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

1 files changed, 95 insertions, 0 deletions

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+% BNT Structure Learning Package 
+% http://banquiseasi.insa-rouen.fr/projects/bnt-slp/
+%
+% Some usefull add-ons for Bayes Net Toolbox
+% ==================================================================================================
+% v1.5 : 20 avril 2008
+% ==================================================================================================
+%
+% Data manipulation
+% -----------------
+%
+%  - mat_to_bnt             :   matrix to cell array conversion (with missing data encoding in the matrix)
+%  - hist_ic                :   optimal Histogram based on IC information criterion (PhL)
+%  - histc_ic               :   Histogram count , for HIST_IC edges (PhL)
+%
+%
+% Bayes Net Structure Learning
+% ----------------------------
+%
+%  - cond_indep_chisquare   :   test if X indep Y given Z using ChiSquare test (Pearson's or Likelihood Ratio Test) (PhL,OF)
+%                               (for use with LEARN_STRUCT_PDAG_PC or LEARN_STRUCT_PDAG_IC_STAR)
+%  - test_chisquare         :   test for COND_INDEP_CHISQUARE function
+%  - test_pc                :   test for LEARN_STRUCT_PDAG_PC function with ChiSquare test (PhL)
+%
+%  - mk_alarm_bnet	    :   make the bnet of ALARM network (WH) (used in test_sem3)
+%  - mk_asia_bnet	    :   make the bnet of ASIA network (PhL,OF) (used in test functions)
+%
+%  - mk_naive_struct        :   generate the naive bayes structure (for a given class node)
+%
+%  - learn_struct_mwst      :   structure learning using maximum spanning tree (OF, PhL)
+%  - mutual_info_score      :   mutual information scoring (OF, PhL, WXY)
+%  - test_mwst              :   test for LEARN_STRUCT_MWST function (PhL)
+%
+%  - learn_struct_tan       :   structure learning giving the best tree augmented naive bayes classifier (OF, PHL, NS)
+%
+%  - learn_struct_hc        :   structure learning using hill climbing (GL)
+%  - learn_struct_gs        :   structure learning using greedy search (GL)
+%  - learn_struct_gs2       :   structure learning using greedy search with cache implementation (GL, WH, OF, PhL)
+%                               (use mk_nbrs_of_dag_topo (developped WH) instead of mk_nbrs_of_dag)
+%  - score_init_cache       :   cache initialisation for local score computation (OF, PhL) 
+%  - score_family           :   new version of BNT function with cache implementation (OF, PhL)
+%  - score_dags             :   new version of BNT function with cache implementation (OF, PhL, DH)
+%  - test_gs2               :   test for LEARN_STRUCT_GS2 function (PhL)
+%
+%  - learn_struct_ges	    :   structure learning using Greedy Equivalence Search (PhL)
+%  - mk_nbrs_of_pdag_add    :   generate the sup. inclusion boundary of a given pdag (PhL)
+%  - mk_nbrs_of_pdag_del    :   generate the inf. inclusion boundary of a given pdag (PhL)
+%  - test_ges               :   test for LEARN_STRUCT_GES function (PhL)
+%
+%  - learn_struct_EM        :   structure learning using structural EM (WH)
+%  - multiply_one_marginal.c
+%  - mk_nbrs_of_dag_topo    :   generate the neighbours of a given dag (WH)
+%                               (better implementation than mk_nbrs_of_dag)
+%  - test_sem1              :   demo 1 (SPRINKER)
+%  - test_sem2              :   demo 2 (DISCRETE1)
+%  - test_sem3              :   demo 3 (ALARM)
+%
+%  - learn_struct_mwst_EM   :   MWST structure learning with missing data (OF, PhL)
+%
+%  - cpdag_to_dag           :   return a dag for a given CPDAG (OF, PhL)
+%  - dag_to_cpdag           :   return the CPDAG, representant of the equivalent class of the dag (OF, PhL)
+%  - pdag_to_dag            :   return a DAG that instantiates the given pdag [Dor&Tarsi] (PhL, OF)
+%  - test_cpdag             :   test CPDAG_to_DAG and DAG_to_CPDAG functions (PhL)
+%  - kl_divergence          :   Kullback-Leibler divergence between two bnet distributions (PhL)
+%  - kl_divergence2         :   Kullback-Leibler divergence between two bnet distributions (PhL) 
+%
+%  - test_structure         :   runs all the test functions (PhL)
+%
+%  - gener_MCAR_net         :   to gener a BN which modelise a process of incomplete data generation with MCAR assumptions (OF)
+%  - gener_MAR_net          :   to gener a BN which modelise a process of incomplete data generation with MAR assumptions (OF)
+%  - gener_data_from_bnet_miss: to gener a incomplete dataset from a BN creted with gener_MCAR_net or gener_MAR_net (OF)
+%
+%  - editing_dist           :   editing distance between two DAG (PhL)
+%                               (memory optimization, but quite slow !)
+%  - learn_struct_bnpc      :   structure learning with BN-Power constructor (OF, PhL)
+%  - learn_struct_tan_EM    :   Tree Augmented Naive Bayes structure learning with missing data (OF, PhL)
+%  - learn_struct_ges_EM    :   structure learning in Markov equivalent space with missing data (OF, HB, PhL)
+%  - ......
+%
+%
+% Contributors :
+% ------------
+% LITIS Rouen, France
+%   - PhL   : Philippe Leray (philippe.leray@univ-nantes.fr)
+%   - OF    : Olivier Francois (francois.olivier.c.h@gmail.com)
+%
+% External parts
+%   - GL    : Gang Li, Deakin University (gangli@deakin.edu.au)
+%   - WH    : Wei Hu, Intel (wei.hu@intel.com)
+%   - DH    : Derek Hoiem (dhoiem@cs.cmu.edu)
+%   - NS    : Navid Serrano, Jet Propulsion Laboratory (Navid.Serrano@jpl.nasa.gov)
+%   - WXY   : Wang Xiang Yang, Shanghai JiaoTong University (wangxiangyang@sjtu.edu.cn)
+%   - HB    : Hanene Borchani (hanene.borchani@gmail.com)
+%
+