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-rw-r--r--sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/tabular_CPD.m3
1 files changed, 3 insertions, 0 deletions
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 = [];