通过BNT Toolbox建立GMM分类模型

参考文档：http://www.media.mit.edu/wearables/mithril/BNT/mixtureBNT.txt

代码下载地址：http://www.media.mit.edu/wearables/mithril/BNT/mixtureBNT.zip

案例：通过建立一个GMM分类器对人类活动（跑、走）进行分类，实验数据为可穿戴设备获取的31维特征向量。

说明数据：

walkingX——walking features, 570 samples, 31 elements

runningX——running features, 120 samples, 31 elements

GMM的DBN示意图：

 

load 'mixtureBNT.mat' size(walkingX) size(runningX) dag = [ 0 1 1 ; 0 0 1 ; 0 0 0 ]; discrete_nodes = [1 2]; nodes = [1 : 3]; node_sizes=[ 2 2 31]; bnet = mk_bnet(dag, node_sizes, 'discrete', discrete_nodes); bnet.CPD{1} = tabular_CPD(bnet,1); bnet.CPD{2} = tabular_CPD(bnet,2); bnet.CPD{3} = gaussian_CPD(bnet, 3); %bnet.CPD{3} = gaussian_CPD(bnet, 3,'cov_type','diag'); trainingX = walkingX(1:100,:); trainingX(101:200,:)=runningX(1:100,:); trainingC(1:100) = 1;   %% Class 1 is walking trainingC(101:200) = 2; %% Class 2 is running testX(1:20,:) = walkingX(101:120,:);   %% The first 20 are walking testX(21:40,:) = runningX(101:120,:);  %% The next 20 are running training= cell(3,length(trainingX)); training(3,:) = num2cell(trainingX',1); training(1,:) = num2cell(trainingC,1); engine = jtree_inf_engine(bnet); maxiter=10;     %% The number of iterations of EM (max) epsilon=1e-100; %% A very small stopping criterion [bnet2, ll, engine2] = learn_params_em(engine,training,maxiter,epsilon); class0= cell(3,1); %% Create an empty cell array for observations class1 = class0; class2 = class0; class1{1} = 1;     %% The class node is observed to be walking class2{1} = 2;     %% The class node is observed to be running for i=1:100   sample1=sample_bnet(bnet2,'evidence',class1);   sample2=sample_bnet(bnet2,'evidence',class2);   modelX(i,:)=sample1{3}';   modelX(i+100,:)=sample2{3}'; end figure subplot(2,1,1); plot(trainingX); subplot(2,1,2); plot(modelX); evidence=class0;   %% Start out with nothing observed for i=1:40   evidence{3}=testX(i,:)';   [engine3, ll] = enter_evidence(engine2,evidence);   marg = marginal_nodes(engine3,1);   p(i,:)=marg.T'; end figure; subplot(2,1,1); plot(testX); hold plot(p(:,1));  %% Plot the output of the walking classifier subplot(2,1,2); plot(testX); hold plot(p(:,2));  %% Plot the output of the running classifier

size(walkingX) size(runningX) dag = [ 0 1 1 ; 0 0 1 ; 0 0 0 ]; discrete_nodes = [1 2]; nodes = [1 : 3]; node_sizes=[ 2 2 31]; bnet = mk_bnet(dag, node_sizes, 'discrete', discrete_nodes); bnet.CPD{1} = tabular_CPD(bnet,1); bnet.CPD{2} = tabular_CPD(bnet,2); bnet.CPD{3} = gaussian_CPD(bnet, 3); %bnet.CPD{3} = gaussian_CPD(bnet, 3,'cov_type','diag'); trainingX = walkingX(1:100,:); trainingX(101:200,:)=runningX(1:100,:); trainingC(1:100) = 1; %% Class 1 is walking trainingC(101:200) = 2; %% Class 2 is running testX(1:20,:) = walkingX(101:120,:); %% The first 20 are walking testX(21:40,:) = runningX(101:120,:); %% The next 20 are running training= cell(3,length(trainingX)); training(3,:) = num2cell(trainingX',1); training(1,:) = num2cell(trainingC,1); engine = jtree_inf_engine(bnet); maxiter=10; %% The number of iterations of EM (max) epsilon=1e-100; %% A very small stopping criterion [bnet2, ll, engine2] = learn_params_em(engine,training,maxiter,epsilon); class0= cell(3,1); %% Create an empty cell array for observations class1 = class0; class2 = class0; class1{1} = 1; %% The class node is observed to be walking class2{1} = 2; %% The class node is observed to be running for i=1:100 sample1=sample_bnet(bnet2,'evidence',class1); sample2=sample_bnet(bnet2,'evidence',class2); modelX(i,:)=sample1{3}'; modelX(i+100,:)=sample2{3}'; end figure subplot(2,1,1); plot(trainingX); subplot(2,1,2); plot(modelX); evidence=class0; %% Start out with nothing observed for i=1:40 evidence{3}=testX(i,:)'; [engine3, ll] = enter_evidence(engine2,evidence); marg = marginal_nodes(engine3,1); p(i,:)=marg.T'; end figure; subplot(2,1,1); plot(testX); hold plot(p(:,1)); %% Plot the output of the walking classifier subplot(2,1,2); plot(testX); hold plot(p(:,2)); %% Plot the output of the running classifier