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| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
|---|---|---|
| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square | |
| parent | 7cc31810d53176e805532b2789955f4eedbce6bb (diff) | |
| download | BNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz | |
BNW using Octave instead of Matlab.
This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02
Diffstat (limited to 'sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square')
23 files changed, 2667 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/CVS/Entries b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/CVS/Entries new file mode 100644 index 00000000..fdee19ae --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/CVS/Entries @@ -0,0 +1,14 @@ +/get_square_data.m/1.1.1.1/Wed May 29 15:59:54 2002// +/hhmm_inference.m/1.1.1.1/Wed May 29 15:59:54 2002// +/is_F2_true_D3.m/1.1.1.1/Wed May 29 15:59:54 2002// +/learn_square_hhmm_cts.m/1.1.1.1/Thu Jun 20 00:19:22 2002// +/learn_square_hhmm_discrete.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/plot_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_square_hhmm_cts.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_square_hhmm_discrete.m/1.1.1.1/Wed May 29 15:59:54 2002// +/square4.mat/1.1.1.1/Wed May 29 15:59:54 2002// +/square4_cases.mat/1.1.1.1/Wed May 29 15:59:54 2002// +/test_square_fig.m/1.1.1.1/Wed May 29 15:59:54 2002// +/test_square_fig.mat/1.1.1.1/Wed May 29 15:59:54 2002// +D/Old//// diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/CVS/Repository b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/CVS/Repository new file mode 100644 index 00000000..e926a0d5 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/CVS/Repository @@ -0,0 +1 @@ +FullBNT/BNT/examples/dynamic/HHMM/Square diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/CVS/Root b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/CVS/Root new file mode 100644 index 00000000..f3bd14a6 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/CVS/Root @@ -0,0 +1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Entries b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Entries new file mode 100644 index 00000000..6d415d0d --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Entries @@ -0,0 +1,5 @@ +/learn_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/plot_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +D diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Repository b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Repository new file mode 100644 index 00000000..47df1a8c --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Repository @@ -0,0 +1 @@ +FullBNT/BNT/examples/dynamic/HHMM/Square/Old diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Root b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Root new file mode 100644 index 00000000..f3bd14a6 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Root @@ -0,0 +1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/learn_square_hhmm.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/learn_square_hhmm.m new file mode 100644 index 00000000..695ae047 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/learn_square_hhmm.m @@ -0,0 +1,294 @@ +% Learn a 3 level HHMM similar to mk_square_hhmm + +% Because startprob should be shared for t=1:T, +% but in the DBN is shared for t=2:T, we train using a single long sequence. + +discrete_obs = 0; +supervised = 1; +obs_finalF2 = 0; +% It is not possible to observe F2 if we learn +% because the update_ess method for hhmmF_CPD and hhmmQ_CPD assume +% the F nodes are always hidden (for speed). +% However, for generating, we might want to set the final F2=true +% to force all subroutines to finish. + +ss = 6; +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; + +seed = 1; +rand('state', seed); +randn('state', seed); + +if discrete_obs + Qsizes = [2 4 2]; +else + Qsizes = [2 4 1]; +end + +D = 3; +Qnodes = 1:D; +startprob = cell(1,D); +transprob = cell(1,D); +termprob = cell(1,D); + +startprob{1} = 'unif'; +transprob{1} = 'unif'; + +% In the unsupervised case, it is essential that we break symmetry +% in the initial param estimates. +%startprob{2} = 'unif'; +%transprob{2} = 'unif'; +%termprob{2} = 'unif'; +startprob{2} = 'rnd'; +transprob{2} = 'rnd'; +termprob{2} = 'rnd'; + +leftright = 0; +if leftright + % Initialise base-level models as left-right. + % If we initialise with delta functions, + % they will remain delat funcitons after learning + startprob{3} = 'leftstart'; + transprob{3} = 'leftright'; + termprob{3} = 'rightstop'; +else + % If we want to be able to run a base-level model backwards... + startprob{3} = 'rnd'; + transprob{3} = 'rnd'; + termprob{3} = 'rnd'; +end + +if discrete_obs + % Initialise observations of lowest level primitives in a way which we can interpret + chars = ['L', 'l', 'U', 'u', 'R', 'r', 'D', 'd']; + L=find(chars=='L'); l=find(chars=='l'); + U=find(chars=='U'); u=find(chars=='u'); + R=find(chars=='R'); r=find(chars=='r'); + D=find(chars=='D'); d=find(chars=='d'); + Osize = length(chars); + + p = 0.9; + obsprob = (1-p)*ones([4 2 Osize]); + % Q2 Q3 O + obsprob(1, 1, L) = p; + obsprob(1, 2, l) = p; + obsprob(2, 1, U) = p; + obsprob(2, 2, u) = p; + obsprob(3, 1, R) = p; + obsprob(3, 2, r) = p; + obsprob(4, 1, D) = p; + obsprob(4, 2, d) = p; + obsprob = mk_stochastic(obsprob); + Oargs = {'CPT', obsprob}; + +else + % Initialise means of lowest level primitives in a way which we can interpret + % These means are little vectors in the east, south, west, north directions. + % (left-right=east, up-down=south, right-left=west, down-up=north) + Osize = 2; + mu = zeros(2, Qsizes(2), Qsizes(3)); + noise = 0; + scale = 3; + for q3=1:Qsizes(3) + mu(:, 1, q3) = scale*[1;0] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 2, q3) = scale*[0;-1] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 3, q3) = scale*[-1;0] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 4, q3) = scale*[0;1] + noise*rand(2,1); + end + Sigma = repmat(reshape(scale*eye(2), [2 2 1 1 ]), [1 1 Qsizes(2) Qsizes(3)]); + Oargs = {'mean', mu, 'cov', Sigma, 'cov_type', 'diag'}; +end + +bnet = mk_hhmm('Qsizes', Qsizes, 'Osize', Osize', 'discrete_obs', discrete_obs,... + 'Oargs', Oargs, 'Ops', Qnodes(2:3), ... + 'startprob', startprob, 'transprob', transprob, 'termprob', termprob); + +if supervised + bnet.observed = [Q1 Q2 Onode]; +else + bnet.observed = [Onode]; +end + +if obs_finalF2 + engine = jtree_dbn_inf_engine(bnet); + % can't use ndx version because sometimes F2 is hidden, sometimes observed + error('can''t observe F when learning') +else + if supervised + engine = jtree_ndx_dbn_inf_engine(bnet); + else + engine = jtree_hmm_inf_engine(bnet); + end +end + +if discrete_obs + % generate some synthetic data (easier to debug) + cases = {}; + + T = 8; + ev = cell(ss, T); + ev(Onode,:) = num2cell([L l U u R r D d]); + if supervised + ev(Q1,:) = num2cell(1*ones(1,T)); + ev(Q2,:) = num2cell( [1 1 2 2 3 3 4 4]); + end + cases{1} = ev; + cases{3} = ev; + + T = 8; + ev = cell(ss, T); + if leftright % base model is left-right + ev(Onode,:) = num2cell([R r U u L l D d]); + else + ev(Onode,:) = num2cell([r R u U l L d D]); + end + if supervised + ev(Q1,:) = num2cell(2*ones(1,T)); + ev(Q2,:) = num2cell( [3 3 2 2 1 1 4 4]); + end + + cases{2} = ev; + cases{4} = ev; + + if obs_finalF2 + for i=1:length(cases) + T = size(cases{i},2); + cases{i}(F2,T)={2}; % force F2 to be finished at end of seq + end + end + + if 0 + ev = cases{4}; + engine2 = enter_evidence(engine2, ev); + T = size(ev,2); + for t=1:T + m=marginal_family(engine2, F2, t); + fprintf('t=%d\n', t); + reshape(m.T, [2 2]) + end + end + + % [bnet2, LL] = learn_params_dbn_em(engine, cases, 'max_iter', 10); + long_seq = cat(2, cases{:}); + [bnet2, LL, engine2] = learn_params_dbn_em(engine, {long_seq}, 'max_iter', 200); + + % figure out which subsequence each model is responsible for + mpe = calc_mpe_dbn(engine2, long_seq); + pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, chars); + +else + load 'square4_cases' % cases{seq}{i,t} for i=1:ss + %plot_square_hhmm(cases{1}) + %long_seq = cat(2, cases{:}); + train_cases = cases(1:2); + long_seq = cat(2, train_cases{:}); + if ~supervised + T = size(long_seq,2); + for t=1:T + long_seq{Q1,t} = []; + long_seq{Q2,t} = []; + end + end + [bnet2, LL, engine2] = learn_params_dbn_em(engine, {long_seq}, 'max_iter', 100); + + CPDO=struct(bnet2.CPD{eclass(Onode,1)}); + mu = CPDO.mean; + Sigma = CPDO.cov; + CPDO_full = CPDO; + + % force diagonal covs after training + for k=1:size(Sigma,3) + Sigma(:,:,k) = diag(diag(Sigma(:,:,k))); + end + bnet2.CPD{6} = set_fields(bnet.CPD{6}, 'cov', Sigma); + + if 0 + % visualize each model by concatenating means for each model for nsteps in a row + nsteps = 5; + ev = cell(ss, nsteps*prod(Qsizes(2:3))); + t = 1; + for q2=1:Qsizes(2) + for q3=1:Qsizes(3) + for i=1:nsteps + ev{Onode,t} = mu(:,q2,q3); + ev{Q2,t} = q2; + t = t + 1; + end + end + end + plot_square_hhmm(ev) + end + + % bnet3 is the same as the learned model, except we will use it in testing mode + if supervised + bnet3 = bnet2; + bnet3.observed = [Onode]; + engine3 = hmm_inf_engine(bnet3); + %engine3 = jtree_ndx_dbn_inf_engine(bnet3); + else + bnet3 = bnet2; + engine3 = engine2; + end + + if 0 + % segment whole sequence + mpe = calc_mpe_dbn(engine3, long_seq); + pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, []); + end + + % segment each sequence + test_cases = cases(3:4); + for i=1:2 + ev = test_cases{i}; + T = size(ev, 2); + for t=1:T + ev{Q1,t} = []; + ev{Q2,t} = []; + end + mpe = calc_mpe_dbn(engine3, ev); + subplot(1,2,i) + plot_square_hhmm(mpe) + %pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, []); + q1s = cell2num(mpe(Q1,:)); + h = hist(q1s, 1:Qsizes(1)); + map_q1 = argmax(h); + str = sprintf('test seq %d is of type %d\n', i, map_q1); + title(str) + end + +end + +if 0 +% Estimate gotten by couting transitions in the labelled data +% Note that a self transition shouldnt count if F2=off. +Q2ev = cell2num(ev(Q2,:)); +Q2a = Q2ev(1:end-1); +Q2b = Q2ev(2:end); +counts = compute_counts([Q2a; Q2b], [4 4]); +end + +eclass = bnet2.equiv_class; +CPDQ1=struct(bnet2.CPD{eclass(Q1,2)}); +CPDQ2=struct(bnet2.CPD{eclass(Q2,2)}); +CPDQ3=struct(bnet2.CPD{eclass(Q3,2)}); +CPDF2=struct(bnet2.CPD{eclass(F2,1)}); +CPDF3=struct(bnet2.CPD{eclass(F3,1)}); + + +A=add_hhmm_end_state(CPDQ2.transprob, CPDF2.termprob(:,:,2)); +squeeze(A(:,1,:)) +squeeze(A(:,2,:)) +CPDQ2.startprob + +if 0 +S=struct(CPDF2.sub_CPD_term); +S.nsamples +reshape(S.counts, [2 4 2]) +end diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/mk_square_hhmm.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/mk_square_hhmm.m new file mode 100644 index 00000000..608b6784 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/mk_square_hhmm.m @@ -0,0 +1,183 @@ +function bnet = mk_square_hhmm(discrete_obs, true_params, topright) + +% Make a 3 level HHMM described by the following grammar +% +% Square -> CLK | CCK % clockwise or counterclockwise +% CLK -> LR UD RL DU start on top left (1 2 3 4) +% CCK -> RL UD LR DU if start at top right (3 2 1 4) +% CCK -> UD LR DU RL if start at top left (2 1 4 3) +% +% LR = left-right, UD = up-down, RL = right-left, DU = down-up +% LR, UD, RL, DU are sub HMMs. +% +% For discrete observations, the subHMMs are 2-state left-right. +% LR emits L then l, etc. +% +% For cts observations, the subHMMs are 1 state. +% LR emits a vector in the -> direction, with a little noise. +% Since there is no constraint that we remain in the LR state as long as the RL state, +% the sides of the square might have different lengths, +% so the result is not really a square! +% +% If true_params = 0, we use random parameters at the top 2 levels +% (ready for learning). At the bottom level, we use noisy versions +% of the "true" observations. +% +% If topright=1, counter-clockwise starts at top right, not top left +% This example was inspired by Ivanov and Bobick. + +if nargin < 3, topright = 1; end + +if 1 % discrete_obs + Qsizes = [2 4 2]; +else + Qsizes = [2 4 1]; +end + +D = 3; +Qnodes = 1:D; +startprob = cell(1,D); +transprob = cell(1,D); +termprob = cell(1,D); + +% LEVEL 1 + +startprob{1} = 'unif'; +transprob{1} = 'unif'; + +% LEVEL 2 + +if true_params + startprob{2} = zeros(2, 4); + startprob{2}(1, :) = [1 0 0 0]; + if topright + startprob{2}(2, :) = [0 0 1 0]; + else + startprob{2}(2, :) = [0 1 0 0]; + end + + transprob{2} = zeros(4, 2, 4); + + transprob{2}(:,1,:) = [0 1 0 0 + 0 0 1 0 + 0 0 0 1 + 0 0 0 1]; % 4->e + if topright + transprob{2}(:,2,:) = [0 0 0 1 + 1 0 0 0 + 0 1 0 0 + 0 0 0 1]; % 4->e + else + transprob{2}(:,2,:) = [0 0 0 1 + 1 0 0 0 + 0 0 1 0 % 3->e + 0 0 1 0]; + end + + %termprob{2} = 'rightstop'; + termprob{2} = zeros(2,4,2); + pfin = 0.8; + termprob{2}(1,:,2) = [0 0 0 pfin]; % finish in state 4 (DU) + termprob{2}(1,:,1) = 1 - [0 0 0 pfin]; + if topright + termprob{2}(2,:,2) = [0 0 0 pfin]; + termprob{2}(2,:,1) = 1 - [0 0 0 pfin]; + else + termprob{2}(2,:,2) = [0 0 pfin 0]; % finish in state 3 (RL) + termprob{2}(2,:,1) = 1 - [0 0 pfin 0]; + end +else + % In the unsupervised case, it is essential that we break symmetry + % in the initial param estimates. + %startprob{2} = 'unif'; + %transprob{2} = 'unif'; + %termprob{2} = 'unif'; + startprob{2} = 'rnd'; + transprob{2} = 'rnd'; + termprob{2} = 'rnd'; +end + +% LEVEL 3 + +if 1 | true_params + startprob{3} = 'leftstart'; + transprob{3} = 'leftright'; + termprob{3} = 'rightstop'; +else + % If we want to be able to run a base-level model backwards... + startprob{3} = 'rnd'; + transprob{3} = 'rnd'; + termprob{3} = 'rnd'; +end + + +% OBS LEVEl + +if discrete_obs + % Initialise observations of lowest level primitives in a way which we can interpret + chars = ['L', 'l', 'U', 'u', 'R', 'r', 'D', 'd']; + L=find(chars=='L'); l=find(chars=='l'); + U=find(chars=='U'); u=find(chars=='u'); + R=find(chars=='R'); r=find(chars=='r'); + D=find(chars=='D'); d=find(chars=='d'); + Osize = length(chars); + + if true_params + p = 1; % makes each state fully observed + else + p = 0.9; + end + + obsprob = (1-p)*ones([4 2 Osize]); + % Q2 Q3 O + obsprob(1, 1, L) = p; + obsprob(1, 2, l) = p; + obsprob(2, 1, U) = p; + obsprob(2, 2, u) = p; + obsprob(3, 1, R) = p; + obsprob(3, 2, r) = p; + obsprob(4, 1, D) = p; + obsprob(4, 2, d) = p; + obsprob = mk_stochastic(obsprob); + Oargs = {'CPT', obsprob}; +else + % Initialise means of lowest level primitives in a way which we can interpret + % These means are little vectors in the east, south, west, north directions. + % (left-right=east, up-down=south, right-left=west, down-up=north) + Osize = 2; + mu = zeros(2, Qsizes(2), Qsizes(3)); + scale = 3; + if true_params + noise = 0; + else + noise = 0.5*scale; + end + for q3=1:Qsizes(3) + mu(:, 1, q3) = scale*[1;0] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 2, q3) = scale*[0;-1] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 3, q3) = scale*[-1;0] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 4, q3) = scale*[0;1] + noise*rand(2,1); + end + Sigma = repmat(reshape(scale*eye(2), [2 2 1 1 ]), [1 1 Qsizes(2) Qsizes(3)]); + Oargs = {'mean', mu, 'cov', Sigma, 'cov_type', 'diag'}; +end + +if discrete_obs + selfprob = 0.5; +else + selfprob = 0.95; + % If less than this, it won't look like a square + % because it doesn't spend enough time in each state + % Unfortunately, the variance on durations (lengths of each side) + % is very large +end +bnet = mk_hhmm('Qsizes', Qsizes, 'Osize', Osize', 'discrete_obs', discrete_obs, ... + 'Oargs', Oargs, 'Ops', Qnodes(2:3), 'selfprob', selfprob, ... + 'startprob', startprob, 'transprob', transprob, 'termprob', termprob); + diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/plot_square_hhmm.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/plot_square_hhmm.m new file mode 100644 index 00000000..e6701e45 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/plot_square_hhmm.m @@ -0,0 +1,32 @@ +function plot_square_hhmm(ev) +% Plot the square shape implicit in the evidence. +% ev{i,t} is the value of node i in slice t. +% The observed node contains a velocity (delta increment), which is converted +% into a position. +% The Q2 node specifies which model is used; each segment is color-coded +% in the order red, green, blue, black. + +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; + +delta = cell2num(ev(Onode,:)); % delta(:,t) +Q2label = cell2num(ev(Q2,:)); + +T = size(delta, 2); +pos = zeros(2,T+1); +clf +hold on +cols = {'r', 'g', 'b', 'k'}; +boundary = 0; +coli = 1; +for t=2:T+1 + pos(:,t) = pos(:,t-1) + delta(:,t-1); + plot(pos(1,t), pos(2,t), sprintf('%c.', cols{coli})); + if t < T + boundary = (Q2label(t) ~= Q2label(t-1)); + end + if boundary + coli = coli + 1; + coli = mod(coli-1, length(cols)) + 1; + end +end + diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/sample_square_hhmm.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/sample_square_hhmm.m new file mode 100644 index 00000000..a0f9007e --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/sample_square_hhmm.m @@ -0,0 +1,160 @@ + +seed = 0; +rand('state', seed); +randn('state', seed); + +discrete_obs = 1; +topright = 0; + +Qsizes = [2 4 2]; +D = 3; +Qnodes = 1:D; +startprob = cell(1,D); +transprob = cell(1,D); +termprob = cell(1,D); + +% LEVEL 1 + +startprob{1} = 'ergodic'; +transprob{1} = 'ergodic'; + +% LEVEL 2 + +startprob{2} = zeros(2, 4); +startprob{2}(1, :) = [1 0 0 0]; +if topright + startprob{2}(2, :) = [0 0 1 0]; +else + startprob{2}(2, :) = [0 1 0 0]; +end + +transprob{2} = zeros(4, 2, 4); + +transprob{2}(:,1,:) = [0 1 0 0 + 0 0 1 0 + 0 0 0 1 + 0 0 0 1]; % 4->e +if topright + transprob{2}(:,2,:) = [0 0 0 1 + 1 0 0 0 + 0 1 0 0 + 0 0 0 1]; % 4->e +else + transprob{2}(:,2,:) = [0 0 0 1 + 1 0 0 0 + 0 0 1 0 % 3->e + 0 0 1 0]; +end + +%termprob{2} = 'rightstop'; +termprob{2} = zeros(2,4,2); +pfin = 0.8; +termprob{2}(1,:,2) = [0 0 0 pfin]; % finish in state 4 (DU) +termprob{2}(1,:,1) = 1 - [0 0 0 pfin]; +if topright + termprob{2}(2,:,2) = [0 0 0 pfin]; + termprob{2}(2,:,1) = 1 - [0 0 0 pfin]; +else + termprob{2}(2,:,2) = [0 0 pfin 0]; % finish in state 3 (RL) + termprob{2}(2,:,1) = 1 - [0 0 pfin 0]; +end + +% LEVEL 3 + +startprob{3} = 'leftstart'; +transprob{3} = 'leftright'; +termprob{3} = 'rightstop'; + + +% OBS LEVEl + +if discrete_obs + chars = ['L', 'l', 'U', 'u', 'R', 'r', 'D', 'd']; + L=find(chars=='L'); l=find(chars=='l'); + U=find(chars=='U'); u=find(chars=='u'); + R=find(chars=='R'); r=find(chars=='r'); + D=find(chars=='D'); d=find(chars=='d'); + Osize = length(chars); + + obsprob = zeros([4 2 Osize]); + % Q2 Q3 O + obsprob(1, 1, L) = 1.0; + obsprob(1, 2, l) = 1.0; + obsprob(2, 1, U) = 1.0; + obsprob(2, 2, u) = 1.0; + obsprob(3, 1, R) = 1.0; + obsprob(3, 2, r) = 1.0; + obsprob(4, 1, D) = 1.0; + obsprob(4, 2, d) = 1.0; + + Oargs = {'CPT', obsprob}; +else + Osize = 2; + mu = zeros(2, 4, 2); + noise = 0; + scale = 10; + for q3=1:2 + mu(:, 1, q3) = scale*[1;0] + noise*rand(2,1); + end + for q3=1:2 + mu(:, 2, q3) = scale*[0;-1] + noise*rand(2,1); + end + for q3=1:2 + mu(:, 3, q3) = scale*[-1;0] + noise*rand(2,1); + end + for q3=1:2 + mu(:, 4, q3) = scale*[0;1] + noise*rand(2,1); + end + Sigma = repmat(reshape(0.01*eye(2), [2 2 1 1 ]), [1 1 4 2]); + Oargs = {'mean', mu, 'cov', Sigma}; +end + +bnet = mk_hhmm('Qsizes', Qsizes, 'Osize', Osize', 'discrete_obs', discrete_obs, ... + 'Oargs', Oargs, 'Ops', Qnodes(2:3), ... + 'startprob', startprob, 'transprob', transprob, 'termprob', termprob); + +if discrete_obs + Tmax = 30; +else + Tmax = 200; +end +usecell = ~discrete_obs; +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; + +for seqi=1:3 + evidence = sample_dbn(bnet, Tmax, usecell, 'stop_sampling_F2'); + T = size(evidence, 2) + if discrete_obs + pretty_print_hhmm_parse(evidence, Qnodes, Fnodes, Onode, chars); + else + pos = zeros(2,T+1); + delta = cell2num(evidence(Onode,:)); + clf + hold on + cols = {'r', 'g', 'k', 'b'}; + boundary = cell2num(evidence(F3,:))-1; + coli = 1; + for t=2:T+1 + pos(:,t) = pos(:,t-1) + delta(:,t-1); + plot(pos(1,t), pos(2,t), sprintf('%c.', cols{coli})); + if boundary(t-1) + coli = coli + 1; + coli = mod(coli-1, length(cols)) + 1; + end + end + %plot(pos(1,:), pos(2,:), '.') + %pretty_print_hhmm_parse(evidence, Qnodes, Fnodes, Onode, []); + pause + end +end + +eclass = bnet.equiv_class; +S=struct(bnet.CPD{eclass(Q2,2)}); + + + + + + + diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/get_square_data.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/get_square_data.m new file mode 100644 index 00000000..9790221c --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/get_square_data.m @@ -0,0 +1,70 @@ +% Let the user draw a square with the mouse, +% and then click on the corners to do a manual segmentation + +ss = 6; +Q1 = 1; Q2 = 2; Q3 = 3; obsvel = 6; +CLOCKWISE = 1; ANTICLOCK = 2; +LR = 1; UD = 2; RL = 3; DU = 4; + +% repeat this block manually incrementing the sequence number +% and setting ori. +% (since I don't know how to call getmouse as a call-return function). +seq = 4; +%ori = CLOCKWISE +ori = ANTICLOCK; +clear xpos ypos +getmouse +% end block + +% manual segmentation with the mouse +startseg(1) = 1; +for i=2:4 + fprintf('click on start of segment %d\n', i); + [x,y] = ginput(1); + plot(x,y,'ro') + d = dist2([xpos; ypos]', [x y]); + startseg(i) = argmin(d); +end + +% plot corners in green +%ti = first point in (i+1)st segment +t1 = startseg(1); t2 = startseg(2); t3 = startseg(3); t4 = startseg(4); +plot(xpos(t2), ypos(t2), 'g*') +plot(xpos(t3), ypos(t3), 'g*') +plot(xpos(t4), ypos(t4), 'g*') + + +xvel = xpos(2:end) - xpos(1:end-1); +yvel = ypos(2:end) - ypos(1:end-1); +speed = [xvel(:)'; yvel(:)']; +pos_data{seq} = [xpos(:)'; ypos(:)']; +vel_data{seq} = [xvel(:)'; yvel(:)']; +T = length(xvel); +Q1label{seq} = num2cell(repmat(ori, 1, T)); +Q2label{seq} = zeros(1, T); +if ori == CLOCKWISE + Q2label{seq}(t1:t2) = LR; + Q2label{seq}(t2+1:t3) = UD; + Q2label{seq}(t3+1:t4) = RL; + Q2label{seq}(t4+1:T) = DU; +else + Q2label{seq}(t1:t2) = RL; + Q2label{seq}(t2+1:t3) = UD; + Q2label{seq}(t3+1:t4) = LR; + Q2label{seq}(t4+1:T) = DU; +end + +% pos_data{seq}(:,t), vel_data{seq}(:,t) Q1label{seq}(t) Q2label{seq}(t) +save 'square4' pos_data vel_data Q1label Q2label + +nseq = 4; +cases = cell(1,nseq); +for seq=1:nseq + T = size(vel_data{seq},2); + ev = cell(ss,T); + ev(obsvel,:) = num2cell(vel_data{seq},1); + ev(Q1,:) = Q1label{seq}; + ev(Q2,:) = num2cell(Q2label{seq}); + cases{seq} = ev; +end +save 'square4_cases' cases diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/hhmm_inference.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/hhmm_inference.m new file mode 100644 index 00000000..c3bc8441 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/hhmm_inference.m @@ -0,0 +1,13 @@ +bnet = mk_square_hhmm(1, 1); + +engine = {}; +engine{end+1} = hmm_inf_engine(bnet); +engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet)); + +exact = 1:length(engine); +filter = 0; +single = 0; +maximize = 0; +T = 4; + +[err, inf_time, engine] = cmp_inference(bnet, engine, exact, T, filter, single, maximize); diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/is_F2_true_D3.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/is_F2_true_D3.m new file mode 100644 index 00000000..38d0b6e8 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/is_F2_true_D3.m @@ -0,0 +1,12 @@ +function stop = is_F2_true_D3(vals) +% function stop = is_F2_true_D3(vals) +% +% If vals(F2)=2 then level 2 has finished, so we return stop=1 +% to stop sample_dbn. Otherwise we return stop=0. +% We assume this is for a D=3 level HHMM. + +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; +stop = 0; +if (iscell(vals) & vals{F2}==2) | (~iscell(vals) & vals(F2)==2) + stop = 1; +end diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/learn_square_hhmm_cts.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/learn_square_hhmm_cts.m new file mode 100644 index 00000000..77bdaec3 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/learn_square_hhmm_cts.m @@ -0,0 +1,152 @@ +% Try to learn a 3 level HHMM similar to mk_square_hhmm +% from hand-drawn squares. + +% Because startprob should be shared for t=1:T, +% but in the DBN is shared for t=2:T, we train using a single long sequence. + +discrete_obs = 0; +supervised = 1; +obs_finalF2 = 0; +% It is not possible to observe F2 if we learn +% because the update_ess method for hhmmF_CPD and hhmmQ_CPD assume +% the F nodes are always hidden (for speed). +% However, for generating, we might want to set the final F2=true +% to force all subroutines to finish. + +seed = 1; +rand('state', seed); +randn('state', seed); + +bnet = mk_square_hhmm(discrete_obs, 0); + +ss = 6; +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; +Qsizes = [2 4 1]; + +if supervised + bnet.observed = [Q1 Q2 Onode]; +else + bnet.observed = [Onode]; +end + +if obs_finalF2 + engine = jtree_dbn_inf_engine(bnet); + % can't use ndx version because sometimes F2 is hidden, sometimes observed + error('can''t observe F when learning') +else + if supervised + engine = jtree_ndx_dbn_inf_engine(bnet); + else + engine = jtree_hmm_inf_engine(bnet); + end +end + +load 'square4_cases' % cases{seq}{i,t} for i=1:ss +%plot_square_hhmm(cases{1}) +%long_seq = cat(2, cases{:}); +train_cases = cases(1:2); +long_seq = cat(2, train_cases{:}); +if ~supervised + T = size(long_seq,2); + for t=1:T + long_seq{Q1,t} = []; + long_seq{Q2,t} = []; + end +end +[bnet2, LL, engine2] = learn_params_dbn_em(engine, {long_seq}, 'max_iter', 2); + +eclass = bnet2.equiv_class; +CPDO=struct(bnet2.CPD{eclass(Onode,1)}); +mu = CPDO.mean; +Sigma = CPDO.cov; +CPDO_full = CPDO; + +% force diagonal covs after training +for k=1:size(Sigma,3) + Sigma(:,:,k) = diag(diag(Sigma(:,:,k))); +end +bnet2.CPD{6} = set_fields(bnet.CPD{6}, 'cov', Sigma); + +if 0 + % visualize each model by concatenating means for each model for nsteps in a row + nsteps = 5; + ev = cell(ss, nsteps*prod(Qsizes(2:3))); + t = 1; + for q2=1:Qsizes(2) + for q3=1:Qsizes(3) + for i=1:nsteps + ev{Onode,t} = mu(:,q2,q3); + ev{Q2,t} = q2; + t = t + 1; + end + end + end + plot_square_hhmm(ev) +end + +% bnet3 is the same as the learned model, except we will use it in testing mode +if supervised + bnet3 = bnet2; + bnet3.observed = [Onode]; + engine3 = hmm_inf_engine(bnet3); + %engine3 = jtree_ndx_dbn_inf_engine(bnet3); +else + bnet3 = bnet2; + engine3 = engine2; +end + +if 0 + % segment whole sequence + mpe = calc_mpe_dbn(engine3, long_seq); + pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, []); +end + +% segment each sequence +test_cases = cases(3:4); +for i=1:2 + ev = test_cases{i}; + T = size(ev, 2); + for t=1:T + ev{Q1,t} = []; + ev{Q2,t} = []; + end + %mpe = calc_mpe_dbn(engine3, ev); + mpe = find_mpe(engine3, ev) + subplot(1,2,i) + plot_square_hhmm(mpe) + %pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, []); + q1s = cell2num(mpe(Q1,:)); + h = hist(q1s, 1:Qsizes(1)); + map_q1 = argmax(h); + str = sprintf('test seq %d is of type %d\n', i, map_q1); + title(str) +end + + +if 0 +% Estimate gotten by couting transitions in the labelled data +% Note that a self transition shouldnt count if F2=off. +Q2ev = cell2num(ev(Q2,:)); +Q2a = Q2ev(1:end-1); +Q2b = Q2ev(2:end); +counts = compute_counts([Q2a; Q2b], [4 4]); +end + +eclass = bnet2.equiv_class; +CPDQ1=struct(bnet2.CPD{eclass(Q1,2)}); +CPDQ2=struct(bnet2.CPD{eclass(Q2,2)}); +CPDQ3=struct(bnet2.CPD{eclass(Q3,2)}); +CPDF2=struct(bnet2.CPD{eclass(F2,1)}); +CPDF3=struct(bnet2.CPD{eclass(F3,1)}); + + +A=add_hhmm_end_state(CPDQ2.transprob, CPDF2.termprob(:,:,2)); +squeeze(A(:,1,:)); +CPDQ2.startprob; + +if 0 +S=struct(CPDF2.sub_CPD_term); +S.nsamples +reshape(S.counts, [2 4 2]) +end diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/learn_square_hhmm_discrete.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/learn_square_hhmm_discrete.m new file mode 100644 index 00000000..3110ae5e --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/learn_square_hhmm_discrete.m @@ -0,0 +1,171 @@ +% Try to learn a 3 level HHMM similar to mk_square_hhmm +% from synthetic discrete sequences + + +discrete_obs = 1; +supervised = 0; +obs_finalF2 = 0; + +seed = 1; +rand('state', seed); +randn('state', seed); + +bnet_init = mk_square_hhmm(discrete_obs, 0); + +ss = 6; +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; + +if supervised + bnet_init.observed = [Q1 Q2 Onode]; +else + bnet_init.observed = [Onode]; +end + +if obs_finalF2 + engine_init = jtree_dbn_inf_engine(bnet_init); + % can't use ndx version because sometimes F2 is hidden, sometimes observed + error('can''t observe F when learning') + % It is not possible to observe F2 if we learn + % because the update_ess method for hhmmF_CPD and hhmmQ_CPD assume + % the F nodes are always hidden (for speed). + % However, for generating, we might want to set the final F2=true + % to force all subroutines to finish. +else + if supervised + engine_init = jtree_ndx_dbn_inf_engine(bnet_init); + else + engine_init = hmm_inf_engine(bnet_init); + end +end + +% generate some synthetic data (easier to debug) +chars = ['L', 'l', 'U', 'u', 'R', 'r', 'D', 'd']; +L=find(chars=='L'); l=find(chars=='l'); +U=find(chars=='U'); u=find(chars=='u'); +R=find(chars=='R'); r=find(chars=='r'); +D=find(chars=='D'); d=find(chars=='d'); + +cases = {}; + +T = 8; +ev = cell(ss, T); +ev(Onode,:) = num2cell([L l U u R r D d]); +if supervised + ev(Q1,:) = num2cell(1*ones(1,T)); + ev(Q2,:) = num2cell( [1 1 2 2 3 3 4 4]); +end +cases{1} = ev; +cases{3} = ev; + +T = 8; +ev = cell(ss, T); +%we start with R then r, even though we are running the model 'backwards'! +ev(Onode,:) = num2cell([R r U u L l D d]); + +if supervised + ev(Q1,:) = num2cell(2*ones(1,T)); + ev(Q2,:) = num2cell( [3 3 2 2 1 1 4 4]); +end + +cases{2} = ev; +cases{4} = ev; + +if obs_finalF2 + for i=1:length(cases) + T = size(cases{i},2); + cases{i}(F2,T)={2}; % force F2 to be finished at end of seq + end +end + + +% startprob should be shared for t=1:T, +% but in the DBN it is shared for t=2:T, +% so we train using a single long sequence. +long_seq = cat(2, cases{:}); +[bnet_learned, LL, engine_learned] = ... + learn_params_dbn_em(engine_init, {long_seq}, 'max_iter', 200); + +% figure out which subsequence each model is responsible for +mpe = calc_mpe_dbn(engine_learned, long_seq); +pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, chars); + + +% The "true" segmentation of the training sequence is +% Q1: 1 2 +% O: L l U u R r D d | R r U u L l D d | etc. +% +% When we learn in a supervised fashion, we recover the "truth". + +% When we learn in an unsupervised fashion with seed=1, we get +% Q1: 2 1 +% O: L l U u R r D d R r | U u L l D d | etc. +% +% This means for model 1: +% starts in state 2 +% transitions 2->1, 1->4, 4->e, 3->2 +% +% For model 2, +% starts in state 1 +% transitions 1->2, 2->3, 3->4 or e, 4->3 + +% examine the params +eclass = bnet_learned.equiv_class; +CPDQ1=struct(bnet_learned.CPD{eclass(Q1,2)}); +CPDQ2=struct(bnet_learned.CPD{eclass(Q2,2)}); +CPDQ3=struct(bnet_learned.CPD{eclass(Q3,2)}); +CPDF2=struct(bnet_learned.CPD{eclass(F2,1)}); +CPDF3=struct(bnet_learned.CPD{eclass(F3,1)}); +CPDO=struct(bnet_learned.CPD{eclass(Onode,1)}); + +A_learned =add_hhmm_end_state(CPDQ2.transprob, CPDF2.termprob(:,:,2)); +squeeze(A_learned(:,1,:)) +squeeze(A_learned(:,2,:)) + + +% Does the "true" model have higher likelihood than the learned one? +% i.e., Does the unsupervised method learn the wrong model because +% we have the wrong cost fn, or because of local minima? + +bnet_true = mk_square_hhmm(discrete_obs,1); + +% examine the params +eclass = bnet_learned.equiv_class; +CPDQ1_true=struct(bnet_true.CPD{eclass(Q1,2)}); +CPDQ2_true=struct(bnet_true.CPD{eclass(Q2,2)}); +CPDQ3_true=struct(bnet_true.CPD{eclass(Q3,2)}); +CPDF2_true=struct(bnet_true.CPD{eclass(F2,1)}); +CPDF3_true=struct(bnet_true.CPD{eclass(F3,1)}); + +A_true =add_hhmm_end_state(CPDQ2_true.transprob, CPDF2_true.termprob(:,:,2)); +squeeze(A_true(:,1,:)) + + +if supervised + engine_true = jtree_ndx_dbn_inf_engine(bnet_true); +else + engine_true = hmm_inf_engine(bnet_true); +end + +%[engine_learned, ll_learned] = enter_evidence(engine_learned, long_seq); +%[engine_true, ll_true] = enter_evidence(engine_true, long_seq); +[engine_learned, ll_learned] = enter_evidence(engine_learned, cases{2}); +[engine_true, ll_true] = enter_evidence(engine_true, cases{2}); +ll_learned +ll_true + + +% remove concatentation artefacts +ll_learned = 0; +ll_true = 0; +for m=1:length(cases) + [engine_learned, ll_learned_tmp] = enter_evidence(engine_learned, cases{m}); + [engine_true, ll_true_tmp] = enter_evidence(engine_true, cases{m}); + ll_learned = ll_learned + ll_learned_tmp; + ll_true = ll_true + ll_true_tmp; +end +ll_learned +ll_true + +% In both cases, ll_learned >> ll_true +% which shows we are using the wrong cost function! diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/mk_square_hhmm.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/mk_square_hhmm.m new file mode 100644 index 00000000..41cfc539 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/mk_square_hhmm.m @@ -0,0 +1,180 @@ +function bnet = mk_square_hhmm(discrete_obs, true_params, topright) + +% Make a 3 level HHMM described by the following grammar +% +% Square -> CLK | CCK % clockwise or counterclockwise +% CLK -> LR UD RL DU start on top left (1 2 3 4) +% CCK -> RL UD LR DU if start at top right (3 2 1 4) +% CCK -> UD LR DU RL if start at top left (2 1 4 3) +% +% LR = left-right, UD = up-down, RL = right-left, DU = down-up +% LR, UD, RL, DU are sub HMMs. +% +% For discrete observations, the subHMMs are 2-state left-right. +% LR emits L then l, etc. +% +% For cts observations, the subHMMs are 1 state. +% LR emits a vector in the -> direction, with a little noise. +% Since there is no constraint that we remain in the LR state as long as the RL state, +% the sides of the square might have different lengths, +% so the result is not really a square! +% +% If true_params = 0, we use random parameters at the top 2 levels +% (ready for learning). At the bottom level, we use noisy versions +% of the "true" observations. +% +% If topright=1, counter-clockwise starts at top right, not top left +% This example was inspired by Ivanov and Bobick. + +if nargin < 3, topright = 1; end + +if 1 % discrete_obs + Qsizes = [2 4 2]; +else + Qsizes = [2 4 1]; +end + +D = 3; +Qnodes = 1:D; +startprob = cell(1,D); +transprob = cell(1,D); +termprob = cell(1,D); + +% LEVEL 1 + +startprob{1} = 'unif'; +transprob{1} = 'unif'; + +% LEVEL 2 + +if true_params + startprob{2} = zeros(2, 4); + startprob{2}(1, :) = [1 0 0 0]; + if topright + startprob{2}(2, :) = [0 0 1 0]; + else + startprob{2}(2, :) = [0 1 0 0]; + end + + transprob{2} = zeros(4, 2, 4); + + transprob{2}(:,1,:) = [0 1 0 0 + 0 0 1 0 + 0 0 0 1 + 0 0 0 1]; % 4->e + if topright + transprob{2}(:,2,:) = [0 0 0 1 + 1 0 0 0 + 0 1 0 0 + 0 0 0 1]; % 4->e + else + transprob{2}(:,2,:) = [0 0 0 1 + 1 0 0 0 + 0 0 1 0 % 3->e + 0 0 1 0]; + end + + %termprob{2} = 'rightstop'; + termprob{2} = zeros(2,4); + pfin = 0.8; + termprob{2}(1,:) = [0 0 0 pfin]; % finish in state 4 (DU) + if topright + termprob{2}(2,:) = [0 0 0 pfin]; + else + termprob{2}(2,:) = [0 0 pfin 0]; % finish in state 3 (RL) + end +else + % In the unsupervised case, it is essential that we break symmetry + % in the initial param estimates. + %startprob{2} = 'unif'; + %transprob{2} = 'unif'; + %termprob{2} = 'unif'; + startprob{2} = 'rnd'; + transprob{2} = 'rnd'; + termprob{2} = 'rnd'; +end + +% LEVEL 3 + +if 1 | true_params + startprob{3} = 'leftstart'; + transprob{3} = 'leftright'; + termprob{3} = 'rightstop'; +else + % If we want to be able to run a base-level model backwards... + startprob{3} = 'rnd'; + transprob{3} = 'rnd'; + termprob{3} = 'rnd'; +end + + +% OBS LEVEl + +if discrete_obs + % Initialise observations of lowest level primitives in a way which we can interpret + chars = ['L', 'l', 'U', 'u', 'R', 'r', 'D', 'd']; + L=find(chars=='L'); l=find(chars=='l'); + U=find(chars=='U'); u=find(chars=='u'); + R=find(chars=='R'); r=find(chars=='r'); + D=find(chars=='D'); d=find(chars=='d'); + Osize = length(chars); + + if true_params + p = 1; % makes each state fully observed + else + p = 0.9; + end + + obsprob = (1-p)*ones([4 2 Osize]); + % Q2 Q3 O + obsprob(1, 1, L) = p; + obsprob(1, 2, l) = p; + obsprob(2, 1, U) = p; + obsprob(2, 2, u) = p; + obsprob(3, 1, R) = p; + obsprob(3, 2, r) = p; + obsprob(4, 1, D) = p; + obsprob(4, 2, d) = p; + obsprob = mk_stochastic(obsprob); + Oargs = {'CPT', obsprob}; +else + % Initialise means of lowest level primitives in a way which we can interpret + % These means are little vectors in the east, south, west, north directions. + % (left-right=east, up-down=south, right-left=west, down-up=north) + Osize = 2; + mu = zeros(2, Qsizes(2), Qsizes(3)); + scale = 3; + if true_params + noise = 0; + else + noise = 0.5*scale; + end + for q3=1:Qsizes(3) + mu(:, 1, q3) = scale*[1;0] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 2, q3) = scale*[0;-1] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 3, q3) = scale*[-1;0] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 4, q3) = scale*[0;1] + noise*rand(2,1); + end + Sigma = repmat(reshape(scale*eye(2), [2 2 1 1 ]), [1 1 Qsizes(2) Qsizes(3)]); + Oargs = {'mean', mu, 'cov', Sigma, 'cov_type', 'diag'}; +end + +if discrete_obs + selfprob = 0.5; +else + selfprob = 0.95; + % If less than this, it won't look like a square + % because it doesn't spend enough time in each state + % Unfortunately, the variance on durations (lengths of each side) + % is very large +end +bnet = mk_hhmm('Qsizes', Qsizes, 'Osize', Osize', 'discrete_obs', discrete_obs, ... + 'Oargs', Oargs, 'Ops', Qnodes(2:3), 'selfprob', selfprob, ... + 'startprob', startprob, 'transprob', transprob, 'termprob', termprob); + diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/plot_square_hhmm.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/plot_square_hhmm.m new file mode 100644 index 00000000..e61e5669 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/plot_square_hhmm.m @@ -0,0 +1,27 @@ +function plot_square_hhmm(ev) +% Plot the square shape implicit in the evidence. +% ev{i,t} is the value of node i in slice t. +% The observed node contains a velocity (delta increment), which is converted +% into a position. +% The Q2 node specifies which model is used, and hence which color +% to use: 1=red, 2=green, 3=blue, 4=black. + +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; + +delta = cell2num(ev(Onode,:)); % delta(:,t) +Q2label = cell2num(ev(Q2,:)); + +T = size(delta, 2); +pos = zeros(2,T+1); +hold on +cols = {'r', 'g', 'b', 'k'}; +for t=2:T+1 + pos(:,t) = pos(:,t-1) + delta(:,t-1); + plot(pos(1,t), pos(2,t), sprintf('%c.', cols{Q2label(t-1)})); + if (t==2) + text(pos(1,t-1),pos(2,t-1),sprintf('%d',t)) + elseif (mod(t,20)==0) + text(pos(1,t),pos(2,t),sprintf('%d',t)) + end +end + diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/sample_square_hhmm_cts.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/sample_square_hhmm_cts.m new file mode 100644 index 00000000..3ab2abb5 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/sample_square_hhmm_cts.m @@ -0,0 +1,20 @@ +% Generate samples from the HHMM with the true params. + +seed = 1; +rand('state', seed); +randn('state', seed); + +discrete_obs = 0; + +bnet = mk_square_hhmm(discrete_obs, 1); +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; + +for seqi=1:1 + evidence = sample_dbn(bnet, 'stop_test', 'is_F2_true_D3'); + clf + plot_square_hhmm(evidence); + %pretty_print_hhmm_parse(evidence, Qnodes, Fnodes, Onode, []); + fprintf('sequence %d has length %d; press key to continue\n', seqi, size(evidence,2)) + pause +end diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/sample_square_hhmm_discrete.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/sample_square_hhmm_discrete.m new file mode 100644 index 00000000..20279899 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/sample_square_hhmm_discrete.m @@ -0,0 +1,20 @@ +% Generate samples from the HHMM with the true params. + +seed = 0; +rand('state', seed); +randn('state', seed); + +discrete_obs = 1; + +bnet = mk_square_hhmm(discrete_obs, 1); + +Tmax = 30; +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; +chars = ['L', 'l', 'U', 'u', 'R', 'r', 'D', 'd']; + +for seqi=1:3 + evidence = cell2num(sample_dbn(bnet, 'stop_test', 'is_F2_true_D3')); + T = size(evidence, 2) + pretty_print_hhmm_parse(evidence, Qnodes, Fnodes, Onode, chars); +end diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/square4.mat b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/square4.mat new file mode 100644 index 00000000..cda0585b --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/square4.mat Binary files differdiff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/square4_cases.mat b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/square4_cases.mat new file mode 100644 index 00000000..788c3239 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/square4_cases.mat Binary files differdiff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/test_square_fig.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/test_square_fig.m new file mode 100644 index 00000000..e983af14 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/test_square_fig.m @@ -0,0 +1,1310 @@ +function fig = test_square_fig() +% This is the machine-generated representation of a Handle Graphics object +% and its children. Note that handle values may change when these objects +% are re-created. This may cause problems with any callbacks written to +% depend on the value of the handle at the time the object was saved. +% +% To reopen this object, just type the name of the M-file at the MATLAB +% prompt. The M-file and its associated MAT-file must be on your path. + +load test_square_fig + +h0 = figure('Color',[0.8 0.8 0.8], ... + 'Colormap',mat0, ... + 'PointerShapeCData',mat1, ... + 'Position',[540 374 476 292]); +h1 = axes('Parent',h0, ... + 'CameraUpVector',[0 1 0], ... + 'Color',[1 1 1], ... + 'ColorOrder',mat2, ... + 'NextPlot','add', ... + 'Position',[0.13 0.11 0.3270231213872832 0.8149999999999998], ... + 'XColor',[0 0 0], ... + 'XLim',[-10 50], ... + 'XLimMode','manual', ... + 'YColor',[0 0 0], ... + 'YLim',[-60 10], ... + 'YLimMode','manual', ... + 'ZColor',[0 0 0]); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',0.4608294930875587, ... + 'YData',0.2923976608187218); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'String','2'); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',1.152073732718893, ... + 'YData',0.2923976608187218); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',2.995391705069125, ... + 'YData',0.8771929824561511); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',3.686635944700463, ... + 'YData',0.8771929824561511); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',6.451612903225808, ... + 'YData',0.8771929824561511); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',9.677419354838712, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',10.36866359447005, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',15.43778801843318, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',17.51152073732719, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',19.81566820276498, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',20.50691244239631, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',23.73271889400922, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',25.57603686635945, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',29.95391705069125, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',31.79723502304147, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',35.02304147465438, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',35.71428571428572, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',38.47926267281106, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',40.3225806451613, ... + 'YData',1.461988304093566); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[40.3225806451613 1.461988304093566 0], ... + 'String','20'); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',42.62672811059908, ... + 'YData',mat3); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',43.31797235023042, ... + 'YData',mat4); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',43.31797235023042, ... + 'YData',0.8771929824561511); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',43.54838709677419, ... + 'YData',0); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',43.77880184331798, ... + 'YData',-0.5847953216374293); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',44.47004608294931, ... + 'YData',-2.339181286549703); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',44.93087557603687, ... + 'YData',-4.385964912280699); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',46.7741935483871, ... + 'YData',-9.064327485380119); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.00460829493088, ... + 'YData',-10.81871345029239); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.69585253456221, ... + 'YData',mat5); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.69585253456221, ... + 'YData',-15.20467836257309); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-19.00584795321637); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-19.88304093567251); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-22.51461988304093); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-23.09941520467836); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-26.02339181286549); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-26.31578947368421); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-27.77777777777777); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-28.3625730994152); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-30.99415204678362); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[47.23502304147466 -30.99415204678362 0], ... + 'String','40'); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.46543778801843, ... + 'YData',-31.57894736842105); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.46543778801843, ... + 'YData',-33.62573099415204); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.46543778801843, ... + 'YData',-34.50292397660818); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.46543778801843, ... + 'YData',-37.42690058479531); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.46543778801843, ... + 'YData',-38.01169590643274); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.00460829493088, ... + 'YData',-42.39766081871344); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.00460829493088, ... + 'YData',-42.98245614035087); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.00460829493088, ... + 'YData',-46.49122807017543); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',46.7741935483871, ... + 'YData',-46.78362573099415); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',46.54377880184332, ... + 'YData',-49.41520467836257); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',46.54377880184332, ... + 'YData',-49.70760233918128); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',45.85253456221199, ... + 'YData',-51.46198830409356); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',45.85253456221199, ... + 'YData',-51.75438596491227); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',44.93087557603687, ... + 'YData',-53.21637426900584); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',44.70046082949308, ... + 'YData',-53.21637426900584); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',44.00921658986175, ... + 'YData',-54.09356725146198); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',43.77880184331798, ... + 'YData',-54.38596491228069); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',41.93548387096774, ... + 'YData',-54.97076023391811); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',41.47465437788019, ... + 'YData',-55.26315789473683); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',39.1705069124424, ... + 'YData',-55.55555555555554); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[39.1705069124424 -55.55555555555554 0], ... + 'String','60'); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',38.94009216589862, ... + 'YData',-55.84795321637426); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',36.63594470046083, ... + 'YData',-55.55555555555554); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',36.17511520737327, ... + 'YData',-55.55555555555554); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',32.94930875576037, ... + 'YData',-54.97076023391811); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',32.48847926267281, ... + 'YData',-54.97076023391811); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',28.11059907834102, ... + 'YData',-53.80116959064326); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',27.64976958525346, ... + 'YData',-53.50877192982455); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',23.963133640553, ... + 'YData',-53.50877192982455); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',23.27188940092166, ... + 'YData',-53.50877192982455); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',19.5852534562212, ... + 'YData',-54.97076023391811); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',19.12442396313364, ... + 'YData',-54.97076023391811); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',mat6, ... + 'YData',-56.14035087719297); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',mat7, ... + 'YData',-56.14035087719297); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',9.907834101382491, ... + 'YData',-57.30994152046782); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',9.447004608294932, ... + 'YData',-57.30994152046782); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',6.221198156682029, ... + 'YData',-57.30994152046782); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',4.838709677419356, ... + 'YData',-56.7251461988304); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',2.764976958525345, ... + 'YData',-56.14035087719297); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',2.534562211981569, ... + 'YData',-56.14035087719297); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',0.9216589861751174, ... + 'YData',-53.80116959064327); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[0.9216589861751174 -53.80116959064327 0], ... + 'String','80'); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',0.6912442396313381, ... + 'YData',-53.21637426900584); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.152073732718893, ... + 'YData',-48.24561403508771); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.152073732718893, ... + 'YData',-47.953216374269); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.843317972350228, ... + 'YData',-44.73684210526315); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.843317972350228, ... + 'YData',-44.44444444444444); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-2.304147465437787, ... + 'YData',-39.76608187134502); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-2.764976958525345, ... + 'YData',-38.01169590643274); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-3.225806451612904, ... + 'YData',-30.99415204678362); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-3.225806451612904, ... + 'YData',-29.82456140350877); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-3.225806451612904, ... + 'YData',-24.85380116959064); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-3.225806451612904, ... + 'YData',-24.26900584795321); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-2.534562211981566, ... + 'YData',-17.5438596491228); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-2.304147465437787, ... + 'YData',-16.95906432748537); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.612903225806452, ... + 'YData',-11.98830409356725); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.612903225806452, ... + 'YData',-11.40350877192982); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',mat8, ... + 'YData',-8.47953216374269); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',mat9, ... + 'YData',-8.187134502923968); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.152073732718893, ... + 'YData',-5.263157894736835); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.152073732718893, ... + 'YData',-4.970760233918128); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-0.9216589861751139, ... + 'YData',-2.923976608187132); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[-0.9216589861751139 -2.923976608187132 0], ... + 'String','100'); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-0.9216589861751139, ... + 'YData',-2.631578947368411); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-0.6912442396313345, ... + 'YData',mat10); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-0.6912442396313345, ... + 'YData',-0.8771929824561369); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-0.6912442396313345, ... + 'YData',-0.5847953216374293); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','center', ... + 'Position',[19.80645161290322 12.0675105485232 17.32050807568877], ... + 'VerticalAlignment','bottom'); +set(get(h2,'Parent'),'Title',h2); +h1 = axes('Parent',h0, ... + 'CameraUpVector',[0 1 0], ... + 'Color',[1 1 1], ... + 'ColorOrder',mat11, ... + 'NextPlot','add', ... + 'Position',[0.5779768786127169 0.11 0.3270231213872832 0.8149999999999998], ... + 'XColor',[0 0 0], ... + 'YColor',[0 0 0], ... + 'ZColor',[0 0 0]); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-0.4608294930875587, ... + 'YData',0); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'String','2'); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-2.764976958525345, ... + 'YData',-0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-3.456221198156683, ... + 'YData',-0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-7.834101382488477, ... + 'YData',-0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-11.52073732718894, ... + 'YData',-0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',mat12, ... + 'YData',-0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-19.35483870967742, ... + 'YData',0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-23.50230414746544, ... + 'YData',0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-24.88479262672811, ... + 'YData',0.8771929824561369); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-28.11059907834102, ... + 'YData',0.8771929824561369); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-29.49308755760369, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-31.10599078341014, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-32.02764976958525, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-33.17972350230414, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-33.6405529953917, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-34.7926267281106, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-35.02304147465438, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-35.48387096774194, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-35.71428571428572, ... + 'YData',1.461988304093566); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[-35.71428571428572 1.461988304093566 0], ... + 'String','20'); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.17511520737327, ... + 'YData',mat13); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.40552995391705, ... + 'YData',0.8771929824561369); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.63594470046083, ... + 'YData',0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.63594470046083, ... + 'YData',0); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.63594470046083, ... + 'YData',mat14); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.40552995391705, ... + 'YData',-2.339181286549703); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.40552995391705, ... + 'YData',-2.631578947368425); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.40552995391705, ... + 'YData',-4.67836257309942); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.40552995391705, ... + 'YData',-5.555555555555557); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.40552995391705, ... + 'YData',-8.187134502923982); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.40552995391705, ... + 'YData',-8.771929824561397); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.86635944700461, ... + 'YData',-13.15789473684211); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',mat15); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',-16.08187134502924); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',-17.54385964912281); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',-18.12865497076023); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.32718894009217, ... + 'YData',-19.88304093567251); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.32718894009217, ... + 'YData',-20.17543859649123); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',-21.92982456140351); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',-22.22222222222222); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[-37.09677419354839 -22.22222222222222 0], ... + 'String','40'); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',-23.09941520467836); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',-23.39181286549707); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.32718894009217, ... + 'YData',-25.14619883040935); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.32718894009217, ... + 'YData',-25.43859649122807); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.32718894009217, ... + 'YData',-28.3625730994152); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.55760368663595, ... + 'YData',-28.94736842105263); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-31.87134502923976); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-32.16374269005848); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-34.7953216374269); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-35.38011695906432); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-38.88888888888889); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-39.76608187134503); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-43.27485380116958); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-43.5672514619883); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-44.44444444444444); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.55760368663595, ... + 'YData',-45.32163742690058); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.55760368663595, ... + 'YData',-45.61403508771929); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.32718894009217, ... + 'YData',-47.36842105263158); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.32718894009217, ... + 'YData',-47.95321637426901); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.86635944700461, ... + 'YData',-49.70760233918129); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[-36.86635944700461 -49.70760233918129 0], ... + 'String','60'); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.86635944700461, ... + 'YData',-50); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-35.71428571428572, ... + 'YData',-50.29239766081872); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-35.25345622119816, ... + 'YData',-50.29239766081872); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-32.02764976958527, ... + 'YData',-50.29239766081872); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-31.33640552995393, ... + 'YData',-50.29239766081872); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-27.88018433179725, ... + 'YData',-50.58479532163743); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-27.41935483870969, ... + 'YData',-50.58479532163743); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-18.20276497695854, ... + 'YData',-50.58479532163743); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-16.82027649769586, ... + 'YData',-51.16959064327486); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-12.21198156682029, ... + 'YData',-50.58479532163743); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-11.52073732718895, ... + 'YData',-50.58479532163743); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-6.912442396313377, ... + 'YData',-51.16959064327486); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-5.069124423963142, ... + 'YData',-51.75438596491229); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',mat16, ... + 'YData',-52.046783625731); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-0.9216589861751281, ... + 'YData',-52.33918128654972); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',0.2304147465437687, ... + 'YData',-52.33918128654972); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',0.4608294930875481, ... + 'YData',-52.33918128654972); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',2.304147465437776, ... + 'YData',-52.63157894736843); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',2.534562211981548, ... + 'YData',-52.63157894736843); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',3.917050691244224, ... + 'YData',-52.63157894736843); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[3.917050691244224 -52.63157894736843 0], ... + 'String','80'); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',4.147465437788011, ... + 'YData',-52.63157894736843); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',4.147465437788011, ... + 'YData',-52.33918128654972); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.529953917050673, ... + 'YData',-46.19883040935674); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.990783410138231, ... + 'YData',-44.44444444444446); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',7.834101382488466, ... + 'YData',-28.0701754385965); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',8.294930875576025, ... + 'YData',-22.80701754385966); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',8.755760368663584, ... + 'YData',mat17); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',8.525345622119797, ... + 'YData',-14.9122807017544); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',7.373271889400908, ... + 'YData',-10.23391812865498); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',7.142857142857135, ... + 'YData',-9.94152046783627); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',6.221198156682018, ... + 'YData',-7.602339181286567); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',6.221198156682018, ... + 'YData',-7.309941520467845); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.760368663594459, ... + 'YData',-5.555555555555571); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.529953917050673, ... + 'YData',-5.555555555555571); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.2995391705069, ... + 'YData',-4.093567251462005); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.069124423963128, ... + 'YData',-2.631578947368439); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.069124423963128, ... + 'YData',-2.339181286549717); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.069124423963128, ... + 'YData',mat18); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.069124423963128, ... + 'YData',-1.169590643274873); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',4.838709677419342, ... + 'YData',-0.2923976608187218); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[4.838709677419342 -0.2923976608187218 0], ... + 'String','100'); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','center', ... + 'Position',[-10.38961038961038 12.0675105485232 17.32050807568877], ... + 'VerticalAlignment','bottom'); +set(get(h2,'Parent'),'Title',h2); +if nargout > 0, fig = h0; end diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/test_square_fig.mat b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/test_square_fig.mat new file mode 100644 index 00000000..5b2b5f53 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/test_square_fig.mat Binary files differ |