diff options
Diffstat (limited to 'sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD')
6 files changed, 54 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/CPD_to_CPT_orig.m b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/CPD_to_CPT_orig.m new file mode 100644 index 00000000..707de900 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/CPD_to_CPT_orig.m @@ -0,0 +1,5 @@ +function CPT = CPD_to_CPT_orig(CPD) +% CPD_TO_CPT Convert the discrete CPD to tabular form (tabular) +% CPT = CPD_to_CPT_orig(CPD) + +CPT = CPD.CPT_orig; diff --git a/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/CPD_to_CPT_orig.m~ b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/CPD_to_CPT_orig.m~ new file mode 100644 index 00000000..351f103c --- /dev/null +++ b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/CPD_to_CPT_orig.m~ @@ -0,0 +1,5 @@ +function CPT = CPD_to_CPT(CPD) +% CPD_TO_CPT Convert the discrete CPD to tabular form (tabular) +% CPT = CPD_to_CPT(CPD) + +CPT = CPD.CPT; diff --git a/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/get_field.m b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/get_field.m index ba233db9..5812330d 100644 --- a/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/get_field.m +++ b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/get_field.m @@ -10,6 +10,7 @@ function val = get_field(CPD, name) switch name case 'cpt', val = CPD.CPT; + case 'cpt_orig', val = CPD.CPT_orig; case 'counts', val = CPD.counts; otherwise, error(['invalid argument name ' name]); diff --git a/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/learn_params_orig.m b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/learn_params_orig.m new file mode 100644 index 00000000..c0948566 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/learn_params_orig.m @@ -0,0 +1,20 @@ +function CPD = learn_params_orig(CPD,j,data,ns,cnodes) +% LEARN_PARAMS_ORIG +% Calculate the original distributions of the data. +% The original distributions are just the percentages of states in the +% data file. + +local_data = data(j, :); +nobs = size(local_data,2); +if iscell(local_data) + local_data = cell2num(local_data); +end +counts = compute_counts(local_data,ns(j)); +counts = counts/nobs; +switch CPD.prior_type + case 'none', CPD.CPT_orig = counts; +% case 'dirichlet', CPD.CPT = mk_stochastic(counts + CPD.dirichlet); +% I will use 'dirichlet' priors incorrectly here. + case 'dirichlet', CPD.CPT_orig = counts; + otherwise, error(['unrecognized prior ' CPD.prior_type]) +end diff --git a/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/learn_params_orig.m~ b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/learn_params_orig.m~ new file mode 100644 index 00000000..7a19a42d --- /dev/null +++ b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/learn_params_orig.m~ @@ -0,0 +1,20 @@ +function CPD = learn_params_orig(CPD,j,data,data,ns,cnodes) +% LEARN_PARAMS_ORIG +% Calculate the original distributions of the data. +% The original distributions are just the percentages of states in the +% data file. + +local_data = data(j, :); +nobs = size(local_data,2); +if iscell(local_data) + local_data = cell2num(local_data); +end +counts = compute_counts(local_data,ns(j)); +counts = counts/nobs; +switch CPD.prior_type + case 'none', CPD.CPT_orig = counts; +% case 'dirichlet', CPD.CPT = mk_stochastic(counts + CPD.dirichlet); +% I will use 'dirichlet' priors incorrectly here. + case 'dirichlet', CPD.CPT_orig = counts; + otherwise, error(['unrecognized prior ' CPD.prior_type]) +end diff --git a/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/tabular_CPD.m b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/tabular_CPD.m index 728302d4..a41a23d9 100644 --- a/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/tabular_CPD.m +++ b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/tabular_CPD.m @@ -9,6 +9,7 @@ function CPD = tabular_CPD(bnet, self, varargin) % - T means use table T; it will be reshaped to the size of node's family. % - 'rnd' creates rnd params (drawn from uniform) % - 'unif' creates a uniform distribution +% CPT_orig - specifies the distribution based on original data % adjustable - 0 means don't adjust the parameters during learning [1] % prior_type - defines type of prior ['none'] % - 'none' means do ML estimation @@ -60,6 +61,7 @@ CPD.sparse = 0; % set defaults CPD.CPT = mk_stochastic(myrand(fam_sz)); +CPD.CPT_orig = mk_stochastic(myrand(ns([self]))); CPD.adjustable = 1; CPD.prior_type = 'none'; dirichlet_type = 'BDeu'; @@ -158,6 +160,7 @@ function CPD = init_fields() % or create it from scratch. (Matlab requires this.) CPD.CPT = []; +CPD.CPT_orig = []; CPD.sizes = []; CPD.prior_type = []; CPD.dirichlet = []; |