate coordinate systems.
%
% Author: Sylvain Calinon, 2014
% http://programming-by-demonstration.org/SylvainCalinon
% Writing code takes time. Polishing it and making it available to others takes longer!
% If some parts of the code were useful for your research of for a better understanding
% of the algorithms, please reward the authors by citing the related publications,
% and consider making your own research available in this way.
%
% This source code is given for free! In exchange, I would be grateful if you cite
% the following reference in any academic publication that uses this code or part of it:
%
% @inproceedings{Calinon14ICRA,
% author="Calinon, S. and Bruno, D. and Caldwell, D. G.",
% title="A task-parameterized probabilistic model with minimal intervention control",
% booktitle="Proc. {IEEE} Intl Conf. on Robotics and Automation ({ICRA})",
% year="2014",
% month="May-June",
% address="Hong Kong, China",
% pages="3339--3344"
% @article{Calinon15,
% author="Calinon, S.",
% title="A Tutorial on Task-Parameterized Movement Learning and Retrieval",
% journal="Intelligent Service Robotics",
% year="2015"
% }
%
% Copyright (c) 2015 Idiap Research Institute, http://idiap.ch/
% Written by Sylvain Calinon, http://calinon.ch/
%
% This file is part of PbDlib, http://www.idiap.ch/software/pbdlib/
%
% PbDlib is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License version 3 as
% published by the Free Software Foundation.
%
% PbDlib is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with PbDlib. If not, see <http://www.gnu.org/licenses/>.
%Parameters of the EM algorithm
nbMinSteps = 5; %Minimum number of iterations allowed
......@@ -25,8 +39,8 @@ nbMaxSteps = 100; %Maximum number of iterations allowed
maxDiffLL = 1E-5; %Likelihood increase threshold to stop the algorithm
nbData = size(Data,3);
diagRegularizationFactor = 1E-5; %Regularization term is optional, see Eq. (2.1.2) in doc/TechnicalReport.pdf
%diagRegularizationFactor = 0; %Regularization term is optional, see Eq. (2.1.2) in doc/TechnicalReport.pdf
diagRegularizationFactor = 1E-5; %Optional regularization term
%diagRegularizationFactor = 0; %Optional regularization term
for nbIter=1:nbMaxSteps
fprintf('.');
......@@ -38,7 +52,7 @@ for nbIter=1:nbMaxSteps
%M-step
for i=1:model.nbStates
%Update Priors, see Eq. (6.0.2) in doc/TechnicalReport.pdf
%Update Priors
model.Priors(i) = sum(sum(GAMMA(i,:))) / nbData;
for m=1:model.nbFrames
......@@ -46,10 +60,10 @@ for nbIter=1:nbMaxSteps
DataMat=[];
DataMat(1:model.nbVars(m),:) = Data(1:model.nbVars(m),m,:);
%Update Mu, see Eq. (6.0.3) in doc/TechnicalReport.pdf
%Update Mu
model.Mu(1:model.nbVars(m),m,i) = DataMat * GAMMA2(i,:)';
%Update Sigma (regularization term is optional), see Eq. (6.0.4) in doc/TechnicalReport.pdf
%Update Sigma (regularization term is optional)
DataTmp = DataMat - repmat(model.Mu(1:model.nbVars(m),m,i),1,nbData);
model.Sigma(1:model.nbVars(m),1:model.nbVars(m),m,i) = DataTmp * diag(GAMMA2(i,:)) * DataTmp' + eye(model.nbVars(m))*diagRegularizationFactor;
end
......@@ -68,9 +82,9 @@ end
disp(['The maximum number of ' num2str(nbMaxSteps) ' EM iterations has been reached.']);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [Lik, GAMMA, GAMMA0] = computeGamma(Data, model)
%See Eq. (6.0.1) in doc/TechnicalReport.pdf
nbData = size(Data, 3);
Lik = ones(model.nbStates, nbData);
GAMMA0 = zeros(model.nbStates, model.nbFrames, nbData);
......@@ -85,7 +99,3 @@ for i=1:model.nbStates
end
GAMMA = Lik ./ repmat(sum(Lik,1)+realmin, size(Lik,1), 1);
end
m_fcts/EM_stdPGMM.m
View file @ 2cb8784d
function [model, s, LL] = EM_stdPGMM(s, model)
% Training of a parametric Gaussian mixture model (PGMM) with expectation-maximization (EM).
% The implementation follows the approach described by Wilson and Bobick (1999) "Parametric Hidden Markov
% Models for Gesture Recognition", IEEE Trans. on Pattern Analysis and Machine Intelligence, with an
% implementation applied to the special case of Gaussian mixture models (GMM).
% The approach is inspired by Wilson and Bobick (1999), with an implementation applied to
% the special case of Gaussian mixture models (GMM).
%
% Author: Sylvain Calinon, 2013
% http://programming-by-demonstration.org/SylvainCalinon
% Writing code takes time. Polishing it and making it available to others takes longer!
% If some parts of the code were useful for your research of for a better understanding
% of the algorithms, please reward the authors by citing the related publications,
% and consider making your own research available in this way.
%
% This source code is given for free! In exchange, I would be grateful if you cite
% the following references in any academic publication that uses this code or part of it:
%
% @inproceedings{Calinon12Hum,
% author="Calinon, S. and Li, Z. and Alizadeh, T. and Tsagarakis, N. G. and Caldwell, D. G.",
% title="Statistical dynamical systems for skills acquisition in humanoids",
% booktitle="Proc. {IEEE} Intl Conf. on Humanoid Robots ({H}umanoids)",
% year="2012",
% address="Osaka, Japan"
% @article{Calinon15,
% author="Calinon, S.",
% title="A Tutorial on Task-Parameterized Movement Learning and Retrieval",
% journal="Intelligent Service Robotics",
% year="2015"
% }
%
% @article{Wilson99,
% author="Wilson, A. D. and Bobick, A. F.",
% title="Parametric Hidden {M}arkov Models for Gesture Recognition",
......@@ -27,8 +25,23 @@ function [model, s, LL] = EM_stdPGMM(s, model)
% pages="884--900"
% }
%
% The first reference presents an implementation of the approach described in the second reference, and
% applies it to the special case of Gaussian mixture model (GMM).
% Copyright (c) 2015 Idiap Research Institute, http://idiap.ch/
% Written by Sylvain Calinon, http://calinon.ch/
%
% This file is part of PbDlib, http://www.idiap.ch/software/pbdlib/
%
% PbDlib is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License version 3 as