From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001 From: ziejd2 Date: Thu, 28 Sep 2017 15:04:40 -0500 Subject: 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 --- sourcecodes/bnt-master/SLP/SLP_doc_1.5.pdf | Bin 0 -> 673387 bytes sourcecodes/bnt-master/SLP/contents.m | 95 + sourcecodes/bnt-master/SLP/copyright.txt | 1 + .../SLP/examples/UCI_DataSets/SPECT.names | 89 + .../bnt-master/SLP/examples/UCI_DataSets/SPECTD.m | 22 + .../bnt-master/SLP/examples/UCI_DataSets/abalone | 9 + .../SLP/examples/UCI_DataSets/abalone.names | 145 + .../SLP/examples/UCI_DataSets/abaloneD.m | 22 + .../SLP/examples/UCI_DataSets/australian | 15 + .../SLP/examples/UCI_DataSets/australian.doc | 90 + .../SLP/examples/UCI_DataSets/australianD.m | 22 + .../bnt-master/SLP/examples/UCI_DataSets/car | 7 + .../bnt-master/SLP/examples/UCI_DataSets/car.names | 82 + .../bnt-master/SLP/examples/UCI_DataSets/carD.m | 22 + .../SLP/examples/UCI_DataSets/contracep.names | 44 + .../bnt-master/SLP/examples/UCI_DataSets/contrasep | 10 + .../SLP/examples/UCI_DataSets/contrasepD.m | 22 + .../bnt-master/SLP/examples/UCI_DataSets/diabetes 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sourcecodes/bnt-master/SLP/scoring/score_init_cache.m (limited to 'sourcecodes/bnt-master/SLP') diff --git a/sourcecodes/bnt-master/SLP/SLP_doc_1.5.pdf b/sourcecodes/bnt-master/SLP/SLP_doc_1.5.pdf new file mode 100644 index 00000000..5ce9ef4c Binary files /dev/null and b/sourcecodes/bnt-master/SLP/SLP_doc_1.5.pdf differ diff --git a/sourcecodes/bnt-master/SLP/contents.m b/sourcecodes/bnt-master/SLP/contents.m new file mode 100644 index 00000000..cb4b97a0 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/contents.m @@ -0,0 +1,95 @@ +% 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) +% + diff --git a/sourcecodes/bnt-master/SLP/copyright.txt b/sourcecodes/bnt-master/SLP/copyright.txt new file mode 100644 index 00000000..ca3eb959 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/copyright.txt @@ -0,0 +1 @@ +% Written by the contributors (cf. contents.m) 2005 \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/SPECT.names b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/SPECT.names new file mode 100644 index 00000000..636299d6 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/SPECT.names @@ -0,0 +1,89 @@ +1. Title of Database: SPECT heart data + +2. Sources: + -- Original owners: Krzysztof J. Cios, Lukasz A. Kurgan + University of Colorado at Denver, Denver, CO 80217, U.S.A. + Krys.Cios@cudenver.edu + Lucy S. Goodenday + Medical College of Ohio, OH, U.S.A. + + -- Donors: Lukasz A.Kurgan, Krzysztof J. Cios + -- Date: 10/01/01 + +3. Past Usage: + 1. Kurgan, L.A., Cios, K.J., Tadeusiewicz, R., Ogiela, M. & Goodenday, L.S. + "Knowledge Discovery Approach to Automated Cardiac SPECT Diagnosis" + Artificial Intelligence in Medicine, vol. 23:2, pp 149-169, Oct 2001 + + Results: The CLIP3 machine learning algorithm achieved 84.0% accuracy + References: + Cios, K.J., Wedding, D.K. & Liu, N. + CLIP3: cover learning using integer programming. + Kybernetes, 26:4-5, pp 513-536, 1997 + + Cios, K.J. & Kurgan, L. + Hybrid Inductive Machine Learning: An Overview of CLIP Algorithms, + In: Jain, L.C., and Kacprzyk, J. (Eds.) + New Learning Paradigms in Soft Computing, + Physica-Verlag (Springer), 2001 + + SPECT is a good data set for testing ML algorithms; it has 267 instances + that are descibed by 23 binary attributes + + Other results (in press): + -- CLIP4 algorithm achieved 86.1% accuracy + -- ensemble of CLIP4 classifiers achieved 90.4% accuracy + -- Predicted attribute: OVERALL_DIAGNOSIS (binary) + +4. Relevant Information: + The dataset describes diagnosing of cardiac Single Proton Emission Computed Tomography (SPECT) images. + Each of the patients is classified into two categories: normal and abnormal. + The database of 267 SPECT image sets (patients) was processed to extract features that summarize the original SPECT images. + As a result, 44 continuous feature pattern was created for each patient. + The pattern was further processed to obtain 22 binary feature patterns. + The CLIP3 algorithm was used to generate classification rules from these patterns. + The CLIP3 algorithm generated rules that were 84.0% accurate (as compared with cardilogists' diagnoses). + +5. Number of Instances: 267 +6. Number of Attributes: 23 (22 binary + 1 binary class) +7. Attribute Information: + 1. OVERALL_DIAGNOSIS: 0,1 (class attribute, binary) + 2. F1: 0,1 (the partial diagnosis 1, binary) + 3. F2: 0,1 (the partial diagnosis 2, binary) + 4. F3: 0,1 (the partial diagnosis 3, binary) + 5. F4: 0,1 (the partial diagnosis 4, binary) + 6. F5: 0,1 (the partial diagnosis 5, binary) + 7. F6: 0,1 (the partial diagnosis 6, binary) + 8. F7: 0,1 (the partial diagnosis 7, binary) + 9. F8: 0,1 (the partial diagnosis 8, binary) + 10. F9: 0,1 (the partial diagnosis 9, binary) + 11. F10: 0,1 (the partial diagnosis 10, binary) + 12. F11: 0,1 (the partial diagnosis 11, binary) + 13. F12: 0,1 (the partial diagnosis 12, binary) + 14. F13: 0,1 (the partial diagnosis 13, binary) + 15. F14: 0,1 (the partial diagnosis 14, binary) + 16. F15: 0,1 (the partial diagnosis 15, binary) + 17. F16: 0,1 (the partial diagnosis 16, binary) + 18. F17: 0,1 (the partial diagnosis 17, binary) + 19. F18: 0,1 (the partial diagnosis 18, binary) + 20. F19: 0,1 (the partial diagnosis 19, binary) + 21. F20: 0,1 (the partial diagnosis 20, binary) + 22. F21: 0,1 (the partial diagnosis 21, binary) + 23. F22: 0,1 (the partial diagnosis 22, binary) + -- dataset is divided into: + -- training data ("SPECT.train" 80 instances) + -- testing data ("SPECT.test" 187 instances) +8. Missing Attribute Values: None +9. Class Distribution: + -- entire data + Class # examples + 0 55 + 1 212 + -- training dataset + Class # examples + 0 40 + 1 40 + -- testing dataset + Class # examples + 0 15 + 1 172 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/SPECTD.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/SPECTD.m new file mode 100644 index 00000000..b2bfa416 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/SPECTD.m @@ -0,0 +1,22 @@ +load -ascii SPECT_A +load -ascii SPECT_T + +[N, m] = size(SPECT_A); + +class = N + +app = SPECT_A;size(app) +test = SPECT_T;size(test) + +Napp = size(app,2); +Ntest = size(test,2); + +unique(app(class,:)) +unique(test(class,:)) + +ns1 = max(SPECT_A'); +ns2 = max(SPECT_T'); +ns = max(ns1, ns2) +clear SPECT_A SPECT_T ns1 ns2 + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/abalone b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/abalone new file mode 100644 index 00000000..50980d1a --- 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1.1000000e+01 1.1000000e+01 1.0000000e+01 1.0000000e+01 1.1000000e+01 1.3000000e+01 1.3000000e+01 1.1000000e+01 1.1000000e+01 1.0000000e+01 1.1000000e+01 1.1000000e+01 6.0000000e+00 7.0000000e+00 6.0000000e+00 7.0000000e+00 8.0000000e+00 6.0000000e+00 6.0000000e+00 8.0000000e+00 8.0000000e+00 8.0000000e+00 9.0000000e+00 1.1000000e+01 1.1000000e+01 8.0000000e+00 7.0000000e+00 7.0000000e+00 7.0000000e+00 1.0000000e+01 9.0000000e+00 8.0000000e+00 1.0000000e+01 1.0000000e+01 8.0000000e+00 1.1000000e+01 1.0000000e+01 9.0000000e+00 1.0000000e+01 1.2000000e+01 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/abalone.names b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/abalone.names new file mode 100644 index 00000000..8fbe142d --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/abalone.names @@ -0,0 +1,145 @@ +1. Title of Database: Abalone data + +2. Sources: + + (a) Original owners of database: + Marine Resources Division + Marine Research Laboratories - Taroona + Department of Primary Industry and Fisheries, Tasmania + GPO Box 619F, Hobart, Tasmania 7001, Australia + (contact: Warwick Nash +61 02 277277, wnash@dpi.tas.gov.au) + + (b) Donor of database: + Sam Waugh (Sam.Waugh@cs.utas.edu.au) + Department of Computer Science, University of Tasmania + GPO Box 252C, Hobart, Tasmania 7001, Australia + + (c) Date received: December 1995 + + +3. Past Usage: + + Sam Waugh (1995) "Extending and benchmarking Cascade-Correlation", PhD + thesis, Computer Science Department, University of Tasmania. + + -- Test set performance (final 1044 examples, first 3133 used for training): + 24.86% Cascade-Correlation (no hidden nodes) + 26.25% Cascade-Correlation (5 hidden nodes) + 21.5% C4.5 + 0.0% Linear Discriminate Analysis + 3.57% k=5 Nearest Neighbour + (Problem encoded as a classification task) + + -- Data set samples are highly overlapped. Further information is required + to separate completely using affine combinations. Other restrictions + to data set examined. + + David Clark, Zoltan Schreter, Anthony Adams "A Quantitative Comparison of + Dystal and Backpropagation", submitted to the Australian Conference on + Neural Networks (ACNN'96). Data set treated as a 3-category classification + problem (grouping ring classes 1-8, 9 and 10, and 11 on). + + -- Test set performance (3133 training, 1044 testing as above): + 64% Backprop + 55% Dystal + -- Previous work (Waugh, 1995) on same data set: + 61.40% Cascade-Correlation (no hidden nodes) + 65.61% Cascade-Correlation (5 hidden nodes) + 59.2% C4.5 + 32.57% Linear Discriminate Analysis + 62.46% k=5 Nearest Neighbour + + +4. Relevant Information Paragraph: + + Predicting the age of abalone from physical measurements. The age of + abalone is determined by cutting the shell through the cone, staining it, + and counting the number of rings through a microscope -- a boring and + time-consuming task. Other measurements, which are easier to obtain, are + used to predict the age. Further information, such as weather patterns + and location (hence food availability) may be required to solve the problem. + + From the original data examples with missing values were removed (the + majority having the predicted value missing), and the ranges of the + continuous values have been scaled for use with an ANN (by dividing by 200). + + Data comes from an original (non-machine-learning) study: + + Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and + Wes B Ford (1994) "The Population Biology of Abalone (_Haliotis_ + species) in Tasmania. I. Blacklip Abalone (_H. rubra_) from the North + Coast and Islands of Bass Strait", Sea Fisheries Division, Technical + Report No. 48 (ISSN 1034-3288) + + +5. Number of Instances: 4177 + + +6. Number of Attributes: 8 + + +7. Attribute information: + + Given is the attribute name, attribute type, the measurement unit and a + brief description. The number of rings is the value to predict: either + as a continuous value or as a classification problem. + + Name Data Type Meas. Description + ---- --------- ----- ----------- + Sex nominal M, F, and I (infant) + Length continuous mm Longest shell measurement + Diameter continuous mm perpendicular to length + Height continuous mm with meat in shell + Whole weight continuous grams whole abalone + Shucked weight continuous grams weight of meat + Viscera weight continuous grams gut weight (after bleeding) + Shell weight continuous grams after being dried + Rings integer +1.5 gives the age in years + + Statistics for numeric domains: + + Length Diam Height Whole Shucked Viscera Shell Rings + Min 0.075 0.055 0.000 0.002 0.001 0.001 0.002 1 + Max 0.815 0.650 1.130 2.826 1.488 0.760 1.005 29 + Mean 0.524 0.408 0.140 0.829 0.359 0.181 0.239 9.934 + SD 0.120 0.099 0.042 0.490 0.222 0.110 0.139 3.224 + Correl 0.557 0.575 0.557 0.540 0.421 0.504 0.628 1.0 + + +8. Missing Attribute Values: None + + +9. Class Distribution: + + Class Examples + ----- -------- + 1 1 + 2 1 + 3 15 + 4 57 + 5 115 + 6 259 + 7 391 + 8 568 + 9 689 + 10 634 + 11 487 + 12 267 + 13 203 + 14 126 + 15 103 + 16 67 + 17 58 + 18 42 + 19 32 + 20 26 + 21 14 + 22 6 + 23 9 + 24 2 + 25 1 + 26 1 + 27 2 + 29 1 + ----- ---- + Total 4177 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/abaloneD.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/abaloneD.m new file mode 100644 index 00000000..525f4f41 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/abaloneD.m @@ -0,0 +1,22 @@ +load -ascii abalone + +[N, m] = size(abalone) + +class = N + +%rand('state',0); randn('state',0); +%abalone = abalone(:,randperm(m)); + +Napp = 3133; +Ntest = m-Napp + +app = abalone(:,1:Napp);size(app) +test = abalone(:,Napp+1:end);size(test) + +unique(app(class,:)) +unique(test(class,:)) + +ns = max(abalone') +clear abalone + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/australian b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/australian new file mode 100644 index 00000000..ef6fb878 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/australian @@ -0,0 +1,15 @@ + 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 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1.0000000e+00 1.0000000e+00 1.0000000e+00 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/australian.doc b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/australian.doc new file mode 100644 index 00000000..100d37b8 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/australian.doc @@ -0,0 +1,90 @@ +Description of the Dataset: + +THIS CREDIT DATA ORIGINATES FROM QUINLAN (see below). + +1. Title: Australian Credit Approval + +2. Sources: + (confidential) + Submitted by quinlan@cs.su.oz.au + +3. Past Usage: + + See Quinlan, + * "Simplifying decision trees", Int J Man-Machine Studies 27, + Dec 1987, pp. 221-234. + * "C4.5: Programs for Machine Learning", Morgan Kaufmann, Oct 1992 + +4. Relevant Information: + + This file concerns credit card applications. All attribute names + and values have been changed to meaningless symbols to protect + confidentiality of the data. + + This dataset is interesting because there is a good mix of + attributes -- continuous, nominal with small numbers of + values, and nominal with larger numbers of values. There + are also a few missing values. + +5. Number of Instances: 690 + +6. Number of Attributes: 14 + class attribute + +7. Attribute Information: THERE ARE 6 NUMERICAL AND 8 CATEGORICAL ATTRIBUTES. + + THE LABELS HAVE BEEN CHANGED FOR THE CONVENIENCE + OF THE STATISTICAL ALGORITHMS. FOR EXAMPLE, + ATTRIBUTE 4 ORIGINALLY HAD 3 LABELS p,g,gg AND + THESE HAVE BEEN CHANGED TO LABELS 1,2,3. + + + A1: 0,1 CATEGORICAL + a,b + A2: continuous. + A3: continuous. + A4: 1,2,3 CATEGORICAL + p,g,gg + A5: 1, 2,3,4,5, 6,7,8,9,10,11,12,13,14 CATEGORICAL + ff,d,i,k,j,aa,m,c,w, e, q, r,cc, x + + A6: 1, 2,3, 4,5,6,7,8,9 CATEGORICAL + ff,dd,j,bb,v,n,o,h,z + + A7: continuous. + A8: 1, 0 CATEGORICAL + t, f. + A9: 1, 0 CATEGORICAL + t, f. + A10: continuous. + A11: 1, 0 CATEGORICAL + t, f. + A12: 1, 2, 3 CATEGORICAL + s, g, p + A13: continuous. + A14: continuous. + A15: 1,2 + +,- (class attribute) + +8. Missing Attribute Values: + 37 cases (5%) HAD one or more missing values. The missing + values from particular attributes WERE: + + A1: 12 + A2: 12 + A4: 6 + A5: 6 + A6: 9 + A7: 9 + A14: 13 + + THESE WERE REPLACED BY THE MODE OF THE ATTRIBUTE (CATEGORICAL) + MEAN OF THE ATTRIBUTE (CONTINUOUS) + +9. Class Distribution + + +: 307 (44.5%) CLASS 2 + -: 383 (55.5%) CLASS 1 + + +10. There is no cost matrix. + diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/australianD.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/australianD.m new file mode 100644 index 00000000..1332d5e4 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/australianD.m @@ -0,0 +1,22 @@ +load -ascii australian + +[N, m] = size(australian) + +class = 15 + +%rand('state',0); randn('state',0); +%australian = australian(:,randperm(m)); + +Napp = 400 +Ntest = m-Napp + +app = australian(:,1:Napp);size(app) +test = australian(:,Napp+1:end);size(test) + +unique(app(class,:)) +unique(test(class,:)) + +ns = max(australian') +clear australian + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/car b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/car new file mode 100644 index 00000000..7775663b --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/car @@ -0,0 +1,7 @@ + 4.0000000e+00 4.0000000e+00 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1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 3.0000000e+00 1.0000000e+00 3.0000000e+00 4.0000000e+00 1.0000000e+00 3.0000000e+00 4.0000000e+00 1.0000000e+00 2.0000000e+00 3.0000000e+00 1.0000000e+00 3.0000000e+00 4.0000000e+00 1.0000000e+00 3.0000000e+00 4.0000000e+00 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/car.names b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/car.names new file mode 100644 index 00000000..9491039a --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/car.names @@ -0,0 +1,82 @@ +1. Title: Car Evaluation Database + +2. Sources: + (a) Creator: Marko Bohanec + (b) Donors: Marko Bohanec (marko.bohanec@ijs.si) + Blaz Zupan (blaz.zupan@ijs.si) + (c) Date: June, 1997 + +3. Past Usage: + + The hierarchical decision model, from which this dataset is + derived, was first presented in + + M. Bohanec and V. Rajkovic: Knowledge acquisition and explanation for + multi-attribute decision making. In 8th Intl Workshop on Expert + Systems and their Applications, Avignon, France. pages 59-78, 1988. + + Within machine-learning, this dataset was used for the evaluation + of HINT (Hierarchy INduction Tool), which was proved to be able to + completely reconstruct the original hierarchical model. This, + together with a comparison with C4.5, is presented in + + B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by + function decomposition. ICML-97, Nashville, TN. 1997 (to appear) + +4. Relevant Information Paragraph: + + Car Evaluation Database was derived from a simple hierarchical + decision model originally developed for the demonstration of DEX + (M. Bohanec, V. Rajkovic: Expert system for decision + making. Sistemica 1(1), pp. 145-157, 1990.). The model evaluates + cars according to the following concept structure: + + CAR car acceptability + . PRICE overall price + . . buying buying price + . . maint price of the maintenance + . TECH technical characteristics + . . COMFORT comfort + . . . doors number of doors + . . . persons capacity in terms of persons to carry + . . . lug_boot the size of luggage boot + . . safety estimated safety of the car + + Input attributes are printed in lowercase. Besides the target + concept (CAR), the model includes three intermediate concepts: + PRICE, TECH, COMFORT. Every concept is in the original model + related to its lower level descendants by a set of examples (for + these examples sets see http://www-ai.ijs.si/BlazZupan/car.html). + + The Car Evaluation Database contains examples with the structural + information removed, i.e., directly relates CAR to the six input + attributes: buying, maint, doors, persons, lug_boot, safety. + + Because of known underlying concept structure, this database may be + particularly useful for testing constructive induction and + structure discovery methods. + +5. Number of Instances: 1728 + (instances completely cover the attribute space) + +6. Number of Attributes: 6 + +7. Attribute Values: + + buying v-high, high, med, low + maint v-high, high, med, low + doors 2, 3, 4, 5-more + persons 2, 4, more + lug_boot small, med, big + safety low, med, high + +8. Missing Attribute Values: none + +9. Class Distribution (number of instances per class) + + class N N[%] + ----------------------------- + unacc 1210 (70.023 %) + acc 384 (22.222 %) + good 69 ( 3.993 %) + v-good 65 ( 3.762 %) diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/carD.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/carD.m new file mode 100644 index 00000000..e0048783 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/carD.m @@ -0,0 +1,22 @@ +load -ascii car + +[N, m] = size(car); + +class = N + +rand('state',0); randn('state',0); +car = car(:,randperm(m)); + +Napp = 1152 +Ntest = m-Napp + +app = car(:,1:Napp);size(app) +test = car(:,Napp+1:end);size(test) + +unique(app(class,:)) +unique(test(class,:)) + +ns = max(car') +clear car + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/contracep.names b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/contracep.names new file mode 100644 index 00000000..c40896ac --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/contracep.names @@ -0,0 +1,44 @@ +1. Title: Contraceptive Method Choice + +2. Sources: + (a) Origin: This dataset is a subset of the 1987 National Indonesia + Contraceptive Prevalence Survey + (b) Creator: Tjen-Sien Lim (limt@stat.wisc.edu) + (c) Donor: Tjen-Sien Lim (limt@stat.wisc.edu) + (c) Date: June 7, 1997 + +3. Past Usage: + Lim, T.-S., Loh, W.-Y. & Shih, Y.-S. (1999). A Comparison of + Prediction Accuracy, Complexity, and Training Time of Thirty-three + Old and New Classification Algorithms. Machine Learning. Forthcoming. + (ftp://ftp.stat.wisc.edu/pub/loh/treeprogs/quest1.7/mach1317.pdf or + (http://www.stat.wisc.edu/~limt/mach1317.pdf) + +4. Relevant Information: + This dataset is a subset of the 1987 National Indonesia Contraceptive + Prevalence Survey. The samples are married women who were either not + pregnant or do not know if they were at the time of interview. The + problem is to predict the current contraceptive method choice + (no use, long-term methods, or short-term methods) of a woman based + on her demographic and socio-economic characteristics. + +5. Number of Instances: 1473 + +6. Number of Attributes: 10 (including the class attribute) + +7. Attribute Information: + + 1. Wife's age (numerical) + 2. Wife's education (categorical) 1=low, 2, 3, 4=high + 3. Husband's education (categorical) 1=low, 2, 3, 4=high + 4. Number of children ever born (numerical) + 5. Wife's religion (binary) 0=Non-Islam, 1=Islam + 6. Wife's now working? (binary) 0=Yes, 1=No + 7. Husband's occupation (categorical) 1, 2, 3, 4 + 8. Standard-of-living index (categorical) 1=low, 2, 3, 4=high + 9. Media exposure (binary) 0=Good, 1=Not good + 10. Contraceptive method used (class attribute) 1=No-use + 2=Long-term + 3=Short-term + +8. Missing Attribute Values: None diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/contrasep b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/contrasep new file mode 100644 index 00000000..2e3eb29a --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/contrasep @@ -0,0 +1,10 @@ + 3.0000000e+00 1.0000000e+01 1.0000000e+01 9.0000000e+00 7.0000000e+00 2.0000000e+00 8.0000000e+00 2.0000000e+00 4.0000000e+00 1.0000000e+01 8.0000000e+00 9.0000000e+00 1.0000000e+01 9.0000000e+00 8.0000000e+00 4.0000000e+00 1.1000000e+01 8.0000000e+00 8.0000000e+00 8.0000000e+00 4.0000000e+00 3.0000000e+00 1.1000000e+01 8.0000000e+00 1.1000000e+01 9.0000000e+00 8.0000000e+00 5.0000000e+00 3.0000000e+00 1.0000000e+01 6.0000000e+00 6.0000000e+00 4.0000000e+00 6.0000000e+00 3.0000000e+00 9.0000000e+00 1.0000000e+01 4.0000000e+00 9.0000000e+00 5.0000000e+00 5.0000000e+00 1.1000000e+01 1.1000000e+01 9.0000000e+00 5.0000000e+00 4.0000000e+00 6.0000000e+00 1.0000000e+01 4.0000000e+00 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3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 3.0000000e+00 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/contrasepD.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/contrasepD.m new file mode 100644 index 00000000..8a9cd9a6 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/contrasepD.m @@ -0,0 +1,22 @@ +load -ascii contrasep + +[N, m] = size(contrasep); + +class = N + +%rand('state',0); randn('state',0); +%contrasep = contrasep(:,randperm(m)); + +Napp = 850 +Ntest = m-Napp + +app = contrasep(:,1:Napp);size(app) +test = contrasep(:,Napp+1:end);size(test) + +unique(app(class,:)) +unique(test(class,:)) + +ns = max(contrasep') +clear contrasep + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/diabetes b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/diabetes new file mode 100644 index 00000000..9447028d --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/diabetes @@ -0,0 +1,9 @@ + 1.3000000e+01 3.0000000e+00 1.7000000e+01 3.0000000e+00 1.0000000e+00 1.1000000e+01 7.0000000e+00 2.1000000e+01 5.0000000e+00 1.7000000e+01 9.0000000e+00 2.1000000e+01 2.1000000e+01 3.0000000e+00 1.1000000e+01 1.5000000e+01 1.0000000e+00 1.5000000e+01 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1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/diabetes.names b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/diabetes.names new file mode 100644 index 00000000..93ff94e7 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/diabetes.names @@ -0,0 +1,75 @@ +1. Title: Pima Indians Diabetes Database + +2. Sources: + (a) Original owners: National Institute of Diabetes and Digestive and + Kidney Diseases + (b) Donor of database: Vincent Sigillito (vgs@aplcen.apl.jhu.edu) + Research Center, RMI Group Leader + Applied Physics Laboratory + The Johns Hopkins University + Johns Hopkins Road + Laurel, MD 20707 + (301) 953-6231 + (c) Date received: 9 May 1990 + +3. Past Usage: + 1. Smith,~J.~W., Everhart,~J.~E., Dickson,~W.~C., Knowler,~W.~C., \& + Johannes,~R.~S. (1988). Using the ADAP learning algorithm to forecast + the onset of diabetes mellitus. In {\it Proceedings of the Symposium + on Computer Applications and Medical Care} (pp. 261--265). IEEE + Computer Society Press. + + The diagnostic, binary-valued variable investigated is whether the + patient shows signs of diabetes according to World Health Organization + criteria (i.e., if the 2 hour post-load plasma glucose was at least + 200 mg/dl at any survey examination or if found during routine medical + care). The population lives near Phoenix, Arizona, USA. + + Results: Their ADAP algorithm makes a real-valued prediction between + 0 and 1. This was transformed into a binary decision using a cutoff of + 0.448. Using 576 training instances, the sensitivity and specificity + of their algorithm was 76% on the remaining 192 instances. + +4. Relevant Information: + Several constraints were placed on the selection of these instances from + a larger database. In particular, all patients here are females at + least 21 years old of Pima Indian heritage. ADAP is an adaptive learning + routine that generates and executes digital analogs of perceptron-like + devices. It is a unique algorithm; see the paper for details. + +5. Number of Instances: 768 + +6. Number of Attributes: 8 plus class + +7. For Each Attribute: (all numeric-valued) + 1. Number of times pregnant + 2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test + 3. Diastolic blood pressure (mm Hg) + 4. Triceps skin fold thickness (mm) + 5. 2-Hour serum insulin (mu U/ml) + 6. Body mass index (weight in kg/(height in m)^2) + 7. Diabetes pedigree function + 8. Age (years) + 9. Class variable (0 or 1) + +8. Missing Attribute Values: None + +9. Class Distribution: (class value 1 is interpreted as "tested positive for + diabetes") + + Class Value Number of instances + 0 500 + 1 268 + +10. Brief statistical analysis: + + Attribute number: Mean: Standard Deviation: + 1. 3.8 3.4 + 2. 120.9 32.0 + 3. 69.1 19.4 + 4. 20.5 16.0 + 5. 79.8 115.2 + 6. 32.0 7.9 + 7. 0.5 0.3 + 8. 33.2 11.8 + diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/diabetesD.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/diabetesD.m new file mode 100644 index 00000000..2b8406e2 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/diabetesD.m @@ -0,0 +1,22 @@ +load -ascii diabetes + +[N, m] = size(diabetes) + +class = N + +%rand('state',0); randn('state',0); +%diabetes = diabetes(:,randperm(m)); + +Napp = 400 +Ntest = m-Napp + +app = diabetes(:,1:Napp);size(app) +test = diabetes(:,Napp+1:end);size(test) + +unique(app(class,:)) +unique(test(class,:)) + +ns = max(diabetes') +clear diabetes + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/german b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/german new file mode 100644 index 00000000..f613ac50 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/german @@ -0,0 +1,25 @@ + 1.0000000e+00 2.0000000e+00 4.0000000e+00 1.0000000e+00 1.0000000e+00 4.0000000e+00 4.0000000e+00 2.0000000e+00 4.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 4.0000000e+00 1.0000000e+00 2.0000000e+00 4.0000000e+00 4.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 4.0000000e+00 1.0000000e+00 4.0000000e+00 3.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 4.0000000e+00 3.0000000e+00 2.0000000e+00 4.0000000e+00 3.0000000e+00 3.0000000e+00 2.0000000e+00 4.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 4.0000000e+00 4.0000000e+00 1.0000000e+00 4.0000000e+00 4.0000000e+00 2.0000000e+00 2.0000000e+00 4.0000000e+00 4.0000000e+00 2.0000000e+00 4.0000000e+00 2.0000000e+00 4.0000000e+00 3.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 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2.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/german.doc b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/german.doc new file mode 100644 index 00000000..38b80dcb --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/german.doc @@ -0,0 +1,175 @@ +Description of the German credit dataset. + +1. Title: German Credit data + +2. Source Information + +Professor Dr. Hans Hofmann +Institut f"ur Statistik und "Okonometrie +Universit"at Hamburg +FB Wirtschaftswissenschaften +Von-Melle-Park 5 +2000 Hamburg 13 + +3. Number of Instances: 1000 + +Two datasets are provided. the original dataset, in the form provided +by Prof. Hofmann, contains categorical/symbolic attributes and +is in the file "german.data". + +For algorithms that need numerical attributes, Strathclyde University +produced the file "german.data-numeric". This file has been edited +and several indicator variables added to make it suitable for +algorithms which cannot cope with categorical variables. Several +attributes that are ordered categorical (such as attribute 17) have +been coded as integer. This was the form used by StatLog. + + +6. Number of Attributes german: 20 (7 numerical, 13 categorical) + Number of Attributes german.numer: 24 (24 numerical) + + +7. Attribute description for german + +Attribute 1: (qualitative) + Status of existing checking account + A11 : ... < 0 DM + A12 : 0 <= ... < 200 DM + A13 : ... >= 200 DM / + salary assignments for at least 1 year + A14 : no checking account + +Attribute 2: (numerical) + Duration in month + +Attribute 3: (qualitative) + Credit history + A30 : no credits taken/ + all credits paid back duly + A31 : all credits at this bank paid back duly + A32 : existing credits paid back duly till now + A33 : delay in paying off in the past + A34 : critical account/ + other credits existing (not at this bank) + +Attribute 4: (qualitative) + Purpose + A40 : car (new) + A41 : car (used) + A42 : furniture/equipment + A43 : radio/television + A44 : domestic appliances + A45 : repairs + A46 : education + A47 : (vacation - does not exist?) + A48 : retraining + A49 : business + A410 : others + +Attribute 5: (numerical) + Credit amount + +Attibute 6: (qualitative) + Savings account/bonds + A61 : ... < 100 DM + A62 : 100 <= ... < 500 DM + A63 : 500 <= ... < 1000 DM + A64 : .. >= 1000 DM + A65 : unknown/ no savings account + +Attribute 7: (qualitative) + Present employment since + A71 : unemployed + A72 : ... < 1 year + A73 : 1 <= ... < 4 years + A74 : 4 <= ... < 7 years + A75 : .. >= 7 years + +Attribute 8: (numerical) + Installment rate in percentage of disposable income + +Attribute 9: (qualitative) + Personal status and sex + A91 : male : divorced/separated + A92 : female : divorced/separated/married + A93 : male : single + A94 : male : married/widowed + A95 : female : single + +Attribute 10: (qualitative) + Other debtors / guarantors + A101 : none + A102 : co-applicant + A103 : guarantor + +Attribute 11: (numerical) + Present residence since + +Attribute 12: (qualitative) + Property + A121 : real estate + A122 : if not A121 : building society savings agreement/ + life insurance + A123 : if not A121/A122 : car or other, not in attribute 6 + A124 : unknown / no property + +Attribute 13: (numerical) + Age in years + +Attribute 14: (qualitative) + Other installment plans + A141 : bank + A142 : stores + A143 : none + +Attribute 15: (qualitative) + Housing + A151 : rent + A152 : own + A153 : for free + +Attribute 16: (numerical) + Number of existing credits at this bank + +Attribute 17: (qualitative) + Job + A171 : unemployed/ unskilled - non-resident + A172 : unskilled - resident + A173 : skilled employee / official + A174 : management/ self-employed/ + highly qualified employee/ officer + +Attribute 18: (numerical) + Number of people being liable to provide maintenance for + +Attribute 19: (qualitative) + Telephone + A191 : none + A192 : yes, registered under the customers name + +Attribute 20: (qualitative) + foreign worker + A201 : yes + A202 : no + + + +8. Cost Matrix + +This dataset requires use of a cost matrix (see below) + + + 1 2 +---------------------------- + 1 0 1 +----------------------- + 2 5 0 + +(1 = Good, 2 = Bad) + +the rows represent the actual classification and the columns +the predicted classification. + +It is worse to class a customer as good when they are bad (5), +than it is to class a customer as bad when they are good (1). + diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/germanD.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/germanD.m new file mode 100644 index 00000000..bf597f58 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/germanD.m @@ -0,0 +1,22 @@ +load -ascii german + +[N, m] = size(german) + +class = N + +%rand('state',0); randn('state',0); +%german = german(:,randperm(m)); + +Napp = 600 +Ntest = m-Napp + +app = german(:,1:Napp);size(app) +test = german(:,Napp+1:end);size(test) + +unique(app(class,:)) +unique(test(class,:)) + +ns = max(german') +clear german + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/heart b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/heart new file mode 100644 index 00000000..005c69f2 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/heart @@ -0,0 +1,14 @@ + 1.1000000e+01 1.1000000e+01 6.0000000e+00 1.0000000e+01 1.2000000e+01 1.1000000e+01 6.0000000e+00 8.0000000e+00 8.0000000e+00 9.0000000e+00 8.0000000e+00 6.0000000e+00 5.0000000e+00 8.0000000e+00 6.0000000e+00 1.2000000e+01 5.0000000e+00 6.0000000e+00 1.0000000e+01 5.0000000e+00 1.1000000e+01 5.0000000e+00 5.0000000e+00 5.0000000e+00 6.0000000e+00 5.0000000e+00 5.0000000e+00 6.0000000e+00 7.0000000e+00 1.2000000e+01 6.0000000e+00 1.1000000e+01 4.0000000e+00 8.0000000e+00 6.0000000e+00 5.0000000e+00 8.0000000e+00 8.0000000e+00 5.0000000e+00 5.0000000e+00 5.0000000e+00 9.0000000e+00 5.0000000e+00 5.0000000e+00 8.0000000e+00 7.0000000e+00 6.0000000e+00 5.0000000e+00 1.1000000e+01 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2.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/heart.doc b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/heart.doc new file mode 100644 index 00000000..a75fa046 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/heart.doc @@ -0,0 +1,44 @@ +This database contains 13 attributes (which have been extracted from +a larger set of 75) + + + +Attribute Information: +------------------------ + -- 1. age + -- 2. sex + -- 3. chest pain type (4 values) + -- 4. resting blood pressure + -- 5. serum cholestoral in mg/dl + -- 6. fasting blood sugar > 120 mg/dl + -- 7. resting electrocardiographic results (values 0,1,2) + -- 8. maximum heart rate achieved + -- 9. exercise induced angina + -- 10. oldpeak = ST depression induced by exercise relative to rest + -- 11. the slope of the peak exercise ST segment + -- 12. number of major vessels (0-3) colored by flourosopy + -- 13. thal: 3 = normal; 6 = fixed defect; 7 = reversable defect + +Attributes types +----------------- + +Real: 1,4,5,8,10,12 +Ordered:11, +Binary: 2,6,9 +Nominal:7,3,13 + +Variable to be predicted +------------------------ +Absence (1) or presence (2) of heart disease + +Cost Matrix + + abse pres +absence 0 1 +presence 5 0 + +where the rows represent the true values and the columns the predicted. + +No missing values. + +270 observations diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/heart2 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1.0 0.0 3.0 1 +71.0 0.0 2.0 160.0 302.0 0.0 0.0 162.0 0.0 0.4 1.0 2.0 3.0 1 +61.0 1.0 3.0 150.0 243.0 1.0 0.0 137.0 1.0 1.0 2.0 0.0 3.0 1 +55.0 1.0 4.0 132.0 353.0 0.0 0.0 132.0 1.0 1.2 2.0 1.0 7.0 2 +64.0 1.0 3.0 140.0 335.0 0.0 0.0 158.0 0.0 0.0 1.0 0.0 3.0 2 +43.0 1.0 4.0 150.0 247.0 0.0 0.0 171.0 0.0 1.5 1.0 0.0 3.0 1 +58.0 0.0 3.0 120.0 340.0 0.0 0.0 172.0 0.0 0.0 1.0 0.0 3.0 1 +60.0 1.0 4.0 130.0 206.0 0.0 2.0 132.0 1.0 2.4 2.0 2.0 7.0 2 +58.0 1.0 2.0 120.0 284.0 0.0 2.0 160.0 0.0 1.8 2.0 0.0 3.0 2 +49.0 1.0 2.0 130.0 266.0 0.0 0.0 171.0 0.0 0.6 1.0 0.0 3.0 1 +48.0 1.0 2.0 110.0 229.0 0.0 0.0 168.0 0.0 1.0 3.0 0.0 7.0 2 +52.0 1.0 3.0 172.0 199.0 1.0 0.0 162.0 0.0 0.5 1.0 0.0 7.0 1 +44.0 1.0 2.0 120.0 263.0 0.0 0.0 173.0 0.0 0.0 1.0 0.0 7.0 1 +56.0 0.0 2.0 140.0 294.0 0.0 2.0 153.0 0.0 1.3 2.0 0.0 3.0 1 +57.0 1.0 4.0 140.0 192.0 0.0 0.0 148.0 0.0 0.4 2.0 0.0 6.0 1 +67.0 1.0 4.0 160.0 286.0 0.0 2.0 108.0 1.0 1.5 2.0 3.0 3.0 2 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/heart2.mat b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/heart2.mat new file mode 100644 index 00000000..d276285e Binary files /dev/null and b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/heart2.mat differ diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/heartD.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/heartD.m new file mode 100644 index 00000000..b74bd941 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/heartD.m @@ -0,0 +1,22 @@ +load -ascii heart + +[N, m] = size(heart) + +class = N + +%rand('state',0); randn('state',0); +%heart = heart(:,randperm(m)); + +Napp = 150 +Ntest = m-Napp + +app = heart(:,1:Napp);size(app) +test = heart(:,Napp+1:end);size(test) + +unique(app(class,:)) +unique(test(class,:)) + +ns = max(heart') +clear heart + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/hepatitis.dat b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/hepatitis.dat new file mode 100644 index 00000000..071fabec --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/hepatitis.dat @@ -0,0 +1,155 @@ +2 30 2 1 2 2 2 2 1 2 2 2 2 2 1.00 85 18 4.0 -9999 1 +2 50 1 1 2 1 2 2 1 2 2 2 2 2 0.90 135 42 3.5 -9999 1 +2 78 1 2 2 1 2 2 2 2 2 2 2 2 0.70 96 32 4.0 -9999 1 +2 31 1 -9999 1 2 2 2 2 2 2 2 2 2 0.70 46 52 4.0 80 1 +2 34 1 2 2 2 2 2 2 2 2 2 2 2 1.00 -9999 200 4.0 -9999 1 +2 34 1 2 2 2 2 2 2 2 2 2 2 2 0.90 95 28 4.0 75 1 +1 51 1 1 2 1 2 1 2 2 1 1 2 2 -9999 -9999 -9999 -9999 -9999 1 +2 23 1 2 2 2 2 2 2 2 2 2 2 2 1.00 -9999 -9999 -9999 -9999 1 +2 39 1 2 2 1 2 2 2 1 2 2 2 2 0.70 -9999 48 4.4 -9999 1 +2 30 1 2 2 2 2 2 2 2 2 2 2 2 1.00 -9999 120 3.9 -9999 1 +2 39 1 1 1 2 2 2 1 1 2 2 2 2 1.30 78 30 4.4 85 1 +2 32 1 2 1 1 2 2 2 1 2 1 2 2 1.00 59 249 3.7 54 1 +2 41 1 2 1 1 2 2 2 1 2 2 2 2 0.90 81 60 3.9 52 1 +2 30 1 2 2 1 2 2 2 1 2 2 2 2 2.20 57 144 4.9 78 1 +2 47 1 1 1 2 2 2 2 2 2 2 2 2 -9999 -9999 60 -9999 -9999 1 +2 38 1 1 2 1 1 1 2 2 2 2 1 2 2.00 72 89 2.9 46 1 +2 66 1 2 2 1 2 2 2 2 2 2 2 2 1.20 102 53 4.3 -9999 1 +2 40 1 1 2 1 2 2 2 1 2 2 2 2 0.60 62 166 4.0 63 1 +2 38 1 2 2 2 2 2 2 2 2 2 2 2 0.70 53 42 4.1 85 2 +2 38 1 1 1 2 2 2 1 1 2 2 2 2 0.70 70 28 4.2 62 1 +2 22 2 2 1 1 2 2 2 2 2 2 2 2 0.90 48 20 4.2 64 1 +2 27 1 2 2 1 1 1 1 1 1 1 2 2 1.20 133 98 4.1 39 1 +2 31 1 2 2 2 2 2 2 2 2 2 2 2 1.00 85 20 4.0 100 1 +2 42 1 2 2 2 2 2 2 2 2 2 2 2 0.90 60 63 4.7 47 1 +2 25 2 1 1 2 2 2 2 2 2 2 2 2 0.40 45 18 4.3 70 1 +2 27 1 1 2 1 1 2 2 2 2 2 2 2 0.80 95 46 3.8 100 1 +2 49 1 1 1 1 1 1 2 1 2 1 2 2 0.60 85 48 3.7 -9999 1 +2 58 2 2 2 1 2 2 2 1 2 1 2 2 1.40 175 55 2.7 36 1 +2 61 1 1 2 1 2 2 1 1 2 2 2 2 1.30 78 25 3.8 100 1 +2 51 1 1 1 1 1 2 2 2 2 2 2 2 1.00 78 58 4.6 52 1 +1 39 1 1 1 1 1 2 2 1 2 2 2 2 2.30 280 98 3.8 40 1 +1 62 1 1 2 1 1 2 -9999 -9999 2 2 2 2 1.00 -9999 60 -9999 -9999 1 +2 41 2 2 1 1 1 1 2 2 2 2 2 2 0.70 81 53 5.0 74 1 +2 26 2 1 2 2 2 2 2 1 2 2 2 2 0.50 135 29 3.8 60 1 +2 35 1 2 2 1 2 2 2 2 2 2 2 2 0.90 58 92 4.3 73 1 +1 37 1 2 2 1 2 2 2 2 2 1 2 2 0.60 67 28 4.2 -9999 1 +2 23 1 2 2 1 1 1 2 2 1 2 2 2 1.30 194 150 4.1 90 1 +2 20 2 1 2 1 1 1 1 1 1 1 2 2 2.30 150 68 3.9 -9999 1 +2 42 1 1 2 2 2 2 2 2 2 2 2 2 1.00 85 14 4.0 100 1 +2 65 1 2 2 1 1 2 2 1 1 1 1 2 0.30 180 53 2.9 74 2 +2 52 1 1 1 2 2 2 2 2 2 2 2 2 0.70 75 55 4.0 21 1 +2 23 1 2 2 2 2 2 -9999 -9999 -9999 -9999 -9999 -9999 4.60 56 16 4.6 -9999 1 +2 33 1 2 2 2 2 2 2 2 2 2 2 2 1.00 46 90 4.4 60 1 +2 56 1 1 2 1 2 2 2 2 2 2 2 2 0.70 71 18 4.4 100 1 +2 34 1 2 2 2 2 2 2 2 2 2 2 2 -9999 -9999 86 -9999 -9999 1 +2 28 1 2 2 1 1 2 2 2 2 2 2 2 0.70 74 110 4.4 -9999 1 +2 37 1 1 2 2 2 2 2 1 2 1 2 2 0.60 80 80 3.8 -9999 1 +2 28 2 2 2 1 1 2 2 1 2 2 2 2 1.80 191 420 3.3 46 1 +2 36 1 1 2 2 2 2 2 2 1 2 2 2 0.80 85 44 4.2 85 1 +2 38 1 2 1 1 1 1 2 2 2 1 2 2 0.70 125 65 4.2 77 1 +2 39 1 1 2 2 2 2 2 2 2 2 2 2 0.90 85 60 4.0 -9999 1 +2 39 1 2 2 2 2 2 2 2 2 2 2 2 1.00 85 20 4.0 -9999 1 +2 44 1 2 2 2 2 2 2 2 2 2 2 2 0.60 110 145 4.4 70 1 +2 40 1 2 1 1 2 2 2 1 1 2 2 2 1.20 85 31 4.0 100 1 +2 30 1 2 2 1 2 2 2 2 2 2 2 2 0.70 50 78 4.2 74 1 +2 37 1 1 2 1 1 1 2 2 2 2 2 2 0.80 92 59 -9999 -9999 1 +2 34 1 1 2 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 1 +2 30 1 2 1 2 2 2 2 2 2 2 2 2 0.70 52 38 3.9 52 1 +2 64 1 2 1 1 1 2 1 1 2 2 2 2 1.00 80 38 4.3 74 1 +2 45 2 1 2 1 1 2 2 2 1 2 2 2 1.00 85 75 -9999 -9999 1 +2 37 1 2 2 2 2 2 2 2 2 2 2 2 0.70 26 58 4.5 100 1 +2 32 1 2 2 2 2 2 2 2 2 2 2 2 0.70 102 64 4.0 90 1 +2 32 1 2 2 1 1 1 2 2 2 1 2 1 3.50 215 54 3.4 29 1 +2 36 1 1 2 2 2 2 1 1 1 2 2 2 0.70 164 44 3.1 41 1 +2 49 1 2 2 1 1 2 2 2 2 2 2 2 0.80 103 43 3.5 66 1 +2 27 1 2 2 2 2 2 2 2 2 2 2 2 0.80 -9999 38 4.2 -9999 1 +2 56 1 1 2 2 2 2 2 2 2 2 2 2 0.70 62 33 3.0 -9999 1 +1 57 1 2 2 1 1 1 2 2 2 1 1 2 4.10 -9999 48 2.6 73 1 +2 39 1 2 2 1 2 2 2 2 2 2 2 2 1.00 34 15 4.0 54 1 +2 44 1 1 2 1 1 2 2 2 2 2 2 2 1.60 68 68 3.7 -9999 1 +2 24 1 2 2 2 2 2 2 2 2 2 2 2 0.80 82 39 4.3 -9999 1 +1 34 1 1 2 1 1 2 1 1 2 1 2 2 2.80 127 182 -9999 -9999 1 +2 51 1 2 2 1 1 1 -9999 -9999 -9999 -9999 -9999 -9999 0.90 76 271 4.4 -9999 1 +2 36 1 1 2 1 1 1 2 1 2 2 2 2 1.00 -9999 45 4.0 57 1 +2 50 1 2 2 2 2 2 2 2 2 2 2 2 1.50 100 100 5.3 -9999 1 +2 32 1 1 1 1 1 2 2 2 2 2 2 2 1.00 55 45 4.1 56 1 +1 58 1 2 2 1 2 2 1 1 1 1 2 2 2.00 167 242 3.3 -9999 1 +2 34 2 1 1 2 2 2 2 1 2 2 2 2 0.60 30 24 4.0 76 1 +2 34 1 1 2 1 2 2 1 1 2 1 2 2 1.00 72 46 4.4 57 1 +2 28 1 2 2 2 2 2 2 2 2 2 2 2 0.70 85 31 4.9 -9999 1 +2 23 1 2 2 1 1 1 2 2 2 2 2 2 0.80 -9999 14 4.8 -9999 1 +2 36 1 2 2 2 2 2 2 2 2 2 2 2 0.70 62 224 4.2 100 1 +2 30 1 1 2 2 2 2 2 2 2 2 2 2 0.70 100 31 4.0 100 1 +2 67 2 1 2 1 1 2 2 2 -9999 -9999 -9999 -9999 1.50 179 69 2.9 -9999 1 +2 62 2 2 2 1 1 2 2 1 2 1 2 2 1.30 141 156 3.9 58 1 +2 28 1 1 2 1 1 1 2 1 2 2 2 2 1.60 44 123 4.0 46 1 +1 44 1 1 2 1 1 2 2 2 1 2 2 1 0.90 135 55 -9999 41 2 +1 30 1 2 2 1 1 1 2 1 2 1 1 1 2.50 165 64 2.8 -9999 2 +1 38 1 1 2 1 1 1 2 1 2 1 1 1 1.20 118 16 2.8 -9999 2 +2 38 1 1 2 1 1 1 1 1 2 2 2 2 0.60 76 18 4.4 84 2 +2 50 2 1 2 1 2 2 1 1 1 1 2 2 0.90 230 117 3.4 41 2 +1 42 1 1 2 1 1 1 2 2 1 1 2 1 4.60 -9999 55 3.3 -9999 2 +2 33 1 2 2 2 2 2 -9999 -9999 2 2 2 2 1.00 -9999 60 4.0 -9999 2 +2 52 1 1 2 2 2 2 2 2 2 2 2 2 1.50 -9999 69 2.9 -9999 2 +1 59 1 1 2 1 1 2 2 1 1 1 2 2 1.50 107 157 3.6 38 2 +2 40 1 1 1 1 1 1 1 1 2 2 2 2 0.60 40 69 4.2 67 2 +2 30 1 1 2 1 1 2 2 1 2 1 2 2 0.80 147 128 3.9 100 2 +2 44 1 1 2 1 1 2 1 1 2 1 2 2 3.00 114 65 3.5 -9999 2 +1 47 1 2 2 2 2 2 2 2 2 1 2 1 2.00 84 23 4.2 66 2 +2 60 1 1 2 1 2 2 1 1 1 1 2 2 -9999 -9999 40 -9999 -9999 2 +1 48 1 1 2 1 1 2 2 1 2 1 1 1 4.80 123 157 2.7 31 2 +2 22 1 2 2 2 2 2 2 2 2 2 2 2 0.70 -9999 24 -9999 -9999 2 +2 27 1 1 2 1 2 2 2 1 2 2 2 2 2.40 168 227 3.0 66 2 +2 51 1 1 2 1 1 1 2 1 1 1 2 1 4.60 215 269 3.9 51 2 +1 47 1 2 2 1 1 2 2 1 2 2 1 1 1.70 86 20 2.1 46 2 +2 25 1 2 2 2 2 2 2 2 2 2 2 2 0.60 -9999 34 6.4 -9999 2 +1 35 1 1 2 1 2 2 -9999 -9999 1 1 1 2 1.50 138 58 2.6 -9999 2 +2 45 1 1 2 1 1 1 2 2 2 2 2 2 2.30 -9999 648 -9999 -9999 2 +2 54 1 1 1 2 2 2 1 1 2 2 2 2 1.00 155 225 3.6 67 2 +1 33 1 1 2 1 1 2 2 2 2 2 1 2 0.70 63 80 3.0 31 2 +2 7 1 2 2 2 2 2 2 1 1 2 2 2 0.70 256 25 4.2 -9999 2 +1 42 1 1 1 1 1 2 2 2 2 1 2 2 0.50 62 68 3.8 29 2 +2 52 1 1 2 1 2 2 2 2 2 2 2 2 1.00 85 30 4.0 -9999 2 +2 45 1 1 2 1 2 2 2 1 1 2 2 2 1.20 81 65 3.0 -9999 1 +2 36 1 1 2 2 2 2 2 2 2 2 2 2 1.10 141 75 3.3 -9999 2 +2 69 2 2 2 1 2 2 2 2 2 2 2 2 3.20 119 136 -9999 -9999 2 +2 24 1 1 2 1 2 2 2 2 2 2 2 2 1.00 -9999 34 4.1 -9999 2 +2 50 1 2 2 2 2 2 2 2 2 2 2 2 1.00 139 81 3.9 62 2 +1 61 1 1 2 1 1 2 -9999 -9999 2 1 2 2 -9999 -9999 -9999 -9999 -9999 2 +2 54 1 2 2 1 2 2 1 1 2 2 2 2 3.20 85 28 3.8 -9999 2 +1 56 1 1 2 1 1 1 1 1 2 1 2 2 2.90 90 153 4.0 -9999 2 +2 20 1 1 2 1 1 1 2 2 2 1 1 2 1.00 160 118 2.9 23 2 +2 42 1 2 2 2 2 2 2 2 1 2 2 2 1.50 85 40 -9999 -9999 2 +2 37 1 1 2 1 2 2 2 2 2 1 2 2 0.90 -9999 231 4.3 -9999 2 +2 50 1 2 2 2 2 2 2 1 1 1 2 2 1.00 85 75 4.0 72 2 +2 34 2 2 2 1 1 1 1 1 2 1 2 2 0.70 70 24 4.1 100 2 +2 28 1 2 2 1 1 1 -9999 -9999 2 1 1 2 1.00 -9999 20 4.0 -9999 2 +1 50 1 2 2 1 2 2 2 1 1 2 1 1 2.80 155 75 2.4 32 2 +2 54 1 1 2 1 1 2 2 2 2 2 1 2 1.20 85 92 3.1 66 2 +1 57 1 1 2 1 1 2 2 2 2 1 1 2 4.60 82 55 3.3 30 2 +2 54 1 2 2 2 2 2 2 2 2 2 2 2 1.00 85 30 4.5 0 2 +1 31 1 1 2 1 1 1 2 2 1 2 2 2 8.00 -9999 101 2.2 -9999 2 +2 48 1 2 2 1 1 1 2 1 2 1 2 2 2.00 158 278 3.8 -9999 2 +2 72 1 2 1 1 2 2 2 1 2 2 2 2 1.00 115 52 3.4 50 2 +1 38 1 1 2 2 2 2 2 1 2 2 2 2 0.40 243 49 3.8 90 2 +2 25 1 2 2 1 2 2 1 1 1 1 1 1 1.30 181 181 4.5 57 2 +2 51 1 2 2 2 2 2 1 1 2 1 2 2 0.80 -9999 33 4.5 -9999 2 +2 38 1 2 2 2 2 2 2 1 2 1 2 1 1.60 130 140 3.5 56 2 +1 47 1 2 2 1 1 2 2 1 2 1 1 1 1.00 166 30 2.6 31 2 +2 45 1 2 1 2 2 2 2 2 2 2 2 2 1.30 85 44 4.2 85 2 +2 36 1 1 2 1 1 1 1 1 2 1 2 1 1.70 295 60 2.7 -9999 2 +1 54 1 1 2 1 1 2 -9999 -9999 1 2 1 2 3.90 120 28 3.5 43 2 +2 51 1 2 2 1 2 2 2 1 1 1 2 1 1.00 -9999 20 3.0 63 2 +1 49 1 1 2 1 1 2 2 2 1 1 2 2 1.40 85 70 3.5 35 2 +1 45 1 2 2 1 1 1 2 2 2 1 1 2 1.90 -9999 114 2.4 -9999 2 +2 31 1 1 2 1 2 2 2 2 2 2 2 2 1.20 75 173 4.2 54 2 +1 41 1 2 2 1 2 2 2 1 1 1 2 1 4.20 65 120 3.4 -9999 2 +1 70 1 1 2 1 1 1 -9999 -9999 -9999 -9999 -9999 -9999 1.70 109 528 2.8 35 2 +2 20 1 1 2 2 2 2 2 -9999 2 2 2 2 0.90 89 152 4.0 -9999 2 +2 36 1 2 2 2 2 2 2 2 2 2 2 2 0.60 120 30 4.0 -9999 2 +1 46 1 2 2 1 1 1 2 2 2 1 1 1 7.60 -9999 242 3.3 50 2 +2 44 1 2 2 1 2 2 2 1 2 2 2 2 0.90 126 142 4.3 -9999 2 +2 61 1 1 2 1 1 2 1 1 2 1 2 2 0.80 75 20 4.1 -9999 2 +2 53 2 1 2 1 2 2 2 2 1 1 2 1 1.50 81 19 4.1 48 2 +1 43 1 2 2 1 2 2 2 2 1 1 1 2 1.20 100 19 3.1 42 2 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/hepatitisL.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/hepatitisL.m new file mode 100644 index 00000000..43d89fb1 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/hepatitisL.m @@ -0,0 +1,35 @@ +load -ascii hepatitis.dat +hepatitisD=hepatitis'; +clear hepatitis + +[N, m] = size(hepatitisD) +misv=-9999; + +class = 1 + +%rand('state',0); randn('state',0); +%abalone = abalone(:,randperm(m)); + +ns = max(hepatitisD'); +Cont=find(ns>2); +for node = Cont + [I,J]=find(hepatitisD(node,:)~=misv); + [N,EDGES,NBEDGES,XECHAN] = hist_ic(hepatitisD(node,J),3); + hepatitisD(node,J)=XECHAN; +end + +[N, m] = size(hepatitisD); +ns = max(hepatitisD') + +Napp = 90; +Ntest = m-Napp + +app = hepatitisD(:,1:Napp);size(app) +test = hepatitisD(:,Napp+1:end);size(test) + +unique(app(class,:)) +unique(test(class,:)) + +clear hepatitisD node Cont EDGES NBEDGES XECHAN + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/horse.dat b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/horse.dat new file mode 100644 index 00000000..488f82b6 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/horse.dat @@ -0,0 +1,300 @@ +2 1 530101 38.50 66 28 3 3 -9999 2 5 4 4 -9999 -9999 -9999 3 5 45.00 8.40 -9999 -9999 2 2 11300 00000 00000 2 +1 1 534817 39.2 88 20 -9999 -9999 4 1 3 4 2 -9999 -9999 -9999 4 2 50 85 2 2 3 2 02208 00000 00000 2 +2 1 530334 38.30 40 24 1 1 3 1 3 3 1 -9999 -9999 -9999 1 1 33.00 6.70 -9999 -9999 1 2 00000 00000 00000 1 +1 9 5290409 39.10 164 84 4 1 6 2 2 4 4 1 2 5.00 3 -9999 48.00 7.20 3 5.30 2 1 02208 00000 00000 1 +2 1 530255 37.30 104 35 -9999 -9999 6 2 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 74.00 7.40 -9999 -9999 2 2 04300 00000 00000 2 +2 1 528355 -9999 -9999 -9999 2 1 3 1 2 3 2 2 1 -9999 3 3 -9999 -9999 -9999 -9999 1 2 00000 00000 00000 2 +1 1 526802 37.90 48 16 1 1 1 1 3 3 3 1 1 -9999 3 5 37.00 7.00 -9999 -9999 1 1 03124 00000 00000 2 +1 1 529607 -9999 60 -9999 3 -9999 -9999 1 -9999 4 2 2 1 -9999 3 4 44.00 8.30 -9999 -9999 2 1 02208 00000 00000 2 +2 1 530051 -9999 80 36 3 4 3 1 4 4 4 2 1 -9999 3 5 38.00 6.20 -9999 -9999 3 1 03205 00000 00000 2 +2 9 5299629 38.30 90 -9999 1 -9999 1 1 5 3 1 2 1 -9999 3 -9999 40.00 6.20 1 2.20 1 2 00000 00000 00000 1 +1 1 528548 38.10 66 12 3 3 5 1 3 3 1 2 1 3.00 2 5 44.00 6.00 2 3.60 1 1 02124 00000 00000 1 +2 1 527927 39.10 72 52 2 -9999 2 1 2 1 2 1 1 -9999 4 4 50.00 7.80 -9999 -9999 1 1 02111 00000 00000 2 +1 1 528031 37.20 42 12 2 1 1 1 3 3 3 3 1 -9999 4 5 -9999 7.00 -9999 -9999 1 2 04124 00000 00000 2 +2 9 5291329 38.00 92 28 1 1 2 1 1 3 2 3 -9999 7.20 1 1 37.00 6.10 1 -9999 2 2 00000 00000 00000 1 +1 1 534917 38.2 76 28 3 1 1 1 3 4 1 2 2 -9999 4 4 46 81 1 2 1 1 02112 00000 00000 2 +1 1 530233 37.60 96 48 3 1 4 1 5 3 3 2 3 4.50 4 -9999 45.00 6.80 -9999 -9999 2 1 03207 00000 00000 2 +1 9 5301219 -9999 128 36 3 3 4 2 4 4 3 3 -9999 -9999 4 5 53.00 7.80 3 4.70 2 2 01400 00000 00000 1 +2 1 526639 37.50 48 24 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 1 2 00000 00000 00000 2 +1 1 5290481 37.60 64 21 1 1 2 1 2 3 1 1 1 -9999 2 5 40.00 7.00 1 -9999 1 1 04205 00000 00000 1 +2 1 532110 39.4 110 35 4 3 6 -9999 -9999 3 3 -9999 -9999 -9999 -9999 -9999 55 8.7 -9999 -9999 1 2 00000 00000 00000 2 +1 1 530157 39.90 72 60 1 1 5 2 5 4 4 3 1 -9999 4 4 46.00 6.10 2 -9999 1 1 02111 00000 00000 2 +2 1 529340 38.40 48 16 1 -9999 1 1 1 3 1 2 3 5.50 4 3 49.00 6.80 -9999 -9999 1 2 00000 00000 00000 2 +1 1 521681 38.60 42 34 2 1 4 -9999 2 3 1 -9999 -9999 -9999 1 -9999 48.00 7.20 -9999 -9999 1 1 03111 00000 00000 2 +1 9 534998 38.3 130 60 -9999 3 -9999 1 2 4 -9999 -9999 -9999 -9999 -9999 -9999 50 70 -9999 -9999 1 1 03111 00000 00000 2 +1 1 533692 38.1 60 12 3 3 3 1 -9999 4 3 3 2 2 -9999 -9999 51 65 -9999 -9999 1 1 03111 00000 00000 2 +2 1 529518 37.80 60 42 -9999 -9999 -9999 1 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 1 2 00000 00000 00000 2 +1 1 530526 38.30 72 30 4 3 3 2 3 3 3 2 1 -9999 3 5 43.00 7.00 2 3.90 1 1 03111 00000 00000 1 +1 1 528653 37.80 48 12 3 1 1 1 -9999 3 2 1 1 -9999 1 3 37.00 5.50 2 1.30 1 2 04122 00000 00000 1 +1 1 5279442 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 2 2 04300 00000 00000 2 +2 1 535415 37.7 48 -9999 2 1 1 1 1 1 1 1 1 -9999 -9999 -9999 45 76 -9999 -9999 1 2 00000 00000 00000 2 +2 1 529475 37.70 96 30 3 3 4 2 5 4 4 3 2 4.00 4 5 66.00 7.50 -9999 -9999 2 1 04205 00000 00000 2 +2 1 530242 37.20 108 12 3 3 4 2 2 4 2 -9999 3 6.00 3 3 52.00 8.20 3 7.40 3 1 02207 00000 00000 1 +1 1 529427 37.20 60 -9999 2 1 1 1 3 3 3 2 1 -9999 4 5 43.00 6.60 -9999 -9999 1 1 02209 00000 00000 2 +1 1 529663 38.20 64 28 1 1 1 1 3 1 -9999 -9999 -9999 -9999 4 4 49.00 8.60 2 6.60 1 1 02208 00000 00000 1 +1 1 529796 -9999 100 30 3 3 4 2 5 4 4 3 3 -9999 4 4 52.00 6.60 -9999 -9999 1 1 01124 00000 00000 2 +2 1 528812 -9999 104 24 4 3 3 2 4 4 3 -9999 3 -9999 -9999 2 73.00 8.40 -9999 -9999 3 1 07111 00000 00000 2 +2 1 529493 38.30 112 16 -9999 3 5 2 -9999 -9999 1 1 2 -9999 -9999 5 51.00 6.00 2 1.00 3 2 05205 00000 00000 1 +1 1 533847 37.8 72 -9999 -9999 3 -9999 1 5 3 1 -9999 1 -9999 1 1 56 80 1 2 1 1 03111 00000 00000 2 +2 1 528996 38.60 52 -9999 1 1 1 1 3 3 2 1 1 -9999 1 3 32.00 6.60 1 5.00 1 2 00000 00000 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1.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/horseL.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/horseL.m new file mode 100644 index 00000000..6fc3c023 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/horseL.m @@ -0,0 +1,41 @@ +load -ascii horseD +load -ascii horseTD + +% horse = horse'; +% horse(2,:)=(horse(2,:)-1)/8+1; +% horse(27,:)=horse(27,:)/2209+1; +% horseT = horseT'; +% horseT(2,:)=(horseT(2,:)-1)/8+1; +% horseT(27,:)=horseT(27,:)/2209+1; +% [horseD, horseTD] = discretization(3, [3,4,5,6,16,19,20,22,25], -9999, horse, horseT); +% horseD(26,:)= nominalval(horseD(26,:)); +% horseTD(26,:)= nominalval(horseTD(26,:)); + +[N, Napp] = size(horseD); +ns=max([horseD,horseTD]') +UNI=find(ns==1); +horseD=horseD(mysetdiff(1:N,UNI),:); +horseTD=horseTD(mysetdiff(1:N,UNI),:); +ns=max([horseD,horseTD]') + +[N, Napp] = size(horseD); +[N, Ntest] = size(horseTD); + +N, m=Napp+Ntest, Napp, Ntest, +class = 1 + +%rand('state',0); randn('state',0); + +app = horseD; +test = horseTD; +fotest = test(:,9); % la classe est absente :-/ +newtest = app(:,299); +app(:,299) = fotest; +test(:,9) = newtest; + +unique(app(class,:)) +unique(test(class,:)) + +clear horseD horseTD fotest newtest + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/horseTD b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/horseTD new file mode 100644 index 00000000..de8b95df --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/horseTD @@ -0,0 +1,28 @@ + 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 -9.9990000e+03 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 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1 1 2 1 2 2 1 1 1 +2 2 1 1 2 2 -9999 1 1 1 1 1 2 -9999 1 1 1 +2 2 2 1 2 2 2 1 1 2 1 1 2 2 2 1 -9999 +2 1 2 1 2 2 2 1 1 1 1 2 2 2 2 1 1 +1 2 2 2 1 1 1 1 1 2 1 2 1 1 1 2 1 +2 -9999 -9999 -9999 2 2 2 1 1 1 1 2 2 1 2 1 1 +2 1 2 1 2 -9999 2 1 1 1 1 2 1 2 -9999 1 1 +1 2 2 1 1 1 1 2 2 1 1 2 1 1 1 2 1 +2 2 2 1 2 2 2 1 1 1 1 2 2 2 2 2 1 +1 2 -9999 2 1 1 1 2 2 2 2 1 1 1 1 2 1 +1 2 2 2 1 1 1 -9999 -9999 -9999 -9999 2 1 1 1 2 1 +1 2 1 2 1 1 1 2 2 2 1 2 1 1 1 -9999 2 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/houseL.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/houseL.m new file mode 100644 index 00000000..d2e92617 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/houseL.m @@ -0,0 +1,22 @@ +load -ascii house.dat +houseD=house'; +clear house + +[N, m] = size(houseD) + +class = 1 + +%rand('state',0); randn('state',0); +%houseD = houseD(:,randperm(m)); + +Napp = ceil(m*2/3); +Ntest = m-Napp + +app = houseD(:,1:Napp);size(app) +test = houseD(:,Napp+1:end);size(test) + +unique(app(class,:)) +unique(test(class,:)) + +ns = max(houseD') +clear houseD diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/letterD.data b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/letterD.data new file mode 100644 index 00000000..6e709591 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/letterD.data @@ -0,0 +1,20000 @@ +20,2,8,3,5,1,8,13,0,6,6,10,8,0,8,0,8 +9,5,12,3,7,2,10,5,5,4,13,3,9,2,8,4,10 +4,4,11,6,8,6,10,6,2,6,10,3,7,3,7,3,9 +14,7,11,6,6,3,5,9,4,6,4,4,10,6,10,2,8 +7,2,1,3,1,1,8,6,6,6,6,5,9,1,7,5,10 +19,4,11,5,8,3,8,8,6,9,5,6,6,0,8,9,7 +2,4,2,5,4,4,8,7,6,6,7,6,6,2,8,7,10 +1,1,1,3,2,1,8,2,2,2,8,2,8,1,6,2,7 +10,2,2,4,4,2,10,6,2,6,12,4,8,1,6,1,7 +13,11,15,13,9,7,13,2,6,2,12,1,9,8,1,1,8 +24,3,9,5,7,4,8,7,3,8,5,6,8,2,8,6,7 +15,6,13,4,7,4,6,7,6,3,10,7,9,5,9,5,8 +7,4,9,6,7,6,7,8,6,2,6,5,11,4,8,7,8 +13,6,9,8,6,9,7,8,6,5,7,5,8,8,9,8,6 +18,5,9,5,7,6,6,11,7,3,7,3,9,2,7,5,11 +6,6,9,5,4,3,10,6,3,5,10,5,7,3,9,6,9 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a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/letterD.names b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/letterD.names new file mode 100644 index 00000000..33a5eae9 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/letterD.names @@ -0,0 +1,62 @@ +1. Title: Letter Image Recognition Data + +2. Source Information + -- Creator: David J. Slate + -- Odesta Corporation; 1890 Maple Ave; Suite 115; Evanston, IL 60201 + -- Donor: David J. Slate (dave@math.nwu.edu) (708) 491-3867 + -- Date: January, 1991 + +3. Past Usage: + -- P. W. Frey and D. J. Slate (Machine Learning Vol 6 #2 March 91): + "Letter Recognition Using Holland-style Adaptive Classifiers". + + The research for this article investigated the ability of several + variations of Holland-style adaptive classifier systems to learn to + correctly guess the letter categories associated with vectors of 16 + integer attributes extracted from raster scan images of the letters. + The best accuracy obtained was a little over 80%. It would be + interesting to see how well other methods do with the same data. + +4. Relevant Information: + + The objective is to identify each of a large number of black-and-white + rectangular pixel displays as one of the 26 capital letters in the English + alphabet. The character images were based on 20 different fonts and each + letter within these 20 fonts was randomly distorted to produce a file of + 20,000 unique stimuli. Each stimulus was converted into 16 primitive + numerical attributes (statistical moments and edge counts) which were then + scaled to fit into a range of integer values from 0 through 15. We + typically train on the first 16000 items and then use the resulting model + to predict the letter category for the remaining 4000. See the article + cited above for more details. + +5. Number of Instances: 20000 + +6. Number of Attributes: 17 (Letter category and 16 numeric features) + +7. Attribute Information: + 1. lettr capital letter (26 values from A to Z) + 2. x-box horizontal position of box (integer) + 3. y-box vertical position of box (integer) + 4. width width of box (integer) + 5. high height of box (integer) + 6. onpix total # on pixels (integer) + 7. x-bar mean x of on pixels in box (integer) + 8. y-bar mean y of on pixels in box (integer) + 9. x2bar mean x variance (integer) + 10. y2bar mean y variance (integer) + 11. xybar mean x y correlation (integer) + 12. x2ybr mean of x * x * y (integer) + 13. xy2br mean of x * y * y (integer) + 14. x-ege mean edge count left to right (integer) + 15. xegvy correlation of x-ege with y (integer) + 16. y-ege mean edge count bottom to top (integer) + 17. yegvx correlation of y-ege with x (integer) + +8. Missing Attribute Values: None + +9. Class Distribution: + 789 A 766 B 736 C 805 D 768 E 775 F 773 G + 734 H 755 I 747 J 739 K 761 L 792 M 783 N + 753 O 803 P 783 Q 758 R 748 S 796 T 813 U + 764 V 752 W 787 X 786 Y 734 Z diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/letter_A b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/letter_A new file mode 100644 index 00000000..0a465011 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/letter_A @@ -0,0 +1,17 @@ + 3.0000000e+00 5.0000000e+00 8.0000000e+00 5.0000000e+00 7.0000000e+00 5.0000000e+00 7.0000000e+00 2.0000000e+00 6.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 4.0000000e+00 4.0000000e+00 9.0000000e+00 8.0000000e+00 6.0000000e+00 3.0000000e+00 5.0000000e+00 5.0000000e+00 6.0000000e+00 4.0000000e+00 2.0000000e+00 3.0000000e+00 9.0000000e+00 6.0000000e+00 6.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00 5.0000000e+00 4.0000000e+00 5.0000000e+00 4.0000000e+00 5.0000000e+00 5.0000000e+00 7.0000000e+00 4.0000000e+00 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1.3000000e+01 7.0000000e+00 1.5000000e+01 1.5000000e+01 1.7000000e+01 1.5000000e+01 6.0000000e+00 1.3000000e+01 2.6000000e+01 2.1000000e+01 1.7000000e+01 1.6000000e+01 5.0000000e+00 2.4000000e+01 2.0000000e+00 1.7000000e+01 1.7000000e+01 1.3000000e+01 6.0000000e+00 2.4000000e+01 2.3000000e+01 7.0000000e+00 2.3000000e+01 2.4000000e+01 9.0000000e+00 1.8000000e+01 2.5000000e+01 1.6000000e+01 8.0000000e+00 3.0000000e+00 2.6000000e+01 1.5000000e+01 1.2000000e+01 1.7000000e+01 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks.names b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks.names new file mode 100644 index 00000000..bee644b0 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks.names @@ -0,0 +1,78 @@ + +1. Title: The Monk's Problems + +2. Sources: + (a) Donor: Sebastian Thrun + School of Computer Science + Carnegie Mellon University + Pittsburgh, PA 15213, USA + + E-mail: thrun@cs.cmu.edu + + (b) Date: October 1992 + +3. Past Usage: + + - See File: thrun.comparison.ps.Z + + - Wnek, J., "Hypothesis-driven Constructive Induction," PhD dissertation, + School of Information Technology and Engineering, Reports of Machine + Learning and Inference Laboratory, MLI 93-2, Center for Artificial + Intelligence, George Mason University, March 1993. + + - Wnek, J. and Michalski, R.S., "Comparing Symbolic and + Subsymbolic Learning: Three Studies," in Machine Learning: A + Multistrategy Approach, Vol. 4., R.S. Michalski and G. Tecuci (Eds.), + Morgan Kaufmann, San Mateo, CA, 1993. + +4. Relevant Information: + + The MONK's problem were the basis of a first international comparison + of learning algorithms. The result of this comparison is summarized in + "The MONK's Problems - A Performance Comparison of Different Learning + algorithms" by S.B. Thrun, J. Bala, E. Bloedorn, I. Bratko, B. + Cestnik, J. Cheng, K. De Jong, S. Dzeroski, S.E. Fahlman, D. Fisher, + R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J. Kreuziger, R.S. + Michalski, T. Mitchell, P. Pachowicz, Y. Reich H. Vafaie, W. Van de + Welde, W. Wenzel, J. Wnek, and J. Zhang has been published as + Technical Report CS-CMU-91-197, Carnegie Mellon University in Dec. + 1991. + + One significant characteristic of this comparison is that it was + performed by a collection of researchers, each of whom was an advocate + of the technique they tested (often they were the creators of the + various methods). In this sense, the results are less biased than in + comparisons performed by a single person advocating a specific + learning method, and more accurately reflect the generalization + behavior of the learning techniques as applied by knowledgeable users. + + There are three MONK's problems. The domains for all MONK's problems + are the same (described below). One of the MONK's problems has noise + added. For each problem, the domain has been partitioned into a train + and test set. + +5. Number of Instances: 432 + +6. Number of Attributes: 8 (including class attribute) + +7. Attribute information: + 1. class: 0, 1 + 2. a1: 1, 2, 3 + 3. a2: 1, 2, 3 + 4. a3: 1, 2 + 5. a4: 1, 2, 3 + 6. a5: 1, 2, 3, 4 + 7. a6: 1, 2 + 8. Id: (A unique symbol for each instance) + +8. Missing Attribute Values: None + +9. Target Concepts associated to the MONK's problem: + + MONK-1: (a1 = a2) or (a5 = 1) + + MONK-2: EXACTLY TWO of {a1 = 1, a2 = 1, a3 = 1, a4 = 1, a5 = 1, a6 = 1} + + MONK-3: (a5 = 3 and a4 = 1) or (a5 /= 4 and a2 /= 3) + (5% class noise added to the training set) + diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks1.dat b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks1.dat new file mode 100644 index 00000000..9f31e51e --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks1.dat @@ -0,0 +1,124 @@ + 1 1 1 1 1 3 1 + 1 1 1 1 1 3 2 + 1 1 1 1 3 2 1 + 1 1 1 1 3 3 2 + 1 1 1 2 1 2 1 + 1 1 1 2 1 2 2 + 1 1 1 2 2 3 1 + 1 1 1 2 2 4 1 + 1 1 1 2 3 1 2 + 1 1 2 1 1 1 2 + 0 1 2 1 1 2 1 + 0 1 2 1 1 3 1 + 0 1 2 1 1 4 2 + 1 1 2 1 2 1 1 + 0 1 2 1 2 3 1 + 0 1 2 1 2 3 2 + 0 1 2 1 2 4 2 + 0 1 2 1 3 2 1 + 0 1 2 1 3 4 2 + 0 1 2 2 1 2 2 + 0 1 2 2 2 3 2 + 0 1 2 2 2 4 1 + 0 1 2 2 2 4 2 + 0 1 2 2 3 2 2 + 0 1 2 2 3 3 1 + 0 1 2 2 3 3 2 + 0 1 3 1 1 2 1 + 0 1 3 1 1 4 1 + 0 1 3 1 2 2 1 + 0 1 3 1 2 4 1 + 1 1 3 1 3 1 2 + 0 1 3 1 3 2 2 + 0 1 3 1 3 3 1 + 0 1 3 1 3 4 1 + 0 1 3 1 3 4 2 + 0 1 3 2 1 2 2 + 1 1 3 2 2 1 2 + 0 1 3 2 2 2 2 + 0 1 3 2 2 3 2 + 0 1 3 2 2 4 1 + 0 1 3 2 2 4 2 + 1 1 3 2 3 1 1 + 0 1 3 2 3 2 1 + 0 1 3 2 3 4 1 + 0 1 3 2 3 4 2 + 0 2 1 1 1 3 1 + 0 2 1 1 1 3 2 + 1 2 1 1 2 1 1 + 1 2 1 1 2 1 2 + 0 2 1 1 2 2 2 + 0 2 1 1 2 3 1 + 0 2 1 1 2 4 1 + 0 2 1 1 2 4 2 + 0 2 1 1 3 4 1 + 0 2 1 2 1 2 2 + 0 2 1 2 1 3 1 + 0 2 1 2 1 4 2 + 0 2 1 2 2 3 1 + 0 2 1 2 2 4 2 + 0 2 1 2 3 2 2 + 0 2 1 2 3 4 1 + 1 2 2 1 1 2 1 + 1 2 2 1 1 2 2 + 1 2 2 1 1 3 1 + 1 2 2 1 2 3 2 + 1 2 2 1 3 1 1 + 1 2 2 1 3 1 2 + 1 2 2 1 3 2 2 + 1 2 2 1 3 3 2 + 1 2 2 1 3 4 2 + 1 2 2 2 1 1 1 + 1 2 2 2 1 3 2 + 1 2 2 2 1 4 1 + 1 2 2 2 1 4 2 + 1 2 2 2 2 2 1 + 1 2 2 2 3 4 1 + 1 2 3 1 1 1 1 + 1 2 3 1 2 1 1 + 0 2 3 1 2 3 1 + 1 2 3 1 3 1 2 + 0 2 3 1 3 3 1 + 0 2 3 1 3 4 2 + 0 2 3 2 1 3 2 + 1 2 3 2 2 1 1 + 1 2 3 2 2 1 2 + 0 2 3 2 2 2 1 + 0 2 3 2 3 3 2 + 1 3 1 1 1 1 1 + 1 3 1 1 1 1 2 + 1 3 1 1 2 1 1 + 0 3 1 1 2 2 2 + 0 3 1 1 3 2 2 + 1 3 1 2 1 1 1 + 0 3 1 2 1 2 2 + 0 3 1 2 2 2 2 + 0 3 1 2 2 3 2 + 0 3 1 2 3 2 2 + 1 3 2 1 1 1 1 + 0 3 2 1 1 4 2 + 1 3 2 1 2 1 2 + 0 3 2 1 2 4 2 + 1 3 2 2 1 1 1 + 1 3 2 2 1 1 2 + 0 3 2 2 1 3 2 + 1 3 2 2 3 1 1 + 0 3 2 2 3 2 1 + 0 3 2 2 3 4 1 + 1 3 3 1 1 1 1 + 1 3 3 1 1 2 1 + 1 3 3 1 1 4 2 + 1 3 3 1 2 3 2 + 1 3 3 1 2 4 2 + 1 3 3 1 3 1 2 + 1 3 3 1 3 2 1 + 1 3 3 1 3 2 2 + 1 3 3 1 3 4 2 + 1 3 3 2 1 1 1 + 1 3 3 2 1 3 2 + 1 3 3 2 1 4 1 + 1 3 3 2 1 4 2 + 1 3 3 2 3 1 2 + 1 3 3 2 3 2 2 + 1 3 3 2 3 3 2 + 1 3 3 2 3 4 2 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks1D.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks1D.m new file mode 100644 index 00000000..4dd2f047 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks1D.m @@ -0,0 +1,21 @@ +load -ascii monks_A1 +load -ascii monks_T + +[N, m] = size(monks_T); + +class = 1 + +app = monks_A1;size(app) + +test = monks_T;size(test) + +Napp = size(app,2); +Ntest = size(test,2); + +unique(app(class,:)) +unique(test(class,:)) + +ns = max(monks_T'); +clear monks_A1 monks_T + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks2.dat b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks2.dat new file mode 100644 index 00000000..b15834e9 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks2.dat @@ -0,0 +1,169 @@ + 0 1 1 1 1 2 2 + 0 1 1 1 1 4 1 + 0 1 1 1 2 1 1 + 0 1 1 1 2 1 2 + 0 1 1 1 2 2 1 + 0 1 1 1 2 3 1 + 0 1 1 1 2 4 1 + 0 1 1 1 3 2 1 + 0 1 1 1 3 4 1 + 0 1 1 2 1 1 1 + 0 1 1 2 1 1 2 + 0 1 1 2 2 3 1 + 0 1 1 2 2 4 1 + 1 1 1 2 2 4 2 + 0 1 1 2 3 1 2 + 1 1 1 2 3 2 2 + 0 1 2 1 1 1 2 + 0 1 2 1 2 1 2 + 1 1 2 1 2 2 2 + 0 1 2 1 2 3 1 + 1 1 2 1 2 3 2 + 0 1 2 1 2 4 1 + 0 1 2 1 3 1 1 + 0 1 2 1 3 1 2 + 1 1 2 1 3 2 2 + 0 1 2 1 3 3 1 + 1 1 2 1 3 3 2 + 0 1 2 1 3 4 1 + 1 1 2 1 3 4 2 + 0 1 2 2 1 2 1 + 0 1 2 2 1 4 1 + 1 1 2 2 2 3 1 + 1 1 2 2 2 4 1 + 0 1 2 2 3 1 1 + 1 1 2 2 3 1 2 + 1 1 2 2 3 3 1 + 0 1 2 2 3 3 2 + 1 1 2 2 3 4 1 + 0 1 2 2 3 4 2 + 0 1 3 1 1 1 2 + 0 1 3 1 1 2 2 + 0 1 3 1 1 3 1 + 0 1 3 1 1 3 2 + 0 1 3 1 2 2 1 + 1 1 3 1 2 2 2 + 1 1 3 1 2 3 2 + 0 1 3 1 2 4 1 + 1 1 3 1 3 2 2 + 0 1 3 1 3 3 1 + 1 1 3 1 3 4 2 + 0 1 3 2 1 3 1 + 1 1 3 2 1 3 2 + 0 1 3 2 1 4 1 + 1 1 3 2 2 1 2 + 0 1 3 2 2 3 2 + 0 1 3 2 2 4 2 + 1 1 3 2 3 2 1 + 0 2 1 1 1 1 1 + 0 2 1 1 1 2 2 + 0 2 1 1 1 3 1 + 1 2 1 1 2 2 2 + 0 2 1 1 3 1 2 + 1 2 1 1 3 2 2 + 1 2 1 1 3 3 2 + 0 2 1 1 3 4 1 + 0 2 1 2 1 1 1 + 1 2 1 2 1 2 2 + 0 2 1 2 1 4 1 + 1 2 1 2 2 2 1 + 0 2 1 2 2 4 2 + 0 2 1 2 3 1 1 + 1 2 1 2 3 1 2 + 0 2 1 2 3 2 2 + 0 2 1 2 3 3 2 + 0 2 1 2 3 4 2 + 0 2 2 1 1 3 1 + 1 2 2 1 1 4 2 + 0 2 2 1 2 1 1 + 1 2 2 1 2 3 1 + 1 2 2 1 3 3 1 + 0 2 2 1 3 3 2 + 1 2 2 1 3 4 1 + 0 2 2 2 1 1 1 + 0 2 2 2 1 2 2 + 0 2 2 2 1 3 2 + 1 2 2 2 1 4 1 + 0 2 2 2 1 4 2 + 1 2 2 2 2 1 1 + 0 2 2 2 2 2 2 + 0 2 2 2 2 3 1 + 1 2 2 2 3 1 1 + 0 2 2 2 3 2 1 + 0 2 2 2 3 2 2 + 0 2 2 2 3 4 2 + 0 2 3 1 1 1 1 + 0 2 3 1 1 1 2 + 1 2 3 1 1 3 2 + 0 2 3 1 2 1 1 + 1 2 3 1 2 3 1 + 0 2 3 1 2 3 2 + 0 2 3 1 2 4 2 + 1 2 3 1 3 1 2 + 1 2 3 1 3 2 1 + 1 2 3 1 3 4 1 + 1 2 3 2 1 1 2 + 1 2 3 2 1 2 1 + 1 2 3 2 1 3 1 + 0 2 3 2 1 4 2 + 1 2 3 2 2 1 1 + 0 2 3 2 2 2 1 + 0 2 3 2 2 3 2 + 0 2 3 2 3 3 1 + 0 2 3 2 3 3 2 + 0 2 3 2 3 4 2 + 0 3 1 1 1 4 1 + 0 3 1 1 2 1 2 + 1 3 1 1 2 2 2 + 1 3 1 1 2 3 2 + 0 3 1 1 2 4 1 + 1 3 1 1 2 4 2 + 0 3 1 1 3 1 1 + 0 3 1 1 3 1 2 + 1 3 1 1 3 2 2 + 1 3 1 1 3 3 2 + 0 3 1 2 1 1 1 + 1 3 1 2 1 2 2 + 0 3 1 2 1 3 1 + 1 3 1 2 1 3 2 + 0 3 1 2 1 4 1 + 1 3 1 2 1 4 2 + 1 3 1 2 2 2 1 + 1 3 1 2 3 1 2 + 1 3 1 2 3 2 1 + 0 3 1 2 3 2 2 + 0 3 1 2 3 4 2 + 0 3 2 1 1 1 2 + 1 3 2 1 1 2 2 + 0 3 2 1 1 3 1 + 1 3 2 1 1 3 2 + 1 3 2 1 2 1 2 + 1 3 2 1 2 2 1 + 0 3 2 1 3 1 1 + 1 3 2 1 3 2 1 + 1 3 2 1 3 3 1 + 0 3 2 1 3 3 2 + 0 3 2 2 1 1 1 + 0 3 2 2 1 2 2 + 1 3 2 2 1 3 1 + 0 3 2 2 1 3 2 + 1 3 2 2 2 1 1 + 0 3 2 2 2 2 1 + 0 3 2 2 2 2 2 + 0 3 2 2 2 3 2 + 1 3 2 2 3 1 1 + 0 3 2 2 3 3 2 + 0 3 2 2 3 4 2 + 0 3 3 1 1 1 1 + 0 3 3 1 1 2 1 + 0 3 3 1 1 3 1 + 1 3 3 1 1 3 2 + 0 3 3 1 2 3 2 + 0 3 3 2 1 1 1 + 1 3 3 2 2 1 1 + 0 3 3 2 2 2 1 + 0 3 3 2 2 3 1 + 0 3 3 2 2 3 2 + 1 3 3 2 3 1 1 + 0 3 3 2 3 2 1 + 0 3 3 2 3 4 2 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks2D.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks2D.m new file mode 100644 index 00000000..7322a4de --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks2D.m @@ -0,0 +1,21 @@ +load -ascii monks_A2 +load -ascii monks_T + +[N, m] = size(monks_T); + +class = 1 + +app = monks_A2;size(app) + +test = monks_T;size(test) + +Napp = size(app,2); +Ntest = size(test,2); + +unique(app(class,:)) +unique(test(class,:)) + +ns = max(monks_T'); +clear monks_A2 monks_T + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks3.dat b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks3.dat new file mode 100644 index 00000000..9e1bc1f7 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks3.dat @@ -0,0 +1,122 @@ + 1 1 1 1 1 1 2 + 1 1 1 1 1 2 1 + 1 1 1 1 1 2 2 + 0 1 1 1 1 3 1 + 0 1 1 1 1 4 1 + 1 1 1 1 2 1 1 + 1 1 1 1 2 2 2 + 0 1 1 1 2 4 2 + 1 1 1 2 1 2 2 + 0 1 1 2 1 4 2 + 1 1 1 2 2 2 2 + 0 1 1 2 2 4 1 + 0 1 1 2 2 4 2 + 1 1 1 2 3 1 1 + 1 1 1 2 3 1 2 + 1 1 1 2 3 3 1 + 1 1 1 2 3 3 2 + 1 1 2 1 1 3 1 + 1 1 2 1 2 2 1 + 1 1 2 1 2 2 2 + 0 1 2 1 2 3 1 + 1 1 2 1 3 1 1 + 1 1 2 1 3 1 2 + 1 1 2 1 3 2 1 + 1 1 2 1 3 2 2 + 1 1 2 1 3 3 2 + 0 1 2 1 3 4 1 + 1 1 2 2 1 3 1 + 0 1 2 2 1 4 2 + 1 1 2 2 2 1 1 + 1 1 2 2 2 2 1 + 1 1 2 2 2 2 2 + 1 1 2 2 3 1 1 + 1 1 2 2 3 2 1 + 1 1 2 2 3 2 2 + 0 1 3 1 1 2 1 + 0 1 3 1 1 4 1 + 0 1 3 1 2 3 2 + 0 1 3 1 2 4 1 + 0 1 3 1 3 1 1 + 0 1 3 1 3 3 1 + 0 1 3 2 1 1 1 + 0 1 3 2 1 1 2 + 0 1 3 2 1 2 1 + 0 1 3 2 1 4 2 + 0 1 3 2 2 3 2 + 0 1 3 2 2 4 2 + 0 1 3 2 3 4 1 + 1 2 1 1 1 1 1 + 1 2 1 1 1 1 2 + 0 2 1 1 1 4 1 + 0 2 1 1 1 4 2 + 1 2 1 1 2 1 1 + 1 2 1 1 2 1 2 + 1 2 1 1 3 2 2 + 1 2 1 1 3 3 2 + 0 2 1 1 3 4 1 + 1 2 1 2 1 2 2 + 0 2 1 2 2 4 1 + 1 2 1 2 3 1 2 + 1 2 2 1 1 3 2 + 0 2 2 1 1 4 2 + 1 2 2 1 2 1 2 + 0 2 2 1 2 2 1 + 1 2 2 1 3 1 1 + 1 2 2 1 3 2 2 + 0 2 2 1 3 3 1 + 0 2 2 1 3 3 2 + 0 2 2 1 3 4 2 + 1 2 2 2 1 2 2 + 1 2 2 2 2 1 2 + 1 2 2 2 2 3 1 + 1 2 2 2 2 3 2 + 0 2 2 2 3 4 1 + 1 2 3 1 1 3 1 + 0 2 3 1 2 1 1 + 0 2 3 1 2 2 1 + 0 2 3 1 2 2 2 + 0 2 3 1 2 3 2 + 0 2 3 1 3 3 1 + 0 2 3 2 1 1 2 + 0 2 3 2 1 2 2 + 0 2 3 2 1 4 1 + 0 2 3 2 2 3 1 + 0 2 3 2 2 4 2 + 0 2 3 2 3 1 1 + 0 2 3 2 3 2 1 + 0 2 3 2 3 4 2 + 1 3 1 1 1 1 1 + 1 3 1 1 1 2 1 + 1 3 1 1 1 3 1 + 0 3 1 1 2 4 2 + 1 3 1 1 3 1 2 + 0 3 1 1 3 4 2 + 1 3 1 2 1 2 1 + 1 3 1 2 2 3 2 + 0 3 1 2 2 4 2 + 1 3 1 2 3 1 1 + 1 3 2 1 1 2 2 + 0 3 2 1 1 4 1 + 1 3 2 1 2 3 1 + 1 3 2 1 3 1 2 + 1 3 2 2 1 2 2 + 1 3 2 2 1 3 2 + 1 3 2 2 2 1 2 + 1 3 2 2 3 1 1 + 1 3 2 2 3 3 2 + 0 3 2 2 3 4 1 + 1 3 3 1 1 3 2 + 1 3 3 1 1 4 1 + 0 3 3 1 2 4 2 + 0 3 3 1 3 1 1 + 0 3 3 1 3 2 1 + 0 3 3 1 3 2 2 + 0 3 3 1 3 4 1 + 0 3 3 2 1 1 1 + 0 3 3 2 1 1 2 + 0 3 3 2 2 2 2 + 0 3 3 2 2 3 2 + 0 3 3 2 3 1 1 + 0 3 3 2 3 3 2 + 0 3 3 2 3 4 2 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks3D.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks3D.m new file mode 100644 index 00000000..bc6c4b55 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks3D.m @@ -0,0 +1,20 @@ +load -ascii monks_A3 +load -ascii monks_T + +[N, m] = size(monks_T); + +class = 1 + +app = monks_A3;size(app) +test = monks_T;size(test) + +Napp = size(app,2); +Ntest = size(test,2); + +unique(app(class,:)) +unique(test(class,:)) + +ns = max(monks_T'); +clear monks_A3 monks_T + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks_A1 b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks_A1 new file mode 100644 index 00000000..2d84efa4 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/monks_A1 @@ -0,0 +1,7 @@ + 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 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14 1 3 2 15 5 -9999 19 19 15 15 16 14 15 16 2 22 12 +16 11 25 5 6 25 6 3 14 2 20 -9999 11 11 16 16 16 23 15 5 23 22 4 +16 6 25 3 6 13 1 3 2 25 5 3 11 25 3 3 16 23 14 14 23 3 4 +5 24 19 14 6 14 1 3 2 25 5 -9999 19 19 15 15 16 15 15 16 15 22 12 +16 11 25 14 6 19 6 3 14 2 20 -9999 19 11 16 23 16 23 15 5 23 22 12 +16 11 19 5 6 25 6 3 14 2 20 -9999 11 19 16 23 16 23 15 5 23 22 4 +16 11 25 14 6 6 6 3 14 2 20 -9999 11 19 16 23 16 23 15 5 23 22 4 +5 11 19 14 6 14 1 3 2 25 5 -9999 19 19 15 15 16 15 15 16 2 3 12 +5 24 19 14 6 14 1 3 2 25 5 -9999 19 19 15 15 16 14 15 16 2 22 12 +5 6 19 14 6 14 1 3 2 14 5 -9999 19 19 15 15 16 15 15 16 2 3 12 +16 11 25 14 6 25 6 3 14 2 20 -9999 19 11 23 23 16 23 15 5 23 22 12 +5 24 19 14 6 14 1 3 2 25 5 -9999 19 19 15 15 16 15 15 16 15 3 12 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/mushroomsL.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/mushroomsL.m new file mode 100644 index 00000000..87dad5e0 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/mushroomsL.m @@ -0,0 +1,34 @@ +load -ascii mushrooms.dat +mushroomsD=mushrooms'; +clear mushrooms + +[N, m] = size(mushroomsD); +class = 1 + +%rand('state',0); randn('state',0); +%abalone = abalone(:,randperm(m)); + +for node = 1:N + UNI=setdiff(unique(mushroomsD(node,:)),-9999); + for val = 1:length(UNI) + [I,J]=find(mushroomsD(node,:)==UNI(val)); + mushroomsD(node,J)=val; + end +end + +ns = max(mushroomsD'); +seul=find(ns==1); +mushroomsD=mushroomsD(setdiff(1:N,seul),:); +[N, m] = size(mushroomsD) +ns = max(mushroomsD') + +Napp = ceil(m*2/3); +Ntest = m-Napp + +app = mushroomsD(:,1:Napp);size(app) +test = mushroomsD(:,Napp+1:end);size(test) + +unique(app(class,:)) +unique(test(class,:)) + +clear mushroomsD seul UNI node I J diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/nursery b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/nursery new file mode 100644 index 00000000..6da1d386 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/nursery @@ -0,0 +1,9 @@ + 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 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1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 2.0000000e+00 1.0000000e+00 5.0000000e+00 2.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/nursery.names b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/nursery.names new file mode 100644 index 00000000..09b489f8 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/nursery.names @@ -0,0 +1,101 @@ +1. Title: Nursery Database + +2. Sources: + (a) Creator: Vladislav Rajkovic et al. (13 experts) + (b) Donors: Marko Bohanec (marko.bohanec@ijs.si) + Blaz Zupan (blaz.zupan@ijs.si) + (c) Date: June, 1997 + +3. Past Usage: + + The hierarchical decision model, from which this dataset is + derived, was first presented in + + M. Olave, V. Rajkovic, M. Bohanec: An application for admission in + public school systems. In (I. Th. M. Snellen and W. B. H. J. van de + Donk and J.-P. Baquiast, editors) Expert Systems in Public + Administration, pages 145-160. Elsevier Science Publishers (North + Holland)}, 1989. + + Within machine-learning, this dataset was used for the evaluation + of HINT (Hierarchy INduction Tool), which was proved to be able to + completely reconstruct the original hierarchical model. This, + together with a comparison with C4.5, is presented in + + B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by + function decomposition. ICML-97, Nashville, TN. 1997 (to appear) + +4. Relevant Information Paragraph: + + Nursery Database was derived from a hierarchical decision model + originally developed to rank applications for nursery schools. It + was used during several years in 1980's when there was excessive + enrollment to these schools in Ljubljana, Slovenia, and the + rejected applications frequently needed an objective + explanation. The final decision depended on three subproblems: + occupation of parents and child's nursery, family structure and + financial standing, and social and health picture of the family. + The model was developed within expert system shell for decision + making DEX (M. Bohanec, V. Rajkovic: Expert system for decision + making. Sistemica 1(1), pp. 145-157, 1990.). + + The hierarchical model ranks nursery-school applications according + to the following concept structure: + + NURSERY Evaluation of applications for nursery schools + . EMPLOY Employment of parents and child's nursery + . . parents Parents' occupation + . . has_nurs Child's nursery + . STRUCT_FINAN Family structure and financial standings + . . STRUCTURE Family structure + . . . form Form of the family + . . . children Number of children + . . housing Housing conditions + . . finance Financial standing of the family + . SOC_HEALTH Social and health picture of the family + . . social Social conditions + . . health Health conditions + + Input attributes are printed in lowercase. Besides the target + concept (NURSERY) the model includes four intermediate concepts: + EMPLOY, STRUCT_FINAN, STRUCTURE, SOC_HEALTH. Every concept is in + the original model related to its lower level descendants by a set + of examples (for these examples sets see + http://www-ai.ijs.si/BlazZupan/nursery.html). + + The Nursery Database contains examples with the structural + information removed, i.e., directly relates NURSERY to the eight input + attributes: parents, has_nurs, form, children, housing, finance, + social, health. + + Because of known underlying concept structure, this database may be + particularly useful for testing constructive induction and + structure discovery methods. + +5. Number of Instances: 12960 + (instances completely cover the attribute space) + +6. Number of Attributes: 8 + +7. Attribute Values: + + parents usual, pretentious, great_pret + has_nurs proper, less_proper, improper, critical, very_crit + form complete, completed, incomplete, foster + children 1, 2, 3, more + housing convenient, less_conv, critical + finance convenient, inconv + social non-prob, slightly_prob, problematic + health recommended, priority, not_recom + +8. Missing Attribute Values: none + +9. Class Distribution (number of instances per class) + + class N N[%] + ------------------------------ + not_recom 4320 (33.333 %) + recommend 2 ( 0.015 %) + very_recom 328 ( 2.531 %) + priority 4266 (32.917 %) + spec_prior 4044 (31.204 %) diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/nurseryD.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/nurseryD.m new file mode 100644 index 00000000..c1307509 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/nurseryD.m @@ -0,0 +1,22 @@ +load -ascii nursery + +[N, m] = size(nursery); + +class = N + +rand('state',0); randn('state',0); +nursery = nursery(:,randperm(m)); + +Napp = 8500 +Ntest = m-Napp + +app = nursery(:,1:Napp);size(app) +test = nursery(:,Napp+1:end);size(test) + +unique(app(class,:)) +unique(test(class,:)) + +ns = max(nursery') +clear nursery + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/penD.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/penD.m new file mode 100644 index 00000000..a99dda71 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/penD.m @@ -0,0 +1,22 @@ +load -ascii pen_A +load -ascii pen_T + +[N, m] = size(pen_A); + +class = N + +app = pen_A;size(app) +test = pen_T;size(test) + +Napp = size(app,2); +Ntest = size(test,2); + +unique(app(class,:)) +unique(test(class,:)) + +ns1 = max(pen_A'); +ns2 = max(pen_T'); +ns = max(ns1, ns2) +clear pen_A pen_T ns1 ns2 + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/pen_A b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/pen_A new file mode 100644 index 00000000..dde4d8bc --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/pen_A @@ -0,0 +1,17 @@ + 5.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+01 1.0000000e+00 1.0000000e+00 2.0000000e+00 6.0000000e+00 8.0000000e+00 5.0000000e+00 1.0000000e+01 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a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/pendigits.names b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/pendigits.names new file mode 100644 index 00000000..2d8a87f7 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/pendigits.names @@ -0,0 +1,136 @@ +1. Title of Database: Pen-Based Recognition of Handwritten Digits + +2. Source: + E. Alpaydin, Fevzi. Alimoglu + Department of Computer Engineering + Bogazici University, 80815 Istanbul Turkey + alpaydin@boun.edu.tr + July 1998 + +3. Past Usage: + F. Alimoglu (1996) Combining Multiple Classifiers for Pen-Based + Handwritten Digit Recognition, + MSc Thesis, Institute of Graduate Studies in Science and + Engineering, Bogazici University. + http://www.cmpe.boun.edu.tr/~alimoglu/alimoglu.ps.gz + + F. Alimoglu, E. Alpaydin, "Methods of Combining Multiple Classifiers + Based on Different Representations for Pen-based Handwriting + Recognition," Proceedings of the Fifth Turkish Artificial + Intelligence and Artificial Neural Networks Symposium (TAINN 96), + June 1996, Istanbul, Turkey. + http://www.cmpe.boun.edu.tr/~alimoglu/tainn96.ps.gz + + +4. Relevant Information: + + We create a digit database by collecting 250 samples from 44 writers. + The samples written by 30 writers are used for training, + cross-validation and writer dependent testing, and the digits + written by the other 14 are used for writer independent testing. This + database is also available in the UNIPEN format. + + We use a WACOM PL-100V pressure sensitive tablet with an integrated + LCD display and a cordless stylus. The input and display areas are + located in the same place. Attached to the serial port of an Intel + 486 based PC, it allows us to collect handwriting samples. The tablet + sends $x$ and $y$ tablet coordinates and pressure level values of the + pen at fixed time intervals (sampling rate) of 100 miliseconds. + + These writers are asked to write 250 digits in random order inside + boxes of 500 by 500 tablet pixel resolution. Subject are monitored + only during the first entry screens. Each screen contains five boxes + with the digits to be written displayed above. Subjects are told to + write only inside these boxes. If they make a mistake or are unhappy + with their writing, they are instructed to clear the content of a box + by using an on-screen button. The first ten digits are ignored + because most writers are not familiar with this type of input devices, + but subjects are not aware of this. + + In our study, we use only ($x, y$) coordinate information. The stylus + pressure level values are ignored. First we apply normalization to + make our representation invariant to translations and scale + distortions. The raw data that we capture from the tablet consist of + integer values between 0 and 500 (tablet input box resolution). The + new coordinates are such that the coordinate which has the maximum + range varies between 0 and 100. Usually $x$ stays in this range, since + most characters are taller than they are wide. + + In order to train and test our classifiers, we need to represent + digits as constant length feature vectors. A commonly used technique + leading to good results is resampling the ( x_t, y_t) points. + Temporal resampling (points regularly spaced in time) or spatial + resampling (points regularly spaced in arc length) can be used here. + Raw point data are already regularly spaced in time but the distance + between them is variable. Previous research showed that spatial + resampling to obtain a constant number of regularly spaced points + on the trajectory yields much better performance, because it provides + a better alignment between points. Our resampling algorithm uses + simple linear interpolation between pairs of points. The resampled + digits are represented as a sequence of T points ( x_t, y_t )_{t=1}^T, + regularly spaced in arc length, as opposed to the input sequence, + which is regularly spaced in time. + + So, the input vector size is 2*T, two times the number of points + resampled. We considered spatial resampling to T=8,12,16 points in our + experiments and found that T=8 gave the best trade-off between + accuracy and complexity. + + +5. Number of Instances + pendigits.tra Training 7494 + pendigits.tes Testing 3498 + + The way we used the dataset was to use first half of training for + actual training, one-fourth for validation and one-fourth + for writer-dependent testing. The test set was used for + writer-independent testing and is the actual quality measure. + +6. Number of Attributes + 16 input+1 class attribute + +7. For Each Attribute: + All input attributes are integers in the range 0..100. + The last attribute is the class code 0..9 + +8. Missing Attribute Values + None + +9. Class Distribution + Class: No of examples in training set + 0: 780 + 1: 779 + 2: 780 + 3: 719 + 4: 780 + 5: 720 + 6: 720 + 7: 778 + 8: 719 + 9: 719 + Class: No of examples in testing set + 0: 363 + 1: 364 + 2: 364 + 3: 336 + 4: 364 + 5: 335 + 6: 336 + 7: 364 + 8: 336 + 9: 336 + +Accuracy on the testing set with k-nn +using Euclidean distance as the metric + + k = 1 : 97.74 + k = 2 : 97.37 + k = 3 : 97.80 + k = 4 : 97.66 + k = 5 : 97.60 + k = 6 : 97.57 + k = 7 : 97.54 + k = 8 : 97.54 + k = 9 : 97.46 + k = 10 : 97.48 + k = 11 : 97.34 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/segment b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/segment new file mode 100644 index 00000000..9a01840b --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/segment @@ -0,0 +1,20 @@ + 2.6000000e+01 1.3000000e+01 2.4000000e+01 4.0000000e+00 9.0000000e+00 1.8000000e+01 2.4000000e+01 3.0000000e+00 1.0000000e+00 9.0000000e+00 1.8000000e+01 2.9000000e+01 1.5000000e+01 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5.0000000e+00 2.0000000e+00 4.0000000e+00 5.0000000e+00 7.0000000e+00 3.0000000e+00 3.0000000e+00 5.0000000e+00 5.0000000e+00 6.0000000e+00 5.0000000e+00 6.0000000e+00 4.0000000e+00 1.0000000e+00 6.0000000e+00 4.0000000e+00 3.0000000e+00 2.0000000e+00 1.0000000e+00 6.0000000e+00 4.0000000e+00 1.0000000e+00 2.0000000e+00 3.0000000e+00 3.0000000e+00 2.0000000e+00 3.0000000e+00 5.0000000e+00 5.0000000e+00 6.0000000e+00 6.0000000e+00 4.0000000e+00 1.0000000e+00 2.0000000e+00 4.0000000e+00 3.0000000e+00 1.0000000e+00 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/segment.doc b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/segment.doc new file mode 100644 index 00000000..09b085ab --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/segment.doc @@ -0,0 +1,68 @@ +Description of Datasets + + +1. Title: Image Segmentation data + +2. Source Information + -- Creators: Vision Group, University of Massachusetts + -- Donor: Vision Group (Carla Brodley, brodley@cs.umass.edu) + -- Date: November, 1990 + +3. Past Usage: None yet published + +4. Relevant Information: + + The instances were drawn randomly from a database of 7 outdoor + images. The images were handsegmented to create a classification + for every pixel. + + Each instance is a 3x3 region. + +5. Number of Instances: 2310 + +6. Number of Attributes: 19 continuous attributes + +7. Attribute Information: + + 1. region-centroid-col: the column of the center pixel of the region. + 2. region-centroid-row: the row of the center pixel of the region. + 3. region-pixel-count: the number of pixels in a region = 9. + 4. short-line-density-5: the results of a line extractoin algorithm that + counts how many lines of length 5 (any orientation) with + low contrast, less than or equal to 5, go through the region. + 5. short-line-density-2: same as short-line-density-5 but counts lines + of high contrast, greater than 5. + 6. vedge-mean: measure the contrast of horizontally + adjacent pixels in the region. There are 6, the mean and + standard deviation are given. This attribute is used as + a vertical edge detector. + 7. vegde-sd: (see 6) + 8. hedge-mean: measures the contrast of vertically adjacent + pixels. Used for horizontal line detection. + 9. hedge-sd: (see 8). + 10. intensity-mean: the average over the region of (R + G + B)/3 + 11. rawred-mean: the average over the region of the R value. + 12. rawblue-mean: the average over the region of the B value. + 13. rawgreen-mean: the average over the region of the G value. + 14. exred-mean: measure the excess red: (2R - (G + B)) + 15. exblue-mean: measure the excess blue: (2B - (G + R)) + 16. exgreen-mean: measure the excess green: (2G - (R + B)) + 17. value-mean: 3-d nonlinear transformation + of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals + of Interactive Computer Graphics) + 18. saturatoin-mean: (see 17) + 19. hue-mean: (see 17) + +8. Missing Attribute Values: None + +9. Class Distribution: + + Classes: 1 = brickface, + 2 = sky, + 3 = foliage, + 4 = cement, + 5 = window, + 6 = path, + 7 = grass. + + diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/segmentD.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/segmentD.m new file mode 100644 index 00000000..50a9aeee --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/segmentD.m @@ -0,0 +1,22 @@ +load -ascii segment + +[N, m] = size(segment) + +class = N + +%rand('state',0); randn('state',0); +%segment = segment(:,randperm(m)); + +Napp = 1400 +Ntest = m-Napp + +app = segment(:,1:Napp);size(app) +test = segment(:,Napp+1:end);size(test) + +unique(app(class,:)) +unique(test(class,:)) + +ns = max(segment') +clear segment + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/soybean.dat b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/soybean.dat new file mode 100644 index 00000000..994a0118 --- /dev/null +++ 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Napp] = size(soybeanD); +[N, Ntest] = size(soybeanTD); + +N, m=Napp+Ntest, Napp, Ntest, +class = 1 + +%rand('state',0); randn('state',0); +%abalone = abalone(:,randperm(m)); + +app = soybeanD; +test = soybeanTD; + +unique(app(class,:)) +unique(test(class,:)) + +ns = max(soybeanD') +clear soybeanD soybeanTD + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/soybean_test.dat b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/soybean_test.dat new file mode 100644 index 00000000..21b57de1 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/soybean_test.dat @@ -0,0 +1,376 @@ +1 6 0 2 1 0 1 0 1 0 2 1 1 0 2 2 0 0 0 1 0 3 1 1 1 0 0 0 0 4 0 0 0 0 0 0 +1 3 0 2 1 0 2 0 2 1 1 1 1 0 2 2 0 0 0 1 0 3 0 1 1 0 0 0 0 4 0 0 0 0 0 0 +1 4 0 2 1 0 3 0 2 0 2 1 1 0 2 2 0 0 0 1 0 3 1 1 1 0 0 0 0 4 0 0 0 0 0 0 +1 5 0 2 1 0 1 0 1 0 1 1 1 0 2 2 0 0 0 1 0 3 0 1 1 0 0 0 0 4 0 0 0 0 0 0 +1 3 0 2 1 0 3 0 1 0 1 1 1 0 2 2 0 0 0 1 0 3 0 1 1 0 0 0 0 4 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--git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/spect_t b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/spect_t new file mode 100644 index 00000000..c36128a5 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/spect_t @@ -0,0 +1,23 @@ + 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 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00000000..3a9f2fda --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/tae @@ -0,0 +1,6 @@ + 1.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 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new file mode 100644 index 00000000..435034ae --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/tae.names @@ -0,0 +1,39 @@ +1. Title: Teaching Assistant Evaluation + +2. Sources: + (a) Collector: Wei-Yin Loh (Department of Statistics, UW-Madison) + (b) Donor: Tjen-Sien Lim (limt@stat.wisc.edu) + (b) Date: June 7, 1997 + +3. Past Usage: + 1. Loh, W.-Y. & Shih, Y.-S. (1997). Split Selection Methods for + Classification Trees, Statistica Sinica 7: 815-840. + 2. Lim, T.-S., Loh, W.-Y. & Shih, Y.-S. (1999). A Comparison of + Prediction Accuracy, Complexity, and Training Time of + Thirty-three Old and New Classification Algorithms. Machine + Learning. Forthcoming. + (ftp://ftp.stat.wisc.edu/pub/loh/treeprogs/quest1.7/mach1317.pdf or + (http://www.stat.wisc.edu/~limt/mach1317.pdf) + +4. Relevant Information: + The data consist of evaluations of teaching performance over three + regular semesters and two summer semesters of 151 teaching assistant + (TA) assignments at the Statistics Department of the University of + Wisconsin-Madison. The scores were divided into 3 roughly equal-sized + categories ("low", "medium", and "high") to form the class variable. + +5. Number of Instances: 151 + +6. Number of Attributes: 6 (including the class attribute) + +7. Attribute Information: + + 1. Whether of not the TA is a native English speaker (binary) + 1=English speaker, 2=non-English speaker + 2. Course instructor (categorical, 25 categories) + 3. Course (categorical, 26 categories) + 4. Summer or regular semester (binary) 1=Summer, 2=Regular + 5. Class size (numerical) + 6. Class attribute (categorical) 1=Low, 2=Medium, 3=High + +8. Missing Attribute Values: None diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/taeD.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/taeD.m new file mode 100644 index 00000000..63cde564 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/taeD.m @@ -0,0 +1,26 @@ +load -ascii tae + +[N, m] = size(tae) + +class = 6 + +%rand('state',0); randn('state',0); +%tae = tae(:,randperm(m)); + +Napp = 100 +Ntest = m-Napp + +app = tae(:,1:Napp);size(app) +test = tae(:,Napp+1:151);size(test) + +%unique(app(class,:)) +%unique(test(class,:)) + +%app=tae; +%test=tae; +%disp('test en apprentissage'); + +ns = max(tae') +clear tae + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/thyroidL.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/thyroidL.m new file mode 100644 index 00000000..8c78cc70 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/thyroidL.m @@ -0,0 +1,21 @@ +load thyroid_app +load thyroid_test +thyroid_test=thyroid_test'; + +[N, Napp] = size(thyroid_app); +[N, Ntest] = size(thyroid_test); + +N, m=Napp+Ntest, Napp, Ntest, +class = 1 + +%rand('state',0); randn('state',0); +%abalone = abalone(:,randperm(m)); + +app = thyroid_app; +test = thyroid_test; + +unique(app(class,:)) +unique(test(class,:)) + +ns = max(thyroid_app') +clear thyroid_app thyroid_test diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/thyroid_app.mat b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/thyroid_app.mat new file mode 100644 index 00000000..0228a6b2 Binary files /dev/null and b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/thyroid_app.mat differ diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/thyroid_test.mat b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/thyroid_test.mat new file mode 100644 index 00000000..c8759674 Binary files /dev/null and b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/thyroid_test.mat differ diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/wine b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/wine new file mode 100644 index 00000000..ccf262ac --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/wine @@ -0,0 +1,14 @@ + 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 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2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 3.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 3.0000000e+00 3.0000000e+00 2.0000000e+00 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/wine.names b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/wine.names new file mode 100644 index 00000000..d2831258 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/wine.names @@ -0,0 +1,100 @@ +1. Title of Database: Wine recognition data + Updated Sept 21, 1998 by C.Blake : Added attribute information + +2. Sources: + (a) Forina, M. et al, PARVUS - An Extendible Package for Data + Exploration, Classification and Correlation. Institute of Pharmaceutical + and Food Analysis and Technologies, Via Brigata Salerno, + 16147 Genoa, Italy. + + (b) Stefan Aeberhard, email: stefan@coral.cs.jcu.edu.au + (c) July 1991 +3. Past Usage: + + (1) + S. Aeberhard, D. Coomans and O. de Vel, + Comparison of Classifiers in High Dimensional Settings, + Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of + Mathematics and Statistics, James Cook University of North Queensland. + (Also submitted to Technometrics). + + The data was used with many others for comparing various + classifiers. The classes are separable, though only RDA + has achieved 100% correct classification. + (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) + (All results using the leave-one-out technique) + + In a classification context, this is a well posed problem + with "well behaved" class structures. A good data set + for first testing of a new classifier, but not very + challenging. + + (2) + S. Aeberhard, D. Coomans and O. de Vel, + "THE CLASSIFICATION PERFORMANCE OF RDA" + Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of + Mathematics and Statistics, James Cook University of North Queensland. + (Also submitted to Journal of Chemometrics). + + Here, the data was used to illustrate the superior performance of + the use of a new appreciation function with RDA. + +4. Relevant Information: + + -- These data are the results of a chemical analysis of + wines grown in the same region in Italy but derived from three + different cultivars. + The analysis determined the quantities of 13 constituents + found in each of the three types of wines. + + -- I think that the initial data set had around 30 variables, but + for some reason I only have the 13 dimensional version. + I had a list of what the 30 or so variables were, but a.) + I lost it, and b.), I would not know which 13 variables + are included in the set. + + -- The attributes are (dontated by Riccardo Leardi, + riclea@anchem.unige.it ) + 1) Alcohol + 2) Malic acid + 3) Ash + 4) Alcalinity of ash + 5) Magnesium + 6) Total phenols + 7) Flavanoids + 8) Nonflavanoid phenols + 9) Proanthocyanins + 10)Color intensity + 11)Hue + 12)OD280/OD315 of diluted wines + 13)Proline + +5. Number of Instances + + class 1 59 + class 2 71 + class 3 48 + +6. Number of Attributes + + 13 + +7. For Each Attribute: + + All attributes are continuous + + No statistics available, but suggest to standardise + variables for certain uses (e.g. for us with classifiers + which are NOT scale invariant) + + NOTE: 1st attribute is class identifier (1-3) + +8. Missing Attribute Values: + + None + +9. Class Distribution: number of instances per class + + class 1 59 + class 2 71 + class 3 48 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/wineD.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/wineD.m new file mode 100644 index 00000000..bbe2e392 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/wineD.m @@ -0,0 +1,26 @@ +load -ascii wine + +[N, m] = size(wine) + +class = 1 + +rand('state',0); randn('state',0); +wine = wine(:,randperm(m)); + +Napp = 80 +Ntest = m-Napp + +app = wine(:,1:Napp);size(app) +test = wine(:,Napp+1:151);size(test) + +%unique(app(class,:)) +%unique(test(class,:)) + +%app = wine; +%test = wine; +%disp('test en apprentissage'); + +ns = max(wine') +clear wine + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/zoo b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/zoo new file mode 100644 index 00000000..3ee2e2bc --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/zoo @@ -0,0 +1,17 @@ + 1.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 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4.0000000e+00 7.0000000e+00 7.0000000e+00 7.0000000e+00 2.0000000e+00 1.0000000e+00 4.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00 6.0000000e+00 5.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 6.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 4.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 4.0000000e+00 6.0000000e+00 6.0000000e+00 2.0000000e+00 6.0000000e+00 2.0000000e+00 1.0000000e+00 1.0000000e+00 7.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 6.0000000e+00 5.0000000e+00 7.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 2.0000000e+00 4.0000000e+00 4.0000000e+00 3.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 7.0000000e+00 4.0000000e+00 1.0000000e+00 1.0000000e+00 3.0000000e+00 7.0000000e+00 2.0000000e+00 2.0000000e+00 3.0000000e+00 7.0000000e+00 4.0000000e+00 2.0000000e+00 1.0000000e+00 7.0000000e+00 4.0000000e+00 2.0000000e+00 6.0000000e+00 5.0000000e+00 3.0000000e+00 3.0000000e+00 4.0000000e+00 1.0000000e+00 1.0000000e+00 2.0000000e+00 1.0000000e+00 6.0000000e+00 1.0000000e+00 7.0000000e+00 2.0000000e+00 diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/zoo.names b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/zoo.names new file mode 100644 index 00000000..27b2063e --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/zoo.names @@ -0,0 +1,68 @@ +1. Title: Zoo database + +2. Source Information + -- Creator: Richard Forsyth + -- Donor: Richard S. Forsyth + 8 Grosvenor Avenue + Mapperley Park + Nottingham NG3 5DX + 0602-621676 + -- Date: 5/15/1990 + +3. Past Usage: + -- None known other than what is shown in Forsyth's PC/BEAGLE User's Guide. + +4. Relevant Information: + -- A simple database containing 17 Boolean-valued attributes. The "type" + attribute appears to be the class attribute. Here is a breakdown of + which animals are in which type: (I find it unusual that there are + 2 instances of "frog" and one of "girl"!) + + Class# Set of animals: + ====== =============================================================== + 1 (41) aardvark, antelope, bear, boar, buffalo, calf, + cavy, cheetah, deer, dolphin, elephant, + fruitbat, giraffe, girl, goat, gorilla, hamster, + hare, leopard, lion, lynx, mink, mole, mongoose, + opossum, oryx, platypus, polecat, pony, + porpoise, puma, pussycat, raccoon, reindeer, + seal, sealion, squirrel, vampire, vole, wallaby,wolf + 2 (20) chicken, crow, dove, duck, flamingo, gull, hawk, + kiwi, lark, ostrich, parakeet, penguin, pheasant, + rhea, skimmer, skua, sparrow, swan, vulture, wren + 3 (5) pitviper, seasnake, slowworm, tortoise, tuatara + 4 (13) bass, carp, catfish, chub, dogfish, haddock, + herring, pike, piranha, seahorse, sole, stingray, tuna + 5 (4) frog, frog, newt, toad + 6 (8) flea, gnat, honeybee, housefly, ladybird, moth, termite, wasp + 7 (10) clam, crab, crayfish, lobster, octopus, + scorpion, seawasp, slug, starfish, worm + +5. Number of Instances: 101 + +6. Number of Attributes: 18 (animal name, 15 Boolean attributes, 2 numerics) + +7. Attribute Information: (name of attribute and type of value domain) + 1. animal name: Unique for each instance + 2. hair Boolean + 3. feathers Boolean + 4. eggs Boolean + 5. milk Boolean + 6. airborne Boolean + 7. aquatic Boolean + 8. predator Boolean + 9. toothed Boolean + 10. backbone Boolean + 11. breathes Boolean + 12. venomous Boolean + 13. fins Boolean + 14. legs Numeric (set of values: {0,2,4,5,6,8}) + 15. tail Boolean + 16. domestic Boolean + 17. catsize Boolean + 18. type Numeric (integer values in range [1,7]) + +8. Missing Attribute Values: None + +9. Class Distribution: Given above + diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/zooD.m b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/zooD.m new file mode 100644 index 00000000..40c1962e --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/zooD.m @@ -0,0 +1,26 @@ +load -ascii zoo + +[N, m] = size(zoo) + +class = 17 + +rand('state',0); randn('state',0); +zoo = zoo(:,randperm(m)); + +Napp = 60 +Ntest = m-Napp + +app = zoo(:,1:Napp);size(app) +test = zoo(:,Napp+1:101);size(test) + +%unique(app(class,:)) +%unique(test(class,:)) + +%app = zoo; +%test = zoo; +%disp('Test en apprentissage'); + +ns = max(zoo') +clear zoo + +% N, ns(class), Napp, Ntest, mean(ns), diff --git a/sourcecodes/bnt-master/SLP/examples/test_MWSTEM.m b/sourcecodes/bnt-master/SLP/examples/test_MWSTEM.m new file mode 100644 index 00000000..cb4b4837 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_MWSTEM.m @@ -0,0 +1,154 @@ +% francois.olivier.c.h@gmail.com + +%ddd = datestr(now); +%ddd([12 15 18])='-' ; +%fnd=[ddd '.txt']; +%diary(fnd) + +%dbstop if error + +clear all; +close all; + + rand('state',sum(100*clock)) + + nbloopmax = 5; % number of loop max in MWST-EM + + +names={ 'A' , 'S' , 'T' , 'L' , 'B' , 'O' , 'X' , 'D' }; +node = struct('visit', 1, ... + 'smoking', 2, ... + 'tuberculosis', 3, ... + 'bronchitis', 5, ... + 'lung', 4, ... + 'ou', 6, ... + 'Xray', 7, ... + 'dyspnoea', 8); + +adjacency = zeros(8); +adjacency([node.visit], node.tuberculosis) = 1; +adjacency([node.smoking], node.lung) = 1; +adjacency([node.lung node.tuberculosis], node.ou) = 1; +adjacency([node.ou], node.Xray) = 1; +adjacency([node.smoking], node.bronchitis) = 1; +adjacency([node.bronchitis node.ou], node.dyspnoea) = 1; +carre=ones(1,8); + + figure(1); [xx yy] = make_layout(adjacency); + yy=(yy-0.2)*.8/.6+.1; + xx=(xx-0.2833)*.8/.517+.1; + subplot(2,2,1), [xx yy]=draw_graph(adjacency,names,carre,xx,yy); %,carre); + title('ASIA net.'); + +fprintf('\n============================= Test MWST-EM\n'); + +n=8; +m=500; +bnet=mk_asia2_bnet; +data = cell(n,m); +for l = 1:m, data(:,l) = sample_bnet(bnet); end +asiab=cell2mat(data); +fprintf('Complete data have been created.'); + + DM = 0.1; + BD0 = asiab; + node_sizes = max(BD0'); + [N, m]=size(BD0); + rand('state',0); randn('state',0); + vide = rand(size(BD0))<(1-DM); + data=BD0.*vide; + data = mat_to_bnt(data,0); + +% N=4; +% dagO = diag(ones(N-1,1),1); dag0(1,3)=1; +% figure(1), subplot(4,4,1), title('theoritical'), draw_graph(dagO); +% +% node_sizes=2*ones(1,N); +discrete = ones(1,N); +% +% bnetO = mk_bnet(dagO, node_sizes); +% bnetO.CPD{1} = tabular_CPD(bnetO, 1, 'CPT',[0.2 0.8]); +% bnetO.CPD{2} = tabular_CPD(bnetO, 2, 'CPT',[0.4 0.7 0.6 0.3]); +% bnetO.CPD{3} = tabular_CPD(bnetO, 3); +% bnetO.CPD{4} = tabular_CPD(bnetO, 4, 'CPT', [0.5 0.8 0.5 0.2]); +% +% m = 1000; DM = 0.1; +% +% for l=1:m, dataO(:,l) = sample_bnet(bnetO); end +% rand('state',0); randn('state',0); +% vide = rand(size(dataO))<(1-DM); +% data = bnt_to_mat(dataO); +% data = data.*vide; +% data = mat_to_bnt(data,0); +% clear dataO; + +fprintf('Missing data percentage : %3.1f%%\n',100*DM); + +% engine0=jtree_sparse_inf_engine(bnetO); +% [bnet1, LL1, engine1] = learn_params_em(engine0, data); +% BIC0=0; +% for i=1:N, +% xxx=struct(bnet1.CPD{i}); +% BIC0=BIC0+bic_score_family(xxx.counts, xxx.CPT, xxx.nsamples); +% end +% fprintf('%5.2f\n',BIC0); + +%root = 1; +prior = 0; + tmp=cputime; + +[BT_J11, Sbest0] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, nbloopmax); + tmp=cputime-tmp; + fprintf('\tMWST-EM algorithm spent %3.2f secondes\n',tmp); + +figure(1), subplot(2,2,2), draw_graph(BT_J11.dag,names,carre,xx,yy); %,carre); + title('MSWT-EM'); + + fprintf('\n============================= Test AM-SEM\n'); + + G0 = zeros(N,N); + B0 = mk_bnet(G0, node_sizes); + for i=1:N + B0.CPD{i} = tabular_CPD(B0, i, 'prior_type', 'dirichlet', 'dirichlet_weight', 0);%1, 'dirichlet_type','BDeu'); + end + + tmp=cputime; + max_loop = 10; + + [B0, order, best_score] = learn_struct_EM(B0, data, max_loop); + G1 = B0.dag; + [xxx oo]=sort(order); + dag=G1(oo,oo); + + tmp=cputime-tmp; + fprintf('\tSEM algorithm spent %3.2f secondes\n',tmp); + + + subplot(2,2,3), draw_graph(dag,names,carre,xx,yy); %,carre); + title('AMS-EM'); + + fprintf('\n============================= Test AM-SEM+T\n'); + + G0 = BT_J11.dag; + B0 = mk_bnet(G0, node_sizes); + for i=1:N + B0.CPD{i} = tabular_CPD(B0, i, 'prior_type', 'dirichlet', 'dirichlet_weight', 0);%1, 'dirichlet_type','BDeu'); + end + + tmp=cputime; + max_loop = 10; + + [B0, order, best_score] = learn_struct_EM(B0, data, max_loop); + G1 = B0.dag; + [xxx oo]=sort(order); + dag=G1(oo,oo); + + tmp=cputime-tmp; + fprintf('\tSEM+T algorithm spent %3.2f secondes\n',tmp); + + + subplot(2,2,4), draw_graph(dag,names,carre,xx,yy); %,carre); + title('AMS-EM+T'); + + +%diary off diff --git a/sourcecodes/bnt-master/SLP/examples/test_bnpc.m b/sourcecodes/bnt-master/SLP/examples/test_bnpc.m new file mode 100644 index 00000000..85a21db3 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_bnpc.m @@ -0,0 +1,51 @@ +close all +clear all + +N=5; +names={'A','B','C','D','E'}; +dag=zeros(N); +dag(1,2)=1; +dag(2,[3 4])=1; +dag([3 4],5)=1; +[xx yy] = draw_graph(dag,names,ones(N,1)); +title('Cheng example.'); + +node_sizes=2*ones(1,N); +bnet=mk_bnet(dag,node_sizes); + +if 1 + disp('Generating DataSet'); + bnet.CPD{1} = tabular_CPD(bnet, 1, [0.4 0.6]); + bnet.CPD{2} = tabular_CPD(bnet, 2, [0.2 0.3 0.8 0.7]); + bnet.CPD{3} = tabular_CPD(bnet, 3, [0.1 0.2 0.9 0.8]); + bnet.CPD{4} = tabular_CPD(bnet, 4, [0.6 0.8 0.4 0.2]); + bnet.CPD{5} = tabular_CPD(bnet, 5, [0.9 0.8 0.7 0.6 0.1 0.2 0.3 0.4]); + m=10000; + cheng = cell(N,m); + for i=1:m + cheng(:,i)=sample_bnet(bnet); + end + cheng = cell2num(cheng); + %save -ascii cheng cheng +else + load -ascii cheng.mat +end + +% profile clear +% profile on + [Phase_3, Phase_2, Phase_1, UPhase_3] = learn_struct_bnpc(cheng, node_sizes, 0.05, 0) +% profile off +% profile report report_cheng + +figure +draw_graph(Phase_1,names,ones(N,1),xx,yy); +title('PhaseI'); +figure +draw_graph(Phase_2,names,ones(N,1),xx,yy); +title('PhaseII'); +%figure +%draw_graph(UPhase_3,names,ones(N,1),xx,yy); +%title('undirected PhaseIII'); +figure +draw_graph(Phase_3,names,ones(N,1),xx,yy); +title('PhaseIII'); diff --git a/sourcecodes/bnt-master/SLP/examples/test_bnpc2.m b/sourcecodes/bnt-master/SLP/examples/test_bnpc2.m new file mode 100644 index 00000000..77d38334 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_bnpc2.m @@ -0,0 +1,44 @@ +% test BNPC +close all; +clear all; + +%test='pearson'; % Pearson's Chi2 +%test='LRT'; % Likelihood ratio test G2 +fprintf('\n=== Structure Learning with BN-PC (Power Constructor) algorithm\n'); + +n=8; +m=10000; +names={ 'A' , 'S' , 'T' , 'L' , 'B' , 'O' , 'X' , 'D' }; +names={ '1' , '2' , '3' , '4' , '5' , '6' , '7' , '8' }; +carre=ones(1,n); + +bnet=mk_asia2_bnet; +data = cell(n,m); +for l = 1:m, data(:,l) = sample_bnet(bnet); end +data=cell2mat(data); +fprintf('Complete data have been created.'); + +tmp=cputime; +[Phase_3 Phase_2 Phase_1 U] = learn_struct_bnpc(data); +%,2*ones(1,n),0.05,1); +tmp=cputime-tmp; +fprintf('\t- Execution time : %3.2f seconds\n',tmp); + +figure; +[xx yy] = make_layout(bnet.dag); +yy=(yy-0.2)*.8/.6+.1; +xx=(xx-0.2833)*.8/.517+.1; +subplot(2,3,1), [xx yy]=draw_graph(bnet.dag,names,carre,xx,yy); %,carre); +title('ASIA original graph'); +subplot(2,3,2), draw_graph(Phase_1,names,carre,xx,yy); %,carre); +title('BNPC Phase 1'); +subplot(2,3,3), draw_graph(Phase_2,names,carre,xx,yy); %,carre); +title('BNPC Phase 2'); +subplot(2,3,4), draw_graph(U,names,carre,xx,yy); %,carre); +title('BNPC Phase 3a'); +subplot(2,3,5), draw_graph(Phase_3,names,carre,xx,yy); %,carre); +title('BNPC Phase 3b'); +subplot(2,3,6), draw_graph(pdag_to_dag(Phase_3),names,carre,xx,yy); %,carre); +title('BNPC DAG'); +drawnow; + diff --git a/sourcecodes/bnt-master/SLP/examples/test_cache.m b/sourcecodes/bnt-master/SLP/examples/test_cache.m new file mode 100644 index 00000000..ff8e84a1 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_cache.m @@ -0,0 +1,177 @@ +% INITIALISATIONS +%%%%%%%%%%%%%%%%% + +clear +%load asia5000 + +N=8;L=100; +ns=2*ones(1,N); +%data=asiab(:,1:5000); +m=5000; + +bnet=mk_asia2_bnet; +data = cell(N,m); +for l = 1:m, data(:,l) = sample_bnet(bnet); end +data=cell2mat(data);asiab=data; +fprintf('Complete data have been created.'); + +names={ 'A' , 'S' , 'T' , 'L' , 'B' , 'O' , 'X' , 'D' }; + +scoring_fn='bic'; +%scoring_fn='bayesian'; + +% greedy search sur asia sans le cache +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +res=zeros(2,10);i=0; + +for L=100:100:500 +i=i+1;L + +tps=cputime; +D1=learn_struct_gs(data, ns, zeros(N,N),'scoring_fn',scoring_fn); +tps=cputime-tps +%subplot(1,4,2);draw_graph(D1,names,ones(1,N),xx,yy); + +% score BIC (ce sont des chiffres moyen sur ma machine pour environ 5 test par catégories, et idem pour les autres res) +% 100 exemples : 24s +% 500 exemples : 33s +% 1500 exemples : 49s +% 5000 exemples : 112s +% score Bayesien +% 100 exemples : 30 +% 1500 exemples : 39 +% 5000 exemples : 102 + +res(1,i)=tps; + +% greedy search sur asia avec le cache +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%cache=sparse(L,N+3); +cache=score_init_cache(N,L); + +% QD le cache est vide +tps2=cputime; +[D2 best2]=learn_struct_gs(data, ns, zeros(N,N),'scoring_fn',scoring_fn,'cache',cache); +tps2=cputime-tps2 + +res(2,i)=tps2; +end + +%subplot(1,4,3);draw_graph(D2,names,ones(1,N),xx,yy); +% L=100 +% score BIC +% 100 exemples : 28s +% taille du cache : 4860 octets pour 94 entrées au lieu de 8272 pour une matrice de taille 94*11. +% 1500 exemples : 45s +% 5000 exemples : 77s +% score Bayesien +% 100 exemples : 28 +% 1500 exemples : 35 +% 5000 exemples : 78 + +% QD le cache existe +%tps3=cputime; +%[D3 best3]=learn_struct_gs(data, ns, zeros(N,N),'scoring_fn',scoring_fn,'cache', cache2); +%tps3=cputime-tps3 +%subplot(1,4,4);draw_graph(D3,names,ones(1,N),xx,yy); +% L=100 +% score BIC +% 100 exemples : 37s qd le cache existe. +% 1500 exemples : 69s 62 +% 5000 exemples : 129s +% score Bayesien +% 100 exemples : 27 } +% 1500 exemples : 66 } -> phenomčne du a une taille de cache trop petite +% 5000 exemples : 127 } ca se stabilise ŕ partir de L=200 voir conclusion + + + +L=400; +cache=zeros(L,N+3); +tps0=zeros(2,20);i=0; +for taille=250:250:2000 +i=i+1;taille +cache=score_init_cache(N,L); +data=asiab(:,1:taille); +tps=cputime; +D=learn_struct_gs(data, ns, zeros(N,N),'scoring_fn',scoring_fn); +tps0(1,i)=cputime-tps; +tps=cputime; +[D best]=learn_struct_gs(data, ns, zeros(N,N),'scoring_fn',scoring_fn,'cache',cache); +tps0(2,i)=cputime-tps +end + +% taille BD : 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 +% ss cache : 24.1 24.7 29.8 51.1 31.1 36.8 38.0 30.3 48.7 44.6 47.9 46.2 46.9 51.6 50.3 50.3 56.5 50.9 51.6 48.6 +% avec cache: 22.9 23.0 27.1 46.6 27.6 32.5 32.5 26.0 41.3 36.6 39.3 37.5 37.0 40.3 37.4 39.0 41.9 37.5 40.0 34.4 + + + +%%%%%%%%%%%%%% +% CONCLUSION % +%%%%%%%%%%%%%% + +% Si la base de données est petite, il vaut mieux ne pas utiliser de cache. +% Celui-ci deviens utile ŕ partir de la taille 500 environ... +% Au final on arrive presque ŕ gagner 25% du tps avec 1500 exemple et 40% avec 5000 exemples sur le temps d'execution sur cet exemple. +% Utiliser un cache vide, ou un cache possédant deja des valeurs ne change pas grand chose, ŕ part si celui-ci est de taille trop petite ^^(voir res pour 5000 exemples et score bic et res2 pour 1500 ex et score bayésien et res3 pour 500 ex et bic et res4 pour 1500 et bic)). +% Sinon qd le cache est trop gd cela n'est pas trop grave. +% En fait, utiliser une matrice classique prend moins de place mémoire qd le cache est plein. +% +% res= (L=100 200 300 400 500 600 700 800 900 1000) +% ssc 112 +% cvide 86.5 63.6 64.4 65.1 63.9 64.1 64.4 64.5 64.2 64.8 +% cplein127.7 63.4 63.8 63.7 63.4 63.9 63.5 63.9 63.6 64.2 +% +% res2= (L=20 40 60 80 100 120 140 160 180 200 300 400 500 600) +% ssc 39 +% cvide 46.5 49.2 49.7 52.3 41.9 36.3 46.5 47.1 47.3 46.2 46.4 34.9 35.4 35.9 +% cplein 44.9 46.2 47.7 51.1 69.0 36.0 37.1 37.6 48.2 47.7 48.2 47.7 36.7 36.2 +% +% res3= (L=50 100 150 200 250 300 350 400 450 500 550 600) +% ssc 33 +% cvide 41.9 28.8 28.5 28.6 28.4 28.7 28.4 28.9 28.7 28.8 28.5 28.6 +% cplein 39.1 46.3 28.9 29.3 29.0 29.5 29.1 29.6 29.3 29.0 29.4 29.3 +% +% res4= (L=50 100 150 200 250 300 350 400) +% ssc 49 +% cvide 55.3 39.0 37.2 39.2 37.9 38.3 37.8 38.4 +% +% +% ON voit donc qu'une fois la taille du cache bien choisie, celui-ci ne fait pas perdre de perfs sur les petites BD, +% IL ne reste donc plus qu'a trouver une formule qui donne une taille de cache optimum en fonction du nombre de noeud, +% et esperer que celle-ci ne soit pas trop exponnentielle... + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ANNEXE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ChronoMetrage pour 1500 exemples. +% (pas ŕ jour, c'était comme ca au debut maintenant le prétraitement est passé ŕ ~~0.25s) +%%%%%%%%%%%%%%% + +% n'existe pas ds le cache | existe | ss cache +% -----------------------------------------|-------------|-------------- +% Prétraitement ==> 0.0044~64 secondes | 0.0063~84 s | <== trop long +% Lecture ==> 0.0004~09 secondes | 0.0004~09 s | +% if ~exist | | +% calcul ==> 0.0076~87 secondes | | 0.0076~87 s <== par rapport a ca +% ecriture ==> 0.0005~05 secondes | | +% _________________________________________|_____________|_______________ +% TOTAL === 1,3~1,6 ms | 0,7~,9 ms | 0,9 ms +% | +% |__> ŕ évoluer vers ???? + +% enlever les commentaires sur les tic,toc en dur ds les fonctions + +% tic +% [scorr cac]=score_family(2,[3 4 5],'tabular', 'bic', 2*ones(1,N),1:N, data, {}, cache); +% toc +% tic +% [scorr cac]=score_family(2,[3 4 5],'tabular', 'bic', 2*ones(1,N),1:N, data, {}, cac); +% toc +% tic +% scorr=score_family(2,[3 4 5],'tabular', 'bic', 2*ones(1,N),1:N, data, {}); +% toc diff --git a/sourcecodes/bnt-master/SLP/examples/test_chisquare.m b/sourcecodes/bnt-master/SLP/examples/test_chisquare.m new file mode 100644 index 00000000..5282f1b0 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_chisquare.m @@ -0,0 +1,61 @@ +% +% test CHI2 +% + +% Génération de données jouet +N=20000; +xx=5*rand(N,1); +yy=5*rand(N,1); +zz = 5*rand(N,1); +z2= (xx+yy) ; +Data=1+[round(xx) round(yy) round(z2) round(zz)]; +Data=Data'; + +texte{1}='dependency'; +texte{2}='independency'; + +%test='pearson'; % Pearson's Chi2 +test='LRT'; % Likelihood ratio test G2 + +fprintf('\n\n\n\t=================================================\n'); +fprintf('\t\tChi2 Test for (conditional) independency\n'); +fprintf('\t\t(X, Y and Z are theoritically independant)\n'); + +fprintf('\t=================================================\n'); +fprintf('Variables \t\t: Theory : Results (test=%s)\n',test); +fprintf('------------------------------------------------------------------------\n'); + +S=[]; +x=1; y=2; +[IND CHI2] = cond_indep_chisquare(x,y,S,Data(:,1:100),test); +fprintf('X, Y \t\t\t: DEPDCY : \tCHI2=%8.2f \t%s (not enough data)\n',CHI2,texte{IND+1}); + +S=[]; +x=1; y=2; +[IND CHI2] = cond_indep_chisquare(x,y,S,Data,test); +fprintf('X, Y \t\t\t: INDPCY : \tCHI2=%8.2f \t%s\n',CHI2,texte{IND+1}); + +S=[]; +x=1; y=3; +[IND CHI2] = cond_indep_chisquare(x,y,S,Data,test); +fprintf('X, X+Y \t\t\t: DEPDCY :\tCHI2=%8.2f \t%s\n',CHI2,texte{IND+1}); + +S=[]; +x=1; y=4; +[IND CHI2] = cond_indep_chisquare(x,y,S,Data,test); +fprintf('X, Z \t\t\t: INDPCY :\tCHI2=%8.2f \t%s\n',CHI2,texte{IND+1}); + + +x=1; y=2; S=4; +[IND CHI2] = cond_indep_chisquare(x,y,S,Data,test); +fprintf('X, Y | Z \t\t: INDPCY : \tCHI2=%8.2f \tconditional %s\n',CHI2,texte{IND+1}); + +x=1; y=2; S=3; +[IND CHI2] = cond_indep_chisquare(x,y,S,Data,test); +fprintf('X, Y | X+Y \t\t: DEPDCY : \tCHI2=%8.2f \tconditional %s\n',CHI2,texte{IND+1}); + +x=1; y=2; S=[3 4]; +[IND CHI2] = cond_indep_chisquare(x,y,S,Data,test); +fprintf('X, Y | {X+Y,Z}\t\t: DEPDCY : \tCHI2=%8.2f \tconditional %s\n',CHI2,texte{IND+1}); + +fprintf('------------------------------------------------------------------------\n'); \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/examples/test_cpdag.m b/sourcecodes/bnt-master/SLP/examples/test_cpdag.m new file mode 100644 index 00000000..983a0399 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_cpdag.m @@ -0,0 +1,22 @@ +% Test Dag2Cpdag and Cpdag2Dag functions ... + +close all; +clear all; + +bnet=mk_asia_bnet ; + +n=8; +names={ 'A' , 'S' , 'T' , 'L' , 'B' , 'O' , 'X' , 'D' }; +carre=ones(1,n); + +[xx yy] = make_layout(bnet.dag); +yy=(yy-0.2)*.8/.6+.1; +xx=(xx-0.2833)*.8/.517+.1; + +cpdag=dag_to_cpdag(bnet.dag) ; +dag1 = cpdag_to_dag(cpdag) ; + +figure; +subplot(1,3,1), draw_graph(bnet.dag,names,carre,xx,yy); title('original ASIA'); +subplot(1,3,2), draw_graph(cpdag,names,carre,xx,yy); title('cpasia=DAGtoCPDAG(asia)'); +subplot(1,3,3), draw_graph(dag1,names,carre,xx,yy); title('CPDAGtoDAG(cpasia)'); \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/examples/test_data_generation.m b/sourcecodes/bnt-master/SLP/examples/test_data_generation.m new file mode 100644 index 00000000..a31cf8da --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_data_generation.m @@ -0,0 +1,46 @@ +% gener_mcardat + +%%Jouet 1 +N=5; +dag=zeros(N); +dag(1,2)=1; +dag(2,[3 4])=1; +dag(4,5)=1; +node_sizes= [2 3 4 5 2]; ns = node_sizes; +bnet = mk_bnet(dag, node_sizes); +bnet.CPD{1} = tabular_CPD(bnet, 1, [0.2 0.8]); +bnet.CPD{2} = tabular_CPD(bnet, 2, [0.1 0.6 0.3 0.9 0.4 0.7]); +bnet.CPD{3} = tabular_CPD(bnet, 3, [0.3 0.5 0.7 0.2 0.2 0.1 0.05 0.5 0.5 0.4 0.25 0.3]); +bnet.CPD{4} = tabular_CPD(bnet, 4, [0.35 0.05 0.65 0.15 0.25 0.15 0.15 0.10 0.25 0.45 0.5 0.8 0.25 0.6 0.3]); +bnet.CPD{5} = tabular_CPD(bnet, 5, [0.25 0.15 0.15 0.25 0.10 0.15 0.2 0.05 0.3 0.4]); +bnet_orig=bnet + +base_proba=0.2; + +%%%%%%%% MCAR + +bnet_miss = gener_MCAR_net(bnet_orig, base_proba); + +carre=zeros(1,3*N); +names={'X1','X2','X3','X4','X5','R1','R2','R3','R4','R5','M1','M2','M3','M4','M5'}; +xx=[.1,.3,.5,.7,.9,.05,.25,.45,.65,.85,.15,.35,.55,.75,.95]; +yy=[.85,.70,.90,.75,.80,.65,.40,.60,.45,.50,.2,.2,.2,.2,.2]; +figure;draw_graph(bnet_miss.dag,names,carre,xx,yy) + +[data, comp_data, bnet_miss, taux, bnet_orig, notok] = gener_data_from_bnet_miss(bnet_miss, 500, base_proba ,0,0); + +OK = ~notok + +%%%%%%%% MAR + +bnet_miss = gener_MAR_net(bnet_orig, base_proba); + +carre=zeros(1,3*N); +names={'X1','X2','X3','X4','X5','R1','R2','R3','R4','R5','M1','M2','M3','M4','M5'}; +xx=[.1,.3,.5,.7,.9,.05,.25,.45,.65,.85,.15,.35,.55,.75,.95]; +yy=[.85,.70,.90,.75,.80,.65,.40,.60,.45,.50,.2,.2,.2,.2,.2]; +figure;draw_graph(bnet_miss.dag,names,carre,xx,yy) + +[data, comp_data, bnet_miss, taux, bnet_orig, notok] = gener_data_from_bnet_miss(bnet_miss, 500, base_proba ,0,0); + +OK = ~notok diff --git a/sourcecodes/bnt-master/SLP/examples/test_gener.m b/sourcecodes/bnt-master/SLP/examples/test_gener.m new file mode 100644 index 00000000..d8d5ebb2 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_gener.m @@ -0,0 +1,123 @@ +% data generation +clear all +close all + +ddd = datestr(now); +ddd([12 15 18])='-' ; +fnd=[ddd '.txt']; +diary(fnd) + +% Metaparams +hyp = 'mar'; +ntests = 2; % change it ! +nessais = 2; % change it ! +m = 1000; % change it ! + +% INIT +missing = 2; +misv = -9999; +%test = zeros(nessais,6); +test = zeros(nessais,ntests); +taille = zeros(nessais,ntests); +kiki = zeros(nessais,1); + +%%%%%%%%%% TESTS %%%%%%%%%%% +for essai=1:nessais + + N = ceil(rand*11) + 2 + base_proba = .15 + rand/4; + + fan_in = ceil(rand*4) + dag=mk_rnd_dag(N,fan_in) + discrete=1:N; + for i=1:N, node_sizes(i) = ceil(rand*4)+1; end + bnet = mk_bnet(dag, node_sizes, discrete); + node_sizes + for i=1:N, bnet.CPD{i} = tabular_CPD(bnet, i); end + + if prod([hyp(1:3) == 'mca']+0), + bnet_miss = gener_MCAR_net(bnet, base_proba); + disp(' - MCAR net generated'); + elseif prod([hyp(1:3) == 'mar']+0), + bnet_miss = gener_MAR_net(bnet, base_proba); + disp(' - MAR net generated'); + end + + [data, comp_data, bnet_miss, taux, bnet_orig, notok, dT] = gener_data_from_bnet_miss(bnet_miss, m, base_proba ,1); + base_proba + + kiki(essai) = 1-chi2cdf(dT,1); + + %test(essai,1)=1-chi2cdf(d,df); + node0=[]; param0=[]; + for i=1:ntests + % choose a node + node=ceil(rand*N); + while ~isempty(myintersect(node,node0)) & ichi2_table(chi2(chiv),df), test(essai,i,chiv) = 1; end + %end + cumchi=chi2cdf(d,df) + test(essai,i) = 1-cumchi; + taille(essai,i) = toto; + + %fprintf('%2.1f%% OF MISSING DATA (%2.1f%%, Khi2 : %2.1f > %2.1f)\n', round(base_proba*10000)/100, round(taux*10000)/100, d, chi2(nchi)); + + end + clear bnet_miss bnet datamat data node0 family node_sizes CPT indj tmp +end + +kiki +taille +test + +%eval(['save resgener' ddd 'kiki taille test hyp ntests nessais = 50 m']) + +diary off + diff --git a/sourcecodes/bnt-master/SLP/examples/test_ges.m b/sourcecodes/bnt-master/SLP/examples/test_ges.m new file mode 100644 index 00000000..ba1b6c70 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_ges.m @@ -0,0 +1,45 @@ +% test Greedy Equivalence Search with cache + +clear all; + +%add_BNT_to_path; + +fprintf('\n=== Structure Learning with Greedy Equivalence Search algorithm and cache implementation\n'); + + +bnet=mk_asia_bnet ; + +n=8; +names={ 'A' , 'S' , 'T' , 'L' , 'B' , 'O' , 'X' , 'D' }; +carre=ones(1,n); +node_type={'tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular'}; + +m=10000; +bnet=mk_asia2_bnet; +data = cell(n,m); +for l = 1:m, data(:,l) = sample_bnet(bnet); end +data=cell2mat(data); +fprintf('Complete data have been created.'); + +seeddag = zeros(n,n); + +fprintf('\t- Greedy Search (with cache)\n'); +tmp=cputime; +L=500; +cache=score_init_cache(n,L); +[dag1, best_score, cache] =learn_struct_ges(data,bnet.node_sizes,seeddag,'cache',cache,'scoring_fn','bic'); +%cache +tmp=cputime-tmp; +fprintf('\t- Execution time : %3.2f seconds\n',tmp); + + +figure;[xx yy] = make_layout(bnet.dag); +yy=(yy-0.2)*.8/.6+.1; +xx=(xx-0.2833)*.8/.517+.1; +subplot(1,3,1), [xx yy]=draw_graph(bnet.dag,names,carre,xx,yy); %,carre); +title('ASIA original graph'); +subplot(1,3,2), draw_graph(dag1,names,carre,xx,yy); %,carre); +title('GES CPDAG (cache)'); +subplot(1,3,3), draw_graph(cpdag_to_dag(dag1),names,carre,xx,yy); %,carre); +title('GES DAG (cache)'); +drawnow; \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/examples/test_ges_em.m b/sourcecodes/bnt-master/SLP/examples/test_ges_em.m new file mode 100644 index 00000000..3fe1442c --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_ges_em.m @@ -0,0 +1,25 @@ +clear all +close all + +bnet = mk_asia_bnet(); + +N = length(bnet.dag); + +base_proba = 0.3; +bnet_miss = gener_MCAR_net(bnet, base_proba); + +m=500; +[data, comp_data, bnet_miss, taux, bnet_orig, notok] = gener_data_from_bnet_miss(bnet_miss, m, base_proba); + +dag0 = zeros(N); +bnet0 = mk_bnet(dag0, bnet.node_sizes); +for node = 1:N + bnet0.CPD{node} = tabular_CPD(bnet0, node, 'prior_type', 'dirichlet', 'dirichlet_weight', 0); +end + +max_loop = 6; +[bnet0, cpdag, BIC_score, nloop] = learn_struct_ges_EM(bnet0, data, max_loop); + +[XX, YY] = draw_graph(bnet.dag); +figure; +draw_graph(bnet0.dag, {'1','2','3','4','5','6','7','8'}, zeros(1,N), XX, YY); diff --git a/sourcecodes/bnt-master/SLP/examples/test_gs2.m b/sourcecodes/bnt-master/SLP/examples/test_gs2.m new file mode 100644 index 00000000..0188977e --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_gs2.m @@ -0,0 +1,54 @@ +% test Greedy Search with cache +clear all; + +fprintf('\n=== Structure Learning with Greedy Search algorithm and cache implementation\n'); + +n=8; +names={ 'A' , 'S' , 'T' , 'L' , 'B' , 'O' , 'X' , 'D' }; +carre=ones(1,n); +node_type={'tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular'}; + +m=6000; +bnet=mk_asia2_bnet; +data = cell(n,m); +for l = 1:m, data(:,l) = sample_bnet(bnet); end +data=cell2mat(data); +fprintf('Complete data have been created.\n\n'); + +seeddag = mk_rnd_dag(n); + +fprintf('\t- Greedy Search (with cache)\n'); +tmp=cputime; +L=300; +cache=score_init_cache(n,L); +[dag1, best_score, cache] =learn_struct_gs2(data,bnet.node_sizes,seeddag,'cache',cache,'scoring_fn','bayesian'); +%cache +tmp=cputime-tmp; +fprintf('\t- Execution time : %3.2f seconds\n',tmp); + +fprintf('\t- Greedy Search (without cache)\n'); +tmp=cputime; +dag2=learn_struct_gs2(data,bnet.node_sizes,seeddag,'scoring_fn','bayesian'); +tmp=cputime-tmp; +fprintf('\t- Execution time : %3.2f seconds\n',tmp); + +fprintf('\t- Greedy Search (with cache and MWST initialisation)\n'); +tmp=cputime; +Tdag=learn_struct_mwst(data, ones(n,1), bnet.node_sizes, node_type,'mutual_info',ceil(n*rand)); +dag3=learn_struct_gs2(data,bnet.node_sizes,full(Tdag),'cache',cache,'scoring_fn','bayesian'); +tmp=cputime-tmp; +fprintf('\t- Execution time : %3.2f seconds\n',tmp); + + +figure;[xx yy] = make_layout(bnet.dag); +yy=(yy-0.2)*.8/.6+.1; +xx=(xx-0.2833)*.8/.517+.1; +subplot(1,4,1), [xx yy]=draw_graph(bnet.dag,names,carre,xx,yy); %,carre); +title('ASIA original graph'); +subplot(1,4,2), draw_graph(dag1,names,carre,xx,yy); %,carre); +title('GS (cache)'); +subplot(1,4,3), [xx yy]=draw_graph(dag2,names,carre,xx,yy); %,carre); +title('GS (without cache)'); +subplot(1,4,4), [xx yy]=draw_graph(dag3,names,carre,xx,yy); %,carre); +title('GS (with cache and MWST init)'); +drawnow; diff --git a/sourcecodes/bnt-master/SLP/examples/test_kldiv.m b/sourcecodes/bnt-master/SLP/examples/test_kldiv.m new file mode 100644 index 00000000..0b2b5503 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_kldiv.m @@ -0,0 +1,22 @@ +clear all; + +b1=mk_asia_bnet; +b2=mk_asia_bnet; +res1=[]; res2=[]; + +fprintf('\nComputation of several KL_divergences (quick methode 1): '); +tmp=cputime; +for ii=0.1:.1:1, +b2.CPD{1} = tabular_CPD(b2, 1, [ii 1-ii]); +res1=[res1 kl_divergence(b1,b2)]; +end +fprintf('%3.2f sec.\n', cputime-tmp); + +fprintf('Computation of several KL_divergences (slow methode 2): '); +tmp=cputime; +for ii=0.1:.1:1, +b2.CPD{1} = tabular_CPD(b2, 1, [ii 1-ii]); +res2=[res2 kl_divergence2(b1,b2)]; +end +fprintf('%3.2f sec.\n', cputime-tmp); +[res1' res2'] \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/examples/test_knn.m b/sourcecodes/bnt-master/SLP/examples/test_knn.m new file mode 100644 index 00000000..b34a57a0 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_knn.m @@ -0,0 +1,83 @@ +function [ratio, ratiominus, ratioplus, tps] = test_knn(BD,BDT,class,K) +% [epsilon, tps] = test_knn(BD,BDT,class,K) +% +% epsilon = good classification rate on BDT, +% tps = computation time, +% BD = examples dataset using to find neighbors, +% BDT = set of examples to class, +% class = classification attribute number, +% K = vote on the K nearest neighbor +% +% francois.olivier.c.h@gmail.com + +[N L]=size(BD); +Ltest=size(BDT,2); +fprintf(' test dataset size %d\r\n',Ltest); +E=setdiff(1:N,class); +place=0; +%for k=K +k=K; + place=place+1; + good=0;tic + for i=1:Ltest + %fprintf(' %d\r',i); + res=knn(BD(E,:)',BD(class,:)',[1 2],BDT(E,i)',k); + if res==BDT(class,i) + good=good+1; + end + end + tps(place)=toc; + epsilon(1,place)=k; + epsilon(2,place)=good/(Ltest); +%end + +ratio = epsilon(2,place); +[ratiominus ratioplus] = confiance(ratio,L); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%ďż˝ +function [ypred]=knn(xapp,yapp,valY,X,k) +% knn implementation +% +% USE : [ypred]=knn(xapp,yapp,valY,X,k) +% +% Vincent Guigue 08/01/03 + +if nargin<4 + error('too few argumemnts'); +elseif nargin<5 + k=3; +else + if mod(k,2)==0 + error('k must be odd'); + end +end + +if size(xapp,2)~=size(X,2) + error('dimension incompatibility'); +end +ndim = size(xapp,2); +nptxapp = size(xapp,1); +nptX = size(X,1); + +% distance de X a xapp : +mat1 = repmat(xapp, nptX,1); +%mat21 = reshape(X',1,nptX*ndim) +mat22 = repmat(X,1,nptxapp)'; +mat2 = reshape(mat22 ,ndim, nptxapp*nptX)'; +distance = mat1 - mat2 ; +distance = sum(distance.^2,2); +distance = reshape(distance,nptxapp,nptX); +[val kppv] = sort(distance,1); +% bilan sur les k premieres lignes +kppv = reshape(kppv(1:k,:),k*nptX,1); +Ykppv = yapp(kppv,1); +Ykppv = reshape(Ykppv,k,nptX); +% trouver le plus de reponses identique par colonne +vote = []; +for i=1:nptX + for j=1:length(valY) + vote(j,i)=size(find(Ykppv(:,i)==valY(j)),1); + end +end +[val ind]=max(vote,[],1); +ypred = valY(ind); \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/examples/test_mwst.m b/sourcecodes/bnt-master/SLP/examples/test_mwst.m new file mode 100644 index 00000000..2961729a --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_mwst.m @@ -0,0 +1,33 @@ +% test MWST + +clear all; + +fprintf('\n=== Structure Learning with Maximum Weight Spanning Tree\n'); + +bnet=mk_asia2_bnet ; + +n=8; +names={ 'A' , 'S' , 'T' , 'L' , 'B' , 'O' , 'X' , 'D' }; +carre=ones(1,n); +node_type={'tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular'}; + +m=10000; +bnet=mk_asia2_bnet; +data = cell(n,m); +for l = 1:m, data(:,l) = sample_bnet(bnet); end +data=cell2mat(data); +fprintf('Complete data have been created.'); + +tmp=cputime; +dag=learn_struct_mwst(data, ones(n,1), bnet.node_sizes, node_type,'mutual_info'); +tmp=cputime-tmp; +fprintf('\t- Execution Time : %3.2f seconds\n\n',tmp); + +figure; [xx yy] = make_layout(bnet.dag); +yy=(yy-0.2)*.8/.6+.1; +xx=(xx-0.2833)*.8/.517+.1; +subplot(1,2,1), [xx yy]=draw_graph(bnet.dag,names,carre,xx,yy); %,carre); +title('ASIA original graph'); +subplot(1,2,2), draw_graph(dag,names,carre,xx,yy); %,carre); +title('MWST DAG'); +drawnow; diff --git a/sourcecodes/bnt-master/SLP/examples/test_netica_export.m b/sourcecodes/bnt-master/SLP/examples/test_netica_export.m new file mode 100644 index 00000000..3c35d32c --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_netica_export.m @@ -0,0 +1,85 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Example 1 +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +N=3; +dag=zeros(N); +dag([1 3],2)=1; + +names = {'A1','B2','C3'}; + +bnet = mk_bnet(dag, [2 3 4], 'names', names); +%bnet = mk_bnet(dag, [2 3 4]); + +bnet.CPD{1} = tabular_CPD (bnet, 1, [0.2 0.8] ); +bnet.CPD{3} = tabular_CPD (bnet, 3, [0.1 0.2 0.3 0.4] ); + + % 1 3 2 prob +tab = [[1 1 1 .2];... + [2 1 1 .4];... + [1 2 1 .1];... + [2 2 1 .7];... + [1 3 1 .5];... + [2 3 1 .1];... + [1 4 1 .75];... + [2 4 1 .1];... +... + [1 1 2 .3];... + [2 1 2 .5];... + [1 2 2 .1];... + [2 2 2 .2];... + [1 3 2 .25];... + [2 3 2 .9];... + [1 4 2 .10];... + [2 4 2 .25];... +... + [1 1 3 .5];... + [2 1 3 .1];... + [1 2 3 .8];... + [2 2 3 .1];... + [1 3 3 .25];... + [2 3 3 .0];... + [1 4 3 .15];... + [2 4 3 .65]]; + +bnet.CPD{2} = tabular_CPD (bnet, 2, tab(:,4)); + +export_dnet(bnet,'test1', 1) + +m = 20; +data = cell(N,m); +for l = 1:m, data(:,l) = sample_bnet(bnet); end +remov = rand(1,N*m)>.8; +for i=1:length(remov); if remov(i), data{i}=[];end; end + +export_cases(data, names, 'test1', -9999) + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Example 2 +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +N=4; +dag=zeros(N); +dag([1 2 3],4)=1; +dag(1,2)=1; +ns = [2 2 2 2]; + +names = {'A1','B2','C3','D4'}; + +bnet = mk_bnet(dag, ns, 'names', names); + +bnet.CPD{1} = tabular_CPD (bnet, 1, [0.2 0.8] ); +bnet.CPD{2} = tabular_CPD (bnet, 2, [0.1 0.4 0.9 0.6] ); +bnet.CPD{3} = tabular_CPD (bnet, 3, [0.5 0.5] ); +bnet.CPD{4} = tabular_CPD (bnet, 4, [0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2] ); + +export_dnet(bnet,'test2', 1) + +m = 20; +data = cell(N,m); +for l = 1:m, data(:,l) = sample_bnet(bnet); end +remov = rand(1,N*m)>.8; +for i=1:length(remov); if remov(i), data{i}=[];end; end + +export_cases(data, names, 'test2', -9999) + diff --git a/sourcecodes/bnt-master/SLP/examples/test_pc.m b/sourcecodes/bnt-master/SLP/examples/test_pc.m new file mode 100644 index 00000000..ba2e84c6 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_pc.m @@ -0,0 +1,39 @@ +% test PC + Chi2 statistical test + +clear all; + +test='pearson'; % Pearson's Chi2 +%test='LRT'; % Likelihood ratio test G2 +fprintf('\n=== Structure Learning with PC algorithm (and %s CHI2 test)\n',test); + +bnet=mk_asia2_bnet ; + +n=8; +names={ 'A' , 'S' , 'T' , 'L' , 'B' , 'O' , 'X' , 'D' }; +carre=ones(1,n); +node_type={'tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular'}; + +m=2000; +bnet=mk_asia2_bnet; +data = cell(n,m); +for l = 1:m, data(:,l) = sample_bnet(bnet); end +data=cell2mat(data); +fprintf('Complete data have been created.'); + +tmp=cputime; +PC.dag = learn_struct_pdag_pc('cond_indep_chisquare',n,n-2,data,test); +tmp=cputime-tmp; +fprintf('\t- Execution time : %3.2f seconds\n',tmp); + +figure; +[xx yy] = make_layout(bnet.dag); +yy=(yy-0.2)*.8/.6+.1; +xx=(xx-0.2833)*.8/.517+.1; +subplot(1,3,1), [xx yy]=draw_graph(bnet.dag,names,carre,xx,yy); %,carre); +title('ASIA original graph'); +subplot(1,3,2), draw_graph(abs(PC.dag),names,carre,xx,yy); %,carre); +title('PC PDAG'); +dag = cpdag_to_dag(abs(PC.dag)); +subplot(1,3,3), draw_graph(dag,names,carre,xx,yy); +title('PC DAG'); +drawnow; \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/examples/test_sem1.m b/sourcecodes/bnt-master/SLP/examples/test_sem1.m new file mode 100644 index 00000000..942ec7dd --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_sem1.m @@ -0,0 +1,56 @@ +% Lawn sprinker example from Russell and Norvig p454 +% See www.cs.berkeley.edu/~murphyk/Bayes/usage.html for details. + +rand('state', 0); +randn('state', 0); + +N = 4; +dag = zeros(N,N); +C = 1; S = 2; R = 3; W = 4; +dag(C,[R S]) = 1; +dag(R,W) = 1; +dag(S,W)=1; + +false = 1; true = 2; +ns = 2*ones(1,N); % binary nodes + +bnet = mk_bnet(dag, ns); +bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]); +bnet.CPD{R} = tabular_CPD(bnet, R, [0.8 0.2 0.2 0.8]); +bnet.CPD{S} = tabular_CPD(bnet, S, [0.5 0.9 0.5 0.1]); +bnet.CPD{W} = tabular_CPD(bnet, W, [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); + +nsamples = 500; +samplesM = cell(N, nsamples); +for i=1:nsamples + samplesM(:,i) = sample_bnet(bnet); +end + +hide = rand(N, nsamples) > 0.9; +[I,J]=find(hide); +for k=1:length(I) + samplesM{I(k), J(k)} = []; +end + +% Make a initial chain like dag +G0 = zeros(N,N); +for i=1:N-1 + G0(i, i+1) = 1; +end + +figure; +draw_graph(G0); + +B0 = mk_bnet(G0, ns); +% use random params +for i=1:N + B0.CPD{i} = tabular_CPD(B0, i, 'prior_type', 'dirichlet', 'dirichlet_weight', 0); +end + +max_loop = 30; +%profile on -detail mmex +[B0, order, best_score] = learn_struct_EM(B0, samplesM, max_loop); +%profile report + +dag1 = B0.dag; +dag1 = dag1(order,order); diff --git a/sourcecodes/bnt-master/SLP/examples/test_sem2.m b/sourcecodes/bnt-master/SLP/examples/test_sem2.m new file mode 100644 index 00000000..038afd0b --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/test_sem2.m @@ -0,0 +1,59 @@ +% learn the structure of "discrete1" network. + +rand('state', 0); +randn('state', 0); +N = 9; +dag = zeros(N,N); +dag(1,2)=1; dag(1,3)=1; dag(1,4)=1; +dag(2,5)=1; dag(3,6)=1; dag(4,7)=1; +dag(5,8)=1; dag(6,8)=1; dag(6,9)=1; dag(7,9) = 1; + + +dnodes = 1:N; +false = 1; true = 2; +ns = 2*ones(1,N); % binary nodes + +bnet = mk_bnet(dag, ns); +% use random params +for i=1:N + bnet.CPD{i} = tabular_CPD(bnet, i); +end + +nsamples = 500; +samplesM = cell(N, nsamples); +for i=1:nsamples + samplesM(:,i) = sample_bnet(bnet); +end + +hide = rand(N, nsamples) > 0.8; +[I,J]=find(hide); +for k=1:length(I) + samplesM{I(k), J(k)} = []; +end + +engine = jtree_inf_engine(bnet); +[bnet, LL, engine] = learn_params_em(engine, samplesM, 1); +LL + +G0 = zeros(N,N); +for i=1: N-1 + G0(i, i+1) = 1; +end + +figure; +draw_graph(G0); + +B0 = mk_bnet(G0, ns); +% use random params +for i=1:N + B0.CPD{i} = tabular_CPD(B0, i, 'prior_type', 'dirichlet', 'dirichlet_weight', 0); +end + +max_loop = 10; +profile on -detail mmex +[B0, order, best_score] = learn_struct_EM(B0, samplesM, max_loop); +profile report +G1 = B0.dag; + +G0 = G1(order, order); + diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/cliques_from_engine.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/cliques_from_engine.m new file mode 100644 index 00000000..cd9d871d --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/cliques_from_engine.m @@ -0,0 +1,5 @@ +function cliques = cliques_from_engine(engine) +% CLIQUES_FROM_ENGINE Return the cliques stored inside the inf. engine (jtree) +% cliques = cliques_from_engine(engine) + +cliques = engine.cliques; diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/clq_containing_nodes.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/clq_containing_nodes.m new file mode 100644 index 00000000..8904fa49 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/clq_containing_nodes.m @@ -0,0 +1,24 @@ +function c = clq_containing_nodes(engine, nodes, fam) +% CLQ_CONTAINING_NODES Find the lightest clique (if any) that contains the set of nodes +% c = clq_containing_nodes(engine, nodes, family) +% +% If the optional 'family' argument is specified, it means nodes = family(nodes(end)). +% (This is useful since clq_ass_to_node is not accessible to outsiders.) +% Returns c=-1 if there is no such clique. + +if nargin < 3, fam = 0; else fam = 1; end + +if length(nodes)==1 + c = engine.clq_ass_to_node(nodes(1)); +%elseif fam +% c = engine.clq_ass_to_node(nodes(end)); +else + B = engine.cliques_bitv; + w = engine.clique_weight; + clqs = find(all(B(:,nodes), 2)); % all selected columns must be 1 + if isempty(clqs) + c = -1; + else + c = clqs(argmin(w(clqs))); + end +end diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/collect_evidence.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/collect_evidence.m new file mode 100644 index 00000000..03c00edf --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/collect_evidence.m @@ -0,0 +1,12 @@ +function [clpot, seppot] = collect_evidence(engine, clpot, seppot) +% COLLECT_EVIDENCE Do message passing from leaves to root (children then parents) +% [clpot, seppot] = collect_evidence(engine, clpot, seppot) + +for n=engine.postorder %postorder(1:end-1) + for p=engine.postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, engine.separator{p,n}, engine.maximize); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/disp.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/disp.m new file mode 100644 index 00000000..9fca2aaa --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/disp.m @@ -0,0 +1,40 @@ +% ==== +% disp +% ==== +% +% Description : +% ------------- +% +% disp function for class inf_engine. +% +% Syntax : +% -------- +% +% [] = disp( obj ) +% +% Input(s) : +% ---------- +% +% obj - class inf_engine +% An instance of the class inf_engine +% +% Output(s) : +% ----------- +% +% Example(s) : +% ------------ +% +% disp( obj ); +% +% Reference(s) : +% -------------- +% +% See also : +% ---------- +% +% display +function [] = disp( obj ) + +disp( struct( obj ) ); + +% End of function diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/display.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/display.m new file mode 100644 index 00000000..ca40702c --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/display.m @@ -0,0 +1,42 @@ +% ======= +% display +% ======= +% +% Description : +% ------------- +% +% display function for class inf_engine. +% +% Syntax : +% -------- +% +% [] = display( obj ) +% +% Input(s) : +% ---------- +% +% obj - class inf_engine +% An instance of the class inf_engine +% +% Output(s) : +% ----------- +% +% Example(s) : +% ------------ +% +% display( obj ); +% +% Reference(s) : +% -------------- +% +% See also : +% ---------- +% +% disp +function [] = display( obj ) + +fprintf( '\n%s =\n\n', inputname( 1 ) ); +disp( struct( obj ) ); + +% End of function + diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/distribute_evidence.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/distribute_evidence.m new file mode 100644 index 00000000..403b8970 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/distribute_evidence.m @@ -0,0 +1,11 @@ +function [clpot, seppot] = distribute_evidence(engine, clpot, seppot) +% DISTRIBUTE_EVIDENCE Do message passing from root to leaves (parents then children) +% [clpot, seppot] = distribute_evidence(engine, clpot, seppot) + +for n=engine.preorder + for c=engine.preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, engine.separator{n,c}, engine.maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + end +end diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/enter_evidence.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/enter_evidence.m new file mode 100644 index 00000000..8cda6012 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/enter_evidence.m @@ -0,0 +1,95 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% soft - a cell array of soft/virtual evidence; +% soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] +% +% e.g., engine = enter_evidence(engine, ev, 'soft', soft_ev) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +engine.evidence = evidence; % store this for marginal_nodes with add_ev option +engine.maximize = 0; + +% set default params +exclude = []; +soft_evidence = cell(1,N); + +% parse optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'soft', soft_evidence = args{i+1}; + case 'maximize', engine.maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + if strcmp(pot_type, 'cg') + check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); +end + +if is_mnet(bnet) + pot = engine.user_pot; + clqs = engine.nums_ass_to_user_clqs; +else + % Evaluate CPDs with evidence, and convert to potentials + pot = cell(1, N); + for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + if isempty(bnet.CPD{e}) + error(['must define CPD ' num2str(e)]) + else + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), ... + evidence); + end + end + clqs = engine.clq_ass_to_node(1:N); +end + + +% soft evidence +soft_nodes = find(~isemptycell(soft_evidence)); +S = length(soft_nodes); +if S > 0 + assert(pot_type == 'd'); + assert(mysubset(soft_nodes, bnet.dnodes)); +end +for i=1:S + n = soft_nodes(i); + % Modif RD - 2006/12/22 + % Why end+1 it doesn't work for me... + % replace with n and it is ok + pot{ n } = dpot( n, ns( n ), soft_evidence{ n } ); +end +% Modif RD - 2006/12/22 +% But now we have to comment this line to ensure dimension matching +%clqs = [clqs engine.clq_ass_to_node(soft_nodes)]; + + +[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +[clpot, seppot] = distribute_evidence(engine, clpot, seppot); + +C = length(clpot); +ll = zeros(1, C); +for i=1:C + [clpot{i}, ll(i)] = normalize_pot(clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique + +engine.clpot = clpot; diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/enter_soft_evidence.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/enter_soft_evidence.m new file mode 100644 index 00000000..0a4346c6 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/enter_soft_evidence.m @@ -0,0 +1,21 @@ +function [clpot, loglik] = enter_soft_evidence(engine, clique, potential, onodes, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified potentials to the network (jtree) +% [clpot, loglik] = enter_soft_evidence(engine, clique, potential, onodes, pot_type, maximize) +% +% We multiply potential{i} onto clique(i) before propagating. +% We return all the modified clique potentials. + +% only used by BK! + +[clpot, seppot] = init_pot(engine, clique, potential, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +[clpot, seppot] = distribute_evidence(engine, clpot, seppot); + +C = length(clpot); +ll = zeros(1, C); +for i=1:C + [clpot{i}, ll(i)] = normalize_pot(clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique + + diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/find_max_config.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/find_max_config.m new file mode 100644 index 00000000..5053b1e8 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/find_max_config.m @@ -0,0 +1,35 @@ +function [mpe, clpot, seppot] = find_max_config(engine, clpot, seppot, evidence) +% FIND_MAX_CONFIG Backwards pass of Viterbi fro jtree +% function [mpe, clpot, seppot] = find_max_config(engine, clpot, seppot, evidence) +% See Cowell99 p98 + +bnet = bnet_from_engine(engine); +nnodes = length(bnet.dag); +mpe = cell(1, nnodes); +maximize = 1; + +c = engine.root_clq; +pot = struct(clpot{c}); % violate object privacy +dom = pot.domain; +[indices, clpot{c}] = find_most_prob_entry(clpot{c}); +mpe(dom) = num2cell(indices); + +for n=engine.preorder + for c=engine.preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, engine.separator{n,c}, maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + + pot = struct(clpot{c}); % violate object privacy + dom = pot.domain; + [indices, clpot{c}] = find_most_prob_entry(clpot{c}); + mpe(dom) = num2cell(indices); + end +end + +obs_nodes = find(~isemptycell(evidence)); +% indices for observed nodes will be 1 - need to overwrite these +mpe(obs_nodes) = evidence(obs_nodes); + + + diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/find_mpe.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/find_mpe.m new file mode 100644 index 00000000..8a46c1ed --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/find_mpe.m @@ -0,0 +1,71 @@ +function mpe = find_mpe(engine, evidence, varargin) +% FIND_MPE Find the most probable explanation of the data (assignment to the hidden nodes) +% function mpe = find_mpe(engine, evidence,...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% soft - a cell array of soft/virtual evidence; +% soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] +% + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +engine.evidence = evidence; + +% set default params +exclude = []; +soft_evidence = cell(1,N); + +% parse optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'soft', soft_evidence = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end +engine.maximize = 1; + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + if strcmp(pot_type, 'cg') + check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); +end + +hard_nodes = 1:N; +soft_nodes = find(~isemptycell(soft_evidence)); +S = length(soft_nodes); +if S > 0 + assert(pot_type == 'd'); + assert(mysubset(soft_nodes, bnet.dnodes)); +end + +% Evaluate CPDs with evidence, and convert to potentials +pot = cell(1, N+S); +for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + if isempty(bnet.CPD{e}) + error(['must define CPD ' num2str(e)]) + else + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); + end +end + +for i=1:S + n = soft_nodes(i); + pot{N+i} = dpot(n, ns(n), soft_evidence{n}); +end +clqs = engine.clq_ass_to_node([hard_nodes soft_nodes]); + +[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +mpe = find_max_config(engine, clpot, seppot, evidence); % instead of distribute evidence diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/get.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/get.m new file mode 100644 index 00000000..af4f7d88 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/get.m @@ -0,0 +1,95 @@ +% === +% get +% === +% +% Description : +% ------------- +% +% Reading method for the class' attributes. +% +% Syntax : +% -------- +% +% val = get( obj, attribute_name ); +% +% Input(s) : +% ---------- +% +% obj - class inf_engine +% An instance of the class inf_engine +% +% attribute_name - string +% The name of a class' attribute +% For details about the attribute's names, see the constructor +% (inf_engine.m) +% +% Output(s) : +% ----------- +% +% val - any +% The value of the specified attribute +% +% Example(s) : +% ------------ +% +% val = get( obj, 'attribute_name' ); +% +% Reference(s) : +% -------------- +% +% See also : +% ---------- +% +% inf_engine +% set +function [ val ] = get( engine, attribute_name ) + +val = []; + +% Processing +val = get( engine.inf_engine, attribute_name ); + +% Processing +arg = upper( attribute_name ); + +switch arg + case upper( 'evidence' ) + val = engine.evidence; + case upper( 'jtree' ) + val = engine.jtree; + case upper( 'cliques' ) + val = engine.cliques; + case upper( 'separator' ) + val = engine.separator; + case upper( 'cliques_bitv' ) + val = engine.cliques_bitv; + case upper( 'clique_weight' ) + val = engine.clique_weight; + case upper( 'clpot' ) + val = engine.clpot; + case upper( 'clq_ass_to_node' ) + val = engine.clq_ass_to_node; + case upper( 'root_clq' ) + val = engine.root_clq; + case upper( 'preorder' ) + val = engine.preorder; + case upper( 'postorder' ) + val = engine.postorder; + case upper( 'preorder_children' ) + val = engine.preorder_children; + case upper( 'postorder_parents' ) + val = engine.postorder_parents; + case upper( 'maximize' ) + val = engine.maximize; + case upper( 'evidence' ) + val = engine.evidence; + + otherwise + % warning_str = [ 'inf_engine - get : attribute ' ... + % arg ' doesn''t exist' ]; + + % warning( warning_str ); +end + +% End of function + diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/init_pot.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/init_pot.m new file mode 100644 index 00000000..857e6266 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/init_pot.m @@ -0,0 +1,20 @@ +function [clpot, seppot] = init_pot(engine, clqs, pots, pot_type, onodes, ndx) +% INIT_POT Initialise potentials with evidence (jtree_inf) +% function [clpot, seppot] = init_pot(engine, clqs, pots, pot_type, onodes) + +cliques = engine.cliques; +bnet = bnet_from_engine(engine); +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, bnet.node_sizes(:), bnet.cnodes(:), onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1 diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/jtree_inf_engine2.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/jtree_inf_engine2.m new file mode 100644 index 00000000..594833de --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/jtree_inf_engine2.m @@ -0,0 +1,239 @@ +function [ engine ] = jtree_inf_engine_rd( bnet, varargin ) +% JTREE_INF_ENGINE2 Junction tree inference engine +% engine = jtree_inf_engine(bnet, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% clusters - a cell array of sets of nodes we want to ensure are in the same clique (in addition to families) [ {} ] +% root - the root of the junction tree will be a clique that contains this set of nodes [N] +% stages - stages{t} is a set of nodes we want to eliminate before stages{t+1}, ... [ {1:N} ] +% +% e.g., engine = jtree_inf_engine(bnet, 'maximize', 1); +% +% For more details on the junction tree algorithm, see +% - "Probabilistic networks and expert systems", Cowell, Dawid, Lauritzen and Spiegelhalter, Springer, 1999 +% - "Inference in Belief Networks: A procedural guide", C. Huang and A. Darwiche, +% Intl. J. Approximate Reasoning, 15(3):225-263, 1996. +% +% modification of the calculus of cliques from JTREE_INF_ENGINE + + +% set default params +N = length( bnet.dag ); + +% Optional argument processing +[ b_verb, clusters, root, stages ] = jtree_inf_engine2_varargin_mgt( N, varargin ); + +% --- Verbose --- % +if b_verb + fprintf( '/ ------------------------------- \\\n' ); + fprintf( '| jtree_inf_engine2 - verbose mode |\n' ); + time_i = datenum( clock ); +end +% --------------- % + +% Initialization +% ============== + +% Class initialization +engine = init_fields; +engine = class( engine, 'jtree_inf_engine2', inf_engine( bnet ) ); + +% Default parameters +maximize = 0; +onodes = bnet.observed; + +% Optional parameters given by user +% engine = set( engine, varargin{ : } ); + +% Building of the junction tree +% ============================= + +% Elimination ordering +% -------------------- +% --- Verbose --- % +if b_verb + fprintf( 'Compute elimination constraints ...' ); + time_i_cur = datenum( clock ); +end +% --------------- % +porder = determine_elim_constraints( bnet, onodes ); +strong = ~isempty( porder ); +% --- Verbose --- % +if b_verb + time_f_cur = datenum( clock ); + time_cur_str = datestr( time_f_cur - time_i_cur, 'HH:MM:SS' ); + fprintf( ' [Done] - elapsed time = %s\n', time_cur_str ); +end +% --------------- % + + +% Moralization +% ------------ +% --- Verbose --- % +if b_verb + fprintf( 'Moralization ...' ); + time_i_cur = datenum( clock ); +end +% --------------- % +ns = bnet.node_sizes( : ); +ns( onodes ) = 1; % observed nodes have only 1 possible value +moral_graph = moralize( bnet.dag ); +% --- Verbose --- % +if b_verb + time_f_cur = datenum( clock ); + time_cur_str = datestr( time_f_cur - time_i_cur, 'HH:MM:SS' ); + fprintf( ' [Done] - elapsed time = %s\n', time_cur_str ); +end +% --------------- % + + +% Junction tree building +% ---------------------- +% --- Verbose --- % +if b_verb + fprintf( 'Building the junction tree ...' ); + time_i_cur = datenum( clock ); +end +% --------------- % +[ engine.jtree, root2, engine.cliques, B, w, elim_order ] = ... + graph_to_jtree( moral_graph, ns, porder, stages, clusters ); +% --- Verbose --- % +if b_verb + time_f_cur = datenum( clock ); + time_cur_str = datestr( time_f_cur - time_i_cur, 'HH:MM:SS' ); + fprintf( ' [Done] - elapsed time = %s\n', time_cur_str ); +end +% --------------- % + + +engine.cliques_bitv = B; +engine.clique_weight = w; +C = length( engine.cliques ); +engine.clpot = cell(1,C); + +% Separators computation +% ---------------------- + +% --- Verbose --- % +if b_verb + fprintf( 'Separators computation ...' ); + time_i_cur = datenum( clock ); +end +% --------------- % +% Compute the separators between connected cliques. +[ is, js ] = find( engine.jtree > 0 ); +engine.separator = cell( C, C ); +for k = 1:length( is ) + i = is( k ); j = js( k ); + % intersect(cliques{i}, cliques{j}); + engine.separator{ i, j } = find( B( i, : ) & B( j, : ) ); +end +% --------------- % +if b_verb + time_f_cur = datenum( clock ); + time_cur_str = datestr( time_f_cur - time_i_cur, 'HH:MM:SS' ); + fprintf( ' [Done] - elapsed time = %s\n', time_cur_str ); +end +% --------------- % + +% A node can be a member of many cliques, but is assigned to exactly one, to avoid +% double-counting its CPD. We assign node i to clique c if c is the "lightest" clique that +% contains i's family, so it can accomodate its CPD. + +engine.clq_ass_to_node = zeros(1, N); +for i=1:N + %c = clq_containing_nodes(engine, family(bnet.dag, i)); + % all selected columns must be 1 + clqs_containing_family = find( all( B( :, family( bnet.dag, i ) ), 2 ) ); + c = clqs_containing_family( ... + argmin( w( clqs_containing_family ) ) ); + engine.clq_ass_to_node( i ) = c; +end + +% Make the jtree rooted, so there is a fixed message passing order. +if strong + % the last clique is guaranteed to be a strong root + engine.root_clq = length( engine.cliques ); +else + % jtree_dbn_inf_engine requires the root to contain the interface. + % This may conflict with the strong root requirement! *********** BUG ************* + engine.root_clq = clq_containing_nodes( engine, root ); + if engine.root_clq <= 0 + error( [ 'no clique contains ' num2str( root ) ] ); + end +end + +[ engine.jtree, engine.preorder, engine.postorder ] = ... + mk_rooted_tree( engine.jtree, engine.root_clq ); + +% collect +engine.postorder_parents = cell( 1, length(engine.postorder ) ); +for n = engine.postorder( : )' + engine.postorder_parents{ n } = parents( engine.jtree, n ); +end +% distribute +engine.preorder_children = cell( 1, length( engine.preorder ) ); +for n = engine.preorder( : )' + engine.preorder_children{ n } = children( engine.jtree, n ); +end + +% --- Verbose --- % +if b_verb + time_f = datenum( clock ); + time_str = datestr( time_f - time_i, 'HH:MM:SS' ); + fprintf( 'Elapsed time = %s\n', time_str' ); + fprintf( '\\ ------------------------------- /\n' ); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function [ b_verb, clusters, root, stages ] = jtree_inf_engine2_varargin_mgt( N, parent_varargin ) + +% Number of variable arguments +nb_varargin = length( parent_varargin ); + +% Default parameters +b_verb = 0; +clusters = {}; +root = N; +stages = { 1:N }; + +% Processing +for i = 1:2:nb_varargin + + arg_i = upper( parent_varargin{ i } ); + val_i = parent_varargin{ i + 1 }; + + switch arg_i + case upper( 'EngineVerbose' ) + b_verb = val_i; + case upper( 'Clusters' ) + clusters = val_i; + case upper( 'Root' ) + root = val_i; + case upper( 'Stages' ) + stages = val_i; + otherwise + end + +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function engine = init_fields() + +engine.jtree = []; +engine.cliques = []; +engine.separator = []; +engine.cliques_bitv = []; +engine.clique_weight = []; +engine.clpot = []; +engine.clq_ass_to_node = []; +engine.root_clq = []; +engine.preorder = []; +engine.postorder = []; +engine.preorder_children = []; +engine.postorder_parents = []; +engine.maximize = []; +engine.evidence = []; + diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/marginal_family.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/marginal_family.m new file mode 100644 index 00000000..eff60ca2 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/marginal_family.m @@ -0,0 +1,11 @@ +function marginal = marginal_family(engine, i, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree) +% marginal = marginal_family(engine, i) + +if nargin < 3, add_ev = 0; end +assert(~add_ev); + +bnet = bnet_from_engine(engine); +fam = family(bnet.dag, i); +c = engine.clq_ass_to_node(i); +marginal = pot_to_marginal(marginalize_pot(engine.clpot{c}, fam)); diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/marginal_nodes.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/marginal_nodes.m new file mode 100644 index 00000000..6413172c --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/marginal_nodes.m @@ -0,0 +1,22 @@ +function marginal = marginal_nodes(engine, query, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (jtree) +% marginal = marginal_nodes(engine, query, add_ev) +% +% 'query' must be a subset of some clique; an error will be raised if not. +% add_ev is an optional argument; if 1, we will "inflate" the marginal of observed nodes +% to their original size, adding 0s to the positions which contradict the evidence + +if nargin < 3, add_ev = 0; end + +c = clq_containing_nodes(engine, query); +if c == -1 + error(['no clique contains ' num2str(query)]); +end +marginal = pot_to_marginal(marginalize_pot(engine.clpot{c}, query, engine.maximize)); + +if add_ev + bnet = bnet_from_engine(engine); + %marginal = add_ev_to_dmarginal(marginal, engine.evidence, bnet.node_sizes); + marginal = add_evidence_to_gmarginal(marginal, engine.evidence, bnet.node_sizes, bnet.cnodes); +end + diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/set.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/set.m new file mode 100644 index 00000000..2ade7cb2 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/set.m @@ -0,0 +1,69 @@ +% === +% set +% === +% +% Description : +% ------------- +% +% Writing method for the class' attributes. +% +% Syntax : +% -------- +% +% obj = set( obj, attrib_name_1, val_1, ..., attrib_name_n, val_n ); +% +% Input(s) : +% ---------- +% +% obj - class inf_engine +% An instance of the class inf_engine +% +% > Optionals : 'attrib_name'/value form +% For details about the attribute's names, see the constructor +% (inf_engine.m) +% +% Output(s) : +% ----------- +% +% obj - class inf_engine +% The updated object +% +% Example(s) : +% ------------ +% +% obj = set( obj, 'attrib_1', val_1, ..., 'attrib_n', val_n ); +% +% Reference(s) : +% -------------- +% +% See also : +% ---------- +% +% inf_engine +% get +function [ engine ] = set( engine, varargin ) + +engine.inf_engine = set( engine.inf_engine, varargin{ : } ); + +% Processing +% Number of variable arguments +nb_varargin = length( varargin ); + +for i = 1:2:nb_varargin + + arg_i = upper( varargin{ i } ); + val_i = varargin{ i + 1 }; + + switch arg_i + case upper( 'maximize' ) + engine.maximize = val_i; + otherwise +% warning_str = [ 'inf_engine - set : attribute ' ... +% arg_i ' doesn''t exist' ]; + +% warning( warning_str ); + end + +end + +% End of function \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/set_fields.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/set_fields.m new file mode 100644 index 00000000..e75cfa45 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/set_fields.m @@ -0,0 +1,13 @@ +function engine = set_fields(engine, varargin) +% SET_FIELDS Set the fields for a generic engine +% engine = set_fields(engine, name/value pairs) +% +% e.g., engine = set_fields(engine, 'maximize', 1) + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'maximize', engine.maximize = args{i+1}; + end +end diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_EM.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_EM.m new file mode 100644 index 00000000..2dc14a2d --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_EM.m @@ -0,0 +1,383 @@ +function [bnet, order, BIC_score, LOGLIKE] = learn_struct_EM(bnet, samplesM, max_loop) +% LEARN_STRUCT_EM(), structural EM algorithm , learn structure and parameters +% from missing data. +% [bnet, order, BIC_score] = learn_struct_EM(bnet, samplesM, max_loop) + +tiny = exp(-700); +improve_factor = 0.001; %when current BIC score is less than old_score+old_score*improve_factor, stop search +N = length(bnet.dag); +ncases = size(samplesM, 2); +log_value = log(ncases); +ns = bnet.node_sizes; +dag = zeros(N,N); +order = zeros(1,N); %save the label of each node in current dag correspond to the original dag +order = 1:N; %original dag has nodes label 1:N +CPT = cell(2); %save the modified node's CPT of the bnet that has the highest score in an iteration + %in cases "del" and "add", there is only one CPT will be save, in "rev" need to save two CPTs. +update_samples = cell(N, ncases); %in this algorithm, because the label of the next dag will be different with the + %last dag, so the label of the trainning data will be modified, too + +loop = 0; +evidence = cell(1,N); +while looptail % now, the "ess" is in ascent manner, accord with the labels in the "domain" field. + n = length(ec{i}.domain); % need permute , so that "ess" contain the last dimension is about the "tail" node. + approx_ess = permute(approx_ess, [1:n-2, n, n-1]); % because there is only one "edge" modified, only need + end % to exchange the last two dimension if needed. + CPT1 = mk_stochastic(approx_ess); + + LL1 = LL; + d1 = d; + LL1(tail) = sum(log(CPT1(:) + tiny) .* approx_ess(:)); + d1(tail) = d(tail) * ns(head); + D1 = sum(d1); + bic_score(i) = sum(LL1) - 0.5 * D1 * log_value; + [a, j] = max(bic_score); + if j==i + CPT{1} = CPT1; + end + + case 'rev' % ops "rev" influent two family, equals the combination of a "del" and an "add" + % "del" an edge + head = edge(1); + tail = edge(2); + approx_ess = ec1{i}.counts; + CPT1 = mk_stochastic(approx_ess); + LL1 = LL; + LL1(tail) = sum(log(CPT1(:) + tiny) .* approx_ess(:)); + d1 = d; + d1(tail) = d(tail) / ns(head); + + % "add" an edge + head = edge(2); + tail = edge(1); + approx_ess = ec{i}.counts; + if head>tail % now, the "ess" is in ascent manner, accord with the labels in the "domain" field. + n = length(ec{i}.domain); % need permute , so that "ess" contain the last dimension is about the "tail" node. + approx_ess = permute(approx_ess, [1:n-2, n, n-1]); % because there is only one "edge" modified, only need + end % to exchange the last two dimension if needed. + CPT2 = mk_stochastic(approx_ess); + LL1(tail) = sum(log(CPT2(:) + tiny) .* approx_ess(:)); + d1(tail) = d(tail) * ns(head); + + D1 = sum(d1); + bic_score(i) = sum(LL1) - 0.5 * D1 * log_value; + [a, j] = max(bic_score); + if j==i + CPT{1} = CPT1; + CPT{2} = CPT2; + end + end + end + + [BIC_score, i] = max(bic_score); + temp = abs(bic_score0) * improve_factor; % search will be finish when the improvment of bic score + % less than 0.1% compare with the previous best result + if BIC_score > (bic_score0 + temp) + dag1 = nbrs{i}; % new best dag + order1 = orders{i}; % labels of each nodes altered from last iteration + + % the labels of each nodes are altered, so the "data" will need to "re-arrange" according to the new order + for j = 1:N + row = order1(j); + for k = 1:ncases + update_samples{j,k} = samplesM{row,k}; + end + end + samplesM = update_samples; + + dag = dag1(order1, order1); % "reshape" the best dag, make it as an "upper trianglar" + ns = ns(order1); % also must modify the order of "ns" + CPDs = bnet.CPD; + bnet = mk_bnet(dag, ns); % use the best dag now to produce a new bnet, with altered nodes labels + for j=1:N % randomly set the CPTs values of each CPDs + bnet.CPD{j} = tabular_CPD(bnet, j, 'prior_type', 'dirichlet', 'dirichlet_weight', 0); + end + edge = nodes(i,:); + + % update the CPDs of new best bnet(dag) using corresponding CPDs of last iteration. + % copy the old CPTs that not altered. + % set the altered CPTs from the saved "CPT" variables + switch ops{i} + case 'del' + tail = edge(2); + tail = find(order1==tail); + bnet.CPD{tail} = set_fields(bnet.CPD{tail}, 'CPT', CPT{1}); + forbidden = [tail]; + bnet.CPD = copy_CPD(bnet.CPD, CPDs, order1, forbidden); + case 'add' + tail = edge(2); + tail = find(order1==tail); + bnet.CPD{tail} = set_fields(bnet.CPD{tail}, 'CPT', CPT{1}); + forbidden = [tail]; + bnet.CPD = copy_CPD(bnet.CPD, CPDs, order1, forbidden); + case 'rev' + head = edge(2); + head = find(order1==head); + bnet.CPD{head} = set_fields(bnet.CPD{head}, 'CPT', CPT{1}); + tail = edge(1); + tail = find(order1==tail); + bnet.CPD{tail} = set_fields(bnet.CPD{tail}, 'CPT', CPT{2}); + forbidden = [head, tail]; + bnet.CPD = copy_CPD(bnet.CPD, CPDs, order1, forbidden); + end + + % draw a graph for the new best dag with nodes labels are the same as the original + order = order(order1); +% labels = cellstr(int2str(order')); +% figure(loop+1); +% draw_graph(dag,labels); + + clear bic_score D d; % for each iteration, re-compute all the expected counts and bic score + clear ec ec1 LL; + clear nbrs ops nodes orders; + else + BIC_score = bic_score0; % if there is no improvement in bic score, stop the search, and return + break; + end +end +BIC_score + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function [D,d]= compute_bnet_nparams(bnet) +% +% +N = length(bnet.dag); +d = zeros(1,N); +for i=1:N + a = struct(bnet.CPD{i}); + d(i) = a.nparams; +end +D = sum(d); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function newCPD = copy_CPD(newCPD, CPDs, order, forbidden) +%copy CPDs from old bnet to new bnet, except those nodes has been modified +% +N = length(order); +for i=1:N + if ~mysubset(i, forbidden) + a = order(i); + s = struct(CPDs{a}); + CPT = s.CPT; + newCPD{i} = set_fields(newCPD{i}, 'CPT', CPT); + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function [ec, ec1, LL] = compute_approx_ess(bnet, samplesM, ops, nodes) +%compute all neighbours' needed approximate ess based on current bnet. +% +tiny = exp(-700); +N = length(bnet.dag); +ns = bnet.node_sizes; +ncases = size(samplesM, 2); +nGs = length(ops); +ec0 = cell(1,N); +ec = cell(1, nGs); %store each neighbours' altered family's approximate ess. +ec1 = cell(1, nGs); %since operator 'rev' need to alter two families, ec1 store the ess of family deleted an edge +copy = zeros(1, nGs); +copy1 = zeros(1, nGs); +LL = zeros(1, N); %For current bnet, LL store each nodes's LogLike based on approximate ess. +for i =1:nGs + ec{i}.domain = []; + ec{i}.counts = []; + ec1{i}.domain = []; + ec1{i}.counts = []; +end +for i =1:N + parents = bnet.parents{i}; + family = [parents, i]; + ec0{i} = 0 * myones(ns(family)); +end +for i =1:nGs + edge = nodes(i, :); + switch ops{i} + case 'del' + head = edge(1); + tail = edge(2); + parents = bnet.parents{tail}; + parents = mysetdiff(parents, head); + domain = [parents, tail]; + copy(i) = find_same_domain(ec, domain, i); + ec{i}.domain = domain; + ec{i}.counts = 0 * myones(ns(domain)); + case 'add' + head = edge(1); + tail = edge(2); + parents = bnet.parents{tail}; + parents = [parents, head, tail]; + domain = sort(parents); + copy(i) = find_same_domain(ec, domain, i); + ec{i}.domain = domain; + ec{i}.counts = 0 * myones(ns(domain)); + case 'rev' + head = edge(1); + tail = edge(2); + parents = bnet.parents{tail}; + parents = mysetdiff(parents, head); + domain = [parents, tail]; + copy1(i) = find_same_domain(ec, domain, i); + ec1{i}.domain = domain; + ec1{i}.counts = 0 * myones(ns(domain)); + + head = edge(2); + tail = edge(1); + parents = bnet.parents{tail}; + parents = [parents, head, tail]; + domain = sort(parents); + copy(i) = find_same_domain(ec, domain, i); + ec{i}.domain = domain; + ec{i}.counts = 0 * myones(ns(domain)); + end +end + +engine = jtree_inf_engine(bnet); + +for l =1:ncases + evidence = samplesM(:, l); + [engine, ll] = enter_evidence(engine, evidence); + ns_eff = ns; + ns_eff(~isemptycell(evidence)) = 1; + Vmarg = cell(1,N); + for i =1:N + Vmarg{i} = marginal_nodes(engine, i); + end + for i = 1:N + parents = bnet.parents{i}; + family = [parents, i]; + nfamily = length(family); + Fmarg = []; + for j = 1:nfamily + Fmarg = multiply_one_marginal(Fmarg, Vmarg{family(j)}, ns_eff); + end + fullm = add_ev_to_dmarginal(Fmarg, evidence, ns); + ec0{i} = ec0{i} + fullm.T; + end + + for i = 1:nGs + switch ops{i} + case 'del' + if ~copy(i) + domain = ec{i}.domain; + Fmarg = []; + for j=1:length(domain) + Fmarg = multiply_one_marginal(Fmarg, Vmarg{domain(j)}, ns_eff); + end + fullm = add_ev_to_dmarginal(Fmarg, evidence, ns); + ec{i}.counts = ec{i}.counts + fullm.T; + end + case 'add' + if ~copy(i) + domain = ec{i}.domain; + Fmarg = []; + for j=1:length(domain) + Fmarg = multiply_one_marginal(Fmarg, Vmarg{domain(j)}, ns_eff); + end + fullm = add_ev_to_dmarginal(Fmarg, evidence, ns); + ec{i}.counts = ec{i}.counts + fullm.T; + end + case 'rev' + if ~copy1(i) + domain = ec1{i}.domain; + Fmarg = []; + for j=1:length(domain) + Fmarg = multiply_one_marginal(Fmarg, Vmarg{domain(j)}, ns_eff); + end + fullm = add_ev_to_dmarginal(Fmarg, evidence, ns); + ec1{i}.counts = ec1{i}.counts + fullm.T; + end + + if ~copy(i) + domain = ec{i}.domain; + Fmarg = []; + for j=1:length(domain) + Fmarg = multiply_one_marginal(Fmarg, Vmarg{domain(j)}, ns_eff); + end + fullm = add_ev_to_dmarginal(Fmarg, evidence, ns); + ec{i}.counts = ec{i}.counts + fullm.T; + end + end + end + clear Vmarg; +end + +for i =1:nGs + if copy(i) + ec{i}.counts = ec{copy(i)}.counts; + end + if copy1(i) + ec1{i}.counts = ec{copy1(i)}.counts; + end +end + +for i=1:N + s = struct(bnet.CPD{i}); + counts = ec0{i}; + LL(i) = sum(log(s.CPT(:) + tiny) .* counts(:)); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function index = find_same_domain(ec, domain, length) +% +% +index = 0; +for i = 1:length + if isequal(domain, ec{i}.domain) + index = i; + break; + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + + + diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_bnpc.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_bnpc.m new file mode 100644 index 00000000..bddce565 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_bnpc.m @@ -0,0 +1,725 @@ +function [Phase_3, Phase_2, Phase_1, UPhase_3] = learn_struct_bnpc(data,node_sizes,epsilon,star) +% G = learn_struct_bnpc(Data,node_sizes,epsilon,star) +% +% Data(i,m) is node i in case m. +% node_sizes and epsilon are optionnals. +% star = 0 to use try_to_separate_B instead of try_to_separate_B_star +% +% see "Learning bayesian Networks from Data: A Efficient Approach Based on Information Theorie" +% Jie Cheng, David Bell and Weird Liu. +% +% Things to do : rewrite function orient_edges ! +% ! sometimes it causes crashes ! +% +% V0.91 : 18 sept 2003 (olivier.francois@insa-rouen.fr) + +verbose=1; +%if nargin < 5, mwst=0; end +if nargin < 4, star=1; end +if nargin < 3, epsilon=0.05; end +if nargin < 2, node_sizes=max(data'); end + +if verbose + fprintf('================== phase I : \n'); +end +tmp1=cputime; +[Phase_1 II JJ score_mat score_mat2] = phaseI(data, node_sizes, epsilon); +tmp1=cputime-tmp1; + +if verbose + fprintf('Execution time : %2.5f\n',tmp1); + fprintf('\n================== phase II : \n'); +end +tmp1=cputime; +Phase_2 = phaseII(Phase_1, data, node_sizes, epsilon, II, JJ, score_mat); +tmp1=cputime-tmp1; + +if verbose + fprintf('Execution time : %2.5f\n',tmp1); + fprintf('\n================== phase III : \n'); +end +tmp1=cputime; +[Phase_3 UPhase_3] = phaseIII(Phase_2, data, node_sizes, epsilon, score_mat2, star); +tmp1=cputime-tmp1; +if verbose + fprintf('Execution time : %2.5f\n',tmp1); +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function [G, II, JJ, score_mat, sc2] = phaseI(data,node_sizes,alpha) +% [G, II , JJ, score_mat, s2] = phaseI(data,node_sizes,epsilon) +% +% G is an acyclic graph +% [II JJ] is the list of important edges not processed in phase I (for phase II) +% score_mat is the mutual information score matrix +% +% data(i,m) is node i in case m. +% alpha is the significant level for CI tests ( default=0.05 ). +% node_sizes is the vector of sizes ( default=max(data') ). +% +% see "Learning bayesian Networks from Data: A Efficient Approach Based on Information Theorie" +% Jie Cheng, David Bell and Weird Liu. + +% 0. +if nargin < 3, alpha=0.05; end +if nargin < 2, node_sizes=max(data'); end +[N m] = size(data); +score_mat = zeros(N); +edges=0; + +% 1. +G = zeros(N); +L=[]; + +% 2. Use of Chi2 instead of MI ... allow using a confidence level alpha instead of an arbitrary epsilon +for i=1:(N-1) + for j=(i+1):N + [I score_mat(i,j)] = cond_indep_chisquare(i,j,[],data,'LRT',alpha,node_sizes); + end +end +sc2=score_mat; + + +[tmp ordre]=sort(-score_mat(:)); +ordre2=ordre(find(-tmp>alpha)); +[II JJ]=ind2sub([N N],ordre2); + +pointer=1 ; +fini=length(II); + +% 3. +edges=2; +for pointer=1:min(2,fini), + %fprintf('%d-%d\n',II(pointer),JJ(pointer)); + G(II(pointer),JJ(pointer))=1; + G(JJ(pointer),II(pointer))=1; + score_mat(II(pointer),JJ(pointer))=-inf; +end + +pointer=min(2,fini); +arret=0; + +while pointeralpha)); +[II JJ]=ind2sub([N N],ordre2); + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function G = phaseII(G1,data,node_sizes,alpha,II, JJ, score_mat) +% G = phaseII(G1,data,node_sizes,epsilon,II,JJ, score_mat) +% +% G1, II, JJ,score_mat are given by phaseI. +% data(i,m) is node i in case m. +% node_sizes is the vector of sizes ( default=max(data') ). +% alpha is the significant level for CI tests ( default=0.05 ). +% +% see "Learning bayesian Networks from Data: A Efficient Approach Based on Information Theorie" +% Jie Cheng, David Bell and Weird Liu. + +st{1}='added'; +st{2}=''; +% 0. +[N m] = size(data); + +G=G1; + +% 6. +II=II(end:-1:1); +JJ=JJ(end:-1:1); + +pointer=length(II); + +while pointer>0 + % 7. + trysep = try_to_separate_A(G,II(pointer),JJ(pointer),data,alpha,node_sizes); + %fprintf('Try to separate %d and %d : %s\n',II(pointer),JJ(pointer),st{trysep+1}); + if ~trysep + G(II(pointer),JJ(pointer))=1; + G(JJ(pointer),II(pointer))=1; + %fprintf('%d-%d\n',II(pointer),JJ(pointer)); + end + % 8. + pointer=pointer-1; +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function [G,U] = phaseIII(G1,data,node_sizes,alpha,s2,star) +% [G] = phaseIII(G1,data,node_sizes,alpha,s2,star) +% +% data(i,m) is node i in case m. +% if star~=0, use try_to_separate_B_star instead of try_to_separate_B (default star=1). +% G is the non-oriented graph and G1 the oriented result. +% +% see "Learning bayesian Networks from Data: A Efficient Approach Based on Information Theorie" +% Jie Cheng, David Bell and Weird Liu. + +% 0. +if nargin < 2, disp('Not enough arguments');return; end +if nargin < 3, node_sizes=max(data'); end +if nargin < 4, alpha = 0.05; end +if nargin < 5, star=1; end +G=G1; +N=length(G); +% reachability_matrix of G +M = expm(full(G)) - eye(length(G)); M = (M>0); + +% 9. +fprintf('Thinning - separateA\n'); + +% Edges are examined in the inverse order of their Chi2 (or MI) score +s2(find(~G))=0; +[tmp ordre]=sort(s2(:)); +ordre2=ordre(find(tmp>0)); +[I J]=ind2sub([N N],ordre2); +ii=1:length(I); +for i=ii, + %fprintf('%d-%d : ',I(i),J(i)); + G(I(i),J(i))=0; + G(J(i),I(i))=0; + trysep = try_to_separate_A(G,I(i),J(i),data,alpha,node_sizes); + + if ~trysep, + G(I(i),J(i))=1; + G(J(i),I(i))=1; + %fprintf(' keep\n'); + %else + %fprintf('delete\n'); + end +end + +% 10. +fprintf('Thinning - separateB'); if star; fprintf('star'); end; fprintf('\n'); +s2(find(~G))=0; +[tmp ordre]=sort(s2(:)); +ordre2=ordre(find(tmp>0)); +[I J]=ind2sub([N N],ordre2); +ii=1:length(I); +for i=ii, + %fprintf('%d-%d : ',I(i),J(i)); + G(I(i),J(i))=0; G(J(i),I(i))=0; + if star==0 + trysep = try_to_separate_B(G,I(i),J(i),data,node_sizes,alpha); + else + trysep = try_to_separate_B_star(G,I(i),J(i),data,node_sizes,alpha); + end + if ~trysep, + G(I(i),J(i))=1; G(J(i),I(i))=1; + %fprintf(' keep\n'); + %else + %fprintf('delete\n'); + end +end + +%11. +fprintf('Thinning - orient_edges\n'); +U=G; +G=orient_edges(U,data,node_sizes,alpha); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function [I, N1, N2] = try_to_separate_A(G,node1,node2,data,alpha,node_sizes) +% [I, N1, N2] = try_to_separate_A(G,node1,node2,data,alpha,node_sizess) +% +% G is the current partially directed graph. +% I is a boolean : I=1 <==> separated. +% N1 : neighbors of node1 that are on an adjacency path between node1 and node2 (ditto for N2). +% +% see "Learning bayesian Networks from Data: A Efficient Approach Based on Information Theorie" +% Jie Cheng, David Bell and Weird Liu. + +% 0. +if node1==node2 | length(G)<2 + disp('Error: Check your arguments in try_to_separate_A.');I=0; return +end +N=size(data,1); +if nargin==4, alpha=0.05; node_sizes=max(data'); end +if nargin==5, node_sizes=max(data'); end + +% 1. +N1=find(G(node1,:)==1);N01=N1; +GG1=G(setdiff(1:N,node1),setdiff(1:N,node1)); +node22=node2-(node2>node1); +% reachability_matrix of GG1 +M = expm(full(GG1)) - eye(length(GG1)); M = (M>0); +% N1 is the neighbors of node1 that are on the adjacency between node1 and node2 +for i=N1 + j=i-(i>node1); + if M(j,node22)~=1, N01=setdiff(N01,i); N1=setdiff(N1,i); end + % 2. + if ~G(node1,i), N1=setdiff(N1,i); end +end + +N2=find(G(node2,:)==1);N02=N2; +GG2=G(setdiff(1:N,node2),setdiff(1:N,node2)); +node12=node1-(node20); +% N2 is the neighbors of node2 that are on the adjacency between node1 and node2 +for i=N2 + j=i-(i>node2); + if M(node12,j)~=1, N02=setdiff(N02,i); N2=setdiff(N2,i); end + % 2. + if ~G(node2,i), N2=setdiff(N2,i); end +end + +%fprintf('%d : N1=',node1); fprintf('%d',N1); fprintf('\n'); +%fprintf('%d : N2=',node2); fprintf('%d',N2); fprintf('\n'); +% 3. +if length(N1)>length(N2), tmp=N1; N1=N2; N2=tmp; clear tmp, end +% 4. +C=N1; +for test=1:2 + if test==2, C=N2; end + % 5. + [I v1] = cond_indep_chisquare(node1,node2,C,data,'LRT',alpha,node_sizes); + if I, %fprintf('%d-%d separated (%2.5f) by C=',node1,node2,v1); fprintf('%d',C); fprintf('\n'); + return, + %else + %fprintf('%d-%d not separated (%2.5f) by C=',node1,node2,v1); fprintf('%d',C); fprintf('\n'); + end; + + + % 6. + step6=1; + while step6 + step6=0; + if length(C)>=1 + v=[]; + for i=C + Ci = setdiff(C,i); + [I(i) v(i)] = cond_indep_chisquare(node1,node2,Ci,data,'LRT',alpha,node_sizes); + end + [vm ind] = min(v); + + % 7. + if I(ind) + I = 1; return + else + if vm < v1 + v1 = vm; + C = setdiff(C,ind); + % goto step 6. + step6 = 1; + end + end + end + + % 8. + if test==2, I=0; return, end + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function I = try_to_separate_B(G,node1,node2,data,node_sizes,alpha,N1,N2) +% I = try_to_separate_B(G,node1,node2,data,node_sizes,epsilon,N1,N2) +% +% G is the current partially directed graph. +% data(i,m), node i in case m. +% node_sizes is the vector of size of the attributs in data ( default max(data') ) +% N1 (optionnal) is the neighbors of node1 that are on an adjacency path between node1 and node2 (ditto for N2). +% I is a boolean : I+1 <==> separated. +% +% see "Learning bayesian Networks from Data: A Efficient Approach Based on Information Theorie" +% Jie Cheng, David Bell and Weird Liu. + +% 0. +if node1==node2 | length(G)<2 + disp('Error: Verify your arguments in try_to_separate_B.');I=0; return +end +if nargin < 4, + disp('Error : not enougth arguments'); I=0; return; +end +N=size(data,1); + +% 1. +if nargin < 8 + N1=find(G(node1,:)==1); + GG1=G(setdiff(1:N,node1),setdiff(1:N,node1)); + node22=node2-(node2>node1); + M = expm(full(GG1)) - eye(length(GG1)); M = (M>0); + for i=N1 + j=i-(i>node1); + if M(j,node22)~=1, N1=setdiff(N1,i); end + end + N2=find(G(node2,:)==1); + GG2=G(setdiff(1:N,node2),setdiff(1:N,node2)); + node12=node1-(node20); + for i=N2 + j=i-(i>node2); + if M(node12,j)~=1, N2=setdiff(N2,i); end + end +end +if nargin < 6, alpha=0.05; end +if nargin < 5, node_sizes=max(data'); end +M = expm(full(G)) - eye(length(G)); M = (M>0); + +% 2. +N1b=[]; +for i=N1 + NN1=find(G(i,:)==1); + for j=NN1 + if M(i,j)~=1 & ~ismember(j,N1), N1b=union(N1b,j); end + end +end + +% 3. +N2b=[]; +for i=N2 + NN2=find(G(i,:)==1); + for j=NN2 + if M(i,j)~=1 & ~ismember(j,N2), N2b=union(N2b,j); end + end +end + +% 4. +if length(union(N1,N1b)) < length(union(N2,N2b)) + C=union(N1,N1b); +else + C=union(N2,N2b); +end + +% 5. +continu=1; +while continu + l=length(C); + %fprintf('%d',continu); + [I v] = cond_indep_chisquare(node1,node2,C,data,'LRT',alpha,node_sizes); + if I==1; return, elseif l<2, I=0; return, end + + % 6. + Cb=C; + for i=1:l + Ci=setdiff(C,C(i)); + [I vi] = cond_indep_chisquare(node1,node2,Ci,data,'LRT',alpha,node_sizes); + e = (v+1)/3; % e is a small value... + if I==1,return, elseif vinode1); + M = expm(full(GG1)) - eye(length(GG1)); M = (M>0); + for i=N1 + j=i-(i>node1); + if M(j,node22)~=1, N1=setdiff(N1,i); end + % 2. + if ~G(node1,i), N1=setdiff(N1,i); end + end + N2=find(G(node2,:)==1); + GG2=G(setdiff(1:N,node2),setdiff(1:N,node2)); + node12=node1-(node20); + for i=N2 + j=i-(i>node2); + if M(node12,j)~=1, N2=setdiff(N2,i); end + % 2. + if ~G(node2,i), N2=setdiff(N2,i); end + end +end +if nargin < 6, alpha=0.05; end +if nargin < 5, node_sizes=max(data'); end + +% 3. +if length(N1)>length(N2), tmp=N1; N1=N2; N2=tmp; end + +% 4. +C=N1; +l=length(C); +I=0; + +% 5. +for test=1:2 + continu=1; + %test + if test==2 & ~isempty(N2) + C=N2; l=length(C); IsInCi=zeros(1,l); IsInCi(l)=1; + else IsInCi=zeros(1,l); + end + s=ones(1,l); + % Pour tous les sous-ensemble Ci de C : + while continu & ~isempty(C) + Ci = setdiff(C.*IsInCi,0); + [I vi] = cond_indep_chisquare(node1,node2,Ci,data,'LRT',alpha,node_sizes); + if I, %fprintf('%d-%d separated (%2.5f) by C=',node1,node2,vi); fprintf('%d',Ci); fprintf('\n'); + return, + %else + %fprintf('%d-%d not separated (%2.5f) by C=',node1,node2,vi); fprintf('%d',Ci); fprintf('\n'); + end; + % if I, return, end + + if IsInCi==s, continu=0; + else + IsInCi(l)=IsInCi(l)+1; + + notOK=1; i=l; + while notOK & i>1 + if IsInCi(i)>s(i), IsInCi(i)=0; IsInCi(i-1)=IsInCi(i-1)+1; else notOK=0; end + i=i-1; + end + end + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function G1 = orient_edges(G,data,node_sizes,alpha) +% G1 = orient_edges(G,data,node_sizes) +% +% G is the partially directed graph. +% data(i,m), node i in case m. +% node_sizes is the vector of size of the attributs in data ( default max(data') ) +% +% see "Learning bayesian Networks from Data: A Efficient Approach Based on Information Theorie" +% Jie Cheng, David Bell and Weird Liu. + +% 0. +if nargin < 4, alpha=0.05; end +if nargin < 3, node_sizes=max(data'); end +if nargin < 2, disp(' Require at least two arguments.'); return, end +N=length(G); +G1=G; + +% 1. +[Lnode1 Lnode2]=find(triu(1-triu(G),1)); %[Lnode1 Lnode2]=ind2sub([N N],find(triu(1-triu(G),1))); +for ii = 1:length(Lnode1), + node1=Lnode1(ii); + node2=Lnode2(ii); + %fprintf('%d %d\n',node1,node2); + N1 = find(G(node1,:)==1); + N2 = find(G(node2,:)==1); + if ~isempty(intersect(N1,N2)) + %fprintf('V1='); fprintf('%d ',N1); fprintf('\n'); + %fprintf('V2='); fprintf('%d ',N2); fprintf('\n'); + GG1 = G(setdiff(1:N,node1),setdiff(1:N,node1)); + node22 = node2-(node2>node1); + % reachability_matrix of GG1 + M = expm(full(GG1)) - eye(length(GG1)); M = (M>0); + % N1 is the neighbors of node1 that are on the adjacency between node1 and node2 + for i = N1 + j = i-(i>node1); + if M(j,node22)~=1, N1=setdiff(N1,i); + end + end + GG2 = G(setdiff(1:N,node2),setdiff(1:N,node2)); + node12 = node1-(node20); + % N2 is the neighbors of node2 that are on the adjacency between node1 and node2 + for i=N2 + j = i-(i>node2); + if M(node12,j)~=1, N2=setdiff(N2,i); end + end + %fprintf('%d %d\n',node1,node2); + %fprintf('N1='); fprintf('%d ',N1); fprintf('\n'); + %fprintf('N2='); fprintf('%d ',N2); fprintf('\n'); + + % 2. + M = expm(full(G)) - eye(length(G)); M = (M>0); + N1b=N1; + for i=N1 + NN1 = find(G(i,:)==1); + for j=NN1 + if M(i,j)~=1 & ~ismember(j,N1), N1b=union(N1b,j); end + end + end + %fprintf('N1''='); fprintf('%d ',N1b); fprintf('\n'); + + % 3. + N2b=N2; + for i=N2 + NN2 = find(G(i,:)==1); + for j=NN2 + if M(i,j)~=1 & ~ismember(j,N2), N2b=union(N2b,j); end + end + end + %fprintf('N2''='); fprintf('%d ',N2b); fprintf('\n'); + + % 4. + if length(N1b) < length(N2b) + C = N1b; + else + C = N2b; + end + %l=length(C); + %fprintf('C='); fprintf('%d ',C); fprintf('\n'); + + % 7. + step5=1; + while step5 + step5=0; + %fprintf('.'); + % 5. + l=length(C); + [I v] = cond_indep_chisquare(node1,node2,C,data,'LRT',alpha,node_sizes); + %fprintf('C='); fprintf('%d ',C); + %fprintf(': %d %2.5f\n',I,v); + + step8=0; + if I==1 & v~=0 % v < epsilon + step8=1; + else + if l==1 + G1(C,node1)=0; G1(C,node2)=0; + fprintf('%d -> %d <- %d\n',node1,C,node2); + step8=1; + end + end + %fprintf('%d\n',step8); + + % 6. + if ~step8 + Cb=C; + for i=1:l + Ci=setdiff(C,C(i)); + [I vi] = cond_indep_chisquare(node1,node2,Ci,data,'LRT',alpha,node_sizes); + % e = (v+1)/3; % e is a small value... + if I==1 % vi < v+e + Cb=setdiff(Cb,C(i)); + if ismember(C(i),N1) & ismember(C(i),N2) + G1(C(i),node1)=0; G1(C(i),node2)=0; + fprintf('%d -> %d <- %d\n',node1,C(i),node2); + end + if I==1 % vi < epsilon + step8=1; + end + end + end % for + end % if + + % 7. + if ~step8 + if length(Cb) < length(C), C=Cb; end + if length(C) > 0, step5==1; end + end + end % while step5 + % step8 : passer ďż˝ la paire de noeud suivant + %fprintf('\n'); + %else + %fprintf(' No common neighbor\n'); + end % if +end % for + +% 11. +step9=0; +fprintf('Infering directions '); +test = pdag_to_dag(G1); +while ~isdag(test) %& step9 %d ... \n',a,b); + if G1(b,c)==1 & G1(c,b)==1 + if G1(a,c)+G1(c,a)==0, + G1(c,b)=0; + fprintf('%d -> %d (9)\n',b,c); + end + end + end + end + end, end, end + + % 10. + for a=1:N-1, for b=a+1:N + if G1(a,b)==1 & G1(b,a)==1 + GGG1=xor(G1,G1'); % matrice des arcs orientďż˝s de G1 + M = expm(full(GGG1)) - eye(length(GGG1)); M = (M>0); + if M(a,b)==1, + G1(b,a)=0; + fprintf('%d -> %d (10)\n',a,b); + end + end + end, end + + test = pdag_to_dag(G1); + if isempty(test), G1=return_one_edge(G1); end + %else + % G1, return, + %end +end % while 11. +G1 = pdag_to_dag(G1); +fprintf('%d boucles\n',step9); + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function b = isdag(G) +b = sum(sum(G.*G')); % How many undirected arcs ? (x2) +b=~b & ~isempty(G); +if b + M = expm(full(G)) - eye(length(G)); M = (M>0); + b = b & find(sum(sum(eye(length(G)).*M))); % is there no cycle ? +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function G=return_one_edge(G) +N=length(G); fam=[]; node2=[]; +permnode = randperm(5); i=0; %fprintf('rev\n'); +while isempty(fam) | isempty(node2) | i==N + i=i+1; + node = permnode(i); %node = ceil(rand(1)*N); + fam=find(G(node,:)==1); + par=find(G(:,node)==1); + fam = myunion(fam, par); + if ~isempty(fam) + node2 = fam(ceil(rand(1)*length(fam))); + par2=find(G(:,node)==1); + if ~isempty(intersect(par2, node)), node2=[]; end + end +end +if isempty(myintersect(node, par2)), + G(node, node2)=0; + G(node2, node)=1; + fprintf('%d -> %d (Rev)\n',node, node2); +else + G(node, node2)=1; + G(node2, node)=0; + fprintf('%d -> %d (Rev)\n',node2, node); +end diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_ges.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_ges.m new file mode 100644 index 00000000..e985195e --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_ges.m @@ -0,0 +1,122 @@ +function [cpdag, best_score, cache] = learn_struct_ges(data, nodesizes, varargin) +% +% LEARN_STRUCT_GES learns a structure of Bayesian net by Greedy Equivalence Search. +% cpdag = learn_struct_ges(Data, Nodesizes, 'cache', cache, 'scoring_fn', 'bic', 'verbose', 'yes') +% +% cpdag: the final cpdag +% Data : training data, data(i,m) is the m obsevation of node i +% Nodesizes: the size array of different nodes +% cache : data structure used to memorize local score computations +% (cf. SCORE_INIT_CACHE function) +% +% V1.1 : 28 july 2003 (Ph. Leray - philippe.leray@univ-nantes.fr, O. francois - francois.olivier.c.h@gmail.com) +% +% Ref: +% Optimal Structure Identification with Greedy Search, Chickering 2002 +% + +[N ncases] = size(data); +seeddag = zeros(N,N); + +% set default params +scoring_fn = 'bayesian'; +verbose = 0; +cache=[]; + +% get params +args = varargin; +nargs = length(args); +if length(args) > 0 + if isstr(args{1}) + for i = 1:2:nargs + switch args{i} + case 'scoring_fn', scoring_fn = args{i+1}; + case 'verbose', verbose = strcmp(args{i+1},'yes'); + case 'cache', cache=args{i+1} ; + end; + end; + end; +end; + +if verbose + names=cellstr(int2str((1:N)')); + carre=zeros(N,1); +end + +done = 0; +[best_score cache] = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn,'cache',cache); +cptt=0; + +% First step : INSERT +while ~done + cptt=cptt+1; + [pdags,nodes] = mk_nbrs_of_pdag_add(seeddag); + seedold=seeddag; + sold=best_score; + nbrs = length(pdags); + dags=pdag_to_dag(pdags); + [scores cache] = score_dags(data, nodesizes, dags,'scoring_fn',scoring_fn,'cache',cache); + max_score = max(scores); + new = find(scores == max_score ); + if ~isempty(new) & (max_score > best_score) + p = sample_discrete(normalise(ones(1, length(new)))); + best_score = max_score; + seeddag = dag_to_cpdag(dags{new(p)}); + new=new(p); + if verbose + figure; + subplot(1,2,1), [xx yy]=draw_graph(seedold,names,carre); + set(gca,'color',[1 1 0]); + title(sprintf('current CPDAG (Smax=%5.2f)',sold)); + subplot(1,2,2), draw_graph(seeddag,names,carre,xx,yy); + s=sprintf(' %d',nodes{new,3}); + title([sprintf('Best in N+ = INSERT(%d, %d,',nodes{new,1},nodes{new,2}) s ')' sprintf(' S=%5.2f',max_score)]); + drawnow; + end + + else + done = 1; + end + +end; + +done = 0; +%[best_score cache] = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn,'cache',cache); +cptt=0; + +if sum(sum(seeddag))==0, done=1;end + +% Second step : DELETE +while ~done + cptt=cptt+1; + [pdags,nodes] = mk_nbrs_of_pdag_del(seeddag); + seedold=seeddag; sold=best_score; + nbrs = length(pdags); + dags=pdag_to_dag(pdags); + [scores cache] = score_dags(data, nodesizes, dags,'scoring_fn',scoring_fn,'cache',cache); + max_score = max(scores); + new = find(scores == max_score ); + if ~isempty(new) & (max_score > best_score) + p = sample_discrete(normalise(ones(1, length(new)))); + best_score = max_score; + seeddag = dag_to_cpdag(dags{new(p)}); + new=new(p); + if verbose + cpdags=dag_to_cpdag(dags); + figure; + subplot(1,2,1), [xx yy]=draw_graph(seedold,names,carre); + set(gca,'color',[1 1 0]); + title(sprintf('current CPDAG (Smax=%5.2f)',best_score)); + subplot(1,2,2), draw_graph(seeddag,names,carre,xx,yy); + s=sprintf('%d',nodes{new,3}); + title([sprintf('Best in N- = DELETE(%d, %d,',nodes{new,1},nodes{new,2}) s ')' sprintf(' S=%5.2f',max_score)]); + drawnow; + end + + else + done = 1; + end + +end + +cpdag = seeddag; diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_ges_EM.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_ges_EM.m new file mode 100644 index 00000000..a4c340e7 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_ges_EM.m @@ -0,0 +1,423 @@ +function [bnet,cpdag,BIC_score,nloop] = learn_struct_ges_EM(bnet, data, max_loop, loop_em) +% GES_EM algorithm, learn Bayesian network equivalence classes from incomplete data. +% [bnet,cpdag,BIC_score,nloop] = learn_struct_ges_EM(bnet, data, max_loop) +% +% 2/11/2006: hanene.borchani@gmail.com, francois.olivier.c.h@gmail.com + +[N ncases] = size(data); +ns = bnet.node_sizes; +cpdag = dag_to_cpdag(bnet.dag); +order=zeros(1,N); +order=1:N; +tiny = exp(-700); +improve_factor = 0.001; %stop search if the current expected BIC score is less than old_score+old_score*improve_factor, +log_value = log(ncases); +update_samples = cell(N, ncases); +loop = 0; +converged = 0; +if nargin<4, loop_em=6; end + +% First phase : INSERT +while ~converged & loop < max_loop + loop=loop+1; + + fprintf('\n Loop INSERT number %d \n\n', loop); + engine = jtree_inf_engine(bnet); + [bnet, LOGLIKE] = learn_params_em(engine, data, loop_em); + + for i=1:N + s = struct(bnet.CPD{i}); + counts = s.counts(:); + ll(i) = sum(log(s.CPT(:) + tiny) .* counts); + end + [D,d] = compute_bnet_nparams(bnet); + + [pdags,nodes] =mk_nbrs_of_pdag_add(cpdag,engine); %generate the neighbors of the current network + nbrs = length(pdags); + if nbrs==0 + break; + end + dags = pdag_to_dag(pdags); %extract the set of consistent extensions + nbrs_dags = length(dags); + + if nbrs_dags==0 + disp('The number of consistent extensions of the pdags neighbors is equal to 0'); + break; + else + c=0; + for k=1:nbrs + if ~isempty(dags{k}) + c=c+1; + cdags{c}=dags{k}; + cnodes(c,:) = nodes(k,:); + end + end + if c==0 + fprintf('There is no consistent extension'); + break; + end + end + + [ess,LL] = compute_ess(bnet,data,cdags); %compute the estimations for all consistent extensions based on the current bnet + bic_score0 = sum(LL); + bic_score0 = bic_score0 - 0.5 * D * log_value; %expected BIC score of the current bnet + fprintf('The expected BIC score of the current bnet, bic_score0 =%8.3f \n', bic_score0); + bic_score = -inf*ones(1,c); + + for i=1:c %compute the expected BIC score of each consistent extension neighbor + for compt=1:N + new_CPT{i,compt}=[]; + end + new_LL = LL; + new_d=d; + for num=1:N + if ~isempty(ess{i,num}) %consider only nodes whose parent set has been changed + par= mysetdiff(ess{i,num}.domain, num); + new_d(num)= prod([ns(par) ns(num)-1]); %compute the new dimension of each node + approx = ess{i,num}.counts; + indnum = find(ess{i,num}.domain > num); + ldom=length(ess{i,num}.domain); + lindnum = length(indnum); + if lindnum>0 + approx = permute(approx, [1:ldom-lindnum-1, indnum(1):ldom, indnum(1)-1]); + end + new_CPT{i,num} = mk_stochastic(approx); + new_LL(num) = sum(log(new_CPT{i,num}(:) + tiny) .* approx(:)); %compute the new LL of each node + end + end + new_D = sum(new_d); + bic_score(i) = sum(new_LL) - 0.5 * new_D * log_value; %deduce the expected BIC score of each neighbor + [a, j] = max(bic_score); + end + + [BIC_score, best] = max(bic_score); + fprintf('End computing of the expected BIC scores of all neighbors, the maximal one is Bic_score =%8.3f \n', BIC_score); + temp = abs(bic_score0) * improve_factor; %search will finish when the improvment of the expected BIC score is less than 0.1% compare with the previous best result + + if BIC_score > (bic_score0 + temp) + best_dag = cdags{best}; + new_order=topological_sort(best_dag); + for j = 1:N + row = new_order(j); + for k = 1:ncases + update_samples{j,k} = data{row,k}; + end + end + data = update_samples; + + forbidden=[]; + reversed =[]; + for j=1:N + old_parents= sort((find(bnet.dag(:,j)==1))'); + new_parents= sort((find(best_dag(:,j)==1))'); + if ~isequal(new_parents,old_parents) + reversed =[reversed,j]; + end + end + + new_dag = best_dag(new_order, new_order); %reshape the best DAG according to new_order + ns = ns(new_order); %modify the order of ns + CPDs = bnet.CPD; + bnet = mk_bnet(new_dag, ns); %make the new best BN structure + + %randomly set the CPD values of each node + for j=1:N + bnet.CPD{j} = tabular_CPD(bnet, j, 'prior_type', 'dirichlet', 'dirichlet_weight', 0); + end + + lreversed= length(reversed); + if lreversed ~=0 + for r=1:lreversed %update the CPDs of nodes whose parent set has been altered using the saved new_CPT + reverse=find(new_order==reversed(r)); + forbidden=[forbidden,reverse]; + bnet.CPD{reverse} = set_fields(bnet.CPD{reverse}, 'CPT', new_CPT{best,reversed(r)}); + end + end + + bnet.CPD = copy_CPD(bnet.CPD, CPDs, new_order, forbidden); %copy the CPDs of remaining nodes from CPDs + + cpdag = dag_to_cpdag(bnet.dag); %get the best equivalence class + + clear bic_score D d new_D new_d; %new computations for each iteration + clear ess LL new_LL new_CPT; + clear pdags dags cdags nodes cnodes; + else + fprintf('No improvement of the expected bic score: End of add phase \n \n'); + BIC_score = bic_score0; + converged=1; + end +end + +nloop=loop; + +loop=0; +converged=0; + +if sum(sum(cpdag))==0, converged=1; else cpdag, end + +% Second phase : Delete + fprintf('Start of delete phase \n '); + while ~converged & loop < max_loop + loop=loop+1; + fprintf('\n Loop DELETE number %d \n\n', loop); + engine = jtree_inf_engine(bnet); + [bnet, LOGLIKE] = learn_params_em(engine, data, loop_em); + for i=1:N + s = struct(bnet.CPD{i}); + counts = s.counts(:); + ll(i) = sum(log(s.CPT(:) + tiny) .* counts); + end + + [D,d] = compute_bnet_nparams(bnet); + + [pdags,nodes] = mk_nbrs_of_pdag_del(cpdag,engine); %generate the neighbors of the current network + nbrs = length(pdags); + if nbrs==0 + disp('The number of pdag neighbors is equal to 0'); + break; + end + + dags = pdag_to_dag(pdags); %extract the set of consistent extensions + nbrs_dags = length(dags); + if nbrs_dags==0 + disp('The number of consistent extensions of the pdag neighbors is equal to 0'); + break; + else + c=0; + for k=1:nbrs + if ~isempty(dags{k}) + c=c+1; + cdags{c}=dags{k}; + cnodes(c,:) = nodes(k,:); + end + end + if c==0 + fprintf('There is no consistent extension'); + return; + end + end + + [ess,LL] = compute_ess(bnet, data,cdags); %compute the estimations for all consistent extensions based on the current bnet + bic_score0 = sum(LL); + bic_score0 = bic_score0 - 0.5 * D * log_value; %expected BIC score of the current bnet + fprintf('The expected BIC score of the current bnet, bic_score0 =%8.3f \n', bic_score0); + bic_score = -inf*ones(1,c); + + for i=1:c %Compute the Expected BIC score for each consistent extension neighbor + for compt=1:N + new_CPT{i,compt}=[]; + end + new_LL = LL; + new_d=d; + for num=1:N + if ~isempty(ess{i,num}) %consider only nodes whose parent set has been changed + par= mysetdiff(ess{i,num}.domain, num); + new_d(num)= prod([ns(par) ns(num)-1]); %compute the new dimension of each node + approx = ess{i,num}.counts; + indnum = find(ess{i,num}.domain > num); + ldom=length(ess{i,num}.domain); + lindnum = length(indnum); + if lindnum>0 + approx = permute(approx, [1:ldom-lindnum-1, indnum(1):ldom, indnum(1)-1]); + end + new_CPT{i,num} = mk_stochastic(approx); + new_LL(num) = sum(log(new_CPT{i,num}(:) + tiny) .* approx(:)); %compute the new LL of each node + end + end + new_D = sum(new_d); + bic_score(i) = sum(new_LL) - 0.5 * new_D * log_value; %deduce the expected BIC score of each neighbor + end + + [BIC_score, best] = max(bic_score); + fprintf('End computing of the expected BIC scores of all neighbor, the maximal one is Bic_score =%8.3f \n', BIC_score); + temp = abs(bic_score0) * improve_factor; + + if BIC_score > (bic_score0 + temp) + best_dag = cdags{best}; + new_order=topological_sort(best_dag); + for j = 1:N + row = new_order(j); + for k = 1:ncases + update_samples{j,k} = data{row,k}; + end + end + data = update_samples; + + forbidden=[]; + reversed =[]; + for j=1:N + old_parents= sort((find(bnet.dag(:,j)==1))'); + new_parents= sort((find(best_dag(:,j)==1))'); + if ~isequal(new_parents,old_parents) + reversed =[reversed,j]; + end + end + + new_dag = best_dag(new_order, new_order); %reshape the best DAG according to new_order + ns = ns(new_order); %modify the order of ns + CPDs = bnet.CPD; + + bnet = mk_bnet(new_dag, ns); %make the new best BN structure + %randomly set the CPD values of each node + for j=1:N + bnet.CPD{j} = tabular_CPD(bnet, j, 'prior_type', 'dirichlet', 'dirichlet_weight', 0); + end + + lreversed= length(reversed); + if lreversed ~=0 + for r=1:lreversed %update the CPDs of nodes whose parent set has been altered using the saved new_CPT + reverse=find(new_order==reversed(r)); + forbidden=[forbidden,reverse]; + bnet.CPD{reverse} = set_fields(bnet.CPD{reverse}, 'CPT', new_CPT{best,reversed(r)}); + end + end + + bnet.CPD = copy_CPD(bnet.CPD, CPDs, new_order, forbidden); %copy the CPDs of remaining nodes from CPDs + + cpdag = dag_to_cpdag(bnet.dag); %get the best equivalence class + + clear bic_score D d new_D new_d; % new computations for each iteration + clear ess LL new_LL new_CPT; + clear pdags dags cdags nodes cnodes; + else + fprintf('No improvement of the expected bic score : End of delete phase \n \n'); + BIC_score = bic_score0; + converged=1; + end +end + +cpdag + +nloop=nloop+loop; %total iteration number + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function [D,d]= compute_bnet_nparams(bnet) +N = length(bnet.dag); +d = zeros(1,N); + for i=1:N + a = struct(bnet.CPD{i}); + d(i) = a.nparams; + end +D = sum(d); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function [ess, LL] = compute_ess(bnet, samplesM, dags) +%compute the estimations for all consistent extensions based on the current bnet +tiny = exp(-700); +N = length(bnet.dag); +ns = bnet.node_sizes; +ncases = size(samplesM, 2); +ess0 = cell(1,N); +LL = zeros(1, N); +nbrs= length(dags); +copy=zeros(nbrs,N); + +for i =1:nbrs + for x=1:N + ess{i,x}=[]; + end +end + +for i =1:N + parents = bnet.parents{i}; + family = [parents, i]; + ess0{i} = 0 * myones(ns(family)); +end + +for i =1:nbrs + neighbor=dags{i}; + for j=1:N + old_parents = sort((find(bnet.dag(:,j)==1))'); + new_parents = sort((find(neighbor(:,j)==1))'); + if ~isequal(new_parents,old_parents) + domain = sort(myunion(new_parents, j)); + copy(i,j)= find_same_domain(ess,j,domain,i); + ess{i,j}.domain = domain; + ess{i,j}.counts = 0 * myones(ns(domain)); + end + end +end + +engine = jtree_inf_engine(bnet); + +for l =1:ncases + evidence = samplesM(:, l); + [engine, ll] = enter_evidence(engine, evidence); + ns_eff = ns; + ns_eff(~isemptycell(evidence)) = 1; + Vmarg = cell(1,N); + for i =1:N + Vmarg{i} = marginal_nodes(engine, i); + end + for i = 1:N + parents = bnet.parents{i}; + family = [parents, i]; + nfamily = length(family); + Fmarg = []; + for j = 1:nfamily + Fmarg = multiply_one_marginal(Fmarg, Vmarg{family(j)}, ns_eff); + end + fullm = add_ev_to_dmarginal(Fmarg, evidence, ns); + ess0{i} = ess0{i} + fullm.T; + end + + for i = 1:nbrs + for j=1:N + if ~isempty(ess{i,j}) + if ~copy(i,j) + domain = ess{i,j}.domain; + Fmarg = []; + for compteur=1:length(domain) + Fmarg = multiply_one_marginal(Fmarg, Vmarg{domain(compteur)}, ns_eff); + end + fullm = add_ev_to_dmarginal(Fmarg, evidence, ns); + ess{i,j}.counts = ess{i,j}.counts + fullm.T; + end + end + end + end + clear Vmarg; +end + +for i = 1:nbrs + for j=1:N + if copy(i,j) + ess{i,j}.counts = ess{copy(i,j),j}.counts; + end + end +end + +for i=1:N + s = struct(bnet.CPD{i}); + counts = ess0{i}; + LL(i) = sum(log(s.CPT(:) + tiny) .* counts(:)); +end +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function index = find_same_domain(ess, j, domain, length) +index = 0; +for i = 1:length + if ~isempty(ess{i,j}) + if isequal(domain, ess{i,j}.domain) + index= i; + break; + end + end +end +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function newCPD = copy_CPD(newCPD, CPDs, order, forbidden) +%copy CPDs from old bnet to best bnet, except those nodes whose parent set has been altered +N = length(order); +for i=1:N + if ~mysubset(i,forbidden) + a = order(i); + s = struct(CPDs{a}); + CPT = s.CPT; + newCPD{i} = set_fields(newCPD{i}, 'CPT', CPT); + end +end +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_gs.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_gs.m new file mode 100644 index 00000000..3dd3ef1c --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_gs.m @@ -0,0 +1,114 @@ +function [dag,best_score] = learn_struct_gs(data, nodesizes, seeddag, varargin) +% +% LEARN_STRUCT_GS(data,seeddag) learns a structure of Bayesian net by Greedy Search. +% dag = learn_struct_gs(data, nodesizes, seeddag) +% +% dag: the final structurre matrix +% Data : training data, data(i,m) is the m obsevation of node i +% Nodesizes: the size array of different nodes +% seeddag: given seed Dag for hill climbing, optional +% +% +% by Gang Li @ Deakin University (gli73@hotmail.com) + +[N ncases] = size(data); +if (nargin < 3 ) + seeddag = zeros(N,N); % mk_rnd_dag(N); %call BNT function +elseif ~acyclic(seeddag) + seeddag = mk_rnd_dag(N); %zeros(N,N); +end; + +% set default params +scoring_fn = 'bic'; +verbose = 'yes'; + +% get params +args = varargin; +nargs = length(args); +if length(args) > 0 + if isstr(args{1}) + for i = 1:2:nargs + switch args{i} + case 'scoring_fn', scoring_fn = args{i+1}; + case 'verbose', verbose = strcmp(args{i+1},'yes'); + end; + end; + end; +end; + +done = 0; +best_score = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn); +while ~done + [dags,op,nodes] = mk_nbrs_of_dag(seeddag); + nbrs = length(dags); + scores = score_dags(data, nodesizes, dags,'scoring_fn',scoring_fn); + max_score = max(scores); + new = find(scores == max_score ); + if ~isempty(new) & (max_score > best_score) + p = sample_discrete(normalise(ones(1, length(new)))); + best_score = max_score; + seeddag = dags{new(p)}; + else + done = 1; + end; +end; + +dag = seeddag; + +outcount = 0; +best_score = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn); +while outcount < 2 + innercount = 0; + for i=1:N + for j=1:N + if i==j, continue; end; + if seeddag(i,j) == 0 % No edge i-->j, then try to add it + tempdag = seeddag; + tempdag(i,j) = 1; + if acyclic(tempdag) + temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn); + if temp_score > best_score + seeddag = tempdag; + best_score= temp_score; + innercount = innercount +1; + end; + end + else % exists edge i--j, then try reverse it or remove it + tempdag = seeddag; + tempdag(i,j) = 0; tempdag(j,i) = 1; + if acyclic(tempdag) + temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn); + if temp_score > best_score + seeddag = tempdag; + best_score = temp_score; + innercount = innercount +1; + else + tempdag = seeddag; + tempdag(i,j) = 0; + temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn); + if temp_score > best_score + seeddag = tempdag; + best_score= temp_score; + innercount = innercount +1; + end; + end; + else + tempdag = seeddag; + tempdag(i,j)=0; + temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn); + if temp_score > best_score + seeddag = tempdag; + best_score= temp_score; + innercount = innercount +1; + end; + end; + end; + end; % end for j + end; % end for i + if innercount == 0 + outcount = outcount +1; + end; +end; % end while + +dag = seeddag; + diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_gs2.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_gs2.m new file mode 100644 index 00000000..4e054800 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_gs2.m @@ -0,0 +1,119 @@ +function [dag, best_score, cache] = learn_struct_gs2(data, nodesizes, seeddag, varargin) +% +% LEARN_STRUCT_GS2(data,seeddag) learns a structure of Bayesian net by Greedy Search. +% dag = learn_struct_gs(data, nodesizes, seeddag) +% +% dag: the final structure matrix +% Data : training data, data(i,m) is the m obsevation of node i +% Nodesizes: the size array of different nodes +% seeddag: given seed Dag for hill climbing, optional +% cache : data structure used to memorize local score computations +% (cf. SCORE_INIT_CACHE function) +% +% by Gang Li @ Deakin University (gli73@hotmail.com) +% (use mk_nbrs_of_dag_topo, developped by Wei Hu, instead of mk_nbrs_of_dag) +% (Caching implementation : ofrancois.olivier.c.h@gmail.com, philippe.leray@univ-nantes.fr) +% + +[N ncases] = size(data); +if (nargin < 3 ) + seeddag = zeros(N,N); % mk_rnd_dag(N); %call BNT function +elseif ~acyclic(seeddag) + seeddag = mk_rnd_dag(N); %zeros(N,N); +end; + +% set default params +scoring_fn = 'bic'; +verbose = 'yes'; +cache=[]; + +% get params +args = varargin; +nargs = length(args); +if length(args) > 0 + if isstr(args{1}) + for i = 1:2:nargs + switch args{i} + case 'scoring_fn', scoring_fn = args{i+1}; + case 'verbose', verbose = strcmp(args{i+1},'yes'); + case 'cache', cache=args{i+1} ; + end; + end; + end; +end; + +done = 0; +[best_score cache] = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn,'cache',cache); +while ~done + [dags,op,nodes] = mk_nbrs_of_dag_topo(seeddag); + nbrs = length(dags); + [scores cache] = score_dags(data, nodesizes, dags,'scoring_fn',scoring_fn,'cache',cache); + max_score = max(scores); + new = find(scores == max_score ); + if ~isempty(new) & (max_score > best_score) + p = sample_discrete(normalise(ones(1, length(new)))); + best_score = max_score; + seeddag = dags{new(p)}; + else + done = 1; + end; +end; + +dag = seeddag; + +outcount = 0; +[best_score cache] = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn,'cache',cache); +while outcount < 2 + innercount = 0; + for i=1:N + for j=1:N + if i==j, continue; end; + if seeddag(i,j) == 0 % No edge i-->j, then try to add it + tempdag = seeddag; + tempdag(i,j) = 1; + if acyclic(tempdag) + [temp_score cache] = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn,'cache',cache); + if temp_score > best_score + seeddag = tempdag + best_score= temp_score; + innercount = innercount +1; + end; + end + else % exists edge i--j, then try reverse it or remove it + tempdag = seeddag; + tempdag(i,j) = 0; tempdag(j,i) = 1; + if acyclic(tempdag) + [temp_score cache] = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn,'cache',cache); + if temp_score > best_score + seeddag = tempdag; + best_score = temp_score; + innercount = innercount +1; + else + tempdag = seeddag; + tempdag(i,j) = 0; + [temp_score cache] = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn,'cache',cache); + if temp_score > best_score + seeddag = tempdag; + best_score= temp_score; + innercount = innercount +1; + end; + end; + else + tempdag = seeddag; + tempdag(i,j)=0; + [temp_score cache] = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn,'cache',cache); + if temp_score > best_score + seeddag = tempdag + best_score= temp_score; + innercount = innercount +1; + end; + end; + end; + end; % end for j + end; % end for i + if innercount == 0 + outcount = outcount +1; + end; +end; % end while + +dag = seeddag; diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_hc.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_hc.m new file mode 100644 index 00000000..e32dee80 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_hc.m @@ -0,0 +1,56 @@ +function [dag,best_score] = learn_struct_hc(data, nodesizes, seeddag, varargin) +% +% LEARN_STRUCT_HC(data,seeddag) learns a structure of Bayesian net by Hill Climbing. +% dag = learn_struct_hc(data, nodesizes, seeddag) +% +% dag: the final structurre matrix +% Data : training data, data(i,m) is the m obsevation of node i +% Nodesizes: the size array of different nodes +% seeddag: given seed Dag for hill climbing, optional +% +% by Gang Li @ Deakin University (gli73@hotmail.com) + +[N ncases] = size(data); +if (nargin < 3 ) + seeddag = zeros(N,N); % mk_rnd_dag(N); %call BNT function +elseif ~acyclic(seeddag) + seeddag = mk_rnd_dag(N); %zeros(N,N); +end; + +% set default params +scoring_fn = 'bic'; +verbose = 'yes'; + +% get params +args = varargin; +nargs = length(args); +if length(args) > 0 + if isstr(args{1}) + for i = 1:2:nargs + switch args{i} + case 'scoring_fn', scoring_fn = args{i+1}; + case 'verbose', verbose = strcmp(args{i+1},'yes'); + end; + end; + end; +end; + +done = 0; +best_score = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn); +while ~done + [dags,op,nodes] = mk_nbrs_of_dag(seeddag); + nbrs = length(dags); + scores = score_dags(data, nodesizes, dags,'scoring_fn',scoring_fn); + max_score = max(scores); + new = find(scores == max_score ); + if ~isempty(new) & (max_score > best_score) + p = sample_discrete(normalise(ones(1, length(new)))); + best_score = max_score; + seeddag = dags{new(p)}; + else + done = 1; + end; +end; + +dag = seeddag; + diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_mcmc.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_mcmc.m new file mode 100644 index 00000000..36207950 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_mcmc.m @@ -0,0 +1,311 @@ +function [sampled_graphs, accept_ratio, num_edges] = learn_struct_mcmc(data, ns, varargin) +% LEARN_STRUCT_MCMC Monte Carlo Markov Chain search over DAGs assuming fully observed data +% [sampled_graphs, accept_ratio, num_edges] = learn_struct_mcmc(data, ns, ...) +% +% data(i,m) is the value of node i in case m. +% ns(i) is the number of discrete values node i can take on. +% +% sampled_graphs{m} is the m'th sampled graph. +% accept_ratio(t) = acceptance ratio at iteration t +% num_edges(t) = number of edges in model at iteration t +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% scoring_fn - 'bayesian' or 'bic' [ 'bayesian' ] +% Currently, only networks with all tabular nodes support Bayesian scoring. +% type - type{i} is the type of CPD to use for node i, where the type is a string +% of the form 'tabular', 'noisy_or', 'gaussian', etc. [ all cells contain 'tabular' ] +% params - params{i} contains optional arguments passed to the CPD constructor for node i, +% or [] if none. [ all cells contain {'prior', 1}, meaning use uniform Dirichlet priors ] +% discrete - the list of discrete nodes [ 1:N ] +% clamped - clamped(i,m) = 1 if node i is clamped in case m [ zeros(N, ncases) ] +% nsamples - number of samples to draw from the chain after burn-in [ 100*N ] +% burnin - number of steps to take before drawing samples [ 5*N ] +% init_dag - starting point for the search [ zeros(N,N) ] +% +% e.g., samples = my_learn_struct_mcmc(data, ns, 'nsamples', 1000); +% +% +% Modified by Mingyi Wang (mingyiwang@hotmail.com) Sep 18, 2006 (based on Sonia Leach (SML)'s version ( 2/4/02, 9/5/03)) +% +% Some bugs in update_ancestor_matrix() were fixed. This function can call mk_nbrs_of_digraph properly +% + +[n ncases] = size(data); + +% set default params +type = cell(1,n); +params = cell(1,n); +for i=1:n + type{i} = 'tabular'; + %params{i} = { 'prior', 1}; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:n; +clamped = zeros(n, ncases); +nsamples = 100*n; +burnin = 5*n; +dag = zeros(n); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'nsamples', nsamples = args{i+1}; + case 'burnin', burnin = args{i+1}; + case 'init_dag', dag = args{i+1}; + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'clamped', clamped = args{i+1}; + case 'gconstraint', gconstraint=args{i+1}; %Added by mingyi + case 'params', if isempty(args{i+1}), params = cell(1,n); else params = args{i+1}; end + + end +end + +% We implement the fast acyclicity check described by P. Giudici and R. Castelo, +% "Improving MCMC model search for data mining", submitted to J. Machine Learning, 2001. + +% SML: also keep descendant matrix C +use_giudici = 1; +%use_giudici = 0; %Revised by MIngyi +if use_giudici + [nbrs, ops, nodes, A] = mk_nbrs_of_digraph(dag); +else + [nbrs, ops, nodes] = mk_nbrs_of_dag(dag); + A = []; +end + +num_accepts = 1; +num_rejects = 1; +T = burnin + nsamples; +accept_ratio = zeros(1, T); +num_edges = zeros(1, T); +sampled_graphs = cell(1, nsamples); +%sampled_bitv = zeros(nsamples, n^2); + +for t=1:T + [dag, nbrs, ops, nodes, A, accept] = take_step(dag, nbrs, ops, ... + nodes, ns, data, clamped, A, ... + scoring_fn, discrete, type, params); + num_edges(t) = sum(dag(:)); + num_accepts = num_accepts + accept; + num_rejects = num_rejects + (1-accept); + accept_ratio(t) = num_accepts/num_rejects; + if t > burnin + sampled_graphs{t-burnin} = dag; + %sampled_bitv(t-burnin, :) = dag(:)'; + end + fprintf('MCMC: %d/%d\n',t,T); +end + + +%%%%%%%%% + + +function [new_dag, new_nbrs, new_ops, new_nodes, A, accept] = ... + take_step(dag, nbrs, ops, nodes, ns, data, clamped, A, ... + scoring_fn, discrete, type, params, prior_w) + +global gconstraint; %Added by Mingyi +use_giudici = ~isempty(A); +if use_giudici + [new_dag, op, i, j, new_A] = pick_digraph_nbr(dag, nbrs, ops, nodes,A); % updates A + [new_nbrs, new_ops, new_nodes] = mk_nbrs_of_digraph(new_dag,new_A); +else + d = sample_discrete(normalise(ones(1, length(nbrs)))); + new_dag = nbrs{d}; + op = ops{d}; + i = nodes(d, 1); j = nodes(d, 2); + [new_nbrs, new_ops, new_nodes] = mk_nbrs_of_dag1(new_dag); +end +%For debug +% fprintf('op:%s,i:%d,j:%d\n',op,i,j); +% if ~acyclic(new_dag) +% error('new dag must be acyclic!') +% end +% if size(find(diag(new_A)),1)>0 +% A=A +% new_A=new_A +% error('new A must be acyclic!') +% end +%debug ends + +bf = bayes_factor(dag, new_dag, op, i, j, ns, data, clamped, scoring_fn, discrete, type, params); + +%R = bf * (new_prior / prior) * (length(nbrs) / length(new_nbrs)); +R = bf * (length(nbrs) / length(new_nbrs)); +u = rand(1,1); +if u > min(1,R) % reject the move + accept = 0; + new_dag = dag; + new_nbrs = nbrs; + new_ops = ops; + new_nodes = nodes; +else + accept = 1; + if use_giudici + A = new_A; % new_A already updated in pick_digraph_nbr + end +end + + +%%%%%%%%% + +function bfactor = bayes_factor(old_dag, new_dag, op, i, j, ns, data, clamped, scoring_fn, discrete, type, params) + +u = find(clamped(j,:)==0); +LLnew = score_family(j, parents(new_dag, j), type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); +LLold = score_family(j, parents(old_dag, j), type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); +bf1 = exp(LLnew - LLold); + +if strcmp(op, 'rev') % must also multiply in the changes to i's family + u = find(clamped(i,:)==0); + LLnew = score_family(i, parents(new_dag, i), type{i}, scoring_fn, ns, discrete, data(:,u), params{i}); + LLold = score_family(i, parents(old_dag, i), type{i}, scoring_fn, ns, discrete, data(:,u), params{i}); + bf2 = exp(LLnew - LLold); +else + bf2 = 1; +end +bfactor = bf1 * bf2; + + +%%%%%%%% Giudici stuff follows %%%%%%%%%% + + +% SML: This now updates A as it goes from digraph it choses +function [new_dag, op, i, j, new_A] = pick_digraph_nbr(dag, digraph_nbrs, ops, nodes, A) + +d = sample_discrete(normalise(ones(1, length(digraph_nbrs)))); +%d = myunidrnd(length(digraph_nbrs),1,1); +i = nodes(d, 1); j = nodes(d, 2); +new_dag = digraph_nbrs(:,:,d); + +op = ops{d}; +new_A = update_ancestor_matrix(A, op, i, j, dag); +%for debug +% if op=='add' +% if ~(dag(i,j)==0 & new_dag(i,j)==1) +% fprintf('error add\n'); +% end +% end +% if op=='del' +% if ~(dag(i,j)==1 & new_dag(i,j)==0) +% fprintf('new dag del calculation is error!\n') +% end +% end +% if op=='rev' +% if ~(dag(i,j)==1 & dag(j,i)==0 & new_dag(i,j)==0 & new_dag(j,i)==1) +% fprintf('new dag rev calculation is error!\n') +% end +% end +% new_AA = reachability_graph(new_dag'); +% if find(diag(new_AA)==1) +% fprintf('cyclic\n'); +% end +% if ~isequal(new_A,new_AA) +% fprintf('new A calculation is error!\n') +% end +%debug ends + +%%%%%%%%%%%%%% + +function A = update_ancestor_matrix(A, op, i, j, dag) + +switch op +case 'add', + A = do_addition(A, op, i, j, dag); +case 'del', + A = do_removal(A, op, i, j, dag); +case 'rev', + A = do_removal(A, op, i, j, dag); + A = do_addition(A, op, j, i, dag); +end + + +%%%%%%%%%%%% + +function A = do_addition(A, op, i, j, dag) + +A(j,i) = 1; % i is an ancestor of j +anci = find(A(i,:)); +if ~isempty(anci) + A(j,anci) = 1; % all of i's ancestors are added to Anc(j) +end + +descj = find(A(:,j)); %all the descendants of j are selected +if ~isempty(descj) + for k=descj(:)' + A(k,i) = 1; % i is the ancestor of descj + if ~isempty(anci) % all of i's ancestors are also the ancestor of each descendant of j + A(k,anci)=1; + end + end +end + + +%%%%%%%%%%% + +function A = do_removal(A, op, i, j, dag) +descj = find(A(:,j)); +A = update_row(A,i, j, dag); % compute the A(j,:) row for dag i->j removal + +if ~isempty(descj) + order = topological_sort(dag); %all the parent nodes are before to the children nodes + [junk, perm] = sort(order); %node i is perm(i)-TH in order + descj_topnum = perm(descj); %descj(i) is descj_topnum(i)-th in order + +% SML: now re-sort descj by rank in descj_topnum + [junk, perm] = sort(descj_topnum); + descj = descj(perm); + for k = descj(:)' + A = old_update_row(A, k, dag); + end +end + +%%%%%%%%% + +function A = update_row(A, i,j, dag) +% We compute row j of A +A(j, :) = 0; +ps = parents(dag, j); +ps=setdiff(ps,i); % All the parents except i +if ~isempty(ps) + A(j, ps) = 1; +end +for k=ps(:)' + anck = find(A(k,:)); + if ~isempty(anck) + A(j, anck) = 1; + end +end + +%%%%%%%%% + +function A = old_update_row(A, j, dag) + +% We compute row j of A +A(j, :) = 0; +ps = parents(dag, j); +if ~isempty(ps) + A(j, ps) = 1; +end +for k=ps(:)' + anck = find(A(k,:)); + if ~isempty(anck) + A(j, anck) = 1; + end +end + +%%%%%%%% + +function A = init_ancestor_matrix(dag) + +order = topological_sort(dag); +A = zeros(length(dag)); +for j=order(:)' + A = update_row(A, j, dag); +end diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_mwst.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_mwst.m new file mode 100644 index 00000000..8437385e --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_mwst.m @@ -0,0 +1,62 @@ +function [T, score_mat] = learn_struct_mwst(data, discrete, node_sizes, node_type, scoring_fn, root) +% LEARN_STRUCT_MWST Learn an oriented tree using the MSWT algorithm +% T = learn_struct_mwst(data, discrete, node_sizes, node_type, scoring_fn, root) +% +% Input : +% data(i,m) is the node i in the case m, +% discrete = [ 1 if discret-node 0 if not ], +% node_sizes = 1 if gaussian node, +% node_type = {'tabular','gaussian',...}, +% score = 'bic' (for complete data and any node types) or 'mutual_info' (tabular nodes), +% root is the futur root-node of the tree T. +% +% Output : +% T = adjacency matrix of the tree +% +% V1.2 : 17 feb 2003 (O. Francois - francois.olivier.c.h@gmail.com, Ph. Leray - philippe.leray@univ-nantes.fr) +% +% +% See Chow&Liu 1968 for the original algorithm using Mutual Information scoring. +% Or Heckerman 1994. + +if nargin <4 + error('Requires at least 4 arguments.') +end + +if nargin == 4 + scoring_fn='bic'; root=1; +end; + +if nargin == 5 + root=1; +end; + + +N=size(data,1); +score_mat=zeros(N,N); + +switch scoring_fn +case 'bic', + for i=1:(N-1) + score2 = score_family(i, [], node_type{i}, scoring_fn, node_sizes, discrete, data,[]); + for j=(i+1):N + score1 = score_family(i, [j], node_type{i}, scoring_fn, node_sizes, discrete, data,[]); + score = score2-score1; + score_mat(i,j)=score; + score_mat(j,i)=score; + end + end +case 'mutual_info', + for i=1:(N-1) + for j=(i+1):N + score_mat(i,j)= -mutual_info_score(i,node_sizes(i),j,node_sizes(j),data); + score_mat(j,i)=score_mat(i,j); + end + end +otherwise, + error(['unrecognized scoring fn ' scoring_fn]); +end + +G = minimum_spanning_tree(score_mat); +T = mk_rooted_tree(G, root); +T=full(T); \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_mwst_EM.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_mwst_EM.m new file mode 100644 index 00000000..7dcf531e --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_mwst_EM.m @@ -0,0 +1,171 @@ +function [bnet0, Sbest, Obest] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, nbloopmax, thresh) +% LEARN_STRUCT_MWST_EM Learn an oriented tree using the MSWT algorithm +% [bnet, Ebic] = learn_struct_mwstem(data, discrete, node_sizes, prior, +% nbloopmax, thresh) +% +% Input : +% data{i,m} a cell where the node i in the case m, +% discrete = [ 1 if discret-node 0 if not ], (1:N) +% node_sizes = 1 if gaussian node, (max on complete samples) +% prior = 1 to use uniform Dirichlet prior (0) +% nbloopmax = max loop number (ceil(log(N*log(N)))) +% thresh = the convergence test's threshold (1e-3) +% +% Output : +% bnet = the output bayesian network +% Ebic = the espected BIC score of bnet given the data +% +% francois.olivier.c.h@gmail.com + +%%%%%%%%%%%%% +%fprintf('-- INITIALIZATION\n'); +%%%%%%%%%%%%% +[N, m]=size(data); + rand('state',sum(100*clock)) + +if nargin<6, thresh = 1e-4; end +if nargin<5, nbloopmax = 15; end +if nargin<4, prior = 0; end +if nargin<3, + misv = -9999; + data_mat = bnt_to_mat(data,misv); + node_sizes = max(data_mat'), +end +if nargin<2, discrete = ones(1,N); end + +nbloop = 1; last=0; +max_iter = 6; % for learn_struct_params + +%%%%%%%%%%%%% +%fprintf(' Choice of the first bnet ');tic +%%%%%%%%%%%%% +% Random Chain like DAG +T = diag(ones(N-1,1),1); T=T+T'; +order=randperm(N); root=randperm(N); root=root(1); +[tmp order2]=sort(order); order2; +T2 = full(mk_rooted_tree(T,order2(root))); +torder=topological_sort(T2(order2,order2)); +[tmp torder2]=sort(torder); torder2; +ordre = 1:N;torder=ordre;torder2=ordre;order2=ordre; +%figure(2), subplot(3,2,1), draw_graph(T2(order2,order2));drawnow + +bnet0 = mk_bnet(T2(order2(torder),order2(torder)), node_sizes(torder)); +%for i=1:N, bnet0.CPD{i} = tabular_CPD(bnet0, i); end % probleme de log of zeros si tous les cas ne sont pas reprďż˝sentďż˝s dans la base +for i=1:N, bnet0.CPD{i} = tabular_CPD(bnet0, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); end % a priori -> change les espected counts +%tmp = toc;fprintf(' : %6.2f seconds\n',tmp); + +Sbest = -Inf; Bbest = bnet0; fini=0;T3=T2; +while not(fini) + %%%%%%%%%%%%% + %fprintf('Loop %d : learning parameters...\n',nbloop);tic +% engine0=jtree_sparse_inf_engine(bnet0); + engine0=jtree_inf_engine(bnet0); + [bnet1, LL1, engine1] = learn_params_em(engine0, data(torder,:), max_iter, thresh); + %tmp = toc; + %fprintf(' Parameters learning : %6.2f seconds\n',tmp); + + BIC0=0; + for i=1:N, + xxx=struct(bnet1.CPD{i}); + BIC0=BIC0+bic_score_family(xxx.counts, xxx.CPT, xxx.nsamples); + end + %fprintf('%d ',torder), fprintf('%5.2f\n',BIC0); + %figure(2), subplot(3,2,nbloop), title(sprintf('%5.2f',BIC0)); + + if BIC0 < Sbest+ thresh*abs(Sbest) | nbloop>nbloopmax + fini=1; + else + Sbest = BIC0; + Bbest = bnet1; + Obest = torder2 ; + Tbest = T3; + + %%%%%%%%%%%%% + %tic; + %%%%%%%%%%%%% + + theta_Xi=cell(N,1); + evidence = cell(1,N); + [engine2, loglik] = enter_evidence(engine1, evidence); + for j=1:N, + SS= marginal_nodes (engine2,torder2(j)); + theta_Xi{j} = SS.T; + end + + theta_Xj_given_Xi = cell(N,N); + for i=1:N + for vali = 1:node_sizes(i) + evidence = cell(1,N); evidence{torder2(i)} = vali; + [engine2, loglik] = enter_evidence(engine1, evidence); + for j=mysetdiff(1:N,i) + SS= marginal_nodes (engine2,torder2(j)); + theta_Xj_given_Xi{j,i} = [theta_Xj_given_Xi{j,i}, SS.T]; + end + end + end + %celldisp(theta_Xi); + + BIC_mat=zeros(N,N); + for i=1:N, + BIC_mat(i,i)=bic_score_family(theta_Xi{i}*m,theta_Xi{i},m); + for j=mysetdiff(1:N,i) + theta_XjXi=(ones(node_sizes(i),1)*theta_Xi{j}').*theta_Xj_given_Xi{i,j}; + BIC_mat(i,j)= bic_score_family(m*theta_XjXi,theta_Xj_given_Xi{i,j},m); + end + end + BIC_delta = BIC_mat-diag(BIC_mat)*ones(1,N); + + BIC1=0; + for i = 1:N + j = find(bnet0.dag(:,i)==1); + if isempty(j) + BIC1 = BIC1 + BIC_mat(torder2(i),torder2(i)); + else + BIC1 = BIC1 + BIC_mat(torder2(i),torder2(j)); + end + end + %fprintf('%d ',torder), fprintf('%5.2f (1)\n',BIC1); + + %fprintf(' Creation of the score matrix '); + %tmp = toc;fprintf(' : %6.2f seconds\n',tmp); + + %%%%%%%%%%%%% + %fprintf(' Creation of the new bnet ');tic + %%%%%%%%%%%%% + T2 = minimum_spanning_tree(-BIC_delta); + root=randperm(N); root=root(1); + T3 = full(mk_rooted_tree(T2, root)); + torder=topological_sort(T3); + [tmp torder2]=sort(torder); torder2; + + bnet0 = mk_bnet(T3(torder,torder), node_sizes(torder)); +%bnet0.order + %BIC=0; + for i = 1:N + j = find(T3(:,i)==1); + if isempty(j) + bnet0.CPD{torder2(i)} = tabular_CPD(bnet0, torder2(i), 'CPT', theta_Xi{i}, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); + %BIC = BIC + BIC_mat(i,i); + else + bnet0.CPD{torder2(i)} = tabular_CPD(bnet0, torder2(i), 'CPT', theta_Xj_given_Xi{i,j}, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); + %BIC=BIC + BIC_mat(i,j); + end + end + nbloop=nbloop+1; + %figure(2), subplot(3,2,nbloop), draw_graph(T3); drawnow + %tmp = toc;fprintf(' : %6.2f seconds\n',tmp); + fprintf('================================================================================\n'); + %fprintf(' --> BIC score = %6.2f\n\n\n',BIC); + theta_Xi_best=theta_Xi; + theta_Xj_given_Xi_best=theta_Xj_given_Xi; + end +end + +% Bbest.dag +% Tbest + +% bnet = mk_bnet(Tbest, node_sizes); +% for i=1:N, bnet.CPD{i} = tabular_CPD(bnet, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); end +% engine=jtree_sparse_inf_engine(bnet); +% [Bbest, LL1] = learn_params_em(engine, data, max_iter, thresh); + diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_pdag_pc_mod.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_pdag_pc_mod.m new file mode 100644 index 00000000..081c70f1 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_pdag_pc_mod.m @@ -0,0 +1,158 @@ +function [pdag, G] = learn_struct_pdag_pc_mod(cond_indep, n, k, varargin) +% LEARN_STRUCT_PDAG_PC Learn a partially oriented DAG (pattern) using the PC algorithm +% P = learn_struct_pdag_pc(cond_indep, n, k, ...) +% +% n is the number of nodes. +% k is an optional upper bound on the fan-in (default: n) +% cond_indep is a boolean function that will be called as follows: +% feval(cond_indep, x, y, S, ...) +% where x and y are nodes, and S is a set of nodes (positive integers), +% and ... are any optional parameters passed to this function. +% +% The output P is an adjacency matrix, in which +% P(i,j) = -1 if there is an i->j edge. +% P(i,j) = P(j,i) = 1 if there is an undirected edge i <-> j +% +% The PC algorithm does structure learning assuming all variables are observed. +% See Spirtes, Glymour and Scheines, "Causation, Prediction and Search", 1993, p117. +% This algorithm may take O(n^k) time if there are n variables and k is the max fan-in, +% but this is quicker than the Verma-Pearl IC algorithm, which is always O(n^n). + + +sep = cell(n,n); +ord = 0; +done = 0; +G = ones(n,n); +G=setdiag(G,0); +while ~done + done = 1; + [X,Y] = find(G); + for i=1:length(X) + x = X(i); y = Y(i); + %nbrs = mysetdiff(myunion(neighbors(G, x), neighbors(G,y)), [x y]); + nbrs = mysetdiff(neighbors(G, y), x); % bug fix by Raanan Yehezkel 6/27/04 + if length(nbrs) >= ord & G(x,y) ~= 0 + done = 0; + %SS = subsets(nbrs, ord, ord); % all subsets of size ord + SS = subsets1(nbrs, ord); + for si=1:length(SS) + S = SS{si}; + if feval(cond_indep, x, y, S, varargin{:}) + %if isempty(S) + % fprintf('%d indep of %d ', x, y); + %else + % fprintf('%d indep of %d given ', x, y); fprintf('%d ', S); + %end + %fprintf('\n'); + + % diagnostic + %[CI, r] = cond_indep_fisher_z(x, y, S, varargin{:}); + %fprintf(': r = %6.4f\n', r); + + G(x,y) = 0; + G(y,x) = 0; + sep{x,y} = myunion(sep{x,y}, S); + sep{y,x} = myunion(sep{y,x}, S); + break; % no need to check any more subsets + end + end + end + end + ord = ord + 1; +end + +% Create the minimal pattern, +% i.e., the only directed edges are V structures. +pdag = G; +[X, Y] = find(G); +% We want to generate all unique triples x,y,z +% This code generates x,y,z and z,y,x. +for i=1:length(X) + x = X(i); + y = Y(i); + Z = find(G(y,:)); + Z = mysetdiff(Z, x); + for z=Z(:)' + if G(x,z)==0 & ~ismember(y, sep{x,z}) & ~ismember(y, sep{z,x}) + %fprintf('%d -> %d <- %d\n', x, y, z); + pdag(x,y) = -1; pdag(y,x) = 0; + pdag(z,y) = -1; pdag(y,z) = 0; + end + end +end + +% Convert the minimal pattern to a complete one, +% i.e., every directed edge in P is compelled +% (must be directed in all Markov equivalent models), +% and every undirected edge in P is reversible. +% We use the rules of Pearl (2000) p51 (derived in Meek (1995)) + +old_pdag = zeros(n); +iter = 0; +while ~isequal(pdag, old_pdag) + iter = iter + 1; + old_pdag = pdag; + % rule 1 + [A,B] = find(pdag==-1); % a -> b + for i=1:length(A) + a = A(i); b = B(i); + C = find(pdag(b,:)==1 & G(a,:)==0); % all nodes adj to b but not a + if ~isempty(C) + pdag(b,C) = -1; pdag(C,b) = 0; + %fprintf('rule 1: a=%d->b=%d and b=%d-c=%d implies %d->%d\n', a, b, b, C, b, C); + end + end + % rule 2 + [A,B] = find(pdag==1); % unoriented a-b edge + for i=1:length(A) + a = A(i); b = B(i); + if any( (pdag(a,:)==-1) & (pdag(:,b)==-1)' ); + pdag(a,b) = -1; pdag(b,a) = 0; + %fprintf('rule 2: %d -> %d\n', a, b); + end + end + % rule 3 + [A,B] = find(pdag==1); % a-b + for i=1:length(A) + a = A(i); b = B(i); + % Bug fix by Imme Ebert-Uphoff (ebert@tree.com), Jan 2007 + % C = find( (G(a,:)==1) & (pdag(:,b)==-1)' ); + C = find( (pdag(a,:)==1) & (pdag(:,b)==-1)' ); + % C contains nodes c s.t. a-c->ba + G2 = setdiag(G(C, C), 1); + if any(G2(:)==0) % there are 2 different non adjacent elements of C + pdag(a,b) = -1; pdag(b,a) = 0; + %fprintf('rule 3: %d -> %d\n', a, b); + end + end +end + + +% % Test Rule 3 of PC algorithm +% +% % Define PDAG +% pdag = zeros(4); +% pdag(2,1)=-1; +% pdag(3,1)=-1; +% pdag(2,4)=-1; +% pdag(3,4)=-1; +% pdag(1,4)=1; +% pdag(4,1)=1; +% +% fprintf('\nSample input PDAG:\n'); +% pdag +% +% fprintf('Sample DAG generated from PDAG:\n'); +% dag = abs(pdag_to_dag(pdag)) +% +% fprintf('Output from current PC algorithm:\n'); +% pdag_PC = learn_struct_pdag_pc('dsep', 4, 3, dag) +% +% % Problem can be fixed by changing Line 120 of learn_struct_pdag_pc.m +% % C = find( (G(a,:)==1) & (pdag(:,b)==-1)' ); +% % to +% % C = find( (pdag(a,:)==1) & (pdag(:,b)==-1)' ); +% +% fprintf('Correct version:\n'); +% pdag_PC_mod = learn_struct_pdag_pc_mod('dsep', 4, 3, dag) + diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_tan.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_tan.m new file mode 100644 index 00000000..df122b43 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_tan.m @@ -0,0 +1,104 @@ +function dag = learn_struct_tan(data, class_node, root, node_sizes, scoring_fn) +% LEARN_STRUCT_TAN Learn the structure of the tree augmented naive bayesian network +% (with discrete nodes) +% dag = learn_struct_tan(app, class, root, node_sizes) +% +% Input : +% data(i,m) is the value of node i in case m +% class_node is the class node +% root is the root node of the tree part of the dag (must be different from the class node) +% node_sizes = 1 if gaussian node, +% scoring_fn = 'bic' (default value) or 'mutual_info' +% +% Output : +% dag = adjacency matrix of the dag +% +% V1.1 : 21 may 2003, (O. Francois - francois.olivier.c.h@gmail.com, Ph. Leray - philippe.leray@univ-nantes.fr) +% V1.2 : may 2005 bug correction about node types (Navid Serrano ) + + +if nargin <4 + error('Requires at least 4 arguments.') +end + +if nargin == 4 + scoring_fn='bic'; +end; + +if class_node==root + error(' The root node can''t be the class node.'); +end + +% if root>class_node +% root=root-1; +% end + +N=size(data,1); +node_types=cell(N-1,1); +notclass=setdiff(1:N,class_node); +for i=1:N + if node_sizes(i)==1 + node_types{i}='gaussian'; + else + node_types{i}='tabular'; + end +end + +dag=zeros(N); +T = learn_struct_mwst4tan(data, ones(1,N), node_sizes, node_types, scoring_fn, root, class_node); +dag=T; +dag(class_node,notclass)=1; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function [T, score_mat] = learn_struct_mwst4tan(data, discrete, node_sizes, node_type, scoring_fn, root, class) + +if nargin <4 + error('Requires at least 4 arguments.') +end + +if nargin == 4 + scoring_fn='bic'; root=1; +end; + +if nargin == 5 + root=1; +end; + +N=size(data,1); +score_mat=zeros(N,N); +score_mat(class,:)=Inf; +score_mat(:,class)=Inf; + +switch scoring_fn +case 'bic', + for i=mysetdiff(1:(N-1), class) + score2 = score_family(i, [class], node_type{i}, scoring_fn, node_sizes, discrete, data,[]); + for j=mysetdiff((i+1):N, class) + score1 = score_family(i, [j,class], node_type{i}, scoring_fn, node_sizes, discrete, data,[]); + score = score2-score1; + score_mat(i,j)=score; + score_mat(j,i)=score; + end + end +case 'mutual_info', % tabular nodes only + for i=mysetdiff(1:(N-1), class) + for j=mysetdiff((i+1):N, class) + score_mat(i,j)= -cond_mutual_info_score(i,node_sizes(i),j,node_sizes(j),class,node_sizes(class),data); + score_mat(j,i)=score_mat(i,j); + end + end +otherwise, + error(['unrecognized scoring fn ' scoring_fn]); +end + +variab = mysetdiff(1:N,class); +%score_mat +G = minimum_spanning_tree(score_mat(variab,variab)); +if root>class, root=root-1;end +T = mk_rooted_tree(G, root); +T1 = full(T); +T=zeros(N); +T(variab,variab)=T1; + + + diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_tan_EM.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_tan_EM.m new file mode 100644 index 00000000..2fa81ac6 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_tan_EM.m @@ -0,0 +1,172 @@ +function dag = learn_struct_tan_EM(data, class, node_sizes, root, prior, nbloopmax, thresh) +% LEARN_STRUCT_TAN_EM +% dag = learn_struct_tan_EM(data, class, node_sizes, root, prior, nbloopmax, thresh) +% +% Learn TAN classifier for discrete variables from incomplete dataset +% +% Input : +% data{i,m} a cell where the node i in the case m, +% class is the number of the class node, +% node_sizes = 1 if gaussian node, (max on complete samples) +% root is the futur root-node of the tree T. (random) +% prior = 1 to use uniform Dirichlet prior (0) +% nbloopmax = max loop number (ceil(log(N*log(N)))) +% thresh = the convergence test's threshold (1e-3) +% +% Output : +% bnet = the output bayesian network +% Ebic = the espected BIC score of bnet given the data +% +% francois.olivier.c.h@gmail.com + +[N, m]=size(data); +log_m = log(m); +if nargin<7, thresh = 1e-4; end +max_iter = 10; % for learn_struct_params +if nargin<6, nbloopmax = 15, end +if nargin<5, prior = 0; end +if nargin<4, root = ceil(N*rand(1)), end +if nargin<3, + misv = -9999; + data_mat = bnt_to_mat(data,misv); + node_sizes = max(data_mat'), + clear data_mat +end + +discrete = 1:N; +nbloop = 0; +variab = mysetdiff(1:N, class); + +[Bbest1, Sbest, Obest] = learn_struct_mwst_EM4tan(data(variab, :), 1:N-1, node_sizes(variab), prior, nbloopmax, thresh); +dag = zeros(N); +dag(variab, variab) = Bbest1.dag; +dag(class, variab) = 1; + +%Bbest = mk_bnet(dag, discrete); +%for i=discrete, Bbest.CPD{i} = tabular_CPD(Bbest, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); end +%engine = jtree_inf_engine(Bbest); +%Bbest = learn_params_em(engine, data, max_iter, thresh); + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function [Bbest, Sbest, Obest] = learn_struct_mwst_EM4tan(data, discrete, node_sizes, prior, nbloopmax, thresh) + +%fprintf('-- INITIALIZATION\n'); +%%%%%%%%%%%%% +[N, m]=size(data); + rand('state',sum(100*clock)) + +if nargin<6, thresh = 1e-4; end +if nargin<5, nbloopmax = 15; end +if nargin<4, prior = 0; end +if nargin<3, + misv = -9999; + data_mat = bnt_to_mat(data,misv); + node_sizes = max(data_mat'), +end +if nargin<2, discrete = ones(1,N); end + +nbloop = 1; last=0; +max_iter = 15; % for learn_struct_params + +%%%%%%%%%%%%% +% Random Chain like DAG +T = diag(ones(N-1,1),1); T=T+T'; +order=randperm(N); root=randperm(N); root=root(1); +[tmp order2]=sort(order); order2; +T2 = full(mk_rooted_tree(T,order2(root))); +torder=topological_sort(T2(order2,order2)); +[tmp torder2]=sort(torder); torder2; + +bnet0 = mk_bnet(T2(order2(torder),order2(torder)), node_sizes(torder)); +for i=1:N, bnet0.CPD{i} = tabular_CPD(bnet0, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); end % a priori -> change les espected counts + +%%%%%%%%%%%%% +Sbest = -Inf; Bbest = bnet0; fini=0; + +while not(fini) + %fprintf('Loop %d : learning parameters...\n',nbloop); + engine0=jtree_inf_engine(bnet0); + [bnet1, LL1, engine1] = learn_params_em(engine0, data(torder,:), max_iter, thresh); + + BIC0=0; + for i=1:N, + xxx=struct(bnet1.CPD{i}); + BIC0=BIC0+bic_score_family(xxx.counts, xxx.CPT, xxx.nsamples); + end + + if BIC0 < Sbest+ thresh*abs(Sbest), + fini=1; + else + Sbest = BIC0; + Bbest = bnet1; + Obest = torder2 ; + %%%%%%%%%%%%% + + theta_Xi=cell(N,1); + evidence = cell(1,N); + [engine2, loglik] = enter_evidence(engine1, evidence); + for j=1:N, + SS= marginal_nodes (engine2,torder2(j)); + theta_Xi{j} = SS.T; + end + + theta_Xj_given_Xi = cell(N,N); + for i=1:N + for vali = 1:node_sizes(i) + evidence = cell(1,N); evidence{torder2(i)} = vali; + [engine2, loglik] = enter_evidence(engine1, evidence); + for j=mysetdiff(1:N,i) + SS= marginal_nodes (engine2,torder2(j)); + theta_Xj_given_Xi{j,i} = [theta_Xj_given_Xi{j,i}, SS.T]; + end + end + end + + BIC_mat=zeros(N,N); + for i=1:N, + BIC_mat(i,i)=bic_score_family(theta_Xi{i}*m,theta_Xi{i},m); + for j=mysetdiff(1:N,i) + theta_XjXi=(ones(node_sizes(i),1)*theta_Xi{j}').*theta_Xj_given_Xi{i,j}; + BIC_mat(i,j)= bic_score_family(m*theta_XjXi,theta_Xj_given_Xi{i,j},m); + end + end + BIC_delta = BIC_mat-diag(BIC_mat)*ones(1,N); + + BIC1=0; + for i = 1:N + j = find(bnet0.dag(:,i)==1); + if isempty(j) + BIC1 = BIC1 + BIC_mat(torder2(i),torder2(i)); + else + BIC1 = BIC1 + BIC_mat(torder2(i),torder2(j)); + end + end + + %fprintf(' Creation of the new bnet ');tic + %%%%%%%%%%%%% + T2 = minimum_spanning_tree(-BIC_delta); + root=randperm(N); root=root(1); + T3 = full(mk_rooted_tree(T2, root)); + torder=topological_sort(T3); + [tmp torder2]=sort(torder); torder2; + + bnet0 = mk_bnet(T3(torder,torder), node_sizes(torder)); + %BIC=0; + for i = 1:N + j = find(T3(:,i)==1); + if isempty(j) + bnet0.CPD{torder2(i)} = tabular_CPD(bnet0, torder2(i), 'CPT', theta_Xi{i}, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); + %BIC = BIC + BIC_mat(i,i); + else + bnet0.CPD{torder2(i)} = tabular_CPD(bnet0, torder2(i), 'CPT', theta_Xj_given_Xi{i,j}, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); + %BIC=BIC + BIC_mat(i,j); + end + end + nbloop=nbloop+1; + fprintf('================================================================================\n'); + if nbloop>nbloopmax + fini=1; + end + end +end diff --git a/sourcecodes/bnt-master/SLP/learning/mk_naive_struct.m b/sourcecodes/bnt-master/SLP/learning/mk_naive_struct.m new file mode 100644 index 00000000..33e6b7d7 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/mk_naive_struct.m @@ -0,0 +1,6 @@ +function S = mk_naive_struct(n,C) +% +% S = mk_naive_struct(Number_of_nodes, Class_node) +% +S = zeros(n); +S(C,setdiff(1:n,C)) = 1; \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/license.gpl b/sourcecodes/bnt-master/SLP/license.gpl new file mode 100644 index 00000000..6a2d5712 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/license.gpl @@ -0,0 +1,450 @@ +This library is free software; 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IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR +REDISTRIBUTE THE LIBRARY AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, +INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING +OUT OF THE USE OR INABILITY TO USE THE LIBRARY (INCLUDING BUT NOT LIMITED TO +LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR +THIRD PARTIES OR A FAILURE OF THE LIBRARY TO OPERATE WITH ANY OTHER +SOFTWARE), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE +POSSIBILITY OF SUCH DAMAGES. + +END OF TERMS AND CONDITIONS + +This library is free software; you can redistribute it and/or +modify it under the terms of the GNU Library General Public +License version 2 as published by the Free Software Foundation. + +This library is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. + + diff --git a/sourcecodes/bnt-master/SLP/misc/CPT_from_bnet.m b/sourcecodes/bnt-master/SLP/misc/CPT_from_bnet.m new file mode 100644 index 00000000..64a62516 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/CPT_from_bnet.m @@ -0,0 +1,16 @@ +function CPT = CPT_from_bnet(bnet,v) +% Export CPT from bnet +% CPT = CPT_from_bnet(bnet,v) +% +% optional : v~=0 --> verbose mode + +if nargin<2, v=0; end +N = size(bnet.dag,1); +CPT = cell(1,N); +for j=1:N + CPD=struct(bnet.CPD{j}); + %counts{j}=CPD.counts; + CPT{j}=CPD.CPT; +end +if v, celldisp(CPT);end +%nsamples = CPD.nsamples; diff --git a/sourcecodes/bnt-master/SLP/misc/Markov_equivalent_dags.m b/sourcecodes/bnt-master/SLP/misc/Markov_equivalent_dags.m new file mode 100644 index 00000000..455037c9 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/Markov_equivalent_dags.m @@ -0,0 +1,42 @@ +function [n_dags, dag_list] = Markov_equivalent_dags(dag) + +% +% [n_dags, dag_list] = Markov_equivalent_dags(dag) +% +% generates a cell array of all Markov equivalent DAGs +% corresponding to the input DAG. +% +% YOU NEED TO HAVE THE STRUCTURE LEARNING PACKAGE IN PLACE TO USE THIS FUNCTION! +% +% Input: DAG (in standard format, i.e. dag(a,b)=1 if and only if a->b) +% +% Output: Number of DAGs generated and +% Cell array of all Markov-equivalent DAGs (in same format as input) +% +% Sample Use: +% +% Example 1: +% % Find all DAGs equivalent to DAG of Asia Network +% BN = mk_asia_bnet(); +% dag = BN.dag; +% [n_dags, dag_list] = Markov_equivalent_dags(dag); +% n_dags % Answer should be: 6 +% dag_list{1} % displays the first DAG, etc. +% +% Example 2: +% % Find all DAGs equivalent to random DAG +% dag = mk_rnd_dag(4); +% [n_dags, dag_list] = Markov_equivalent_dags(dag); +% +% Imme Ebert-Uphoff (ebert@tree.com), 2007 +% + + % find completed PDAG corresponding to DAG + cpdag = dag_to_cpdag(dag); + + % convert to our notation, i.e. directed edge has (-1) instead of (1) + signed_pdag = pdag_unsigned_to_signed(cpdag); + + % find all corresponding DAGs + [n_dags,dag_list] = pdag_to_all_dags( signed_pdag ); + diff --git a/sourcecodes/bnt-master/SLP/misc/bnt_to_mat.m b/sourcecodes/bnt-master/SLP/misc/bnt_to_mat.m new file mode 100644 index 00000000..27608feb --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/bnt_to_mat.m @@ -0,0 +1,18 @@ +function res = bnt_to_mat(data,misv) +% res = bnt_to_mat(data,misv) +% + +if nargin <1, error('Requires at least 1 argument.'), end +if nargin == 1, misv=-9999; end +taille=size(data); +long=taille(1); +larg=taille(2); +for i=1:long + for j=1:larg + if ~isempty(data{i,j}) + res(i,j)=data{i,j}; + else + res(i,j)=misv; + end + end +end diff --git a/sourcecodes/bnt-master/SLP/misc/chi2_table.m b/sourcecodes/bnt-master/SLP/misc/chi2_table.m new file mode 100644 index 00000000..e753d126 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/chi2_table.m @@ -0,0 +1,115 @@ +function value = chi2_table(P, df) +% function value = chi2_table(P, df) +% +% df 0.25 0.20 0.15 0.10 0.05 0.025 0.02 0.01 0.005 0.0025 0.001 0.0005 +% 1 1.32 1.64 2.07 2.71 3.84 5.02 5.41 6.63 7.88 9.14 10.83 12.12 +% 2 2.77 3.22 3.79 4.61 5.99 7.38 7.82 9.21 10.60 11.98 13.82 15.20 +% 3 4.11 4.64 5.32 6.25 7.81 9.35 9.84 11.34 12.84 14.32 16.27 17.73 +% 4 5.39 5.59 6.74 7.78 9.49 11.14 11.67 13.23 14.86 16.42 18.47 20.00 +% 5 6.63 7.29 8.12 9.24 11.07 12.83 13.33 15.09 16.75 18.39 20.51 22.11 +% 6 7.84 8.56 9.45 10.64 12.53 14.45 15.03 16.81 13.55 20.25 22.46 24.10 +% 7 9.04 5.80 10.75 12.02 14.07 16.01 16.62 18.48 20.28 22.04 24.32 26.02 +% 8 10.22 11.03 12.03 13.36 15.51 17.53 18.17 20.09 21.95 23.77 26.12 27.87 +% 9 11.39 12.24 13.29 14.68 16.92 19.02 19.63 21.67 23.59 25.46 27.83 29.67 +% 10 12.55 13.44 14.53 15.99 18.31 20.48 21.16 23.21 25.19 27.11 29.59 31.42 +% 11 13.70 14.63 15.77 17.29 19.68 21.92 22.62 24.72 26.76 28.73 31.26 33.14 +% 12 14.85 15.81 16.99 18.55 21.03 23.34 24.05 26.22 28.30 30.32 32.91 34.82 +% 13 15.93 15.58 18.90 19.81 22.36 24.74 25.47 27.69 29.82 31.88 34.53 36.48 +% 14 17.12 18.15 19.4 21.06 23.68 26.12 26.87 29.14 31.32 33.43 36.12 38.11 +% 15 18.25 19.31 20.60 22.31 25.00 27.49 28.26 30.58 32.80 34.95 37.70 39.72 +% 16 19.37 20.47 21.79 23.54 26.30 28.85 29.63 32.00 34.27 36.46 39.25 41.31 +% 17 20.49 21.61 22.98 24.77 27.59 30.19 31.00 33.41 35.72 37.95 40.79 42.88 +% 18 21.60 22.76 24.16 25.99 28.87 31.53 32.35 34.81 37.16 39.42 42.31 44.43 +% 19 22.72 23.90 25.33 27.20 30.14 32.85 33.69 36.19 38.58 40.88 43.82 45.97 +% 20 23.83 25.04 26.50 28.41 31.41 34.17 35.02 37.57 40.00 42.34 45.31 47.50 +% 21 24.93 26.17 27.66 29.62 39.67 35.48 36.34 38.93 41.40 43.78 46.80 49.01 +% 22 26.04 27.30 28.82 30.81 33.92 36.78 37.66 40.29 42.80 45.20 48.27 50.51 +% 23 27.14 28.43 29.98 32.01 35.17 38.08 38.97 41.64 44.18 46.62 49.73 52.00 +% 24 28.24 29.55 31.13 33.20 36.42 39.36 40.27 42.98 45.56 48.03 51.18 53.48 +% 25 29.34 30.68 32.28 34.38 37.65 40.65 41.57 44.31 46.93 49.44 52.62 54.95 +% 26 30.43 31.79 33.43 35.56 38.89 41.92 42.86 45.64 48.29 50.83 54.05 56.41 +% 27 31.53 32.91 34.57 36.74 40.11 43.19 44.14 46.96 49.64 52.22 55.48 57.86 +% 28 32.62 34.03 35.71 37.92 41.34 44.46 45.42 48.28 50.99 53.59 56.89 59.30 +% 29 33.71 35.14 36.85 39.09 42.56 45.72 46.69 49.59 52.34 54.97 58.30 60.73 +% 30 34.80 36.25 37.99 40.26 43.77 46.98 47.96 50.89 53.67 56.33 59.70 62.16 +% 40 45.62 47.27 49.24 51.81 55.76 59.34 60.44 63.69 66.77 69.70 73.40 76.09 +% 50 56.33 53.16 60.35 63.17 67.50 71.42 72.61 76.15 79.49 82.66 86.66 89.56 +% 60 66.98 68.97 71.34 74.40 79.08 83.30 84.58 88.38 91.95 95.34 99.61 102.7 +% 80 88.13 90.41 93.11 96.58 101.9 106.6 108.1 112.3 116.3 120.1 124.8 128.3 +% 100 109.1 111.7 114.7 118.5 124.3 129.6 131.1 135.8 140.2 144.3 149.4 153.2 +% df 0.25 0.20 0.15 0.10 0.05 0.025 0.02 0.01 0.005 0.0025 0.001 0.0005 +% + +if nargin<2, error('not enought arguments'); end +if P>0.25, error('first argument too big'); end +if P<0.0005, error('first argument too low'); end +if df>100, error('second argument too big'); end +if df<1, error('second argument too low'); end + +possible_P = [0.25,0.20,0.15,0.10,0.05,0.025,0.02 ,0.01,0.005,0.0025,0.001,0.0005]; +possible_df = [1:30, 40 50 60 80 100]; + +if ~ismember(P,possible_P), + res = sort([possible_P P]); res=res(end:-1:1); + [tmp indxP]=find(res==P); + indxP1 = indxP-1; indxP2=indxP; + P2=[possible_P(indxP1), possible_P(indxP2)]; + force_P = normalise(abs(P2-P)); +else + P2=[P P]; force_P=[1,0];[tmp,indxP1]=find(possible_P==P);indxP2=indxP1+1; +end +if ~ismember(df,possible_df), + res = sort([possible_df df]); + [tmp indxdf]=find(res==df); + indxdf1 = indxdf-1; indxdf2=indxdf; + df2=[possible_df(indxdf1), possible_df(indxdf2)]; + force_df = normalise(abs(df2-df)); +else + df2=[df df]; force_df=[1,0];[tmp,indxdf1]=find(possible_df==df);indxdf2=indxdf1+1; +end + +table = [[1.32,1.64,2.07,2.71,3.84,5.02,5.41,6.63 ,7.88,9.14,10.83,12.12];... +[2.77,3.22,3.79,4.61,5.99,7.38,7.82,9.21 ,10.60,11.98,13.82,15.20 ];... +[4.11,4.64,5.32,6.25,7.81,9.35,9.84,11.34 ,12.84,14.32,16.27,17.73 ];... +[5.39,5.59,6.74,7.78,9.49,11.14,11.67,13.23 ,14.86,16.42,18.47,20.00 ];... +[6.63,7.29,8.12,9.24,11.07,12.83,13.33,15.09 ,16.75,18.39,20.51,22.11 ];... +[7.84,8.56,9.45,10.64,12.53,14.45,15.03,16.81 ,13.55,20.25,22.46,24.10 ];... +[9.04,5.80,10.75,12.02,14.07,16.01,16.62 ,18.48,20.28,22.04,24.32,26.02 ];... +[10.22,11.03,12.03,13.36,15.51,17.53,18.17 ,20.09,21.95,23.77,26.12,27.87 ];... +[11.39,12.24,13.29,14.68,16.92,19.02,19.63 ,21.67,23.59,25.46,27.83,29.67 ];... +[12.55,13.44,14.53,15.99,18.31,20.48,21.16 ,23.21,25.19,27.11,29.59,31.42 ];... +[13.70,14.63,15.77,17.29,19.68,21.92,22.62,24.72,26.76,28.73,31.26,33.14 ];... +[14.85,15.81,16.99,18.55,21.03,23.34,24.05,26.22,28.30,30.32,32.91,34.82 ];... +[15.93,15.58,18.90,19.81,22.36,24.74,25.47,27.69,29.82,31.88,34.53,36.48 ];... +[17.12,18.15,19.4,21.06,23.68,26.12,26.87,29.14,31.32,33.43,36.12,38.11 ];... +[18.25,19.31,20.60,22.31,25.00,27.49,28.26,30.58,32.80,34.95,37.70,39.72 ];... +[19.37,20.47,21.79,23.54,26.30,28.85,29.63,32.00,34.27,36.46,39.25,41.31 ];... +[20.49,21.61,22.98,24.77,27.59,30.19,31.00,33.41,35.72,37.95,40.79,42.88 ];... +[21.60,22.76,24.16,25.99,28.87,31.53,32.35,34.81,37.16,39.42,42.31,44.43 ];... +[22.72,23.90,25.33,27.20,30.14,32.85,33.69,36.19,38.58,40.88,43.82,45.97 ];... +[23.83,25.04,26.50,28.41,31.41,34.17,35.02,37.57,40.00,42.34,45.31,47.50 ];... +[24.93,26.17,27.66,29.62,39.67,35.48,36.34,38.93,41.40,43.78,46.80,49.01 ];... +[26.04,27.30,28.82,30.81,33.92,36.78,37.66,40.29,42.80,45.20,48.27,50.51 ];... +[27.14,28.43,29.98,32.01,35.17,38.08,38.97,41.64,44.18,46.62,49.73,52.00 ];... +[28.24,29.55,31.13,33.20,36.42,39.36,40.27,42.98,45.56,48.03,51.18,53.48 ];... +[29.34,30.68,32.28,34.38,37.65,40.65,41.57,44.31,46.93,49.44,52.62,54.95 ];... +[30.43,31.79,33.43,35.56,38.89,41.92,42.86,45.64,48.29,50.83,54.05,56.41 ];... +[31.53,32.91,34.57,36.74,40.11,43.19,44.14,46.96,49.64,52.22,55.48,57.86 ];... +[32.62,34.03,35.71,37.92,41.34,44.46,45.42,48.28,50.99,53.59,56.89,59.30 ];... +[33.71,35.14,36.85,39.09,42.56,45.72,46.69,49.59,52.34,54.97,58.30,60.73 ];... +[34.80,36.25,37.99,40.26,43.77,46.98,47.96,50.89,53.67,56.33,59.70,62.16 ];... +[45.62,47.27,49.24,51.81,55.76,59.34,60.44,63.69,66.77,69.70,73.40,76.09 ];... +[56.33,53.16,60.35,63.17,67.50,71.42,72.61,76.15,79.49,82.66,86.66,89.56 ];... +[66.98,68.97,71.34,74.40,79.08,83.30,84.58,88.38,91.95,95.34,99.61,102.7 ];... +[88.13,90.41,93.11,96.58,101.9,106.6,108.1,112.3,116.3,120.1,124.8,128.3 ];... +[109.1,111.7,114.7,118.5,124.3,129.6,131.1,135.8,140.2,144.3,149.4,153.2]]; + +values = table(indxdf1:indxdf2,indxP1:indxP2); +value1 = values(1,:)*force_P'; +value2 = values(2,:)*force_P'; +value3 = values(:,1)'*force_df'; +value4 = values(:,2)'*force_df'; +value5 = [value1 value2]*force_df'; +value6 = [value3 value4]*force_P'; +value = mean([value5, value6]); + diff --git a/sourcecodes/bnt-master/SLP/misc/classification_evaluation.m b/sourcecodes/bnt-master/SLP/misc/classification_evaluation.m new file mode 100644 index 00000000..a2b107bc --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/classification_evaluation.m @@ -0,0 +1,35 @@ +function [ratio, ratiominus, ratioplus, proba_post, yt] = classification_evaluation(bnet, BDT, class) +% Computes the classification ratio of a bnet structure on a test dataset BDT +% [ratio ratiominus ratioplus] = classification_evaluation(bnet, BDT, class) +% +% [ratiominus rationplus] is the 95 percent confident interval. +% results are in percentage [0 100]. +% +% francois.olivier.c.h@gmail.com +% + + [proba_post,engine] = inference(bnet, mat_to_bnt(BDT), class); + [tmp yt] = max(proba_post, [],2); + [N L] = size(BDT); + count = length(find(BDT(class,:)==yt')); + ratio = 100*count/L; +[ratiominus, ratioplus] = confiance(ratio,L); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [I, J] = confiance(t, N) +% Compute the 95 percent confident interval +% +% see Y. Bennani and F. Bossaert, +% Predictive neural networks for traffic disturbance detection in the telephone network +% In Proceedings of IMACS-CESA 1996, Lille, France. + +Z = 1.96; % this value for the 95 percent confident interval +T = t/100; +tmp = (Z*Z)/N; +D = 1+tmp; +N1 = T+tmp/2; +tmp2 = T*(1-T)/N + tmp/(4*N); +N2 = Z*sqrt(tmp2); +I = 100*(N1-N2)/D; +J = 100*(N1+N2)/D; diff --git a/sourcecodes/bnt-master/SLP/misc/complete_pattern.m b/sourcecodes/bnt-master/SLP/misc/complete_pattern.m new file mode 100644 index 00000000..74cb8db7 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/complete_pattern.m @@ -0,0 +1,119 @@ +function completed_pdag = complete_pattern(pdag) + +% +% completed_dag = complete_pattern(pdag) +% +% uses Rules R1-R4 of Meek (1995) to complete +% orientations in a pdag as far as possible, +% i.e. every compelled edge is oriented. +% +% (Rules R1-R4 are also summarized in Pearl (2000), p.51 +% and Neapolitan (2004), p. 546.) +% +% Since the PC algorithm also uses Rules R1-R3, their implementation was +% copied (with some modifications) from function learn_struct_pdag_pc. +% +% Rule R4 is necessary here, since the orientations in the input +% pdag do not just represent v-structures. +% +% Imme Ebert-Uphoff (ebert@tree.com), 2007 +% + + DIAGNOSTICS_ON = false; + + n = length(pdag); + old_pdag = zeros(n); + %iter = 0; + while ~isequal(pdag, old_pdag) + %iter = iter + 1; + old_pdag = pdag; + + % Rule R1 + [A,B] = find(pdag==-1); % a -> b + for i=1:length(A) + a = A(i); b = B(i); + undirected = abs(pdag) + abs(pdag)'; + % Adjacency test in undirected matrix: + % a adjacent b <=> undirected(a,b) ==0 + % That's easier to use than adjacency test in pdag: + % a adjacent b <=> pdag(a,b)==0 and pdag(b,a)==0 + + % Find all nodes c such that b-c and c not adjacent a + C = find(pdag(b,:)==1 & undirected(a,:)==0); + if ~isempty(C) + pdag(b,C) = -1; pdag(C,b) = 0; + if DIAGNOSTICS_ON + for j=1:length(C) + fprintf('Rule 1: %d -> %d\n', b, C(j)); + end + end + end + end + + % Rule R2 + [A,B] = find(pdag==1); % unoriented a-b edge + for i=1:length(A) + a = A(i); b = B(i); + if any( (pdag(a,:)==-1) & (pdag(:,b)==-1)' ); + pdag(a,b) = -1; pdag(b,a) = 0; + if DIAGNOSTICS_ON + fprintf('Rule 2: %d -> %d\n', a, b); + end + end + end + + % Rule R3 + [A,B] = find(pdag==1); % a-b + for i=1:length(A) + a = A(i); b = B(i); + C = find( (pdag(a,:)==1) & (pdag(:,b)==-1)' ); + % C contains nodes c s.t. a-c->b-a + + % Extract lines and columns corresponding only to the set of nodes C + core = pdag(C,C); + + % Prepare adjacency test: + unoriented = abs(core) + abs(core)'; + % Now: a non-adjacent b <==> unoriented(a,b) == 0 + + % Prepare to detect existence of non-adjacent pairs of nodes in C. + % Set diagonal to 1, to prevent finding pairs of IDENTICAL nodes: + unoriented = setdiag(unoriented, 1); + if any(unoriented(:)==0) % C contains 2 different non adjacent elements + pdag(a,b) = -1; pdag(b,a) = 0; + if DIAGNOSTICS_ON + fprintf('Rule 3: %d -> %d\n', a, b); + end + end + end + + % Rule 4 + [A,B] = find(pdag==1); % unoriented a-b edge + for i=1:length(A) + a = A(i); b = B(i); + + % Prepare adjacency test: + % unoriented(i,j) is 0 (non-adj) or 1 (directed) or 2 (undirected) + unoriented = abs(pdag) + abs(pdag)'; + + % Find c such that c -> b and a,c are adjacent (a-c or a->c or a<-c) + C = find( (pdag(:,b)==-1)' & (unoriented(a,:)>=1) ); + for j=1:length(C) + c = C(j); + % Check whether there is any node d, such that + % d->c AND a-d AND b NOT adjacent to d + if any( (pdag(:,c)==-1)' & (pdag(a,:)==1) & (unoriented(b,:)==0) ) + pdag(a,b) = -1; pdag(b,a) = 0; + if DIAGNOSTICS_ON + fprintf('Rule 4: %d -> %d\n', a, b); + end + end + end + end + + end % end of while + + % Oriented all possible edges. Return result. + completed_pdag = pdag; +end + diff --git a/sourcecodes/bnt-master/SLP/misc/compute_bnet_nparams.m b/sourcecodes/bnt-master/SLP/misc/compute_bnet_nparams.m new file mode 100644 index 00000000..3366375f --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/compute_bnet_nparams.m @@ -0,0 +1,13 @@ +function [D,d]= compute_bnet_nparams(bnet) +% [D,d] = compute_bnet_nparams(bnet) +% +% D is the dimension of the network +% d is the vector containing the number of parameters for all nodes + +N = length(bnet.dag); +d = zeros(1,N); +for i=1:N + a = struct(bnet.CPD{i}); + d(i) = a.nparams; +end +D = sum(d); diff --git a/sourcecodes/bnt-master/SLP/misc/confiance.m b/sourcecodes/bnt-master/SLP/misc/confiance.m new file mode 100644 index 00000000..a9416bf8 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/confiance.m @@ -0,0 +1,16 @@ +function [I, J] = confiance(t, N) +% Compute the 95 percent confident interval +% +% see Y. Bennani and F. Bossaert, +% Predictive neural networks for traffic disturbance detection in the telephone network +% In Proceedings of IMACS-CESA 1996, Lille, France. + +Z = 1.96; % this value for the 95 percent confident interval +T = t/100; +tmp = (Z*Z)/N; +D = 1+tmp; +N1 = T+tmp/2; +tmp2 = T*(1-T)/N + tmp/(4*N); +N2 = Z*sqrt(tmp2); +I = 100*(N1-N2)/D; +J = 100*(N1+N2)/D; diff --git a/sourcecodes/bnt-master/SLP/misc/cpdag_to_dag.m b/sourcecodes/bnt-master/SLP/misc/cpdag_to_dag.m new file mode 100644 index 00000000..8e106610 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/cpdag_to_dag.m @@ -0,0 +1,52 @@ +function dag = cpdag_to_dag(cpdags) +% dags = cpdag_to_dag(cpdags) +% +% CPDAG_TO_DAG produce a N*N matrix of a dag which instantiate cpdag. +% (also works with a cell array of cpdags, returning a cell array of dags) +% make sur that your entry is a completed PDAG +% this function can't be use instead of PDAG_TO_DAG +% +% see Chickering (2002) : Learning equivalence classes of bayesian networks, JMLR2, pp475-479 +% +% francois.olivier.c.h@gmail.com, philippe.leray@univ-nantes.fr +% 31 march 2006 + +if ~iscell(cpdags) + cpdag=cell(1,1); + cpdag{1}=cpdags; +else + cpdag=cpdags; +end + +for da=1:length(cpdag) + + N=length(cpdag{da}); + dag=cpdag{da}; + + unprocessed = find_nodes_in_undirected_component(dag); + while ~isempty(unprocessed) + nbr_parents = []; + for i=1:length(unprocessed) + nbr_parents(end+1)=length(parents(dag-dag.*dag',unprocessed(i))); %nbr_parents(end+1)=length(parents(dag,unprocessed(i))); + end + [tmp, idx] = max(nbr_parents); + node = unprocessed(idx); + dag(parents(dag.*dag',node),node)=0; %dag(parents(dag,node),node)=0; + %dag(myintersect(parents(dag,node),unprocessed), node)=0; % Wei Lu + unprocessed = find_nodes_in_undirected_component(dag); + end + + dags{da}=dag; +end + +if ~iscell(cpdags) + dag=dags{1}; +else + dag=dags; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function unprocessed = find_nodes_in_undirected_component(dag) +undirected_edges = dag.*dag'; +[unprocessed, tmp] = find(undirected_edges); +unprocessed = unique(unprocessed); diff --git a/sourcecodes/bnt-master/SLP/misc/cpdag_to_dag2.m b/sourcecodes/bnt-master/SLP/misc/cpdag_to_dag2.m new file mode 100644 index 00000000..1057a9eb --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/cpdag_to_dag2.m @@ -0,0 +1,52 @@ +function dag = cpdag_to_dag2(cpdags) +% dags = cpdag_to_dag(cpdags) +% +% CPDAG_TO_DAG produce a N*N matrix of a dag which instantiate cpdag. +% (also works with a cell array of cpdags, returning a cell array of dags) +% make sur that your entry is a completed PDAG +% this function can't be use instead of PDAG_TO_DAG +% +% francois.olivier.c.h@gmail.com +% 7 may 2003 - OLD version + +if ~iscell(cpdags) + cpdag=cell(1,1); + cpdag{1}=cpdags; +else + cpdag=cpdags; +end + +for da=1:length(cpdag) + + N=length(cpdag{da}); + dag=cpdag{da}; dag2=dag; + unprocessed = []; + + for i=1:(N-1) + for j=(i+1):N + if dag2(i,j)==1 & dag2(j,i)==1 + if ~myismember(i,unprocessed) + unprocessed = [unprocessed;i]; + end + if ~myismember(j,unprocessed) + unprocessed = [unprocessed;j]; + end + end + end + end + + for i=1:length(unprocessed) + for j=1:N + if dag(unprocessed(i),j)==1 & dag(j,unprocessed(i))==1 + dag(j,unprocessed(i))=0; + end + end + end + dags{da}=dag; +end + +if ~iscell(cpdags) + dag=dags{1}; +else + dag=dags; +end \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/misc/dag_to_cpdag.m b/sourcecodes/bnt-master/SLP/misc/dag_to_cpdag.m new file mode 100644 index 00000000..70ec3a7e --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/dag_to_cpdag.m @@ -0,0 +1,115 @@ +function [cpdag] = dag_to_cpdag(dags) +% (also works with a cell array of dags, returning a cell array of cpdags) +% DAG_TO_CPDAG produce a N*N matrix which values respect : +% +% If the edge is compelled then 1 on the edge. +% If the edge is reversible then 1 on the edge and 1 in the reverse edge. +% +% Make sure that the entry is a DAG. +% +% See D.M. Chickering: "Learning Equivalence Classes of Bayesian-Network Structures". +% +% +% francois.olivier.c.h@gmail.com, philippe.leray@univ-nantes.fr, alain.delaplace@univ-tours.fr + +if ~iscell(dags) + dag=cell(1,1); + dag{1}=dags; +else + dag=dags; +end + +for da=1:length(dag) + cpdags{da} = abs(label_edges(dag{da})); +end + +if ~iscell(dags) + cpdag=cpdags{1}; +else + cpdag=cpdags; +end + +%%============================================================================== + +function [label] = label_edges(dag) +% LABEL-EDGES produce a N*N matrix which values are +% +1 if the edge is compelled or +% -1 if the edge is reversible. +% Make sure that the entry is a DAG. +% +% francois.olivier.c.h@gmail.com + +N=length(dag); +[order xedge yedge] = order_edges(dag); +label = 2*dag; + +NbEdges = length(xedge) ; + +for Edge=1:NbEdges, + xlow=xedge(Edge); + ylow=yedge(Edge); + if label(xlow,ylow)==2 + fin = 0; + wcompelled = find(label(:,xlow)==1); + parenty = find(label(:,ylow)~=0); + + %for w = wcompelled + for s = 1:length(wcompelled) + w = wcompelled(s); + if ~ismember(w,parenty) + label(parenty,ylow)=1; + label(ylow,parenty)=0; + fin = 1; + elseif fin == 0 + label(w,ylow)=1; + label(ylow,w)=0; % + end + end + if fin == 0 + parentx = [xlow ; find(label(:,xlow)~=0)]; + if ~isempty(mysetdiff(parenty,parentx)) + label(xlow,ylow)=1; % + label(ylow,xlow)=0; % + ttp=find(label(:,ylow)==2); + label(ttp,ylow)=1; + label(ylow,ttp)=0; % + else + ttp=find(label(:,ylow)==2); + label(ttp,ylow)=-1; + label(ylow,ttp)=-1; + end + end + end +end + +%%%======================================================================================== +function [order, x, y] = order_edges(dag) +% ORDER_EDGES produce a total (natural) ordering over the edges in a DAG. +% Make sure that the entry is a DAG. +% +% francois.olivier.c.h@gmail.com +% +% 2 mai 2003 + +if acyclic(dag)==0 + error('Requires an acyclic graph'); +end + +N=length(dag); +order = zeros(N,N); + +node_order = topological_sort(dag); +[tmp oo] = sort(node_order); + +dag=dag(node_order,node_order); +[x y]=find(flipud(dag)==1); +nb_edges=length(x); + +if nb_edges~=0 + order(sub2ind([N N],N+1-x,y))=1:nb_edges ; +end + +order=order(oo,oo); +x=node_order(N+1-x); +y=node_order(y); + diff --git a/sourcecodes/bnt-master/SLP/misc/dag_to_cpdag1.m b/sourcecodes/bnt-master/SLP/misc/dag_to_cpdag1.m new file mode 100644 index 00000000..c3189a1d --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/dag_to_cpdag1.m @@ -0,0 +1,128 @@ +function [cpdag] = dag_to_cpdag1(dags) +% 2 +% (also works with a cell array of dags, returning a cell array of cpdags) +% DAG_TO_CPDAG produce a N*N matrix which values respect : +% +% If the edge is compelled then 1 on the edge. +% If the edge is reversible then 1 on the edge and 1 in the reverse edge. +% +% Make sure that the entry is a DAG. +% +% See D.M. Chickering: "Learning Equivalence Classes of Bayesian-Network Structures". +% +% +% francois.olivier.c.h@gmail.com, philippe.leray@univ-nantes.fr + +if ~iscell(dags) + dag=cell(1,1); + dag{1}=dags; +else + dag=dags; +end + +for da=1:length(dag) + cpdags{da} = abs(label_edges(dag{da})); +end + +if ~iscell(dags) + cpdag=cpdags{1}; +else + cpdag=cpdags; +end + +%%============================================================================== + +function [label] = label_edges(dag) +% LABEL-EDGES produce a N*N matrix which values are +% +1 if the edge is compelled or +% -1 if the edge is reversible. +% Make sure that the entry is a DAG. +% +% francois.olivier.c.h@gmail.com + +N=length(dag); +[order xedge yedge] = order_edges(dag); +label = 2*dag; % all edges as unknown + +NbEdges = length(xedge) ; +%xedge=x, yedge=y, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +for Edge=1:NbEdges, + xlow=xedge(Edge); + ylow=yedge(Edge); + if label(xlow,ylow)==2 + fin = 0; + + %wcompelled = find(label(:,xlow)~=2); + wcompelled = find(label(:,xlow)==1); + + parenty = find(label(:,ylow)~=0); + %sonsy = find(label(ylow,:)~=0); + + for s = 1:length(wcompelled) + w = wcompelled(s); + if ~ismember(w,parenty) + label(xlow,ylow)=1; + label(ylow,xlow)=0; % + label(parenty,ylow)=1; + label(ylow,parenty)=0; % + %label(ylow,sonsy)=1; % + %label(sonsy,ylow)=0; % + fin = 1; + + elseif fin == 0 + label(w,ylow)=1; + %label(ylow,w)=0; % + end + end + if fin == 0 + parentx = [xlow ; find(label(:,xlow)~=0)]; + if ~isempty(mysetdiff(parenty,parentx)) + %label(xlow,ylow)=1; % + %label(ylow,xlow)=0; % + + label(find(label(:,ylow)==2),ylow)=1; + label(ylow,find(label(ylow,:)==2))=1; % + else + label(xlow,ylow)=-1; + label(ylow,xlow)=-1; % + ttp = find(label(:,ylow)==2); + label(ttp,ylow)=-1; + label(ylow,ttp)=-1; % + end + end + end +end + +%%%======================================================================================== +function [order, x, y] = order_edges(dag) +% ORDER_EDGES produce a total (natural) ordering over the edges in a DAG. +% Make sure that the entry is a DAG. +% +% francois.olivier.c.h@gmail.com +% +% 2 mai 2003 + +if acyclic(dag)==0 + error('Requires an acyclic graph'); +end + +N=length(dag); +order = zeros(N,N); + +node_order = topological_sort(dag); +[tmp oo] = sort(node_order); + +dag2=dag(node_order,node_order); +[x y]=find(flipud(dag2)==1); +nb_edges=length(x); + +if nb_edges~=0 + order(sub2ind([N N],N+1-x,y))=1:nb_edges ; +end + +order=order(oo,oo); +x=node_order(N+1-x); +y=node_order(y); + diff --git a/sourcecodes/bnt-master/SLP/misc/discretization.m b/sourcecodes/bnt-master/SLP/misc/discretization.m new file mode 100644 index 00000000..870583af --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/discretization.m @@ -0,0 +1,59 @@ +function [appD, testD, bornes] = discretization(critere, continues, miss, app, test) +% [appD, testD, bornes] = discretization(critere, continious, miss, app, test) +% +% Inputs : +% critere = 1, 2, 3 or 4 (see hict_ic for details) +% continious = vector of continious variables to discretize +% miss ~= 0 if it exists missing values coded by 'miss' value +% app = Learning base +% test = Test base (only the learning base is used to make the discretization rules) [optionnal] +% +% Outputs : +% appD = Learning base with discretized entries on 'continious' variables +% testD = Test base with discretized entries on 'continious' variables +% bornes = limits of discretization intervals found by hist_ic +% + +tt=1; +if nargin<5, test=[]; tt=0; end +app = app'; +test = test'; +[ma, Na] = size(app); +[mt, Nt] = size(test); +I = []; +testD = []; + +if miss, + [I J]=find(app==miss); + [I2 J2]=find(test==miss); +end +completes=setdiff(1:ma,I); +donnees_continue=app(completes,continues); + +% echantillonnage +[n,bornes,nbbornes,xx]=hist_ic(donnees_continue,critere); + +% on re-distribue l'ensemble des donnees d'apprentissage continues +[n2,appD_continues]=histc_ic(app(:,continues),bornes); +if tt, [n2test,testD_continues]=histc_ic(test(:,continues),bornes); end + +% on insere les donnees continues discretisees dans les matrices +appD=app; +if tt, testD=test; end +for i=1:length(continues) + appD(:,continues(i))=appD_continues(:,i); + if tt, testD(:,continues(i))=testD_continues(:,i); end +end + +if miss, + for k=1:length(I) + app(I(k),J(k))=miss; + end + if tt, + for l=1:length(I2) + testD(I2(l),J2(l))=miss; + end + end +end +appD = appD'; +testD = testD'; \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/misc/editing_dist.m b/sourcecodes/bnt-master/SLP/misc/editing_dist.m new file mode 100644 index 00000000..5b697752 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/editing_dist.m @@ -0,0 +1,17 @@ +function D = editing_dist(dag1, dag2) +% D = editing_dist(dag1, dag2) +% +% d = 1 if arc1 <> arc2 +% + +[n1 m1]=size(dag1); +[n2 m2]=size(dag2); + +if n1~=m1 | n1~=n2 | n2~=m2 | m1~=m2 + error('formats non compatibles'); + D=-inf; +end + +de=abs(dag1-dag2); +mauv=find(triu(or(de,de'))); +D=size(mauv,1); diff --git a/sourcecodes/bnt-master/SLP/misc/export_cases.m b/sourcecodes/bnt-master/SLP/misc/export_cases.m new file mode 100644 index 00000000..4ab0d790 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/export_cases.m @@ -0,0 +1,57 @@ +function S = export_cases(data, names, file, misv) +% filepath = export_cases(data, names, 'filename', missing_value) +% filename and missing_value [default [] if iscell(data) or -9999 if not] are optional +% +% Exports BNT datasets to Netica cases (* for missing data) +% +% Written by Francois.Olivier.C.H@gmail.com +% +% Informations could be found here http://www.norsys.com/downloads/ +% +% version 80807 + +% inits +if nargin==1, file=['dnet' datestr(now,'-yymmdd-HHMMSS')]; end +if nargin<2, error('Variable Names needed'); end +if length(file)>5, + if prod(double(file((end-3):end)~='.cas')), file=[file '.cas']; end + name = file(1:end-5); +else + name = file; + file = [file '.cas']; +end +if nargin<4, misv=-9999; end +if iscell(data), data = bnt_to_mat(data,misv); end + +[N m]=size(data); +if length(names)~=N, error('Sizes must be the same'); end + +% header of the file +fid = fopen(file, 'w'); +fprintf(fid, '// exported from the Bayes Net Toolbox with export_cases function \n'); +fprintf(fid, '// please report bugs to francois.olivier.c.h@gmail.com\n\n'); + +% write names +for i=1:N + fprintf(fid, '%s\t',names{i}); +end + +% exports +for l=1:m, + fprintf(fid, '\n'); + for i=1:N, + if data(i,l)~=misv, + fprintf(fid, '%s',['x' num2str(data(i,l))]); + else + fprintf(fid, '%s','*'); + end + if i4, + if prod(double(file((end-4):end)~='.dnet')), file=[file '.dnet']; end + name = file(1:end-5); +else + name = file; + file = [file '.dnet']; +end + +% generating node names 1:N if non existant +N=length(bnet.dag); +if isempty(bnet.names), + for i=1:N, + keys{i}=['' num2str(i) '']; + vals{i}=i; + end + bnet.names = assocarray(keys, vals); +end + +% header of the file +fid = fopen(file, 'w'); +fprintf(fid, '// ~->[DNET-1]->~\n\n'); +fprintf(fid, '// exported from the Bayes Net Toolbox with export_dnet function \n'); +fprintf(fid, '// please report bugs to francois.olivier.c.h@gmail.com\n'); +if proba, fprintf(fid, '// Take care ! Parents'' order isn''t the same in probability table comments\n'); end +fprintf(fid, ['\nbnet ' name ' {']); + +% main loop +for node = 1:N + name = get_key(bnet.names,node); + + fprintf(fid, ['\nnode ' name ' {']); + + fprintf(fid, '\n\tkind = NATURE;'); + + fprintf(fid, '\n\tdiscrete = TRUE;'); + +% states names are x1,x2:xsize(node) + fprintf(fid, '\n\tstates = ('); + for l=1:bnet.node_sizes(node), + fprintf(fid, 'x%d',l); + if l~=bnet.node_sizes(node), fprintf(fid, ', '); end + end, fprintf(fid, ');'); + +% declare parent in counter order to be coherent with prob section + fprintf(fid, '\n\tparents = ('); + par = bnet.parents{node}; + for l=length(par):-1:1 + fprintf(fid, '%s',get_key(bnet.names,par(l))); + if l~=1, fprintf(fid, ', '); end + end, fprintf(fid, ');'); + +% fill probs if proba==1 + if proba, + fprintf(fid, '\n\tprobs =\n'); + % inits + fprintf(fid, '\t//\t'); + for l = 1:bnet.node_sizes(node), + fprintf(fid, 'x%d\t', l); + end + fprintf(fid, '\t//'); + if ~isempty(par), + for l = unique(par), %right order this time because of the way both BNT and netica work + fprintf(fid, '\t%s', get_key(bnet.names,l)); + end + end + + % opens tab + fprintf(fid, '\n\t'); + for l = unique(par), + fprintf(fid, '('); + end + + % fullfils probs + CPT = CPT_from_bnet(bnet); + CPT = CPT{node}; + CPT=CPT(:); % good order whatever the node size ???? + if isempty(par), + fprintf(fid, '\t'); + for i=1:length(CPT)-1, + fprintf(fid, '%1.4f, ', CPT(i)); + end + fprintf(fid, '%1.4f);',CPT(end)); + else % if there are parents + endi=0; + parsiz = prod(bnet.node_sizes([par])); + parentstates = ones(1,length(par)); parentstates(end)=0; + for i=1:parsiz + % prints probas + fprintf(fid, '(\t'); + for j = 1:bnet.node_sizes(node) + prob = CPT(i+parsiz*(j-1)); + if j~=bnet.node_sizes(node), fprintf(fid, '%1.4f, ',prob); + else fprintf(fid, '%1.4f',prob); end + end + + % closes parenthesis if needed + endii=endi; + if i~=parsiz, + while endi>0, fprintf(fid, ')');endi=endi-1; end + fprintf(fid, '),\t//\t'); + else % close tab + for l = unique(par), fprintf(fid, ')'); end + fprintf(fid, ');\t//\t'); + end + + % prints node states + res=i; + for l=1:length(par)-1 + if mod(i+1, prod(bnet.node_sizes(par(1:l))))==0, endi=endi+1; end % counts parenthesis + + resaff = mod(res, bnet.node_sizes(l)); + if resaff==0, resaff=bnet.node_sizes(par(l)); end + res = div(res-1, bnet.node_sizes(par(l)))+1; + + fprintf(fid, '%d\t',resaff); + end + + resaff = div(i-1, prod(bnet.node_sizes(par(1:end-1))))+1; + fprintf(fid, '%d',resaff); + + % opens parenthesis if needed + if i~=parsiz, fprintf(fid, '\n'); end + fprintf(fid, '\t'); + if i~=parsiz, while endii>0, fprintf(fid, '(');endii=endii-1; end, end + end + end + end + fprintf(fid, '\n\t};'); +end + +% closes file +fprintf(fid,'\n};\n'); +fclose(fid); + +% outputs string +S = [pwd '/' file]; diff --git a/sourcecodes/bnt-master/SLP/misc/exportfig.m b/sourcecodes/bnt-master/SLP/misc/exportfig.m new file mode 100644 index 00000000..ef734fa3 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/exportfig.m @@ -0,0 +1,991 @@ +function varargout = exportfig(varargin) +%EXPORTFIG Export a figure. +% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is +% a figure handle and FILENAME is a string that specifies the +% name of the output file. +% +% EXPORTFIG(H, FILENAME, OPTIONS) writes the figure H to FILENAME +% with options initially specified by the structure OPTIONS. The +% field names of OPTIONS must be legal parameters listed below +% and the field values must be legal values for the corresponding +% parameter. Default options can be set in releases prior to R12 +% by storing the OPTIONS structure in the root object's appdata +% with the command +% setappdata(0,'exportfigdefaults', OPTIONS) +% and for releases after R12 by setting the preference with the +% command +% setpref('exportfig', 'defaults', OPTIONS) +% +% EXPORTFIG(...,PARAM1,VAL1,PARAM2,VAL2,...) specifies +% parameters that control various characteristics of the output +% file. Any parameter value can be the string 'auto' which means +% the parameter uses the default factory behavior, overriding +% any other default for the parameter. +% +% Format Paramter: +% 'Format' a string +% specifies the output format. Defaults to 'eps'. For a +% list of export formats type 'help print'. +% 'Preview' one of the strings 'none', 'tiff' +% specifies a preview for EPS files. Defaults to 'none'. +% +% Size Parameters: +% 'Width' a positive scalar +% specifies the width in the figure's PaperUnits +% 'Height' a positive scalar +% specifies the height in the figure's PaperUnits +% 'Bounds' one of the strings 'tight', 'loose' +% specifies a tight or loose bounding box. Defaults to 'tight'. +% 'Reference' an axes handle or a string +% specifies that the width and height parameters +% are relative to the given axes. If a string is +% specified then it must evaluate to an axes handle. +% +% Specifying only one dimension sets the other dimension +% so that the exported aspect ratio is the same as the +% figure's or reference axes' current aspect ratio. +% If neither dimension is specified the size defaults to +% the width and height from the figure's or reference +% axes' size. Tight bounding boxes are only computed for +% 2-D views and in that case the computed bounds enclose all +% text objects. +% +% Rendering Parameters: +% 'Color' one of the strings 'bw', 'gray', 'cmyk' +% 'bw' specifies that lines and text are exported in +% black and all other objects in grayscale +% 'gray' specifies that all objects are exported in grayscale +% 'rgb' specifies that all objects are exported in color +% using the RGB color space +% 'cmyk' specifies that all objects are exported in color +% using the CMYK color space +% 'Renderer' one of 'painters', 'zbuffer', 'opengl' +% specifies the renderer to use +% 'Resolution' a positive scalar +% specifies the resolution in dots-per-inch. +% 'LockAxes' one of 0 or 1 +% specifies that all axes limits and ticks should be fixed +% while exporting. +% +% The default color setting is 'bw'. +% +% Font Parameters: +% 'FontMode' one of the strings 'scaled', 'fixed' +% 'FontSize' a positive scalar +% in 'scaled' mode multiplies with the font size of each +% text object to obtain the exported font size +% in 'fixed' mode specifies the font size of all text +% objects in points +% 'DefaultFixedFontSize' a positive scalar +% in 'fixed' mode specified the default font size in +% points +% 'FontSizeMin' a positive scalar +% specifies the minimum font size allowed after scaling +% 'FontSizeMax' a positive scalar +% specifies the maximum font size allowed after scaling +% 'FontEncoding' one of the strings 'latin1', 'adobe' +% specifies the character encoding of the font +% 'SeparateText' one of 0 or 1 +% specifies that the text objects are stored in separate +% file as EPS with the base filename having '_t' appended. +% +% If FontMode is 'scaled' but FontSize is not specified then a +% scaling factor is computed from the ratio of the size of the +% exported figure to the size of the actual figure. +% +% The default 'FontMode' setting is 'scaled'. +% +% Line Width Parameters: +% 'LineMode' one of the strings 'scaled', 'fixed' +% 'LineWidth' a positive scalar +% 'DefaultFixedLineWidth' a positive scalar +% 'LineWidthMin' a positive scalar +% specifies the minimum line width allowed after scaling +% 'LineWidthMax' a positive scalar +% specifies the maximum line width allowed after scaling +% The semantics of 'Line' parameters are exactly the +% same as the corresponding 'Font' parameters, except that +% they apply to line widths instead of font sizes. +% +% Style Map Parameter: +% 'LineStyleMap' one of [], 'bw', or a function name or handle +% specifies how to map line colors to styles. An empty +% style map means styles are not changed. The style map +% 'bw' is a built-in mapping that maps lines with the same +% color to the same style and otherwise cycles through the +% available styles. A user-specified map is a function +% that takes as input a cell array of line objects and +% outputs a cell array of line style strings. The default +% map is []. +% +% Examples: +% exportfig(gcf,'fig1.eps','height',3); +% Exports the current figure to the file named 'fig1.eps' with +% a height of 3 inches (assuming the figure's PaperUnits is +% inches) and an aspect ratio the same as the figure's aspect +% ratio on screen. +% +% opts = struct('FontMode','fixed','FontSize',10,'height',3); +% exportfig(gcf, 'fig2.eps', opts, 'height', 5); +% Exports the current figure to 'fig2.eps' with all +% text in 10 point fonts and with height 5 inches. +% +% See also PREVIEWFIG, APPLYTOFIG, RESTOREFIG, PRINT. + +% Copyright 2000 Ben Hinkle +% Email bug reports and comments to bhinkle@mathworks.com + +if (nargin < 2) + error('Too few input arguments'); +end + +% exportfig(H, filename, [options,] ...) +H = varargin{1}; +if ~LocalIsHG(H,'figure') + error('First argument must be a handle to a figure.'); +end +filename = varargin{2}; +if ~ischar(filename) + error('Second argument must be a string.'); +end +paramPairs = {varargin{3:end}}; +if nargin > 2 + if isstruct(paramPairs{1}) + pcell = LocalToCell(paramPairs{1}); + paramPairs = {pcell{:}, paramPairs{2:end}}; + end +end +verstr = version; +majorver = str2num(verstr(1)); +defaults = []; +if majorver > 5 + if ispref('exportfig','defaults') + defaults = getpref('exportfig','defaults'); + end +elseif exist('getappdata') + defaults = getappdata(0,'exportfigdefaults'); +end +if ~isempty(defaults) + dcell = LocalToCell(defaults); + paramPairs = {dcell{:}, paramPairs{:}}; +end + +% Do some validity checking on param-value pairs +if (rem(length(paramPairs),2) ~= 0) + error(['Invalid input syntax. Optional parameters and values' ... + ' must be in pairs.']); +end + +auto.format = 'eps'; +auto.preview = 'none'; +auto.width = -1; +auto.height = -1; +auto.color = 'rgb'; +auto.defaultfontsize=10; +auto.fontsize = -1; +auto.fontmode='scaled'; +auto.fontmin = 8; +auto.fontmax = 60; +auto.defaultlinewidth = 1.0; +auto.linewidth = -1; +auto.linemode=[]; +auto.linemin = 0.5; +auto.linemax = 100; +auto.fontencoding = 'latin1'; +auto.renderer = []; +auto.resolution = []; +auto.stylemap = []; +auto.applystyle = 0; +auto.refobj = -1; +auto.bounds = 'tight'; +explicitbounds = 0; +auto.lockaxes = 1; +auto.separatetext = 0; +opts = auto; + +% Process param-value pairs +args = {}; +for k = 1:2:length(paramPairs) + param = lower(paramPairs{k}); + if ~ischar(param) + error('Optional parameter names must be strings'); + end + value = paramPairs{k+1}; + + switch (param) + case 'format' + opts.format = LocalCheckAuto(lower(value),auto.format); + if strcmp(opts.format,'preview') + error(['Format ''preview'' no longer supported. Use PREVIEWFIG' ... + ' instead.']); + end + case 'preview' + opts.preview = LocalCheckAuto(lower(value),auto.preview); + if ~strcmp(opts.preview,{'none','tiff'}) + error('Preview must be ''none'' or ''tiff''.'); + end + case 'width' + opts.width = LocalToNum(value, auto.width); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.width) + error('Width must be a numeric scalar > 0'); + end + end + case 'height' + opts.height = LocalToNum(value, auto.height); + if ~ischar(value) | ~strcmp(value,'auto') + if(~LocalIsPositiveScalar(opts.height)) + error('Height must be a numeric scalar > 0'); + end + end + case 'color' + opts.color = LocalCheckAuto(lower(value),auto.color); + if ~strcmp(opts.color,{'bw','gray','rgb','cmyk'}) + error('Color must be ''bw'', ''gray'',''rgb'' or ''cmyk''.'); + end + case 'fontmode' + opts.fontmode = LocalCheckAuto(lower(value),auto.fontmode); + if ~strcmp(opts.fontmode,{'scaled','fixed'}) + error('FontMode must be ''scaled'' or ''fixed''.'); + end + case 'fontsize' + opts.fontsize = LocalToNum(value,auto.fontsize); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.fontsize) + error('FontSize must be a numeric scalar > 0'); + end + end + case 'defaultfixedfontsize' + opts.defaultfontsize = LocalToNum(value,auto.defaultfontsize); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.defaultfontsize) + error('DefaultFixedFontSize must be a numeric scalar > 0'); + end + end + case 'fontsizemin' + opts.fontmin = LocalToNum(value,auto.fontmin); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.fontmin) + error('FontSizeMin must be a numeric scalar > 0'); + end + end + case 'fontsizemax' + opts.fontmax = LocalToNum(value,auto.fontmax); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.fontmax) + error('FontSizeMax must be a numeric scalar > 0'); + end + end + case 'fontencoding' + opts.fontencoding = LocalCheckAuto(lower(value),auto.fontencoding); + if ~strcmp(opts.fontencoding,{'latin1','adobe'}) + error('FontEncoding must be ''latin1'' or ''adobe''.'); + end + case 'linemode' + opts.linemode = LocalCheckAuto(lower(value),auto.linemode); + if ~strcmp(opts.linemode,{'scaled','fixed'}) + error('LineMode must be ''scaled'' or ''fixed''.'); + end + case 'linewidth' + opts.linewidth = LocalToNum(value,auto.linewidth); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.linewidth) + error('LineWidth must be a numeric scalar > 0'); + end + end + case 'defaultfixedlinewidth' + opts.defaultlinewidth = LocalToNum(value,auto.defaultlinewidth); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.defaultlinewidth) + error(['DefaultFixedLineWidth must be a numeric scalar >' ... + ' 0']); + end + end + case 'linewidthmin' + opts.linemin = LocalToNum(value,auto.linemin); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.linemin) + error('LineWidthMin must be a numeric scalar > 0'); + end + end + case 'linewidthmax' + opts.linemax = LocalToNum(value,auto.linemax); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.linemax) + error('LineWidthMax must be a numeric scalar > 0'); + end + end + case 'linestylemap' + opts.stylemap = LocalCheckAuto(value,auto.stylemap); + case 'renderer' + opts.renderer = LocalCheckAuto(lower(value),auto.renderer); + if ~ischar(value) | ~strcmp(value,'auto') + if ~strcmp(opts.renderer,{'painters','zbuffer','opengl'}) + error(['Renderer must be ''painters'', ''zbuffer'' or' ... + ' ''opengl''.']); + end + end + case 'resolution' + opts.resolution = LocalToNum(value,auto.resolution); + if ~ischar(value) | ~strcmp(value,'auto') + if ~(isnumeric(value) & (prod(size(value)) == 1) & (value >= 0)); + error('Resolution must be a numeric scalar >= 0'); + end + end + case 'applystyle' % means to apply the options and not export + opts.applystyle = 1; + case 'reference' + if ischar(value) + if strcmp(value,'auto') + opts.refobj = auto.refobj; + else + opts.refobj = eval(value); + end + else + opts.refobj = value; + end + if ~LocalIsHG(opts.refobj,'axes') + error('Reference object must evaluate to an axes handle.'); + end + case 'bounds' + opts.bounds = LocalCheckAuto(lower(value),auto.bounds); + explicitbounds = 1; + if ~strcmp(opts.bounds,{'tight','loose'}) + error('Bounds must be ''tight'' or ''loose''.'); + end + case 'lockaxes' + opts.lockaxes = LocalToNum(value,auto.lockaxes); + case 'separatetext' + opts.separatetext = LocalToNum(value,auto.separatetext); + otherwise + error(['Unrecognized option ' param '.']); + end +end + +% make sure figure is up-to-date +drawnow; + +allLines = findall(H, 'type', 'line'); +allText = findall(H, 'type', 'text'); +allAxes = findall(H, 'type', 'axes'); +allImages = findall(H, 'type', 'image'); +allLights = findall(H, 'type', 'light'); +allPatch = findall(H, 'type', 'patch'); +allSurf = findall(H, 'type', 'surface'); +allRect = findall(H, 'type', 'rectangle'); +allFont = [allText; allAxes]; +allColor = [allLines; allText; allAxes; allLights]; +allMarker = [allLines; allPatch; allSurf]; +allEdge = [allPatch; allSurf]; +allCData = [allImages; allPatch; allSurf]; + +old.objs = {}; +old.prop = {}; +old.values = {}; + +% Process format +if strncmp(opts.format,'eps',3) & ~strcmp(opts.preview,'none') + args = {args{:}, ['-' opts.preview]}; +end + +hadError = 0; +oldwarn = warning; +try + + % lock axes limits, ticks and labels if requested + if opts.lockaxes + old = LocalManualAxesMode(old, allAxes, 'TickMode'); + old = LocalManualAxesMode(old, allAxes, 'TickLabelMode'); + old = LocalManualAxesMode(old, allAxes, 'LimMode'); + end + + % Process size parameters + figurePaperUnits = get(H, 'PaperUnits'); + oldFigureUnits = get(H, 'Units'); + oldFigPos = get(H,'Position'); + set(H, 'Units', figurePaperUnits); + figPos = get(H,'Position'); + refsize = figPos(3:4); + if opts.refobj ~= -1 + oldUnits = get(opts.refobj, 'Units'); + set(opts.refobj, 'Units', figurePaperUnits); + r = get(opts.refobj, 'Position'); + refsize = r(3:4); + set(opts.refobj, 'Units', oldUnits); + end + aspectRatio = refsize(1)/refsize(2); + if (opts.width == -1) & (opts.height == -1) + opts.width = refsize(1); + opts.height = refsize(2); + elseif (opts.width == -1) + opts.width = opts.height * aspectRatio; + elseif (opts.height == -1) + opts.height = opts.width / aspectRatio; + end + wscale = opts.width/refsize(1); + hscale = opts.height/refsize(2); + sizescale = min(wscale,hscale); + old = LocalPushOldData(old,H,'PaperPositionMode', ... + get(H,'PaperPositionMode')); + set(H, 'PaperPositionMode', 'auto'); + newPos = [figPos(1) figPos(2)+figPos(4)*(1-hscale) ... + wscale*figPos(3) hscale*figPos(4)]; + set(H, 'Position', newPos); + set(H, 'Units', oldFigureUnits); + + % process line-style map + if ~isempty(opts.stylemap) & ~isempty(allLines) + oldlstyle = LocalGetAsCell(allLines,'LineStyle'); + old = LocalPushOldData(old, allLines, {'LineStyle'}, ... + oldlstyle); + newlstyle = oldlstyle; + if ischar(opts.stylemap) & strcmpi(opts.stylemap,'bw') + newlstyle = LocalMapColorToStyle(allLines); + else + try + newlstyle = feval(opts.stylemap,allLines); + catch + warning(['Skipping stylemap. ' lasterr]); + end + end + set(allLines,{'LineStyle'},newlstyle); + end + + % Process rendering parameters + switch (opts.color) + case {'bw', 'gray'} + if ~strcmp(opts.color,'bw') & strncmp(opts.format,'eps',3) + opts.format = [opts.format 'c']; + end + args = {args{:}, ['-d' opts.format]}; + + %compute and set gray colormap + oldcmap = get(H,'Colormap'); + newgrays = 0.30*oldcmap(:,1) + 0.59*oldcmap(:,2) + 0.11*oldcmap(:,3); + newcmap = [newgrays newgrays newgrays]; + old = LocalPushOldData(old, H, 'Colormap', oldcmap); + set(H, 'Colormap', newcmap); + + %compute and set ColorSpec and CData properties + old = LocalUpdateColors(allColor, 'color', old); + old = LocalUpdateColors(allAxes, 'xcolor', old); + old = LocalUpdateColors(allAxes, 'ycolor', old); + old = LocalUpdateColors(allAxes, 'zcolor', old); + old = LocalUpdateColors(allMarker, 'MarkerEdgeColor', old); + old = LocalUpdateColors(allMarker, 'MarkerFaceColor', old); + old = LocalUpdateColors(allEdge, 'EdgeColor', old); + old = LocalUpdateColors(allEdge, 'FaceColor', old); + old = LocalUpdateColors(allCData, 'CData', old); + + case {'rgb','cmyk'} + if strncmp(opts.format,'eps',3) + opts.format = [opts.format 'c']; + args = {args{:}, ['-d' opts.format]}; + if strcmp(opts.color,'cmyk') + args = {args{:}, '-cmyk'}; + end + else + args = {args{:}, ['-d' opts.format]}; + end + otherwise + error('Invalid Color parameter'); + end + if (~isempty(opts.renderer)) + args = {args{:}, ['-' opts.renderer]}; + end + if (~isempty(opts.resolution)) | ~strncmp(opts.format,'eps',3) + if isempty(opts.resolution) + opts.resolution = 0; + end + args = {args{:}, ['-r' int2str(opts.resolution)]}; + end + + % Process font parameters + if ~isempty(opts.fontmode) + oldfonts = LocalGetAsCell(allFont,'FontSize'); + oldfontunits = LocalGetAsCell(allFont,'FontUnits'); + set(allFont,'FontUnits','points'); + switch (opts.fontmode) + case 'fixed' + if (opts.fontsize == -1) + set(allFont,'FontSize',opts.defaultfontsize); + else + set(allFont,'FontSize',opts.fontsize); + end + case 'scaled' + if (opts.fontsize == -1) + scale = sizescale; + else + scale = opts.fontsize; + end + newfonts = LocalScale(oldfonts,scale,opts.fontmin,opts.fontmax); + set(allFont,{'FontSize'},newfonts); + otherwise + error('Invalid FontMode parameter'); + end + old = LocalPushOldData(old, allFont, {'FontSize'}, oldfonts); + old = LocalPushOldData(old, allFont, {'FontUnits'}, oldfontunits); + end + if strcmp(opts.fontencoding,'adobe') & strncmp(opts.format,'eps',3) + args = {args{:}, '-adobecset'}; + end + + % Process line parameters + if ~isempty(opts.linemode) + oldlines = LocalGetAsCell(allMarker,'LineWidth'); + old = LocalPushOldData(old, allMarker, {'LineWidth'}, oldlines); + switch (opts.linemode) + case 'fixed' + if (opts.linewidth == -1) + set(allMarker,'LineWidth',opts.defaultlinewidth); + else + set(allMarker,'LineWidth',opts.linewidth); + end + case 'scaled' + if (opts.linewidth == -1) + scale = sizescale; + else + scale = opts.linewidth; + end + newlines = LocalScale(oldlines, scale, opts.linemin, opts.linemax); + set(allMarker,{'LineWidth'},newlines); + end + end + + % adjust figure bounds to surround axes + if strcmp(opts.bounds,'tight') + if (~strncmp(opts.format,'eps',3) & LocalHas3DPlot(allAxes)) | ... + (strncmp(opts.format,'eps',3) & opts.separatetext) + if (explicitbounds == 1) + warning(['Cannot compute ''tight'' bounds. Using ''loose''' ... + ' bounds.']); + end + opts.bounds = 'loose'; + end + end + warning('off'); + if ~isempty(allAxes) + if strncmp(opts.format,'eps',3) + if strcmp(opts.bounds,'loose') + args = {args{:}, '-loose'}; + end + old = LocalPushOldData(old,H,'Position', oldFigPos); + elseif strcmp(opts.bounds,'tight') + oldaunits = LocalGetAsCell(allAxes,'Units'); + oldapos = LocalGetAsCell(allAxes,'Position'); + oldtunits = LocalGetAsCell(allText,'units'); + oldtpos = LocalGetAsCell(allText,'Position'); + set(allAxes,'units','points'); + apos = LocalGetAsCell(allAxes,'Position'); + oldunits = get(H,'Units'); + set(H,'units','points'); + origfr = get(H,'position'); + fr = []; + for k=1:length(allAxes) + if ~strcmpi(get(allAxes(k),'Tag'),'legend') + axesR = apos{k}; + r = LocalAxesTightBoundingBox(axesR, allAxes(k)); + r(1:2) = r(1:2) + axesR(1:2); + fr = LocalUnionRect(fr,r); + end + end + if isempty(fr) + fr = [0 0 origfr(3:4)]; + end + for k=1:length(allAxes) + ax = allAxes(k); + r = apos{k}; + r(1:2) = r(1:2) - fr(1:2); + set(ax,'Position',r); + end + old = LocalPushOldData(old, allAxes, {'Position'}, oldapos); + old = LocalPushOldData(old, allText, {'Position'}, oldtpos); + old = LocalPushOldData(old, allText, {'Units'}, oldtunits); + old = LocalPushOldData(old, allAxes, {'Units'}, oldaunits); + old = LocalPushOldData(old, H, 'Position', oldFigPos); + old = LocalPushOldData(old, H, 'Units', oldFigureUnits); + r = [origfr(1) origfr(2)+origfr(4)-fr(4) fr(3:4)]; + set(H,'Position',r); + else + args = {args{:}, '-loose'}; + old = LocalPushOldData(old,H,'Position', oldFigPos); + end + end + + % Process text in a separate file if needed + if opts.separatetext & ~opts.applystyle + % First hide all text and export + oldtvis = LocalGetAsCell(allText,'visible'); + set(allText,'visible','off'); + oldax = LocalGetAsCell(allAxes,'XTickLabel',1); + olday = LocalGetAsCell(allAxes,'YTickLabel',1); + oldaz = LocalGetAsCell(allAxes,'ZTickLabel',1); + null = cell(length(oldax),1); + [null{:}] = deal([]); + set(allAxes,{'XTickLabel'},null); + set(allAxes,{'YTickLabel'},null); + set(allAxes,{'ZTickLabel'},null); + print(H, filename, args{:}); + set(allText,{'Visible'},oldtvis); + set(allAxes,{'XTickLabel'},oldax); + set(allAxes,{'YTickLabel'},olday); + set(allAxes,{'ZTickLabel'},oldaz); + % Now hide all non-text and export as eps in painters + [path, name, ext] = fileparts(filename); + tfile = fullfile(path,[name '_t.eps']); + tfile2 = fullfile(path,[name '_t2.eps']); + foundRenderer = 0; + for k=1:length(args) + if strncmp('-d',args{k},2) + args{k} = '-deps'; + elseif strncmp('-zbuffer',args{k},8) | ... + strncmp('-opengl', args{k},6) + args{k} = '-painters'; + foundRenderer = 1; + end + end + if ~foundRenderer + args = {args{:}, '-painters'}; + end + allNonText = [allLines; allLights; allPatch; ... + allImages; allSurf; allRect]; + oldvis = LocalGetAsCell(allNonText,'visible'); + oldc = LocalGetAsCell(allAxes,'color'); + oldaxg = LocalGetAsCell(allAxes,'XGrid'); + oldayg = LocalGetAsCell(allAxes,'YGrid'); + oldazg = LocalGetAsCell(allAxes,'ZGrid'); + [null{:}] = deal('off'); + set(allAxes,{'XGrid'},null); + set(allAxes,{'YGrid'},null); + set(allAxes,{'ZGrid'},null); + set(allNonText,'Visible','off'); + set(allAxes,'Color','none'); + print(H, tfile2, args{:}); + set(allNonText,{'Visible'},oldvis); + set(allAxes,{'Color'},oldc); + set(allAxes,{'XGrid'},oldaxg); + set(allAxes,{'YGrid'},oldayg); + set(allAxes,{'ZGrid'},oldazg); + %hack up the postscript file + fid1 = fopen(tfile,'w'); + fid2 = fopen(tfile2,'r'); + line = fgetl(fid2); + while ischar(line) + if strncmp(line,'%%Title',7) + fprintf(fid1,'%s\n',['%%Title: ', tfile]); + elseif (length(line) < 3) + fprintf(fid1,'%s\n',line); + elseif ~strcmp(line(end-2:end),' PR') & ... + ~strcmp(line(end-1:end),' L') + fprintf(fid1,'%s\n',line); + end + line = fgetl(fid2); + end + fclose(fid1); + fclose(fid2); + delete(tfile2); + + elseif ~opts.applystyle + drawnow; + print(H, filename, args{:}); + end + warning(oldwarn); + +catch + warning(oldwarn); + hadError = 1; +end + +% Restore figure settings +if opts.applystyle + varargout{1} = old; +else + for n=1:length(old.objs) + if ~iscell(old.values{n}) & iscell(old.prop{n}) + old.values{n} = {old.values{n}}; + end + set(old.objs{n}, old.prop{n}, old.values{n}); + end +end + +if hadError + error(deblank(lasterr)); +end + +% +% Local Functions +% + +function outData = LocalPushOldData(inData, objs, prop, values) +outData.objs = {objs, inData.objs{:}}; +outData.prop = {prop, inData.prop{:}}; +outData.values = {values, inData.values{:}}; + +function cellArray = LocalGetAsCell(fig,prop,allowemptycell); +cellArray = get(fig,prop); +if nargin < 3 + allowemptycell = 0; +end +if ~iscell(cellArray) & (allowemptycell | ~isempty(cellArray)) + cellArray = {cellArray}; +end + +function newArray = LocalScale(inArray, scale, minv, maxv) +n = length(inArray); +newArray = cell(n,1); +for k=1:n + newArray{k} = min(maxv,max(minv,scale*inArray{k}(1))); +end + +function gray = LocalMapToGray1(color) +gray = color; +if ischar(color) + switch color(1) + case 'y' + color = [1 1 0]; + case 'm' + color = [1 0 1]; + case 'c' + color = [0 1 1]; + case 'r' + color = [1 0 0]; + case 'g' + color = [0 1 0]; + case 'b' + color = [0 0 1]; + case 'w' + color = [1 1 1]; + case 'k' + color = [0 0 0]; + end +end +if ~ischar(color) + gray = 0.30*color(1) + 0.59*color(2) + 0.11*color(3); +end + +function newArray = LocalMapToGray(inArray); +n = length(inArray); +newArray = cell(n,1); +for k=1:n + color = inArray{k}; + if ~isempty(color) + color = LocalMapToGray1(color); + end + if isempty(color) | ischar(color) + newArray{k} = color; + else + newArray{k} = [color color color]; + end +end + +function newArray = LocalMapColorToStyle(inArray); +inArray = LocalGetAsCell(inArray,'Color'); +n = length(inArray); +newArray = cell(n,1); +styles = {'-','--',':','-.'}; +uniques = []; +nstyles = length(styles); +for k=1:n + gray = LocalMapToGray1(inArray{k}); + if isempty(gray) | ischar(gray) | gray < .05 + newArray{k} = '-'; + else + if ~isempty(uniques) & any(gray == uniques) + ind = find(gray==uniques); + else + uniques = [uniques gray]; + ind = length(uniques); + end + newArray{k} = styles{mod(ind-1,nstyles)+1}; + end +end + +function newArray = LocalMapCData(inArray); +n = length(inArray); +newArray = cell(n,1); +for k=1:n + color = inArray{k}; + if (ndims(color) == 3) & isa(color,'double') + gray = 0.30*color(:,:,1) + 0.59*color(:,:,2) + 0.11*color(:,:,3); + color(:,:,1) = gray; + color(:,:,2) = gray; + color(:,:,3) = gray; + end + newArray{k} = color; +end + +function outData = LocalUpdateColors(inArray, prop, inData) +value = LocalGetAsCell(inArray,prop); +outData.objs = {inData.objs{:}, inArray}; +outData.prop = {inData.prop{:}, {prop}}; +outData.values = {inData.values{:}, value}; +if (~isempty(value)) + if strcmp(prop,'CData') + value = LocalMapCData(value); + else + value = LocalMapToGray(value); + end + set(inArray,{prop},value); +end + +function bool = LocalIsPositiveScalar(value) +bool = isnumeric(value) & ... + prod(size(value)) == 1 & ... + value > 0; + +function value = LocalToNum(value,auto) +if ischar(value) + if strcmp(value,'auto') + value = auto; + else + value = str2num(value); + end +end + +%convert a struct to {field1,val1,field2,val2,...} +function c = LocalToCell(s) +f = fieldnames(s); +v = struct2cell(s); +opts = cell(2,length(f)); +opts(1,:) = f; +opts(2,:) = v; +c = {opts{:}}; + +function c = LocalIsHG(obj,hgtype) +c = 0; +if (length(obj) == 1) & ishandle(obj) + c = strcmp(get(obj,'type'),hgtype); +end + +function c = LocalHas3DPlot(a) +zticks = LocalGetAsCell(a,'ZTickLabel'); +c = 0; +for k=1:length(zticks) + if ~isempty(zticks{k}) + c = 1; + return; + end +end + +function r = LocalUnionRect(r1,r2) +if isempty(r1) + r = r2; +elseif isempty(r2) + r = r1; +elseif max(r2(3:4)) > 0 + left = min(r1(1),r2(1)); + bot = min(r1(2),r2(2)); + right = max(r1(1)+r1(3),r2(1)+r2(3)); + top = max(r1(2)+r1(4),r2(2)+r2(4)); + r = [left bot right-left top-bot]; +else + r = r1; +end + +function c = LocalLabelsMatchTicks(labs,ticks) +c = 0; +try + t1 = num2str(ticks(1)); + n = length(ticks); + tend = num2str(ticks(n)); + c = strncmp(labs(1),t1,length(labs(1))) & ... + strncmp(labs(n),tend,length(labs(n))); +end + +function r = LocalAxesTightBoundingBox(axesR, a) +r = []; +atext = findall(a,'type','text','visible','on'); +if ~isempty(atext) + set(atext,'units','points'); + res=LocalGetAsCell(atext,'extent'); + for n=1:length(atext) + r = LocalUnionRect(r,res{n}); + end +end +if strcmp(get(a,'visible'),'on') + r = LocalUnionRect(r,[0 0 axesR(3:4)]); + oldunits = get(a,'fontunits'); + set(a,'fontunits','points'); + label = text(0,0,'','parent',a,... + 'units','points',... + 'fontsize',get(a,'fontsize'),... + 'fontname',get(a,'fontname'),... + 'fontweight',get(a,'fontweight'),... + 'fontangle',get(a,'fontangle'),... + 'visible','off'); + fs = get(a,'fontsize'); + + % handle y axis tick labels + ry = [0 -fs/2 0 axesR(4)+fs]; + ylabs = get(a,'yticklabels'); + yticks = get(a,'ytick'); + maxw = 0; + if ~isempty(ylabs) + for n=1:size(ylabs,1) + set(label,'string',ylabs(n,:)); + ext = get(label,'extent'); + maxw = max(maxw,ext(3)); + end + if ~LocalLabelsMatchTicks(ylabs,yticks) & ... + strcmp(get(a,'xaxislocation'),'bottom') + ry(4) = ry(4) + 1.5*ext(4); + end + if strcmp(get(a,'yaxislocation'),'left') + ry(1) = -(maxw+5); + else + ry(1) = axesR(3); + end + ry(3) = maxw+5; + r = LocalUnionRect(r,ry); + end + + % handle x axis tick labels + rx = [0 0 0 fs+5]; + xlabs = get(a,'xticklabels'); + xticks = get(a,'xtick'); + if ~isempty(xlabs) + if strcmp(get(a,'xaxislocation'),'bottom') + rx(2) = -(fs+5); + if ~LocalLabelsMatchTicks(xlabs,xticks); + rx(4) = rx(4) + 2*fs; + rx(2) = rx(2) - 2*fs; + end + else + rx(2) = axesR(4); + % exponent is still below axes + if ~LocalLabelsMatchTicks(xlabs,xticks); + rx(4) = rx(4) + axesR(4) + 2*fs; + rx(2) = -2*fs; + end + end + set(label,'string',xlabs(1,:)); + ext1 = get(label,'extent'); + rx(1) = -ext1(3)/2; + set(label,'string',xlabs(size(xlabs,1),:)); + ext2 = get(label,'extent'); + rx(3) = axesR(3) + (ext2(3) + ext1(3))/2; + r = LocalUnionRect(r,rx); + end + set(a,'fontunits',oldunits); + delete(label); +end + +function c = LocalManualAxesMode(old, allAxes, base) +xs = ['X' base]; +ys = ['Y' base]; +zs = ['Z' base]; +oldXMode = LocalGetAsCell(allAxes,xs); +oldYMode = LocalGetAsCell(allAxes,ys); +oldZMode = LocalGetAsCell(allAxes,zs); +old = LocalPushOldData(old, allAxes, {xs}, oldXMode); +old = LocalPushOldData(old, allAxes, {ys}, oldYMode); +old = LocalPushOldData(old, allAxes, {zs}, oldZMode); +set(allAxes,xs,'manual'); +set(allAxes,ys,'manual'); +set(allAxes,zs,'manual'); +c = old; + +function val = LocalCheckAuto(val, auto) +if ischar(val) & strcmp(val,'auto') + val = auto; +end diff --git a/sourcecodes/bnt-master/SLP/misc/find_nodes_in_undirected_component.m b/sourcecodes/bnt-master/SLP/misc/find_nodes_in_undirected_component.m new file mode 100644 index 00000000..7ba14eca --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/find_nodes_in_undirected_component.m @@ -0,0 +1,18 @@ +function unprocessed = find_unprocessed(dag) +% unprocessed = find_unprocessed(dag) +% +% francois.olivier.c.h@gmail.com + +undirected_edges = dag.*dag'; +[unprocessed, tmp] = find(undirected_edges) +unprocessed = unique(unprocessed); +% N = size(dag,1); +% unprocessed = []; +% for i=1:(N-1) +% for j=(i+1):N +% if dag(i,j)==1 & dag(j,i)==1 +% unprocessed = [unprocessed,i,j]; +% end +% end +% end +% unprocessed = unique(unprocessed); diff --git a/sourcecodes/bnt-master/SLP/misc/foptions.m b/sourcecodes/bnt-master/SLP/misc/foptions.m new file mode 100644 index 00000000..f2976bb9 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/foptions.m @@ -0,0 +1,11 @@ +function opt_vect = foptions() +% FOPTIONS Sets default parameters for optimisation routines +% For compatibility with MATLAB's foptions() +% +% Copyright (c) Dharmesh Maniyar, Ian T. Nabney (2004) + +opt_vect = zeros(1, 18); +opt_vect(2:3) = 1e-4; +opt_vect(4) = 1e-6; +opt_vect(16) = 1e-8; +opt_vect(17) = 0.1; \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/misc/gener_MAR_net.m b/sourcecodes/bnt-master/SLP/misc/gener_MAR_net.m new file mode 100644 index 00000000..64740076 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/gener_MAR_net.m @@ -0,0 +1,104 @@ +function bnet_miss = gener_MAR_net(bnet_orig, base_proba) +% function bnet_miss = gener_MAR_net(bnet_orig, base_proba) +% +% bnet_orig : a bnet +% base_proba : a probability for value to be missing +% +% bnet_miss : a bnet that could be used in gener_data_from_bnet_miss function +% to generate incomplete MAR dataset +% +% Francois.Olivier.C.H@gmail.com + +%%%%%%%%%%%% INIT +if nargin<2, error('Not enougth arguments'); end + +% crĂŠation du rĂŠseau +dag = bnet_orig.dag; +N = size(dag,2); +ns = bnet_orig.node_sizes; + + ns_miss = zeros(1,3*N); + ns_miss(1:N) = ns; + ns_miss(N+1:2*N) = 2*ones(1,N); % 1= node i-N present, 2= node i-N missing + ns_miss(2*N+1:3*N) = ns+1; % 1:ns, absent + + dag_miss = zeros(3*N,3*N); + dag_miss(1:N,1:N) = dag; +% dag_miss(2*N+1:3*N,N+1:2*N)=mk_rnd_dag(N,N-ceil(rand*N/2)); + lim = 1+(rand>.4)+(rand>.65)+(rand>.9); + dag_miss(2*N+1:3*N,N+1:2*N)=mk_rnd_dag(N,lim); + + for i=1:N, dag_miss(i,2*N+i)=1; dag_miss(N+i,2*N+i)=1; dag_miss(2*N+i,i)=0; dag_miss(2*N+i,N+i)=0; end + + bnet_miss = mk_bnet(dag_miss, ns_miss); + CPT = CPT_from_bnet(bnet_orig, 0); + for i=1:N + bnet_miss.CPD{i} = tabular_CPD (bnet_miss, i, CPT{i}); + end + + % CPD of nodes M + for i=1:N, + if find(bnet_miss.order==i)N | round(i)~=i, i = input('Which node ? '); end +% fam = find(dag(:,i)==1)'; +% fam_miss = find(dag_miss(:,N+i)==1)'; +% cas = -ones(1, length(fam_miss)+1); +% familly = [fam_miss, N+i]; +% p=-1; +% while p<0 | p>1, p = input(' - A priori probability for this node to be missing ? ');end +% semisize = prod(ns_miss(fam_miss)); +% CPT = zeros(1,2*semisize); +% CPT(1:semisize) = 1-p; +% CPT(semisize+1:2*semisize) = p; +% bnet_miss.CPD{N+i} = tabular_CPD (bnet_miss, N+i, CPT); +% b=-1; +% while ~(b==0 | b==1), b = input('Would you like to change a probability of a node to be missing (1 for yes, 0 for no) ? ');end +% end +% end +% +% +% else % automatic generation + +%%%%%%%%%%%% creating BETAs +% %% To use multiple of 5 percent in probs +% if N<=25, +% BETA = gener_problist(base_proba, N); +% else +% nboucles = floor(N/25); +% BETA = []; +% for i=1:nboucles +% BETA1 = gener_problist(base_proba, 25); +% BETA = [BETA, BETA1]; +% end +% nreste = rem(N,25); +% BETA1 = gener_problist(base_proba, nreste); +% BETA = [BETA, BETA1]; +% end + BETA = gener_discrete_dist(N, base_proba); + + order=[]; + missdagtmp = bnet_miss.dag(N+1:2*N,N+1:2*N); + unprocessed = 1:N; + while ~isempty(unprocessed) + npar=[]; + for i=1:N, npar(end+1)=length(parents(missdagtmp,i));end, + [npar, ord] = sort(npar); + while ~ismember(ord(1),unprocessed) + ord=ord(2:end); + end + order = [order, ord(1)]; + missdagtmp(ord(1),:)=0; + unprocessed = mysetdiff(unprocessed,ord(1)); + end + +%%%%%%%%%%%% Update CPT with MCAR process +for i=1:length(BETA) + fam_miss = find(dag_miss(:,N+order(i))==1)'; + p=BETA(i); + semisize = prod(ns_miss(fam_miss)); + + if isempty(fam_miss), + CPT = zeros(1,2*semisize); + CPT(1:semisize) = 1-p; + CPT(semisize+1:2*semisize) = p; + bnet_miss.CPD{N+order(i)} = tabular_CPD (bnet_miss, N+order(i), CPT); + else + + %node = N+order(i) + %for k=1:semisize + % XI(k) = eval_xi(bnet_miss, N+order(i), k); + % %XI(k+semisize)=1-XI(k); + %end + %MUi1 = zeros(1,semisize); + %MUi1 = gener_mu(p, semisize, XI); + + MUi1k = gener_discrete_dist(semisize, p); + CPT = zeros(1,2*semisize); + CPT(1:semisize) = 1-MUi1k; + CPT(semisize+1:2*semisize) = MUi1k; + bnet_miss.CPD{N+order(i)} = tabular_CPD (bnet_miss, N+order(i), CPT); + + end +% end +end \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/misc/gener_NMAR_data.m b/sourcecodes/bnt-master/SLP/misc/gener_NMAR_data.m new file mode 100644 index 00000000..4cff44d6 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/gener_NMAR_data.m @@ -0,0 +1,290 @@ +function [data, comp_data, bnet_miss] = gener_NMAR_dataset(bnet_orig, m, bnet_miss, upd) +% [NMAR_data] = gener_NMAR_data(bnet_miss, length_of_dataset) +% +% this function takes in input a bnet that can be used to generate NMAR data. +% this bnet bnet_miss can be creating by the function gener_NMAR_bnet. +% +% - comp_data (array) is a dataset that was generate by the bnet_orig that you enter in gener_NMAR_bnet +% - NMAR_data (cell array) is the dataset compdata that was emptyied by the NMAR process encodes in bnet_miss +% +% optional : +% - bnet_miss : an old bnet_miss built by this function +% - upd==1 if you want to update the bnet_miss +% +% [data, comp_data, bnet_miss] = gener_NMAR_dataset(bnet_orig, m, bnet_miss, upd); +% +% version 0.5 : june 8th 2005, olivier.francois@insa-rouen.fr +% +% TO DO : +% - allow the combinaison of a missing state of one variable and another state of another variable to have influence +% - allow the introduction of new nodes and specify which nodes it influence and which nodes has influence on it (it will also satisfy the first task then) +% + + +% INIT +N = size(bnet_orig.dag,2); +if mod(N,2)~=0, error('The number of nodes must be even'); end +if nargin<4, upd =0; end + +% fisrt rules +if nargin<3, + l1=[]; l2=[]; lp=[]; b=-1; + while ~(b==0 | b==1), b = input('Would you like to make a node missing when another one is missing (1 for yes, 0 for no) ? '); end +else + b=-1; + if upd, while ~(b==0 | b==1), b = input('Would you like to add rules (1 for yes, 0 for no) ? '); end + l1=bnet_miss.list{1}; + l2=bnet_miss.list{2}; + lp=bnet_miss.list{3}; + else + l1=[]; l2=[]; lp=[]; b=-1; + end +end + +while b==1, + n = input('The firts node ? '); + s = input('The node that have to be missing when this one is missing ? '); + p = input('The probability of the second node to be missing ? '); + l1 = [l1, n]; l2 = [l2, s]; lp=[lp, p]; + b=-1; + while ~(b==0 | b==1), b = input('Another one (1 for yes, 0 for no) ? '); end +end +bb = length(l1); + + +if nargin>=3, + bnet_miss = gener_NMAR_bnet(upd, bnet_orig, bnet_miss); +else + bnet_miss = gener_NMAR_bnet(1, bnet_orig); + bnet_miss.list={l1; l2; lp}; +%%%%%%%%%%% SAVING FILE + ss = 1; + if nargin == 2 | upd==1, ss = input('Would you like to save the bnet of the NMAR process you have made (1 for yes) ? '); end + if ss == 1, + ddd = datestr(now); + ddd([12 15 18])='-' ; + fnout=['NMAR-bnet-' ddd '.mat']; + eval(['save ' fnout ' bnet_miss']); + fprintf(' The bnet for NMAR process was saved as : %s\n',fnout); + end +end %if nargin + +% Generation of a complete dataset +if N>9 & m>2000, disp(' ! It could take a long time...'); end +data = cell(N,m); +for l = 1:m, data(:,l) = sample_bnet(bnet_orig); end +disp('Complete data have been creating.'); + +% Generation of a NMAR dataset +miss_array = cell(2*N,m); +vide = cell(1,N); l= 1; +while l <= m, + ev(1:N) = data(:,l); ev(N+1:2*N) = vide; + miss_array(:,l) = sample_bnet(bnet_miss, 'evidence', ev); + % apply simple rule of missingness + ev2 = cell2mat(miss_array(N+1:2*N, l)); + if bb, + missl1 = myintersect(find(ev2==2), l1); + if ~isempty(missl1), + for i=1:length(l1), + if ev2(l1(i))==2, if rand1, p = input('Base probability of a value to be missing ? '); end + for i=1:N + fam = find(dag_miss(:,N+i)==1)'; + semisize = prod(ns_miss(fam)); % as node N+i is binary to say i is present or missing + CPT = zeros(1,2*semisize); + CPT(1:semisize) = 1-p; + CPT(semisize+1:2*semisize) = p; + bnet_miss.CPD{N+i} = tabular_CPD (bnet_miss, N+i, CPT); + end +end +end + +b=-1; +while ~(b==0 | b==1), b = input('Would you like to change a probability of a node to be missing (1 for yes, 0 for no) ? '); end +if b, disp(' BE CAREFULL !! New rules can overwrite old ones partialy or fully !! So the order of entries is important'); end + +%%%%%%%%%%%% Update CPT with NMAR process +while b + fprintf('Nodes are from 1 to %d. ',N); + i=0; + while i<1 | i>N | round(i)~=i, i = input('Which node ? '); end + fam = find(dag(:,i)==1)'; + fam_miss = find(dag_miss(:,N+i)==1)'; + cas = -ones(1, length(fam_miss)+1); + familly = [fam_miss, N+i]; + fprintf('States are from 1 to %d (-1 for any states, -2 to cancel). For which state of the variable %d ?', ns(i), i); + state=-3; + while state<-2 | state>ns(i) | round(state)~=state | state==0, state = input(' ');end + if isempty(fam), + if state==-1, + p=-1; + while p<0 | p>1, p = input(' - A priori probability for this node to be missing ? ');end + semisize = prod(ns_miss(fam_miss)); + CPT = zeros(1,2*semisize); + CPT(1:semisize) = 1-p; + CPT(semisize+1:2*semisize) = p; + bnet_miss.CPD{N+i} = tabular_CPD (bnet_miss, N+i, CPT); + elseif state~=-2 + cas = state; + CPT = CPT_from_bnet(bnet_miss); + CPT = CPT{N+i}; + p=-1; + while p<0 | p>1, p = input(' - A priori probability for this node to be missing in this state ? ');end + ind = subv2ind(ns_miss(familly),[cas, 1]); + CPT(ind)=1-p; + ind = subv2ind(ns_miss(familly),[cas, 2]); + CPT(ind)=p; + bnet_miss.CPD{N+i} = tabular_CPD (bnet_miss, N+i, CPT); + end + else + if state>-2, + siz=length(cas); + place = find(fam_miss==i); + cas(place) = state; + + for k = fam, + state=-3; + fprintf(' - For the parent named %d, states are from 1 to %d (-1 for any states of this parent). ',k, ns(k)); + while state<0 | state>ns(k) | round(state)~=state, state = input('Which state ? ');end + %if state==0, + % place = find(fam_miss==(fam_miss(k)+N)); + % cas(place) = 2; % Missing + %elseif state==-2, % a changer ??? + % disp(' This case is buggy, taking missing state instand to minimise influence.'); + % place = find(fam_miss==(fam_miss(k)+N)); + % cas(place) = 2; + %elseif state~=0 & state~=-2, + place = find(fam_miss==(fam_miss(k))); + cas(place) = state; % Present + if state~=-1; place = find(fam_miss==(fam_miss(k)+N)); cas(place) = 1; end + %end + end + + p=-1; + while p<0 | p>1, p = input('Probability in this case of the value to be missing ? ');end + CPT = CPT_from_bnet(bnet_miss); + CPT = CPT{N+i}; + + cas(end) = 1; % i is present + subcas_names = find(cas==-1); + if isempty(subcas_names), + ind = subv2ind(ns_miss(familly),cas); + CPT(ind) = 1-p; + else + subcas = ones(1, length(subcas_names)); + continu = 1; + while continu + cas(subcas_names) = subcas; + ind = subv2ind(ns_miss(familly),cas); + CPT(ind) = 1-p; + [subcas, continu] = next_case(subcas, ns_miss(familly(subcas_names))); + end + end + + cas(end)=2; % i is missing + if isempty(subcas_names), + ind = subv2ind(ns_miss(familly),cas); + CPT(ind) = p; + else + subcas = ones(1, length(subcas_names)); + continu = 1; + while continu + cas(subcas_names) = subcas; + ind = subv2ind(ns_miss(familly),cas); + CPT(ind) = p; + [subcas, continu] = next_case(subcas, ns_miss(familly(subcas_names))); + end + end + mass = sum(CPT, length(size(CPT))); + while length(size(mass))>2, mass = prod(mass, length(size(mass))); end + mass = prod(prod(mass)); + if mass~=1, disp('not a proba...'); end + bnet_miss.CPD{N+i} = tabular_CPD (bnet_miss, N+i, CPT); + end %if state~=-2 for the node + end %if isempty(fam), + b=-1; + while ~(b==0 | b==1), b = input('Would you like to change a probability of a node to be missing (1 for yes, 0 for no) ? ');end +end + diff --git a/sourcecodes/bnt-master/SLP/misc/gener_data_from_bnet_miss.m b/sourcecodes/bnt-master/SLP/misc/gener_data_from_bnet_miss.m new file mode 100644 index 00000000..3508daa0 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/gener_data_from_bnet_miss.m @@ -0,0 +1,103 @@ +function [data, comp_data, bnet_miss, taux, bnet_orig, notok, d] = gener_data_from_bnet_miss(bnet_miss, m, base_proba ,v, testdata) +% [data, comp_data, bnet_miss, taux, bnet_orig, notok] = gener_data_from_bnet_miss(bnet_miss, m, base_proba ,v, aretestdata) +% +% bnet_miss : see gener_[MCAR or MAR]_net function +% m : the length of the dataset +% if base_proba==0 or does not exist the x2_test will be passed +% v==1 to enter the verbose mode +% aretestdata==1 to always build the same dataset <-- rand('state',0) +% +% Francois.Olivier.C.H@gmail.com + +% Initialisation + +if nargin<5, testdata = 0; end +if nargin<4, v = 0; end + +N2 = length(bnet_miss.dag); +if mod(N2,3)~=0, error('The number of nodes must be even in bnet_miss'); end +N = length(bnet_miss.dag)/3; + +if nargin<3, base_proba=0; end +if nargin<2, error('Not enougth parameters'); end + +% CHOOSE THE TEST POWER (only affect 'notok' value) + chi2_0_1_1fd = 2.705 ; + chi2_0_05_1fd = 3.841 ; + chi2_0_01_1fd = 6.635 ; + chi2_0_001_1fd = 10.827 ; + chi2_0_0001_1fd = 15.137 ; + choice = chi2_0_001_1fd; +notok=0; +clear chi2_0_1_1fd chi2_0_05_1fd chi2_0_01_1fd chi2_0_001_1fd chi2_0_0001_1fd + +% Recovering bnet_orig + +dag = bnet_miss.dag(1:N,1:N); +bnet_orig = mk_bnet(dag, bnet_miss.node_sizes(1:N)); +CPT = CPT_from_bnet(bnet_miss, 0); +for i=1:N, bnet_orig.CPD{i} = tabular_CPD(bnet_orig, i, CPT{i}); end + +% Generation of complete data + +if testdata, rand('state',0); randn('state',0); end + +data = cell(N,m); +for l = 1:m, data(:,l) = sample_bnet(bnet_orig); end +fprintf('Complete data have been created.'); + +% Generation of missing array + +miss_array = cell(3*N,m); +vide = cell(1,2*N); l= 1; +while l <= m, + ev(1:N) = data(:,l); ev(N+1:3*N) = vide; + miss_array(:,l) = sample_bnet(bnet_miss, 'evidence', ev); + % apply simple rule of missingness + ev2 = cell2mat(miss_array(N+1:2*N, l)); + % verification that we have not a completly missing sample + ev2 = 3-ev2; + if prod(ev2)==1, + if v, fprintf(' - %d, one completly missing sample removed', l);end + else l=l+1; + end + if v, if mod(l,250)==0, fprintf('\n - %d',l); end, end +end +fprintf('\n'); + +% Generation of incomplete dataset + + %% TO REPLACE THE CELL ARRAY FOR ouput data + %% WITH A MATRIX WITH A SPECIAL CASE (size+1) + %% FOR MISSING DATA, SIMPLY REPLACE 1 by 0 +if 1, %% HERE + miss_array = bnt_to_mat(miss_array(N+1:2*N, :)); + miss_array = 2-miss_array; + data = bnt_to_mat(data); + comp_data = data; + data = data.*miss_array; + data = mat_to_bnt(data, 0); +else + comp_data = bnt_to_mat(miss_array(1:N,:),0); + data = bnt_to_mat(miss_array(2*N+1:3*N,:),0); + miss_array = bnt_to_mat(miss_array(N+1:2*N, :)); + miss_array = 2-miss_array; +end +fprintf('Incomplete dataset have been created.\n'); + +% Verification of the Rate of missing data + +if base_proba, + [XX, YY]=find(miss_array==0); + nbr_miss = length(YY); + taux = nbr_miss/N/m; + if v, fprintf('There is %2.2f percent of missing data\n', round(taux*10000)/100); end + + % Khi2 test between taux and base_proba for m*N + toto = m*N; + d = ((nbr_miss-base_proba*toto)^2)/(base_proba*toto) + (((toto-nbr_miss)-(1-base_proba)*toto)^2)/((1-base_proba)*toto); + if d>choice, + fprintf('THE DATASET DO NOT RESPECT %2.1f%% OF MISSING DATA (%2.1f%%, Khi2 : %2.1f > %2.1f)\n', round(base_proba*10000)/100, round(taux*10000)/100, d, choice); + notok = 1; + end +end diff --git a/sourcecodes/bnt-master/SLP/misc/gener_discrete_dist.m b/sourcecodes/bnt-master/SLP/misc/gener_discrete_dist.m new file mode 100644 index 00000000..4f131fcb --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/gener_discrete_dist.m @@ -0,0 +1,12 @@ +function beta = gener_discrete_dist(N, alpha) +% beta = gener_beta(N, masse) +% +% using the logistic function to create a random vector +% of discrete probability distribution on N values +% if masse~=1 then sum(beta)==masse and this is not a probability +% +% francois.olivier.c.h@gmail.com + +if nargin==1, alpha=1; end +gamma = rand(1,N); +beta = alpha*N*(exp(gamma)/sum(exp(gamma))); diff --git a/sourcecodes/bnt-master/SLP/misc/gener_empty_cache.m b/sourcecodes/bnt-master/SLP/misc/gener_empty_cache.m new file mode 100644 index 00000000..6e9669e5 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/gener_empty_cache.m @@ -0,0 +1,26 @@ +function cache = gener_empty_cache(N,L) +% cache = gener_empty_cache(number_of_nodes,lenght_of_cache) +% +% exemple for 2 nodes with cache of size 5 : +% +% cache = +% 5 b 0 0 0 --> 1st empty place and b==1 iff the cache is full +% 0 0 1 -239.12 1 --> 1st familly in the cache (node 1 without parents) calculate with bic +% 0 0 2 -318.98 1 +% 1 0 2 -189.23 2 --> 3rd familly in the cache (node 2 with 1 as parent) calculate with bayesian +% 0 1 1 -251.09 1 +% 0 0 0 0 0 --> empty entry +% | | | | | +% | | | | |___> 1 for 'bic' or 2 for 'bayesian' +% | | | |__________> score of the familly +% | | |_________________> son node of the familly +% | |__________________________> ==1 iff node 2 is parent of son node +% |______________________________> ==1 iff node 1 is parent of son node +% +% +% +% designed by francois.olivier.c.h@gmail.com +% + +cache=zeros(L+1,N+3); +cache(1,1)=2; \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/misc/hist_ic.m b/sourcecodes/bnt-master/SLP/misc/hist_ic.m new file mode 100644 index 00000000..b16980fd --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/hist_ic.m @@ -0,0 +1,268 @@ +function [n,edges,nbedges,xechan] = hist_ic(x,crit) + +%HIST_IC optimal Histogram based on IC information criterion +% +% [N,EDGES,NBEDGES,XECHAN] = HIST-IC(X,CRIT) +% bins the elements of X into an optimal number of bins according +% to a cost function based on Akaike's Criterion. +% +% +% CRIT = 1 | 2 | 3 (choose one of the 3 possible criterium) (default 3) +% 4 (returns the initial histogram instead of the optimal one) +% +% +% N = cell array containing the distribution of each column of X +% (or a vector if X is a column vector) +% EDGES = cell array containing the bin edges of each column of X +% (or a vector if X is a column vector) +% NBEDGES = vector containing the number of bin edges for each column of X +% (or a number if X is a column vector) +% XECHAN = discretized version of X +% +% Ref : O. Colot et al., Information Criteria and Abrupt Changes in +% Probability Laws, Signal Processing VII: Theory and Applications +% pp.1855-18858, September 1994 +% +% F. El-Matouat, O. Colot 2000 (first version) +% Revised 01-06-2001 by Ph. Leray - philippe.leray@univ-nantes.fr +% +% +% Things to do : +% * Call criteron by a name ('aic','xxx', ...) instead of a number +% + + +if nargin == 0 + error('Requires one or two arguments.') +end + +if nargin == 1 + crit = 3; +end; + +if min(size(x))==1, x = x(:); end + +if isstr(x) + error('Input argument must be numeric.') +end + +if isempty(x), + error('No elements to count') +end + + +[nb_l,nb_c]=size(x); + +% Outputs declaration +xechan=zeros(nb_l,nb_c); + +edges=cell(nb_c,1); +% Local variables +maxi = max(x); +mini = min(x); + +%% Erreur ? ancien code : +%% nb_clas_ini=2*round(sqrt(nb_l)-1); % article Fatima + +nb_clas_ini=round(2*sqrt(nb_l)-1); + +pas_ini=(maxi-mini)/nb_clas_ini; % initial step + +for j=1:nb_c, + + % optimal histogram for each column of X + histo_ini =hist(x(:,j),nb_clas_ini); % initial histogram + + if (crit~=4) + [hist_opt,pas_opt]=hist1_ic(histo_ini,nb_l,pas_ini(j),nb_clas_ini,crit); + else + fprintf('Histo initial\n'); + hist_opt=histo_ini; + pas_opt=ones(1,nb_clas_ini)*pas_ini(j); + end; + nbedges(j)=size(hist_opt,2); + edges{j}=mini(j)+cumsum(pas_opt(1:nbedges(j)-1)); %+1e-7; + [n{j} xechan(:,j)]=histc(x(:,j),[-inf edges{j} inf]); + n{j}=n{j}(1:end-1); +end + +if (nb_c==1) + n=n{1}; edges=edges{1}; +end + + +% ============================== subfunctions + +function [hist_opt,step_opt]=hist1_ic(histo,nb,step_ini,m,critere); + +%HIST1_IC optimal Histogram based on IC information criterion +% +% [HIST_OPT, STEP_OPT] = HIST1_IC(HISTO, NB, STEP_INI, NBSTEP_INI, CRIT) +% fusion of an 1D histogramme (HISTO) according to an IC criterion (CRIT) +% +% This function is mainly an internal function used by HIST_IC +% +% Ref : O. Colot et al., Information Criteria and Abrupt Changes in +% Probability Laws, Signal Processing VII: Theory and Applications +% pp.1855-18858, September 1994 +% +% F. El-Matouat, O. Colot 2000 (first version) +% Revised 11-06-2001 by Ph. Leray +% +% +% Things to do : +% * Call criteron by a name ('aic','xxx', ...) instead of a number +% + + +aic=[]; +aic2=[]; + +% Initialisation +histt=histo; +teta = histt/nb; +pas = step_ini*ones(1,m); + +% Calcul de l'ensemble des histogrammes optimaux + +for z=1:m + + % Calcul de AIC pour l'union entre hist(indice,u) et hist(indice,u+1) + aic2 = [aic2 cal_aic(nb,teta,pas,m+1-z,critere)]; + + if (z~=m) + % Calcul des couples de classes adjacentes + if critere==1 + penalite=(2*(m-z)-1)/nb; + elseif critere==2 + penalite=(m-z-1)*(1+log(nb))/nb; + else + penalite=(m-z)*(1+log(log(nb)))/nb; + end + aic=cla_adj(nb,teta,pas,step_ini,m-z+1,histt,penalite,aic); + % Recherche de la valeur min du critďż˝re pour les classes adjacentes + [min_aic classe]=min(aic(1:(m-z))); + + + % Fusion de hist(classe) et hist(classe+1) + nb_pas1=pas(classe)/step_ini; + nb_pas2=pas(classe+1)/step_ini; + + ess=round( nb_pas1*histt(classe)+nb_pas2*histt(classe+1) ); + teta(classe)=ess / nb; + histt(classe)=ess / (nb_pas1+nb_pas2); + pas(classe)=pas(classe)+pas(classe+1); + + + % Crďż˝ation du nouvel histogramme + itemp = setdiff(1:m+1-z,classe+1); + histt = histt(itemp); + pas = pas(itemp); + teta = teta(itemp); + end +end + +% Recherche du critďż˝re minimun AIC +[min_AIC fusion]=min(aic2(1:m)); + +% Initialisation de histo +histt=histo; +teta = histt/nb; +pas = step_ini*ones(1,m); + +% Calcul de l'histogramme optimal + +for z=1:fusion-1 + + % Calcul des couples de classes adjacentes + if critere==1 + penalite=(2*(m-1)-1)/nb; + elseif critere==2 + penalite=(m-2)*(1+log(nb))/nb; + else + penalite=(m-1)*(1+log(log(nb)))/nb; + end + + aic=cla_adj(nb,teta,pas,step_ini,m,histt,penalite,aic); + + + % Recherche de la valeur min du critďż˝re pour les classes adjacentes + [min_aic classe]=min(aic(1:m-1)); + + % Fusion de hist(indice,classe) et hist(indice,classe+1) + + nb_pas1=pas(classe)/step_ini; + nb_pas2=pas(classe+1)/step_ini; + + teta(classe)=(round(nb_pas1*histt(classe)+nb_pas2*histt(classe+1)))/nb; + histt(classe)=(nb_pas1*histt(classe)+nb_pas2*histt(classe+1))/(nb_pas1+nb_pas2); + pas(classe)=pas(classe)+pas(classe+1); + + % Crďż˝ation du nouvel histogramme + + itemp=setdiff(1:m,classe+1); + histt = histt(itemp); + pas = pas(itemp); + teta = teta(itemp); + %aic=zeros(1,m-1); + + m=m-1; +end +hist_opt=histt; +step_opt=pas; + + +%===================================================== +% Calcul du Critere pour l'ensemble des classes + +function akaike=cal_aic(size_ech,teta,pas,m,critere); + + +if critere==1 + a=(2*m-1)/size_ech; +elseif critere==2 + a=(m-1)*(1+log(size_ech))/size_ech; +else + a=m*(1+log(log(size_ech)))/size_ech; +end + +indu = find(teta); +akaike = a - 2*sum(teta(indu).*log(teta(indu)./pas(indu))); + + +%===================================================== +% Cla_adj.m +% aic=cla_adj(taille,indice,teta,pas,pas_ini,m,hist,penalite,aic) +% taille=nombre d'ďż˝lďż˝ments dans chacune des hypotheses; +% indice=numero de la classe; +% Calcul du critere de Akaike pour l'histogramme totale avec +% fusion de deux classes adjacentes u et (u+1). + +function aic=cla_adj(size_ech,teta,pas,pas_ini,m,hist,penalite,aic); + + +for u=1:m-1 + + cumul=0; + + % This loop is faster than a sum of a vectorised computation ! + for x=1:m + if x~=u & x~=u+1 & teta(x)~=0 + cumul=cumul+teta(x)*log(teta(x)/pas(x)); + end + end + + + nb_pas1=pas(u)/pas_ini; + nb_pas2=pas(u+1)/pas_ini; + + b=( round(nb_pas1*hist(u)+nb_pas2*hist(u+1) ) ) / size_ech; + + if b~=0 + c=2*b*log( b / ( pas(u) + pas(u+1) ) ); + else + c=0; + end + + aic(u)=penalite-2*cumul-c; +end diff --git a/sourcecodes/bnt-master/SLP/misc/histc_ic.m b/sourcecodes/bnt-master/SLP/misc/histc_ic.m new file mode 100644 index 00000000..3e4a5af5 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/histc_ic.m @@ -0,0 +1,35 @@ +function [n,xechan] = histc_ic(x,edges) +%HISTC_IC Histogram count +% +% [N XECHAN] = HISTC_IC(X,EDGES), for vector X, counts the number of values +% in X that fall between the elements in the EDGES vector +% (EDGES = cell array returned by HIST_IC function) +% +% N is cell array containing these counts. +% (or a vector if X is a column vector) +% +% XECHAN = discretized version of X +% +% Example : +% X=randn(100,5); +% Xapp=X(1:50,:); +% Xtest=X(51:100,:); +% +% % bins are computed with Xapp data +% [n1,bornes,nbbornes,xechan]=hist_ic(Xapp); +% % histogram is computed on Xtest data, with previously obtained bins +% [n2,xtechan]=histc_ic(Xtest,bornes); +% +% 05-06-2001 by Ph. Leray - philippe.leray@univ-nantes.fr +% + +[nb_l,nb_c]=size(x); +% Outputs declaration +xechan=zeros(nb_l,nb_c); +if nb_c==1, edges={edges}; end +% edges=mat2cell(edges); modified by francois.olivier.c.h@gmail.com +for j=1:nb_c, + [n{j} xechan(:,j)]=histc(x(:,j),[-inf edges{j} inf]); + n{j}=n{j}(1:end-1); +end +if nb_c==1, n=n{1}; end diff --git a/sourcecodes/bnt-master/SLP/misc/inference.m b/sourcecodes/bnt-master/SLP/misc/inference.m new file mode 100644 index 00000000..3aef9eeb --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/inference.m @@ -0,0 +1,25 @@ +function [proba_post, engin2]= inference(bnet, data, node) +% Make bayesian inference on data +% [proba_post, engine]= inference(bnet, data, node) +% +% INPUTS : +% - bnet, the structure of the bayesian network gived by mk_bnet. +% - data(i,m), node i in case m. +% - node, the node we interrogating. +% +% OUTPUTS : +% - proba_post, the posteriors probabilities. +% - engine, the inference engine. +% +% francois.olivier.c.h@gmail.com + +engine=jtree_inf_engine(bnet); +[N L]=size(data); +proba_post=zeros(L,bnet.node_sizes(node)); +for i=1:L + evidence(1:N)=data(1:N,i); + evidence{node}=[]; + [engin2, ll]=enter_evidence(engine,evidence); + marg=marginal_nodes(engin2,node); + proba_post(i,:)=marg.T'; +end \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/misc/isdag.m b/sourcecodes/bnt-master/SLP/misc/isdag.m new file mode 100644 index 00000000..8335d58c --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/isdag.m @@ -0,0 +1,9 @@ +function b = isdag(G) +% b = isdag(G) +% +b = sum(sum(G.*G')); % How many undirected arcs ? (x2) +b=~b & ~isempty(G); +if b + M = expm(full(G)) - eye(length(G)); M = (M>0); + b = isempty(find(sum(sum(eye(length(G)).*M)))); % is there no cycle ? +end diff --git a/sourcecodes/bnt-master/SLP/misc/ismemberclique.m b/sourcecodes/bnt-master/SLP/misc/ismemberclique.m new file mode 100644 index 00000000..5b1f8771 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/ismemberclique.m @@ -0,0 +1,14 @@ +function resu = ismemberclique(v,cliques) +% b = ismemberclique(v,cliques) +% + +finiclique = 0 ; resu=0 ; +cl=1; ncl=length(cliques) ; + +while (~finiclique) & (cl<=ncl); + if ismember(v,cliques{cl}) + resu=1; + finiclique=1 ; + end + cl=cl+1; +end diff --git a/sourcecodes/bnt-master/SLP/misc/knn.m b/sourcecodes/bnt-master/SLP/misc/knn.m new file mode 100644 index 00000000..43233de1 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/knn.m @@ -0,0 +1,57 @@ +function [ypred]=knn(xapp,yapp,valY,X,k) + +% +% knn implementation +% +% USE : [ypred]=knn(xapp,yapp,valY,X,k) +% +% Vincent Guigue 08/01/03 + +% check nargin + +if nargin<4 + error('too few argumemnts'); +elseif nargin<5 + k=3; +else + if mod(k,2)==0 + error('k must be odd'); + end +end + +if size(xapp,2)~=size(X,2) + error('dimension incompatibility'); +end + + +ndim = size(xapp,2); +nptxapp = size(xapp,1); +nptX = size(X,1); + +% distance de X a xapp : +mat1 = repmat(xapp, nptX,1); +%mat21 = reshape(X',1,nptX*ndim) +mat22 = repmat(X,1,nptxapp)'; +mat2 = reshape(mat22 ,ndim, nptxapp*nptX)'; +distance = mat1 - mat2 ; + +distance = sum(distance.^2,2); +distance = reshape(distance,nptxapp,nptX); +[val kppv] = sort(distance,1); + +% bilan sur les k premieres lignes +kppv = reshape(kppv(1:k,:),k*nptX,1); +Ykppv = yapp(kppv,1); +Ykppv = reshape(Ykppv,k,nptX); + +% trouver le plus de reponses identique par colonne + +vote = []; +for i=1:nptX + for j=1:length(valY) + vote(j,i)=size(find(Ykppv(:,i)==valY(j)),1); + end +end + +[val ind]=max(vote,[],1); +ypred = valY(ind); \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/misc/mat_to_bnt.m b/sourcecodes/bnt-master/SLP/misc/mat_to_bnt.m new file mode 100644 index 00000000..008cabc8 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/mat_to_bnt.m @@ -0,0 +1,58 @@ +function [res] = mat_to_bnt(mat,misv) +% MAT_TO_BNT Convert a matrix to a cell array +% D = mat_to_bnt(data,misv) +% +% Input : +% data(i,m) is the node i in the case m, +% misv is the way you choose to encode missing data +% in the original matrix (-9999 by default) +% +% Output : +% D = cell array containing data(i,m) is the data is OK +% or [] is the data is missing +% +% +% V1.2 : 18 feb 2003 (Ph. Leray - philippe.leray@univ-nantes.fr) +% +% >> m=rand(2,4) +% +% m = +% +% 0.9525 0.4693 0.3907 0.1496 +% 0.9274 0.3157 0.1346 0.9383 +% +% >> m(2,2)=-9 +% +% m = +% +% 0.9525 0.4693 0.3907 0.1496 +% 0.9274 -9.0000 0.1346 0.9383 +% +% >> mat_to_bnt(m,-9) +% +% ans = +% +% [0.9525] [0.4693] [0.3907] [0.1496] +% [0.9274] [] [0.1346] [0.9383] +% + +if nargin <1 + error('Requires at least 1 argument.') +end + +if nargin == 1 + misv=-9999; + end; + +taille=size(mat); +long=taille(1); +larg=taille(2); +for i=1:long + for j=1:larg + res{i,j}=mat(i,j); + if(mat(i,j)==misv) + res{i,j}=[]; + end + end +end + diff --git a/sourcecodes/bnt-master/SLP/misc/mk_nbrs_of_dag_topo.m b/sourcecodes/bnt-master/SLP/misc/mk_nbrs_of_dag_topo.m new file mode 100644 index 00000000..73ba2439 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/mk_nbrs_of_dag_topo.m @@ -0,0 +1,196 @@ +function [new_nbrs, new_ops, new_nodes, new_topos] = mk_nbrs_of_dag_topo(G0) +% MK_NBRS_OF_DAG_TOPO Make all DAGs that differ from G0 by a single edge deletion, addition or reversal +% [new_nbrs, new_ops, new_nodes, new_topos] = mk_nbrs_of_dag_topo(G0) +% +% new_nbrs{i} is the i'th neighbor of G0. +% new_ops{i} = 'add', 'del', or 'rev' is the operation used to create the i'th neighbor. +% new_nodes(i,1:2) are the head and tail of the operated-on arc. +% new_topos are topological orders of the neighbours. +% +% We implement the fast acyclicity check described by P. Giudici and R. Castelo, +% "Improving MCMC model search for data mining", submitted to J. Machine Learning, 2001. +% +% Written by Qian Diao on 19 Nov 01 +% Reference are ..\BNT\graph\mk_nbrs_of_dag.m, ..\BNT\learning\learn_struct_mcmc.m and +% ..\BNT\graph\topological_sort.m +% Copyright Intel 2001 +% +new_nbrs = {}; +new_ops = {}; +new_nodes = []; +new_topos = {}; +cs = {}; + +n = length(G0); +indeg = zeros(1,n); +zero_indeg = []; % a stack of nodes with no parents +for i=1:n + indeg(i) = length(parents(G0,i)); + cs{i} = children(G0, i); + if indeg(i)==0 + zero_indeg = [i zero_indeg]; + end +end + +dag = G0; +[nbrs, ops, nodes] = mk_nbrs_of_digraph(dag); +A = init_ancestor_matrix(dag); +%assert(acyclic(new_dag)); + +d1 = 1; +for d = 1:length(ops) + i = nodes(d, 1); j = nodes(d, 2); + legal = 0; + switch ops{d} + case 'add', + if A(i,j)==0 + legal = 1; + end + case 'del', + legal = 1; + case 'rev', + ps = mysetdiff(parents(dag, j), i); + % if any(A(ps,i)) then there is a path i -> parent of j -> j + % so reversing i->j would create a cycle + legal = ~any(A(ps, i)); + end + + if legal + tmp = nbrs(:,:,d); + new_nbrs{d1} = tmp; + new_ops{d1} = ops{d}; + new_nodes(d1,1:2) = nodes(d,1:2); + + % obtain the topological orders of neighbour dags + zero_indeg_nbr = []; + indeg_nbr = []; + cs_nbr = []; + + switch ops{d} + case 'add' % i is a new parent of j + zero_indeg_nbr = zero_indeg; + indeg_nbr = indeg; + if ~isempty(find(zero_indeg == j)) % j is not a root anymore + zero_indeg_nbr = mysetdiff(zero_indeg, j); + end + indeg_nbr(j) = indeg(j)+1; + + t_nbr=1; + order_nbr = zeros(1,n); + while ~isempty(zero_indeg_nbr) + v_nbr = zero_indeg_nbr(1); % pop v + zero_indeg_nbr = zero_indeg_nbr(2:end); + order_nbr(t_nbr) = v_nbr; + t_nbr = t_nbr + 1; + if v_nbr == i % j is a new child of i + cs_nbr = sort([j cs{i}]); + else + cs_nbr = cs{v_nbr}; + end + for k = 1:length(cs_nbr) + c_nbr = cs_nbr(k); + indeg_nbr(c_nbr) = indeg_nbr(c_nbr) - 1; + if indeg_nbr(c_nbr) == 0 + zero_indeg_nbr = [c_nbr zero_indeg_nbr]; % push c + end + end + end + + case 'del' % i is not a parent of j anymore + zero_indeg_nbr = zero_indeg; + indeg_nbr = indeg; + if length(parents(tmp, j))==0 + zero_indeg_nbr = -sort(-[zero_indeg, j]); % descending order + end + indeg_nbr(j) = indeg(j) - 1; + + t_nbr=1; + order_nbr = zeros(1,n); + while ~isempty(zero_indeg_nbr) + v_nbr = zero_indeg_nbr(1); % pop v + zero_indeg_nbr = zero_indeg_nbr(2:end); + order_nbr(t_nbr) = v_nbr; + t_nbr = t_nbr + 1; + if v_nbr == i % j is not a child of i anymore + cs_nbr = mysetdiff(cs{i}, j); + else + cs_nbr = cs{v_nbr}; + end + for k = 1:length(cs_nbr) + c_nbr = cs_nbr(k); + indeg_nbr(c_nbr) = indeg_nbr(c_nbr) - 1; + if indeg_nbr(c_nbr) == 0 + zero_indeg_nbr = [c_nbr zero_indeg_nbr]; % push c + end + end + end + + case 'rev' %i is a new child of j and j is a new parent of i + zero_indeg_nbr = zero_indeg; + indeg_nbr = indeg; + if ~isempty(find(zero_indeg == i)) + zero_indeg_nbr = mysetdiff(zero_indeg_nbr, i); + end + if length(parents(tmp, j))==0 + zero_indeg_nbr = -sort(-[zero_indeg_nbr, j]); % decending order + end + indeg_nbr(i) = indeg(i)+1; + indeg_nbr(j) = indeg(j)-1; + + t_nbr=1; + order_nbr = zeros(1,n); + while ~isempty(zero_indeg_nbr) + v_nbr = zero_indeg_nbr(1); % pop v + zero_indeg_nbr = zero_indeg_nbr(2:end); + order_nbr(t_nbr) = v_nbr; + t_nbr = t_nbr + 1; + cs_nbr = cs{v_nbr}; + if v_nbr == i % j is not a child of i anymore + cs_nbr = mysetdiff(cs{i}, j); + end + if v_nbr == j % i is a new child of j + cs_nbr = sort([i cs{j}]); + end + for k = 1:length(cs_nbr) + c_nbr = cs_nbr(k); + indeg_nbr(c_nbr) = indeg_nbr(c_nbr) - 1; + if indeg_nbr(c_nbr) == 0 + zero_indeg_nbr = [c_nbr zero_indeg_nbr]; % push c + end + end + end + end + + new_topos{d1} = order_nbr; + d1 = d1+1; + end +end + +clear nbrs ops nodes; + + + +%%%%%%%%% +function A = update_row(A, j, dag) +% We compute row j of A +A(j, :) = 0; +ps = parents(dag, j); +if ~isempty(ps) + A(j, ps) = 1; +end +for k=ps(:)' + anck = find(A(k,:)); + if ~isempty(anck) + A(j, anck) = 1; + end +end + +%%%%%%%% +function A = init_ancestor_matrix(dag) +order = topological_sort(dag); +A = zeros(length(dag)); +for j=order(:)' + A = update_row(A, j, dag); +end + + \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/misc/mk_nbrs_of_pdag_add.m b/sourcecodes/bnt-master/SLP/misc/mk_nbrs_of_pdag_add.m new file mode 100644 index 00000000..f495702b --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/mk_nbrs_of_pdag_add.m @@ -0,0 +1,169 @@ +function [PDAGs, nodes] = mk_nbrs_of_pdag_add(cpdag,engine) +% MK_NBRS_OF_PDAG_ADD Make the superior inclusion boundary of CPDAG. +% [PDAGs, nodes] = mk_nbrs_of_pdag_add(CPDAG) +% +% PDAGs{i} is the i'th neighbor of CPDAG0 generated by INSERT(X,Y,T) with +% nodes{i,1:2}=[X Y] +% nodes{i,3}=T +% +% See D.M. Chickering 2002 : "Optimal Structure Identification with Greedy Search". +% +% philippe.leray@univ-nantes.fr, francois.olivier.c.h@gmail.com +% 24 july 2003 + + +compteur=0 ; +N=length(cpdag); +G=pdag_to_dag(cpdag); +if nargin==1, + bnet_tmp=mk_bnet(pdag_to_dag(cpdag),2*ones(N,1)); + bnet_tmp=mk_bnet(G,2*ones(N,1)); + engine_tmp=struct(jtree_inf_engine(bnet_tmp)); + clear bnet_tmp +else + engine_tmp=struct(engine); +end +cliques=engine_tmp.cliques; +nbcliques=length(cliques); +clear engine_tmp; + +% find in the PDAG all the X Y not connected +[LX LY]=find((cpdag|cpdag')+eye(N)==0); +nlinks=length(LX); % here is a bug when nlinks is zeros, for i=1:nlinks fail 31-37 added by hanbin +if nlinks==0 + [i,j]=find(cpdag);k=unidrnd(size(i,1),1); + cpdag(i(k),j(k))=0; + [LX LY]=find((cpdag|cpdag')+eye(N)==0); + nlinks=length(LX); +end + +for i=1:nlinks + X=LX(i); + Y=LY(i); + % Neighbors of Y + NY = myintersect(find(cpdag(:,Y)), find(cpdag(Y,:))); + % Adjacents of X + AX = myunion(find(cpdag(:,X)), find(cpdag(X,:))); + % Neighbors of Y adjacent to X + NAYX = myintersect(NY,AX); + % Neighbors of Y NOT adjacent to X + NNAYX = mysetdiff(NY, NAYX); + + % this function recursively "walks" (dfs) in the graph representation of NNA powerset + liste=NNAYX; + if ~isempty(liste) + premier=liste(1); + dernier=liste(end); + end; + + current_set=[]; + fini=0; + evite2 = 0 ; + + while ~fini + isclique=0; + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Test 1 + % is [NAYX current_set] a clique ? + if isempty(current_set) + NAYXT=NAYX; + elseif isempty(NAYX) + NAYXT=current_set; + else + NAYXT = union(NAYX,current_set); + end + % if isempty(NAYXT),isclique=1;end + + isclique = isempty(NAYXT) | ismemberclique(NAYXT,cliques) ; + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% End Test 1 + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Test 2 + % is there exist a partially directed path Y...X in PDAG \ NAYXT ? + if isclique + if evite2 + test2=1 ; + else + %%%% calcul test 2 + L2 = setdiff(1:N,NAYXT); + test2=~partialconnected(cpdag(L2,L2),find(L2==Y),find(L2==X)); + + end + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% End Test 2 + + if test2 + evitet2=1; + + % avoid testing INSERT(X,Y,[]) and INSERT(Y,X,[]) if pa(Y)=pa(X) + test0=1 ; + if (X>Y) + if length(current_set)==0 + PaX=setdiff(find(cpdag(X,:)),find(cpdag(:,X))); + PaY=setdiff(find(cpdag(Y,:)),find(cpdag(:,Y))); + test0=(length(setdiff(PaX,PaY))~=0); + end + end + if test0 + %fprintf(' INSERT(%d,%d,',X,Y); fprintf('%d',current_set); + %fprintf(')\n'); + compteur=compteur+1; + nodes{compteur,1}=X; + nodes{compteur,2}=Y; + nodes{compteur,3}=current_set; + ptmp=cpdag; + ptmp(X,Y)=1; + ptmp(current_set,Y)=1; + ptmp(Y,current_set)=0; + PDAGs{compteur}=ptmp; + end + end + end + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Next Set to test ... + % what is the net set in the powerset ? + if length(liste)==0 + fini=1 ; next_set=[]; + elseif length(current_set)==0 + next_set=[premier]; % first node after the root [] + else + actuel=current_set(end); + if actuel==dernier + if length(current_set)==1 + fini=1; % no more node ... + else + ancien=current_set(end-1); % new "branch" + next_set=[current_set(1:end-2) liste(find(liste==ancien)+1)]; + end + else % new node in the "branch" + if ~isclique + if length(current_set)==1 + fini=1; % no more node ... + else + ancien=current_set(end-1); % new "branch" + next_set=[current_set(1:end-2) liste(find(liste==ancien)+1)]; + evite2=0; + end + else + next_set=[current_set liste(find(liste==actuel)+1)]; + end + end + end + current_set=next_set; + end +end + + +%%%%%%%%% +function resu = ismemberclique(v,cliques) +finiclique = 0 ; resu=0 ; +cl=1; ncl=length(cliques) ; +while (~finiclique) & (cl<=ncl); + if ismember(v,cliques{cl}) + resu=1; + finiclique=1 ; + end + cl=cl+1; +end + +%%%%%%%%% +function resu = partialconnected(G,Y,X) +tmp=expm(G); +resu = (tmp(Y,X)~=0); diff --git a/sourcecodes/bnt-master/SLP/misc/mk_nbrs_of_pdag_del.m b/sourcecodes/bnt-master/SLP/misc/mk_nbrs_of_pdag_del.m new file mode 100644 index 00000000..7f0fc35a --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/mk_nbrs_of_pdag_del.m @@ -0,0 +1,175 @@ +function [PDAGs, nodes] = mk_nbrs_of_pdag_del(cpdag,engine) +% MK_NBRS_OF_PDAG_ADD Make the inferior inclusion boundary of CPDAG. +% [PDAGs, nodes] = mk_nbrs_of_pdag_del(CPDAG) +% +% PDAGs{i} is the i'th neighbor of CPDAG0 generated by DELETE(X,Y,H) with +% nodes{i,1:2}=[X Y] +% nodes{i,3}=H +% +% See D.M. Chickering 2002 : "Optimal Structure Identification with Greedy Search". +% +% philippe.leray@univ-nantes.fr +% 25 july 2003 + +compteur=0; +N=length(cpdag); +if nargin==1, + bnet_tmp=mk_bnet(pdag_to_dag(cpdag),2*ones(N,1)); + engine_tmp=struct(jtree_inf_engine(bnet_tmp)); + clear bnet_tmp +else + engine_tmp=struct(engine); +end +cliques=engine_tmp.cliques; +nbcliques=length(cliques); +clear engine_tmp; + +verbose=0; + +if verbose + fprintf('---- Les cliques (maximales) du DAG\n'); + for i=1:nbcliques, + disp(cliques{i}); + end + fprintf('---- Variables \n'); +end + + +% find in the PDAG all the X Y connected +[LX LY]=find(cpdag==1); +nlinks=length(LX); + +for i=1:nlinks + X=LX(i); + Y=LY(i); + % Neighbors of Y + NY = myintersect(find(cpdag(:,Y)), find(cpdag(Y,:))); + % Adjacents of X + AX = myunion(find(cpdag(:,X)), find(cpdag(X,:))); + % Neighbors of Y adjacent to X + NAYX = myintersect(NY,AX); + + % this function recursively "walks" (dfs) in the graph representation of NA powerset + if verbose + X, Y, NY, AX, NAYX + fprintf('---- NA Powerset\n\n'); + end + + liste=NAYX; + if ~isempty(liste) + premier=liste(1); + dernier=liste(end); + end + + current_set=[]; + fini=0; + evite2 = 0 ; + + while ~fini + if verbose + % sert uniquement ďż˝ l'affichage pour le prog de test ... + if isempty(current_set) + fprintf('\t H = []\n\n'); + else + fprintf('\t H = '); disp(current_set); + end + end + + isclique=0; + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Test 1 + % is [NAYX \ current_set] a clique ? + if isempty(current_set) + NAYXH=NAYX; + elseif isempty(NAYX) + NAYXH=current_set; + else + NAYXH = mysetdiff(NAYX,current_set); + end + + if isempty(NAYXH) + if verbose + fprintf('\t\t NA(Y,X) \\ T = \t[]\n\n'); + end + + elseif verbose + fprintf('\t\t NA(Y,X) \\ T ='); disp(NAYXH); + end + + isclique = isempty(NAYXH) | ismemberclique(NAYXH,cliques) ; + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% End Test 1 + if isclique + % avoid testing INSERT(X,Y,H) and INSERT(Y,X,H) if X--Y + test0=1 ; + if (X>Y) + test0=~cpdag(Y,X); + if verbose&~test0 + fprintf(' (inutile de tester %d %d ',X,Y); fprintf('%d',current_set); + fprintf(')\n'); + end + end + if test0 + if verbose + fprintf(' ==DELETE(%d,%d,',X,Y); fprintf('%d',current_set); + fprintf(')\n'); + end + compteur=compteur+1; + nodes{compteur,1}=X; + nodes{compteur,2}=Y; + nodes{compteur,3}=current_set; + ptmp=cpdag; + ptmp(X,Y)=0; + ptmp(Y,X)=0; + ptmp(current_set,Y)=0; + ptmp(Y,current_set)=1; + ptmp(current_set(find(cpdag(X,current_set)==1)),X)=0; + PDAGs{compteur}=ptmp; + end + end + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Next Set to test ... + % what is the net set in the powerset ? + if length(liste)==0 + fini=1 ; next_set=[]; + elseif length(current_set)==0 + next_set=[premier]; % first node after the root [] + else + actuel=current_set(end); + if actuel==dernier + if length(current_set)==1 + fini=1; % no more node ... + else + ancien=current_set(end-1); % new "branch" + next_set=[current_set(1:end-2) liste(find(liste==ancien)+1)]; + end + else % new node in the "branch" + if ~isclique + if length(current_set)==1 + fini=1; % no more node ... + else + ancien=current_set(end-1); % new "branch" + next_set=[current_set(1:end-2) liste(find(liste==ancien)+1)]; + evite2=0; + end + else + next_set=[current_set liste(find(liste==actuel)+1)]; + end + end + end + current_set=next_set; + end +end + + +%%%%%%% +function resu = ismemberclique(v,cliques) + +finiclique = 0 ; resu=0 ; +cl=1; ncl=length(cliques) ; + +while (~finiclique) & (cl<=ncl); + if ismember(v,cliques{cl}) + resu=1; + finiclique=1 ; + end + cl=cl+1; +end diff --git a/sourcecodes/bnt-master/SLP/misc/multiply_one_marginal.c b/sourcecodes/bnt-master/SLP/misc/multiply_one_marginal.c new file mode 100644 index 00000000..3d347f65 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/multiply_one_marginal.c @@ -0,0 +1,137 @@ +/* multiply_one_marginals.c */ +/* the first operand can be a joint marginals of nodes set,*/ +/* but the second operand nust be a single node's marginal.*/ +/* and the result joint marginal has domain like [prhs[0].domain, prhs[2].domain]*/ +/* i.e. cat the second domain at the end of the first domain*/ +/* the third operands will be the eff_node_sizes */ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + mxArray *ptemp, *ptemp1; + double *xdom, *ydom, *xp, *yp, *zp, *pr, *pSizes; + int N, xElements, numElements; + int i, j, nxdom, ndim, ydim; + int *xdim, *s, *sx, *sy, *cpsx, *cpsy; + int *subs, *s1, *cpsx2, *cpsy2; + int rdims[2]; + const char *field_names[] = {"domain", "T", "mu", "Sigma"}; + + if (nrhs != 3) + mexErrMsgTxt("Incorrect number of inputs."); + + if (nlhs > 1) + mexErrMsgTxt("Too many output arguments."); + + if(mxIsEmpty(prhs[0])){ + plhs[0] = mxDuplicateArray(prhs[1]); + return; + } + + N = mxGetNumberOfElements(prhs[2]); + pSizes = mxGetPr(prhs[2]); + + ptemp = mxGetField(prhs[0], 0, "domain"); + nxdom = mxGetNumberOfElements(ptemp); + xdom = mxGetPr(ptemp); + ptemp = mxGetField(prhs[1], 0, "domain"); + ydom = mxGetPr(ptemp); + ndim = nxdom + 1; + + rdims[0] = 1; + rdims[1] = 1; + plhs[0] = mxCreateStructArray(2, rdims, 4, field_names); + ptemp = mxCreateDoubleMatrix(1, ndim, mxREAL); + mxSetField(plhs[0], 0, "domain", ptemp); + pr = mxGetPr(ptemp); + for(i=0; ib represented as pdag(a,b)=-1 pdag(b,a)=0 +% Edge with unknown direction a-b represented as pdag(a,b)=1 pdag(b,a)=1 +% +% Output: Number of DAGs generated and +% Cell array of all permissible extensions of PDAG +% +% Sample Use: +% % Use output of PC algorithm +% dag = mk_rnd_dag(4); % create random DAG +% % Generate pdag through PC algorithm +% pdag = learn_struct_pdag_pc('dsep', length(dag), 3, dag) +% [n_dags,dag_list] = pdag_to_all_dags(pdag); +% n_dags +% +% If you want to generate all DAGs that are Markov equivalent to an +% input DAG (not a pattern), then use function Markov_equivalent_dags(dag) +% instead which calls this function. +% +% ======================================================================= +% Algorithm to generate ALL DAGs (pdag_to_all_dags): +% +% 0) Initialize an empty list of DAGs. +% +% 1) Complete current PDAG as far as possible using Rules R1-R4. +% +% 2) Select an unoriented edge X-Y. +% +% a) If none left: +% Done. Add DAG=abs(PDAG) to list of output DAGs. Return. +% +% b) Otherwise: +% Select an unoriented edge X-Y. +% Create PDAG1 with X->Y. +% Create PDAG2 with Y->X. +% Recursion: For EACH PDAG (PDAG1/2): Go to Step 1. +% +% +% This algorithm is a slight modification of the algorithm by Meek (1995) +% which generates a single DAG extension of a PDAG - here we just add +% recursion to consider both possible orientations for each considered edge. +% +% For the original algorithm by Meek, see +% C. Meek, "Causal inference and causal explanation with background +% knowledge", UAI 1995, Section 3.1.1, "Phase III" algorithm. +% +% Thanks to Daniel Eaton for extensive testing and bug reports. +% +% Imme Ebert-Uphoff (ebert@tree.com), 2007 +% ======================================================================= + + % MAIN + dag_list={}; % init empty list of DAGs + + % Complete pdag as far as possible using Rules R1-R4 of Meek (1995) + cpdag = complete_pattern(pdag); + + % Start recursion + dag_list = recurse_unoriented_edge(cpdag,dag_list); + + % return # of DAGs along with dag_list + n_dags = length(dag_list); + if (n_dags == 0) % no DAGs generated + fprintf('\nPDAG does not have any permissible extension!\n'); + end +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% RECURSE_UNORIENTED_EDGE % +% implements Step 2 of the pdag_to_all_dags algorithm. % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function updated_list = recurse_unoriented_edge(cpdag, dag_list) + + % input must be a COMPLETE pdag + + [A,B] = find(cpdag==1); % find all undirected edges + + updated_list = dag_list; + + if isempty(A) % if no undirected edges left + % End of recursion reached. + % Convert all (-1) values to (1) to yield standard DAG, add DAG to list. + updated_list{end+1} = abs(cpdag); + + else + a = A(1); b = B(1); % choose first unoriented edge + % (any unoriented edge could be used here) + + % choose two different directions for edge and complete BOTH ! + % PDAG1: contains a -> b + %fprintf('PDAG1: %d -> %d\n',a,b); + pdag1 = cpdag; + pdag1(a,b) = -1; pdag1(b,a) = 0; + % complete as far as possible using rules R1-R4: + cpdag1 = complete_pattern(pdag1); + % Continue recursion on another unoriented edge + updated_list = recurse_unoriented_edge(cpdag1,updated_list); + + % PDAG1: contains b -> a + %fprintf('PDAG2: %d -> %d\n',b,a); + pdag2 = cpdag; + pdag2(a,b) = 0; pdag2(b,a) = -1; + % complete as far as possible using rules R1-R4: + cpdag2 = complete_pattern(pdag2); + % Continue recursion on another unoriented edge + updated_list = recurse_unoriented_edge(cpdag2, updated_list); + + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + diff --git a/sourcecodes/bnt-master/SLP/misc/pdag_to_dag.m b/sourcecodes/bnt-master/SLP/misc/pdag_to_dag.m new file mode 100644 index 00000000..00a73aac --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/pdag_to_dag.m @@ -0,0 +1,87 @@ +function G2 = pdag_to_dag(pdags) +% (also works with a cell array of pdags, returning a cell array of dags) +% dag = pdag_to_dag(pdag) +% +% cf Dor and Tarsi (1992) : +% A simple algorithm to construct a consistent extention of a partially oriented graph. +% +% francois.olivier.c.h@gmail.com + + +if ~iscell(pdags) + pdag=cell(1,1); + pdag{1}=pdags; +else + pdag=pdags; +end + +for da=1:length(pdag) + %fprintf('%d ',da) + G=pdag{da}; + G2 = G; A = G; + N = size(G,1); + empty_loop = 0; + + while ~isempty(find(A)) + [x x_y_undirected] = select_vertex(A); + + if x==0 + fprintf('pdag_to_dag error : This pdag does not admit any extension.\n'); + G2=[]; + break + end + G2(x,x_y_undirected) = 0; G2(x_y_undirected,x) = 1; + + A(x,:) = 0; + A(:,x) = 0; + end + dags{da}=G2; +end +if ~iscell(pdags) + G2=dags{1}; +else + G2=dags; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [sol, x_y] = select_vertex(G) +N = size(G,1); +sol = 0; +x = 0; +fini=0 ; +while ~fini + + x = x+1; + if x>N + fini=1; + else + beforex=find(G(:,x)); + afterx=find(G(x,:)); + + if ~(isempty(beforex)&isempty(afterx)) + x_y = myintersect(beforex,afterx); + beforex=mysetdiff(beforex,x_y); + afterx=mysetdiff(afterx,x_y); + + if isempty(afterx) % x is a sink + Ax= myunion(x_y,beforex); + for y=x_y + % Adjacents of y + Ay = myunion(find(G(:,y)), find(G(y,:))); + Ay = myunion(Ay,y); + if isempty(setdiff(Ax,Ay)) + fini=fini+1; + else + break; + end + end + if fini==length(x_y) + sol=x; fini=1; + else + fini=0; + end + end + end + end +end % while diff --git a/sourcecodes/bnt-master/SLP/misc/pdag_unsigned_to_signed.m b/sourcecodes/bnt-master/SLP/misc/pdag_unsigned_to_signed.m new file mode 100644 index 00000000..37b414f1 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/pdag_unsigned_to_signed.m @@ -0,0 +1,22 @@ +function signed_pdag = pdag_unsigned_to_signed(pdag) + +% Input: pdag with '1' in one place for every directed edge and +% '1' in two places for every undirected edge +% +% Output: pdag with '-1' in one place for every directed edge +% '1' in two places for every undirected edge +% +% This function is used by Markov_equivalent_dags(dag) to convert +% output of SLP function dag_to_cpdag(dag) +% to format required as input for pdag_to_all_dags(pdag). +% +% I'm sure there's a prettier way to code this! +% +% Imme Ebert-Uphoff (ebert@tree.com), 2007 +% + + undirected = ( (pdag+pdag')/2 == 1); % extract undirected eges + directed = pdag - undirected; % extract directed edges + signed_pdag = undirected - directed; % 1s for undirected, (-1)s for directed + + diff --git a/sourcecodes/bnt-master/SLP/misc/subsets1.m b/sourcecodes/bnt-master/SLP/misc/subsets1.m new file mode 100644 index 00000000..ca2bf46c --- /dev/null +++ b/sourcecodes/bnt-master/SLP/misc/subsets1.m @@ -0,0 +1,45 @@ +function sub_s=subsets1(s,k) +% SUBSETS1 creates sub-sets of a specific from a given set +% SS = subsets1(S, k) +% +% S is the given set +% k is the required sub-sets size +% +% Example: +% +% >> ss=subsets1([1:4],3); +% >> ss{:} +% ans = +% 1 2 3 +% ans = +% 1 2 4 +% ans = +% 1 3 4 +% ans = +% 2 3 4 +% +% Written by Raanan Yehezkel, 2004 + +if k<0 % special case + error('subset size must be positive'); +elseif k==0 % special case + sub_s={[]}; +else + l=length(s); + ss={}; + if l>=k + if k==1 % Exit condition + for I=1:l + ss{I}=s(I); + end + else + for I=1:l + ss1=subsets1(s([(I+1):l]),k-1); + for J=1:length(ss1) + ss{end+1}=[s(I),ss1{J}]; + end + end + end + end + sub_s=ss; +end diff --git a/sourcecodes/bnt-master/SLP/scoring/calculate_mutual_information_array.m b/sourcecodes/bnt-master/SLP/scoring/calculate_mutual_information_array.m new file mode 100644 index 00000000..1128e411 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/calculate_mutual_information_array.m @@ -0,0 +1,32 @@ +function [mi] = calculate_mutual_information_array(data) +% FUNCTION [MI_ARRAY] = CALCULATE_MUTUAL_INFORMATION_ARRAY(DATA) +% calculates the mutual information between all pairs of variables +% Data must be discrete, and take values 1,2,...,size +% data(i,m) is the node i in the case m. + +[num_nodes num_examples] = size(data); + +node_sizes = max(data'); +for i = 1:num_nodes + for ic = 1:node_sizes(i) % I CLASS ic + px(i,ic) = sum(data(i,:)==ic); + for j = 1:num_nodes % J CLASS jc + for jc = 1:node_sizes(j) + pxy(i,ic,j,jc) = sum( (data(i,:)==ic) & (data(j,:)==jc) ); + end + mi(i,j) = 0; + end + end +end + +for i = 1:num_nodes + for ic = 1:node_sizes(i) + for j = 1:num_nodes + for jc = 1:node_sizes(j) + if( pxy(i,ic,j,jc)~=0 & px(i,ic)~=0 & px(j,jc)~= 0) + mi(i,j) = mi(i,j) + pxy(i,ic,j,jc)*log2( num_examples*pxy(i,ic,j,jc)/(px(i,ic)*px(j,jc)) )/num_examples; + end + end + end + end +end diff --git a/sourcecodes/bnt-master/SLP/scoring/cond_indep_chisquare.m b/sourcecodes/bnt-master/SLP/scoring/cond_indep_chisquare.m new file mode 100644 index 00000000..efc162cc --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/cond_indep_chisquare.m @@ -0,0 +1,184 @@ +function [CI, Chi2, alpha2] = cond_indep_chisquare(X, Y, S, Data, test, alpha, ns) +% COND_INDEP_CHISQUARE Test if X indep Y given Z +% using either chisquare test or likelihood ratio test G2 +% +% [CI Chi2 Prob_Chi2] = cond_indep_chisquare(X, Y, S, Data, test, alpha, node_sizes) +% +% Input : +% Data is the data matrix, N columns * NbVar rows +% X is the index of variable X in Data matrix +% Y is the index of variable Y in Data matrix +% S are the indexes of variables in set S +% alpha is the significance level (default: 0.05) +% test = 'pearson' for Pearson's chi2 test +% 'LRT' for G2 likelihood ratio test (default) +% node_sizes (default: max(Data')) +% +% Output : +% CI = test result (1=conditional independency, 0=no) +% Chi2 = chi2 value (-1 if not enough data to perform the test --> CI=0) +% +% +% V1.4 : 24 july 2003 (Ph. Leray - philippe.leray@univ-nantes.fr) +% +% +% Things to do : +% - do not use 'find' in nij computation (when S=empty set) +% - find a better way than 'warning off/on' in tmpij, tmpijk computation +% + +if nargin < 5, test = 'LRT'; end +if nargin < 6, alpha = 0.05; end +if nargin < 7, ns = max(Data'); end + +Data=Data'; + +N = size(Data,1); +qi=ns(S); +tmp=[1 cumprod(qi(1:end-1))]; +qs=1+(qi-1)*tmp'; +if isempty(qs), + nij=zeros(ns(X),ns(Y)); + df=prod(ns([X Y])-1)*prod(ns(S)); +else + +% Commented by Mingyi +% nijk=zeros(ns(X),ns(Y),qs); +% tijk=zeros(ns(X),ns(Y),qs); +% Commention ends +% Added by Mingyi + nijk=zeros(ns(X),ns(Y),1); + tijk=zeros(ns(X),ns(Y),1); +% Addition ends + df=prod(ns([X Y])-1)*qs; +end + + +if (N<10*df) + % Not enough data to perform the test + Chi2=-1; + CI=0; + +elseif isempty(S) + for i=1:ns(X), + for j=1:ns(Y), + nij(i,j)=length(find((Data(:,X)==i)&(Data(:,Y)==j))) ; + end + end + restr=find(sum(nij,1)==0); + if ~isempty(restr) + nij=nij(:,find(sum(nij,1))); + end + + tij=sum(nij,2)*sum(nij,1)/N ; + + switch test + case 'pearson', + tmpij=nij-tij; + + [xi yj]=find(tij<10); + for i=1:length(xi), + tmpij(xi(i),yj(i))=abs(tmpij(xi(i),yj(i)))-0.5; + end + + warning off; + tmp=(tmpij.^2)./tij; + warning on; + tmp(find(tmp==Inf))=0; + + case 'LRT', + warning off; + tmp=nij./tij; + warning on; + tmp(find(tmp==Inf | tmp==0))=1; + tmp(find(tmp~=tmp))=1; + tmp=2*nij.*log(tmp); + + otherwise, + error(['unrecognized test ' test]); + end + + Chi2=sum(sum(tmp)); + alpha2=1-chisquared_prob(Chi2,df); + CI=(alpha2>=alpha) ; + +else + SizeofSSi=1; + for exemple=1:N, + i=Data(exemple,X); + j=Data(exemple,Y); + Si=Data(exemple,S)-1; + %Added by Mingyi + if exemple==1 + SSi(SizeofSSi,:)=Si; + nijk(i,j,SizeofSSi)=1; + else + flag=0; + for iii=1:SizeofSSi + if isequal(SSi(iii,:),Si) + nijk(i,j,iii)=nijk(i,j,iii)+1; + flag=1; + end + end + if flag==0 + SizeofSSi=SizeofSSi+1; + SSi(SizeofSSi,:)=Si; + nijk(i,j,SizeofSSi)=1; + end + end + %Addition ends + %Commented by Mingyi +% k=1+Si*tmp'; +% nijk(i,j,k)=nijk(i,j,k)+1; + %Commention ends + end + + nik=sum(nijk,2); + njk=sum(nijk,1); + N2=sum(njk); + + % for k=1:qs, %Commented by Mingyi + for k=1:SizeofSSi %Added by Mingyi + if N2(:,:,k)==0 + tijk(:,:,k)=0; + else + tijk(:,:,k)=nik(:,:,k)*njk(:,:,k)/N2(:,:,k); + end + end + + switch test + case 'pearson', + tmpijk=nijk-tijk; + + [xi yj]=find(tijk<10); + for i=1:length(xi), + tmpijk(xi(i),yj(i))=abs(tmpijk(xi(i),yj(i)))-0.5; + end + + warning off; + tmp=(tmpijk.^2)./tijk; + warning on; + tmp(find(tmp==Inf))=0; + + case 'LRT', + warning off; + tmp=nijk./tijk; + warning on; + tmp(find(tmp==Inf | tmp==0))=1; + tmp(find(tmp~=tmp))=1; + tmp=2*nijk.*log(tmp); + + otherwise, + error(['unrecognized test ' test]); + end + + Chi2=sum(sum(sum(tmp))); + alpha2=1-chisquared_prob(Chi2,df); + CI=(alpha2>=alpha) ; + +end +clear tijk +clear nijk +clear nij +clear tij +clear tmpijk \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/scoring/cond_mutual_info_score.m b/sourcecodes/bnt-master/SLP/scoring/cond_mutual_info_score.m new file mode 100644 index 00000000..b43cbfeb --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/cond_mutual_info_score.m @@ -0,0 +1,28 @@ +function score = cond_mutual_info_score(i,si,j,sj,c,sc,data) +% G = cond_mutual_info_score(i,si,j,sj,c,sc,data) +% Only for tabular node which values are 1,2,...,size . +% si is size of node i, sj is size of node j, sc is the size of node c. +% data(i,m) is the node i in the case m. +% +% +% pphilippe.leray@univ-nantes.fr, francois.olivier.c.h@gmail.com + +[n N]=size(data); +Pc=hist(data(c,:),1:sc)/N; +score=0; + +for cvalue=1:sc, + ind=find(data(c,:)==cvalue); + Nj=hist(data(j,ind),1:sj); + Ni=hist(data(i,ind),1:si); + NiNj=Ni'*Nj; + + for k=1:si + ind2=find(data(i,ind)==k) ; + Nij(k,:) = hist(data(j,ind(ind2)),1:sj); + end + + % sommons les valeurs non-infinies: + ind=find(NiNj~=0 & Nij~=0); + score=score+Pc(cvalue)*sum(sum(Nij(ind).*log(N*Nij(ind)./NiNj(ind))/N)); +end diff --git a/sourcecodes/bnt-master/SLP/scoring/kl_divergence.m b/sourcecodes/bnt-master/SLP/scoring/kl_divergence.m new file mode 100644 index 00000000..f4e18147 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/kl_divergence.m @@ -0,0 +1,48 @@ +function KLdiv = KL_divergence(bnetP, bnetQ) +% KL_DIVERGENCE computes the Kullback-Leibler divergence between two BNET distributions +% KLdiv = KL_divergence(bnetP, bnetQ) +% +% Output : +% div = sum_x P(x).log(P(x)/Q(x)) +% +% Rem : +% This version is optimized for speed, but can use too many memory +% ==> if you have a memory problem, use kl_divergence2 instead +% + +% ONLY FOR TABULAR NODES +% Make sure that you have done the params learning. +% +% V1.1 : 8 oct 2004 (Ph. Leray - philippe.leray@univ-nantes.fr) + +N = size(bnetP.dag,1); +N2 = size(bnetQ.dag,1); +ns= bnetP.node_sizes; +ns2= bnetQ.node_sizes; +if N~=N2, error('size of dags must be the same'), end +if ns~=ns2, error('node sizes of dags must be the same'), end +tiny = exp(-700); +KLdiv=0; + +inst = ind2subv(ns, 1:prod(ns)); + %Px=1; Qx=1; + for i=1:N, + ps = parents(bnetP.dag, i); + %e = bnetP.equiv_class(i); + %[tmp Px(:,i)] = prob_node(bnetP.CPD{e}, inst(:,i)', inst(:,ps)'); + [tmp Px(:,i)] = prob_node(bnetP.CPD{i}, inst(:,i)', inst(:,ps)'); + + ps = parents(bnetQ.dag, i); + %e = bnetQ.equiv_class(i); + %[tmp Qx(:,i)] = prob_node(bnetQ.CPD{e}, inst(:,i)', inst(:,ps)'); + [tmp Qx(:,i)] = prob_node(bnetQ.CPD{i}, inst(:,i)', inst(:,ps)'); + end + Px=prod(Px,2); + Px = Px + (Px==0)*tiny; % replace 0s by tiny + Qx=prod(Qx,2); + Qx = Qx + (Qx==0)*tiny; % replace 0s by tiny + + %%%%% Faut-il diviser par le nb de configurations possibles ? (sum => mean) + KLdiv = sum(Px.*log(Px./Qx)); + %end + diff --git a/sourcecodes/bnt-master/SLP/scoring/kl_divergence2.m b/sourcecodes/bnt-master/SLP/scoring/kl_divergence2.m new file mode 100644 index 00000000..7b9664ba --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/kl_divergence2.m @@ -0,0 +1,43 @@ +function KLdiv = KL_divergence2(bnetP, bnetQ) +% KL_DIVERGENCE2 computes the Kullback-Leibler divergence between two BNET distributions +% KLdiv = KL_divergence2(bnetP, bnetQ) +% +% Output : +% div = sum_x P(x).log(P(x)/Q(x)) +% +% Rem : +% This version is optimized for memory use, but quite slow !!! +% ==> if you have no memory problem, use kl_divergence instead +% +% ONLY FOR TABULAR NODES +% Make sure that you have done the params learning. +% +% V1.1 : 8 oct 2004 (Ph. Leray - philippe.leray@univ-nantes.fr) + +N = size(bnetP.dag,1); +N2 = size(bnetQ.dag,1); +ns= bnetP.node_sizes; +ns2= bnetQ.node_sizes; +if N~=N2, error('size of dags must be the same'), end +if ns~=ns2, error('node sizes of dags must be the same'), end +tiny = exp(-700); +KLdiv=0; + +for i=1:prod(ns), + inst = ind2subv(ns, i); % i'th instantiation + Px=1; Qx=1; + for i=1:N, + ps = parents(bnetP.dag, i); + e = bnetP.equiv_class(i); + [tmp Pxi] = prob_node(bnetP.CPD{e}, inst(i), inst(ps)'); + Px=Px*Pxi; + ps = parents(bnetQ.dag, i); + e = bnetQ.equiv_class(i); + [tmp Qxi] = prob_node(bnetQ.CPD{e}, inst(i), inst(ps)'); + Qx=Qx*Qxi; + end + Px = Px + (Px==0)*tiny; % replace 0s by tiny + Qx = Qx + (Qx==0)*tiny; % replace 0s by tiny + KLdiv = KLdiv + Px*log(Px/Qx); +end + diff --git a/sourcecodes/bnt-master/SLP/scoring/mutual_info_score.m b/sourcecodes/bnt-master/SLP/scoring/mutual_info_score.m new file mode 100644 index 00000000..8808a124 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/mutual_info_score.m @@ -0,0 +1,25 @@ +function score = mutual_info_score(i,si,j,sj,data) +% G = mutual_info_score(i,si,j,sj,data) +% Only for tabular node which values are 1,2,...,size . +% si is size of node i, sj is size of node j. +% data(i,m) is the node i in the case m. +% +% Ref : +% C. Chow and C. Liu (1968). Approximating discrete probability distributions with dependence trees. +% IEEE Transactions on Information Theory, 14(3):462--467, May 1968. +% +% francois.olivier.c.h@gmail.com, philippe.leray@univ-nantes.fr, wangxiangyang@sjtu.edu.cn + +[n N]=size(data); +Nj=hist(data(j,:),1:sj); +Ni=hist(data(i,:),1:si); +NiNj=Ni'*Nj; + +for k=1:si + ind=find(data(i,:)==k) ; + Nij(k,:) = hist(data(j,ind),1:sj); +end + +% sommons les valeurs non-infinies: +ind=find(NiNj~=0 & Nij~=0); +score=sum(sum(Nij(ind).*log(N*Nij(ind)./NiNj(ind))/N)); diff --git a/sourcecodes/bnt-master/SLP/scoring/score_add_to_cache.m b/sourcecodes/bnt-master/SLP/scoring/score_add_to_cache.m new file mode 100644 index 00000000..3c68e4d8 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/score_add_to_cache.m @@ -0,0 +1,67 @@ +function [cache, place] = score_add_to_cache(cache,j,ps,score,scoring_fn) +% [cache place] = score_add_to_cache(cache,j,ps,score,scoring_fn) +% +% j is the son node, +% ps is the list of parents of j, for example [12 5 7], +% score is the score to add for this familly. +% scoring_fn is 'bic' or 'bayesian'. +% +% place = where the entry was add. +% +% example for 2 nodes with cache of size 5 : +% +% cache = +% 5 b 0 0 0 --> number of writing in cache +1 and b==1 iff the cache is full +% 0 0 1 -239.12 1 --> 1st familly in the cache (node 1 without parents) calculate with bic +% 0 0 2 -318.98 1 +% 1 0 2 -189.23 2 --> 3rd familly in the cache (node 2 with 1 as parent) calculate with bayesian +% 0 1 1 -251.09 1 +% .ps2bool. j score 1or2 --> new entry +% | | | | | +% | | | | |___> 1 for 'bic' or 2 for 'bayesian' +% | | | |__________> score of the familly +% | | |_________________> son node of the familly +% | |__________________________> ==1 iff node 2 is parent of son node +% |______________________________> ==1 iff node 1 is parent of son node +% +% If the cache is FULL then the new place is RanDoMly choose. +% +% francois.olivier.c.h@gmail.com + +N=size(cache,2)-3; +place=0; + +if ~isempty(find(ps==j)) + disp('This is a cyclic entry, nothing was done.'); +elseif j>N | j<0 + disp('This entry is not valid, nothing was done.'); +else + + switch scoring_fn + case 'bic', + fn=1; + case 'bayesian', + fn=2; + otherwise, + fn=3; + %error(['unrecognized scoring fn ' scoring_fn]); + end + L=size(cache,1); + + if cache(1,2)==0 + place=cache(1,1); + else + place=ceil(rand(1)*(L-1))+1; + end + + cache(place,:)=0 + cache(place,ps)=1; + cache(place,N+1)=j; + cache(place,N+2)=score; + cache(place,N+3)=fn; + cache(1,1)=place+1; + if place==L | cache(1,2)~=0 + cache(1,2)=1 + end + +end diff --git a/sourcecodes/bnt-master/SLP/scoring/score_dag.c/learn_struct_gs_dtab.m b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/learn_struct_gs_dtab.m new file mode 100644 index 00000000..5bc375fc --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/learn_struct_gs_dtab.m @@ -0,0 +1,179 @@ +function [dag,best_score] = learn_struct_gs_dtab(data, nodesizes, seeddag, varargin) +% +% LEARN_STRUCT_GS(data,seeddag) learns a structure of Bayesian net by Greedy Search. +% dag = learn_struct_gs(data, nodesizes, seeddag) +% +% dag: the final structurre matrix +% Data : training data, data(i,m) is the m obsevation of node i +% Nodesizes: the size array of different nodes +% seeddag: given seed Dag for hill climbing, optional +% +% by Gang Li @ Deakin University (gli73@hotmail.com) +% +% ----------------------------------------------------- +% +% Modified from learn_struct_gs (SLP 1.3) +% to learn structure of BN with tabular nodes: +% +% 1) make use of score decomposibility +% 2) still to do +% +% by Darima , 27/12/2005 +% + +[N ncases] = size(data); +if (nargin < 3 ) + seeddag = zeros(N,N); % mk_rnd_dag(N); %call BNT function +elseif ~acyclic(seeddag) + seeddag = mk_rnd_dag(N); %zeros(N,N); +end; + +% set default params (the same as in score_dags) +for i=1:N + type{i} = 'tabular'; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:N; +verbose = 'yes'; + +% get params +args = varargin; +nargs = length(args); +if length(args) > 0 + if isstr(args{1}) + for i = 1:2:nargs + switch args{i} + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'params', + if isempty(args{i+1}), params = cell(1,n); + else params = args{i+1}; end; + case 'verbose', verbose = strcmp(args{i+1},'yes'); + end; + end; + end; +end; + +done = 0; +best_score = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn); + +it = 0; +while ~done + [dags,op,nodes] = mk_nbrs_of_dag(seeddag); + nbrs = length(dags); +%------ DEBUG + fprintf('DEBUG: nbrs = %6d\n',nbrs); + time_inloop = cputime; +%------ DEND + +% scores = score_dags(data, nodesizes, dags,'scoring_fn',scoring_fn); + scores = zeros(nbrs,1); + for i = 1:nbrs + xj = nodes(i,2); + ps_old = parents(seeddag, xj)'; + ps_new = parents(dags{i}, xj)'; + scor_old = score_family(xj, ps_old, type{xj}, scoring_fn, ... + nodesizes, discrete, data, params{xj}); + scor_new = score_family(xj, ps_new, type{xj}, scoring_fn, ... + nodesizes, discrete, data, params{xj}); + scores(i) = best_score - scor_old + scor_new; + if isequal(op{i},'rev') + xi = nodes(i,1); + ps_old = parents(seeddag, xi); + ps_new = parents(dags{i}, xi); + scor_old = score_family(xi, ps_old, type{xi}, scoring_fn, ... + nodesizes, discrete, data, params{xi}); + scor_new = score_family(xi, ps_new, type{xi}, scoring_fn, ... + nodesizes, discrete, data, params{xi}); + scores(i) = scores(i) - scor_old + scor_new; + end + end + + max_score = max(scores); + new = find(scores == max_score ); + +%------ DEBUG + fprintf(' -> max_score = %7.5f\n',max_score); + fprintf(' -> find(scores == max_score): %d... of %d\n',... + new(1),length(new)); +%------ DEND + if ~isempty(new) & (max_score > best_score) + p = sample_discrete(normalise(ones(1, length(new)))); + best_score = max_score; + seeddag = dags{new(p)}; + else + done = 1; + end; + + it = it+1; +%------ DEBUG + time_inloop = cputime-time_inloop; + fprintf(' time = %12.5f\n',time_inloop); +%------ DEND +end; + +dag = seeddag; + +outcount = 0; +best_score = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn); +while outcount < 2 + innercount = 0; + for i=1:N + for j=1:N + if i==j, continue; end; + if seeddag(i,j) == 0 % No edge i-->j, then try to add it + tempdag = seeddag; + tempdag(i,j) = 1; + if acyclic(tempdag) + temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn); + if temp_score > best_score + seeddag = tempdag; + best_score= temp_score; + innercount = innercount +1; + end; + end + else % exists edge i--j, then try reverse it or remove it + tempdag = seeddag; + tempdag(i,j) = 0; tempdag(j,i) = 1; + if acyclic(tempdag) + temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn); + if temp_score > best_score + seeddag = tempdag; + best_score = temp_score; + innercount = innercount +1; + else + tempdag = seeddag; + tempdag(i,j) = 0; + temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn); + if temp_score > best_score + seeddag = tempdag; + best_score= temp_score; + innercount = innercount +1; + end; + end; + else + tempdag = seeddag; + tempdag(i,j)=0; + temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn); + if temp_score > best_score + seeddag = tempdag; + best_score= temp_score; + innercount = innercount +1; + end; + end; + end; + end; % end for j + end; % end for i + if innercount == 0 + outcount = outcount +1; + end; +end; % end while + +%------ DEBUG +fprintf('DEBUG: Number of iterations = %d\n',it); +fprintf('DEBUG: Outcount = %d\n',outcount); +%------ DEND +dag = seeddag; + diff --git a/sourcecodes/bnt-master/SLP/scoring/score_dag.c/learn_struct_gs_dtab_INFO.txt b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/learn_struct_gs_dtab_INFO.txt new file mode 100644 index 00000000..dc076d78 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/learn_struct_gs_dtab_INFO.txt @@ -0,0 +1,91 @@ +Objet: Re: [BayesNetToolbox] cache size in structure learning package (SLP) +De: "Darima Lamazhapova" +Date: Mar 3 janvier 2006 14:13 +to: BayesNetToolbox@yahoogroups.com + +Hi, +I have not done extensive tests with different dataset and cache sizes. +Mostly because I was interested in the case of small dataset, +say dataset size 100. Even in this case seems that using cache +drasticly improves the performance of greedy search. Here are some +numbers (alarm network, dataset 100, greedy search): + no cache - ~11000 sec + cache 100 - 1395 sec + cache 200 - 1386 sec + cache 300 - 1428 sec + cache 500 - 1567 sec + cache 1000 - 1680 sec + cache 3000 - 2196 sec +The reason why greedy search takes so much time without using +cache, i think is that it does not make use of score +decomposibility. +When I have modified the code so that it used decomposibility, + time required for the search decreased from 11000 to 1362 sec +without using cache. Reimplemening score_family in C decreased this +time to 552 sec; after reimplementing score_dag in C and +removing outcount cycle from the learn_struct_gs code (I did not +understand why it is necessary, can anyone explain what is +the use of it, please?) the time required for GS dropped up to 69 sec. +Implementation in C was done only for tabular nodes, Bayesian scoring +function with default priors. If anyone interested I can submit the codes, +although they are not very well tested. +Darima + +Olivier Francois wrote: + +> Hello, +> Can anyone give recommendations on choosing cache size? +> When I used 500, calculations using learn_struct_gs2 for +> ALARM network took about 20 min (dataset size was 100). +> I thought it is a bit slow for 3 GHz computer with 1 Gb memory, +> or am I wrong here? +> I thought that increasing cache size might increase performance, +> however the calculations took even longer. +> I am kinda lost right now. +> Any comments would be highly appreciated. +> Darima +> + +Hi, + +I have seen this phenomenon. +In fact, for all the tests I have done, I advise you to use a cache of size between 200 +and 500. + +When the size is bigger the time spent to search if an entry already exists is quite +similar to the time spend to recalculate the score, specialy if you have a small dataset +(under 1500-2000 samples). +Nevertheless, if you've got a huge dataset (5000 or more), it will be very advantageous +to use the cache option. + +Moreover, I have seen that when you use a big cache (1000 or more), it do not speed up +the computationnal time but you not spent a lot of extented time in using it. +I think it is better to have a too big cache than a too small. + +If the cache is too small you often erase entries that will be recalculate later, +specially if you have a lot of attributes, and the time spent will be equivalent or +higher than if you have not used it. + +But suprisigly, even if you have a lot of nodes and a lot of samples (I have tested to +25x10000 if I remember well), upgrading the size of the cache (1000 and more) do not +seem to improve the computation time. + +- More tests are needed to be sure of that - + +I have not done a lot of tests, and this is only what I believed to see. +Maybe it is better to take a cache of size 2500 or 5000 for a dataset of size 40*50000 +or 60*5000...? + +I also have remarked that, if you use a 'sparse matrix' for the cache inst ead of a +stardart one, that causes a waste of time even if the cache matrix is, in fact, sparse. + + +In your case, with 100 samples, you don't need too use the cache option. + +If you make (or have made for some others ?) more tests with different sample sizes and +different numbers of attributes, I am interrested in getting some comments on your use +of this function. + + +Bonne fetes - Happy Hollidays - Felice Fiestas + Olivier F. diff --git a/sourcecodes/bnt-master/SLP/scoring/score_dag.c/learn_struct_gs_dtabx.m b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/learn_struct_gs_dtabx.m new file mode 100644 index 00000000..be624ee8 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/learn_struct_gs_dtabx.m @@ -0,0 +1,181 @@ +function [dag,best_score] = learn_struct_gs_dtabx(data, nodesizes, seeddag, varargin) +% +% LEARN_STRUCT_GS(data,seeddag) learns a structure of Bayesian net by Greedy Search. +% dag = learn_struct_gs(data, nodesizes, seeddag) +% +% dag: the final structure matrix +% Data : training data, data(i,m) is the m obsevation of node i +% Nodesizes: the size array of different nodes +% seeddag: given seed Dag for hill climbing, optional +% +% by Gang Li @ Deakin University (gli73@hotmail.com) +% +% ----------------------------------------------------- +% +% Modified from learn_struct_gs (SLP 1.3) +% to learn structure of BN with tabular nodes: +% +% 1) make use of score decomposibility +% 2) replace score_family with score_family_x.c that calculates +% Bayesian score with default parameters (non-adjustable) +% +% by Darima , 28/12/2005 +% + +[N ncases] = size(data); +if (nargin < 3 ) + seeddag = zeros(N,N); % mk_rnd_dag(N); %call BNT function +elseif ~acyclic(seeddag) + seeddag = mk_rnd_dag(N); %zeros(N,N); +end; + +% set default params (the same as in score_dags) +for i=1:N + type{i} = 'tabular'; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:N; +verbose = 'yes'; + +% get params +args = varargin; +nargs = length(args); +if length(args) > 0 + if isstr(args{1}) + for i = 1:2:nargs + switch args{i} + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'params', + if isempty(args{i+1}), params = cell(1,n); + else params = args{i+1}; end; + case 'verbose', verbose = strcmp(args{i+1},'yes'); + end; + end; + end; +end; + +%%++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +datax = data-ones(N,ncases); %%+++++++++++++++++++++++ IMPORTANT!!!! +%%++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + +best_score = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn); +done = 0; +it = 0; + +while ~done + [dags,op,nodes] = mk_nbrs_of_dag(seeddag); + nbrs = length(dags); +%------ DEBUG + fprintf('DEBUG: nbrs = %6d\n',nbrs); + time_inloop = cputime; +%------ DEND +% scores = score_dags(data, nodesizes, dags,'scoring_fn',scoring_fn); + scores = zeros(nbrs,1); + for i = 1:nbrs + xj = nodes(i,2); + ps_old = parents(seeddag, xj)'; + ps_new = parents(dags{i}, xj)'; + scor_old = score_family_x([datax(ps_old,:);datax(xj,:)],... + [nodesizes(ps_old),nodesizes(xj)]); + scor_new = score_family_x([datax(ps_new,:);datax(xj,:)],... + [nodesizes(ps_new),nodesizes(xj)]); + scores(i) = best_score - scor_old + scor_new; + if isequal(op{i},'rev') + xi = nodes(i,1); + ps_old = parents(seeddag, xi); + ps_new = parents(dags{i}, xi); + scor_old = score_family_x([datax(ps_old,:);datax(xi,:)],... + [nodesizes(ps_old),nodesizes(xi)]); + scor_new = score_family_x([datax(ps_new,:);datax(xi,:)],... + [nodesizes(ps_new),nodesizes(xi)]); + scores(i) = scores(i) - scor_old + scor_new; + end + end + max_score = max(scores); + new = find(scores == max_score ); +%------ DEBUG + fprintf(' -> max_score = %7.5f\n',max_score); + fprintf(' -> find(scores == max_score): %d... of %d\n',... + new(1),length(new)); +%------ DEND + if ~isempty(new) & (max_score > best_score) + p = sample_discrete(normalise(ones(1, length(new)))); + best_score = max_score; + seeddag = dags{new(p)}; + else + done = 1; + end; + it = it+1; +%------ DEBUG + time_inloop = cputime-time_inloop; + fprintf(' time = %12.5f\n',time_inloop); +%------ DEND +end; +dag = seeddag; + +%---------------------------------------------------------------------- + +outcount = 0; +best_score = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn); +while outcount < 2 + innercount = 0; + for i=1:N + for j=1:N + if i==j, continue; end; + if seeddag(i,j) == 0 % No edge i-->j, then try to add it + tempdag = seeddag; + tempdag(i,j) = 1; + if acyclic(tempdag) + temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn); + if temp_score > best_score + seeddag = tempdag; + best_score= temp_score; + innercount = innercount +1; + end; + end + else % exists edge i--j, then try reverse it or remove it + tempdag = seeddag; + tempdag(i,j) = 0; tempdag(j,i) = 1; + if acyclic(tempdag) + temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn); + if temp_score > best_score + seeddag = tempdag; + best_score = temp_score; + innercount = innercount +1; + else + tempdag = seeddag; + tempdag(i,j) = 0; + temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn); + if temp_score > best_score + seeddag = tempdag; + best_score= temp_score; + innercount = innercount +1; + end; + end; + else + tempdag = seeddag; + tempdag(i,j)=0; + temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn); + if temp_score > best_score + seeddag = tempdag; + best_score= temp_score; + innercount = innercount +1; + end; + end; + end; + end; % end for j + end; % end for i + if innercount == 0 + outcount = outcount +1; + end; +end; % end while + +%------ DEBUG +fprintf('DEBUG: Number of iterations = %d\n',it); +fprintf('DEBUG: Outcount = %d\n',outcount); +%------ DEND +dag = seeddag; + diff --git a/sourcecodes/bnt-master/SLP/scoring/score_dag.c/learn_struct_gs_dtabxx.m b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/learn_struct_gs_dtabxx.m new file mode 100644 index 00000000..c0d00f05 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/learn_struct_gs_dtabxx.m @@ -0,0 +1,141 @@ +function [dag,best_score] = learn_struct_gs_dtabxx(data, nodesizes, seeddag, varargin) +% +% LEARN_STRUCT_GS(data,seeddag) learns a structure of Bayesian net by Greedy Search. +% dag = learn_struct_gs(data, nodesizes, seeddag) +% +% dag: the final structure matrix +% Data : training data, data(i,m) is the m obsevation of node i +% Nodesizes: the size array of different nodes +% seeddag: given seed Dag for hill climbing, optional +% +% by Gang Li @ Deakin University (gli73@hotmail.com) +% +% ----------------------------------------------------- +% +% Modified from learn_struct_gs (SLP 1.3) +% to learn structure of BN with tabular nodes: +% +% 1) make use of score decomposibility +% 2) replace score_family with score_family_x.c that calculates +% Bayesian score with default parameters (non-adjustable)% +% 3) replace score_dags with score_dag_x.c +% +% by Darima , 28/12/2005 +% + +% useold = 0; +% if useold +% best_score = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn); +% else +% best_score = score_dag_x(datax,nodesizes,seeddag); +% end + +[N ncases] = size(data); +if (nargin < 3 ) + seeddag = zeros(N,N); % mk_rnd_dag(N); %call BNT function +elseif ~acyclic(seeddag) + seeddag = mk_rnd_dag(N); %zeros(N,N); +end; + +% set default params (the same as in score_dags) +for i=1:N + type{i} = 'tabular'; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:N; +verbose = 'yes'; + +% get params +args = varargin; +nargs = length(args); +if length(args) > 0 + if isstr(args{1}) + for i = 1:2:nargs + switch args{i} + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'params', + if isempty(args{i+1}), params = cell(1,n); + else params = args{i+1}; end; + case 'verbose', verbose = strcmp(args{i+1},'yes'); + end; + end; + end; +end; + +%%++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +datax = data-ones(N,ncases); %%+++++++++++++++++++++++ IMPORTANT!!!! +%%++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + +useold = 0; +if useold + best_score = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn); +else + best_score = score_dag_x(datax,nodesizes,seeddag); +end +done = 0; +it = 0; + +while ~done + [dags,op,nodes] = mk_nbrs_of_dag(seeddag); + nbrs = length(dags); +%------ DEBUG +% fprintf('DEBUG: nbrs = %6d\n',nbrs); +% time_inloop = cputime; +%------ DEND + +% scores = score_dags(data, nodesizes, dags,'scoring_fn',scoring_fn); + scores = zeros(nbrs,1); + for i = 1:nbrs + xj = nodes(i,2); + ps_old = parents(seeddag, xj)'; + ps_new = parents(dags{i}, xj)'; + scor_old = score_family_x([datax(ps_old,:);datax(xj,:)],... + [nodesizes(ps_old),nodesizes(xj)]); + scor_new = score_family_x([datax(ps_new,:);datax(xj,:)],... + [nodesizes(ps_new),nodesizes(xj)]); + scores(i) = best_score - scor_old + scor_new; + if isequal(op{i},'rev') + xi = nodes(i,1); + ps_old = parents(seeddag, xi); + ps_new = parents(dags{i}, xi); + scor_old = score_family_x([datax(ps_old,:);datax(xi,:)],... + [nodesizes(ps_old),nodesizes(xi)]); + scor_new = score_family_x([datax(ps_new,:);datax(xi,:)],... + [nodesizes(ps_new),nodesizes(xi)]); + scores(i) = scores(i) - scor_old + scor_new; + end + end + + max_score = max(scores); + new = find(scores == max_score ); + +%------ DEBUG +% fprintf(' -> max_score = %7.5f\n',max_score); +% fprintf(' -> find(scores == max_score): %d... of %d\n',... +% new(1),length(new)); +%------ DEND + if ~isempty(new) & (max_score > best_score) + p = sample_discrete(normalise(ones(1, length(new)))); + best_score = max_score; + seeddag = dags{new(p)}; + else + done = 1; + end; + + it = it+1; +%------ DEBUG +% time_inloop = cputime-time_inloop; +% fprintf(' time = %12.5f\n',time_inloop); +%------ DEND +end; +dag = seeddag; + +%------ DEBUG +% fprintf('DEBUG: Number of iterations = %d\n',it); +% fprintf('DEBUG: Outcount = %d\n',0); +%------ DEND + + diff --git a/sourcecodes/bnt-master/SLP/scoring/score_dag.c/score_dag_x.c b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/score_dag_x.c new file mode 100644 index 00000000..d72bf591 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/score_dag_x.c @@ -0,0 +1,72 @@ +// +// File: score_dag_x.c +// +// MATLAB: +// function score = score_dag_x(data,sz,dag) +// +// DESCRIPTION: +// Calculates Bayesian score for the DAG with tabular nodes +// (as in log_marg_prob_node.m): +// data [nsz x ndata] array +// IMPORTANT: <--- !!!! +// - parents go first +// - values of the nodes are in the range 0..sz[i] +// sz sizes of the nodes +// dag DAG +// ndata # of observations +// nsz # of nodes in the DAG +// with default parameters: +// params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 } +// ...and 'dirichlet_type','BDeu' +// scoring_fn = 'bayesian'; +// etc... +// +// EXAMPLE: +// score_family_x([1 2 1; 1 2 2; 1 1 1],[2 2 2]); +// +// by Darima , 27/12/2005 +// + +#include "mex.h" +#include "score_x.h" + +#define IN_DATA prhs[0] +#define IN_SZ prhs[1] +#define IN_DAG prhs[2] +#define OUT_SCORE plhs[0] +#if !defined(MAX) +#define MAX(A, B) ((A) > (B) ? (A) : (B)) +#endif + +void mexFunction( int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[] ) +{ + int nsz, ndata; + double *sz, *data,*dag, *score; + + /* Check for proper number of arguments */ + + if (nrhs != 3) { + mexErrMsgTxt("Three input arguments required."); + } else if (nlhs > 1) { + mexErrMsgTxt("Too many output arguments."); + } + + /* Assign input arguments */ + + data = mxGetPr(IN_DATA); + sz = mxGetPr(IN_SZ); + dag = mxGetPr(IN_DAG); + ndata = mxGetN(IN_DATA); + nsz = MAX(mxGetM(IN_SZ),mxGetN(IN_SZ)); + + /* Create return argument */ + + OUT_SCORE = mxCreateDoubleScalar(mxREAL); + score = mxGetPr(OUT_SCORE); + + /* Do the actual computations in a subroutine */ + + score_dag_x(data,sz,dag,ndata,nsz,score); + return; +} diff --git a/sourcecodes/bnt-master/SLP/scoring/score_dag.c/score_family_x.c b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/score_family_x.c new file mode 100644 index 00000000..ce45fd94 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/score_family_x.c @@ -0,0 +1,71 @@ +// +// File: score_family_x.c +// +// MATLAB: +// function score = score_family_x(data,sz) +// +// DESCRIPTION: +// Calculates Bayesian score for the family of tabular nodes +// (as in log_marg_prob_node.m): +// data [nsz x ndata] array +// IMPORTANT: <--- !!!! +// - parents go first +// - values of the nodes are in the range 0..sz[i] +// sz sizes of the nodes in the family +// ndata # of observations +// nsz # of nodes in the family +// with default parameters: +// params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 } +// ...and 'dirichlet_type','BDeu' +// scoring_fn = 'bayesian'; +// etc... +// +// EXAMPLE: +// score_family_x([1 2 1; 1 2 2; 1 1 1],[2 2 2]); // ans = -2.0794 +// +// by Darima , 27/12/2005 +// + +#include "mex.h" +#include "score_x.h" + +#define IN_DATA prhs[0] +#define IN_SZ prhs[1] +#define OUT_SCORE plhs[0] +#if !defined(MAX) +#define MAX(A, B) ((A) > (B) ? (A) : (B)) +#endif + +void mexFunction( int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[] ) +{ + int nsz, ndata; + double *sz, *data, *score; + int i,j; + + /* Check for proper number of arguments */ + + if (nrhs != 2) { + mexErrMsgTxt("Two input arguments required."); + } else if (nlhs > 1) { + mexErrMsgTxt("Too many output arguments."); + } + + /* Assign input arguments */ + + data = mxGetPr(IN_DATA); + sz = mxGetPr(IN_SZ); + ndata = mxGetN(IN_DATA); + nsz = MAX(mxGetM(IN_SZ),mxGetN(IN_SZ)); + + /* Create return argument */ + + OUT_SCORE = mxCreateDoubleScalar(mxREAL); + score = mxGetPr(OUT_SCORE); + + /* Do the actual computations in a subroutine */ + + score_family_x(data,sz,nsz,ndata,score); + return; +} + diff --git a/sourcecodes/bnt-master/SLP/scoring/score_dag.c/score_x.c b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/score_x.c new file mode 100644 index 00000000..b63111f6 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/score_x.c @@ -0,0 +1,168 @@ +// +// File: score_x.c +// +// +// by Darima , 27/12/2005 +// + +#include +#include +#include +#include +#include "score_x.h" + +void score_dag_x(double *data,double *sz,double *dag, + int ndata,int nsz,double* score) +// +// GOTO: score_dag_x.c +// by Darima , 27/12/2005 +// +{ + double *fdata,*fsz,*fscore; + int fnsz,*family; + int i,j,k; + + fdata = (double*)malloc(nsz*ndata*sizeof(double)); + fsz = (double*)malloc(nsz*sizeof(double)); + fscore = (double*)malloc(sizeof(double)); + family = (int*)malloc(nsz*sizeof(int)); + *score = 0; + + for (j=0;j, 27/12/2005 +// +{ + int self_sz, ps_sz; + double prior1, prior2, *count; + int i,j,k; double idx,tsz,N_ij; + + // self_sz - number of values of current node + // ps_sz - number of configurations of parents + + self_sz = sz[nsz-1]; + ps_sz = 1; for (i=0;i idx + // 2) increment corresponding count: count[idx,node_val]++ + // + // example: + // case1 case2 etc + // ----------------------------- + // p1 | 0 1 ... + // p2 | 0 0 ... + // node | 1 1 ... + // + // notice: array[i,j,k] -> array[i+j*ni+k*ni*nj] + // + // example 1: data[i,j] -> data[i+j*nsz] + // + // example 2: consider family with 5 parents + // - [p1,p2,..,p5] -> IDX5 + // IDX5 = p1 + p2*p1_sz + p3*p1_sz*p2_sz + ... + + // p5*p1_sz*p2_sz*p3_sz*p4_sz + // - [p1,p2,..,p5,node_val] -> IDXFAM + // IDXFAM = IDX5 + node_val*ps_sz + // count[IDX5,node_val] ->count[IDXFAM] + + idx = data[0+j*nsz]; tsz = 1; + for (i=1;i 0. +// from "Numerical Recipes in C" +// +{ + double x,y,tmp,ser; + static double cof[6]={ + 76.18009172947146,-86.50532032941677, + 24.01409824083091,-1.231739572450155, + 0.1208650973866179e-2,-0.5395239384953e-5}; + int j; + y=x=xx; + tmp=x+5.5; + tmp -= (x+0.5)*log(tmp); + ser=1.000000000190015; + for (j=0;j<=5;j++) ser += cof[j]/++y; + return -tmp+log(2.5066282746310005*ser/x); +} diff --git a/sourcecodes/bnt-master/SLP/scoring/score_dag.c/score_x.h b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/score_x.h new file mode 100644 index 00000000..8fc1c213 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/score_dag.c/score_x.h @@ -0,0 +1,19 @@ +// +// File: score_x.h +// +// +// by Darima , 27/12/2005 +// + +#ifndef score_x_h +#define mex_h + +void score_family_x(double *data,double *sz, + int ndata,int nsz,double* score); + +void score_dag_x(double *data,double *sz,double *dag, + int nsz,int ndata,double* score); + +double gammaln(double xx); + +#endif // score_x_h diff --git a/sourcecodes/bnt-master/SLP/scoring/score_dags.m b/sourcecodes/bnt-master/SLP/scoring/score_dags.m new file mode 100644 index 00000000..acda3e0f --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/score_dags.m @@ -0,0 +1,74 @@ +function [score, cache] = score_dags(data, ns, dags, varargin) +% SCORE_DAGS Compute the score of one or more DAGs +% score = score_dags(data, ns, dags, varargin) +% +% data{i,m} = value of node i in case m (can be a cell array). +% node_sizes(i) is the number of size of node i. +% dags{g} is the g'th dag +% score(g) is the score of the i'th dag +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% scoring_fn - 'bayesian' or 'bic' [ 'bayesian' ] +% Currently, only networks with all tabular nodes support Bayesian scoring. +% type - type{i} is the type of CPD to use for node i, where the type is a string +% of the form 'tabular', 'noisy_or', 'gaussian', etc. [ all cells contain 'tabular' ] +% params - params{i} contains optional arguments passed to the CPD constructor for node i, +% or [] if none. [ all cells contain {'prior', 1}, meaning use uniform Dirichlet priors ] +% discrete - the list of discrete nodes [ 1:N ] +% clamped - clamped(i,m) = 1 if node i is clamped in case m [ zeros(N, ncases) ] +% cache - data structure used to memorize local score computations (cf. SCORE_INIT_CACHE function) [ [] ] +% +% e.g., score = score_dags(data, ns, mk_all_dags(n), 'scoring_fn', 'bic', 'params', [],'cache',cache); +% +% (Caching implementation : francois.olivier.c.h@gmail.com, philippe.leray@univ-nantes.fr) +% ("Clamped" optimisation : Derek Hoiem ) + +[n ncases] = size(data); + +% set default params +type = cell(1,n); +params = cell(1,n); +cache=[]; +for i=1:n + type{i} = 'tabular'; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:n; + +isclamped = 0; % DWH +clamped = zeros(n, ncases); +u = [1:ncases]'; % DWH + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'clamped', clamped = args{i+1}, isclamped = 1; %DWH + case 'params', if isempty(args{i+1}), params = cell(1,n); else params = args{i+1}; end + case 'cache', cache=args{i+1} ; + end +end + +NG = length(dags); +score = zeros(1, NG); + +for j=1:n + if isclamped %DWH + u = find(clamped(j,:)==0); + end + for g=1:NG + if isempty(dags{g}) + score(g)=-Inf; + else + ps = parents(dags{g}, j); + [scor cache] = score_family(j, ps, type{j}, scoring_fn, ns, discrete, data(:,u), params{j}, cache); + score(g) = score(g) + scor; + end + end +end \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/scoring/score_family.m b/sourcecodes/bnt-master/SLP/scoring/score_family.m new file mode 100644 index 00000000..085fcf48 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/score_family.m @@ -0,0 +1,218 @@ +function [score, cache] = score_family(j, ps, node_type, scoring_fn, ns, discrete, data, args, cache) +% SCORE_FAMILY_COMPLETE Compute the score of a node and its parents given completely observed data +% score = score_family(j, ps, node_type, scoring_fn, ns, discrete, data, args, cache) +% +% data(i,m) is the value of node i in case m (can be a cell array, if contain missing value, it uses only available complete cases for an entry) +% args is a cell array containing optional arguments passed to the constructor, +% or is [] if none +% cache is a data structure used to memorize local score computations +% (cf. SCORE_INIT_CACHE function) +% +% We create a whole Bayes net which only connects parents to node, +% where node has a CPD of the specified type (with default parameters). +% We then evaluate its score ('bic' or 'bayesian') +% We should use a cache to avoid unnecessary computation. +% In particular, log_marginal_prob_node for tabular CPDs calls gammaln +% and compute_counts, both of which are slow. +% +% (Caching implementation : ofrancois.olivier.c.h@gmail.com, philippe.leray@univ-nantes.fr) +% + +if (nargin<9 || isempty(cache)) , c=0; else c=1; end +%tic +if c==1 + [b,score]=score_find_in_cache(cache,j,ps,scoring_fn); +else + b=0; +end +%Tfind=toc +ccc = iscell(data); +ps = unique(ps); + +if b==0 + misv = -9999; + if ccc, data = bnt_to_mat(data,misv); end + [n ncases] = size(data); + dag = zeros(n,n); + % SML added to sort ps b/c mk_bnet, learn_params use sorted ps to make + % CPTs % Kevin had: if ~isempty(ps), dag(ps, j) = 1; end + if ~isempty(ps), dag(ps, j) = 1;, ps = sort(ps);, end + + bnet = mk_bnet(dag, ns, 'discrete', discrete); + fname = sprintf('%s_CPD', node_type); + if isempty(args) + bnet.CPD{j} = feval(fname, bnet, j); + else + bnet.CPD{j} = feval(fname, bnet, j, args{:}); + end + %tic + switch scoring_fn + case 'bic', + fam = [ps j]; + if ccc, + [tmp, available_case] = find(data(fam,:)==misv); + available_case = mysetdiff(1:ncases, available_case); + else available_case = 1:ncases; + end + bnet.CPD{j} = learn_params(bnet.CPD{j}, fam, data(:,available_case), ns, bnet.cnodes); + %L = log_prob_node(bnet.CPD{j}, data(j,:), data(ps,:)); + L = log_prob_node(bnet.CPD{j}, data(j,available_case), data(ps,available_case)); + S = struct(bnet.CPD{j}); % violate object privacy + score = L - 0.5*S.nparams*log(length(available_case)); + case 'bicmod', + fam = [ps j]; + if ccc, + [tmp, available_case] = find(data(fam,:)==misv); + available_case = mysetdiff(1:ncases, available_case); + else available_case = 1:ncases; + end + bnet.CPD{j} = learn_params(bnet.CPD{j}, fam, data(:,available_case), ns, bnet.cnodes); + L = log_prob_node(bnet.CPD{j}, data(j,available_case), data(ps,available_case)); + S = struct(bnet.CPD{j}); % violate object privacy + score = L - S.nparams*log(length(available_case)); + case 'bayesian', + fam = [ps j]; + if ccc, + [tmp, available_case] = find(data(fam,:)==misv); + available_case = mysetdiff(1:ncases, available_case); + else available_case = 1:ncases; + end + score = log_marg_prob_node(bnet.CPD{j}, data(j,available_case), data(ps,available_case)); + otherwise, + error(['unrecognized scoring fn ' scoring_fn]); + end + %Tcalc=toc + %tic + if c==1 +% fprintf('a\n') + cache=score_add_to_cache(cache,j,ps,score,scoring_fn); + end + %Tecr=toc +% else +% fprintf('*\n') +end + +%===========================Inner functions + +function [cache, place] = score_add_to_cache(cache,j,ps,score,scoring_fn) +% [cache place] = score_add_to_cache(cache,j,ps,score,scoring_fn) +% +% j is the son node, +% ps is the list of parents of j, for example [12 5 7], +% score is the score to add for this familly. +% scoring_fn is 'bic' or 'bayesian'. +% +% place = where the entry was add. +% +% example for 2 nodes with cache of size 5 : +% +% cache = +% Nw b 0 0 0 --> Nw=number of writings in cache (+1) and b==1 iff the cache is full +% 0 0 1 -239.12 1 --> 1st familly in the cache (node 1 without parents) calculate with bic +% 0 0 2 -318.98 1 +% 1 0 2 -189.23 2 --> 3rd familly in the cache (node 2 with 1 as parent) calculate with bayésian +% 0 1 1 -251.09 1 +% .ps2bool. j score 1or2 --> new entry +% | | | | | +% | | | | |___> scoring function : 1 for 'bic' or 2 for 'bayesian' +% | | | |__________> score of the familly +% | | |_________________> son node of the familly +% | |__________________________> ==1 iff node 2 is parent of son node +% |______________________________> ==1 iff node 1 is parent of son node +% +% If the cache is FULL then the new place is RanDomly choose. +% +% V1.1 : 5 may 2003 (O. Francois, Ph. Leray) + +N=size(cache,2)-3; +L=size(cache,1)-1; +cache_full=cache(1,2) ; + +place=0; + +if ismember(j,ps) + disp('This is a cyclic entry, nothing was done.'); +elseif j>N || j<=0 + disp('This entry is not valid, nothing was done.'); +else + switch scoring_fn + case 'bic', + fn=1; + case 'bayesian', + fn=2; + otherwise, + fn=3; + %error(['unrecognized scoring fn ' scoring_fn]); + end + + if ~cache_full + place=cache(1,1); + else + [ignore place]=max(rand(1,L)); place=place+1; + end + + cache(place,:)=0; + cache(place,ps)=1; + cache(place,N+1)=j; + cache(place,N+2)=score; + cache(place,N+3)=fn; + + cache(1,1)=place+1; + if place>L || cache(1,2)~=0 + cache(1,2)=1; + end +end + + +%========================================================================================= +function [bool, score] = score_find_in_cache(cache,j,ps,scoring_fn) +% cache = score_find_in_cache(cache,j,ps,scoring_fn) +% +% V1.1 : 5 may 2003 (O. Francois, Ph. Leray) + + +%tic +L=size(cache,1)-1; +N=size(cache,2)-3; + +if N<1 + bool=0; + score=0; + return +end + +parents=zeros(1,N); +parents(ps)=1; +%parents(N+1)=j; + +switch scoring_fn +case 'bic', + fn=1; +case 'bayesian', + fn=2; +otherwise, + fn=3; + %error(['unrecognized scoring fn ' scoring_fn]); +end + +tmp=find(cache(2:L+1,N+3)==fn); +tmp=tmp+1; +tmp2=find(cache(tmp,N+1)==j); +candidats=tmp(tmp2); + +i=1; +while i<=N & ~isempty(candidats) + tmp=find(cache(candidats,i)==parents(i)); + candidats=candidats(tmp); + i=i+1; +end + +%Tpre=toc + +bool=~isempty(candidats); + +if bool + score=cache(candidats(1),N+2); +else + score=0; +end diff --git a/sourcecodes/bnt-master/SLP/scoring/score_find_in_cache.m b/sourcecodes/bnt-master/SLP/scoring/score_find_in_cache.m new file mode 100644 index 00000000..61f282e9 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/score_find_in_cache.m @@ -0,0 +1,63 @@ +function [bool, score] = score_find_in_cache(cache,j,ps,scoring_fn) +% [bool, score] = score_find_in_cache(cache,j,ps,scoring_fn) +% +% francois.olivier.c.h@gmail.com + +%tic +L=size(cache,1); +N=size(cache,2)-3; + +if N<1 + bool=0; + score=0; + return +end + +parents=zeros(1,N+1); +parents(ps)=1;parents(N+1)=j; + +switch scoring_fn + case 'bic', + fn=1; + case 'bayesian', + fn=2; + otherwise, + fn=3; + %error(['unrecognized scoring fn ' scoring_fn]); +end + +%parent = str2num(num2str(parents,'%1d')); +%[tmp y]=find(cache(:,N+3)==fn); +%if ~isempty(tmp) +% [tmp2 y]=find(str2num(num2str(cache(tmp,1:N+1),'%1d'))==parent); +% candidats=tmp(tmp2); +%else +% candidats=[]; +%end + +[tmp y]=find(cache(2:L,N+3)==fn); +tmp=tmp+1; +[tmp2 y]=find(cache(tmp,N+1)==j); +candidats=tmp(tmp2); +if ~isempty(candidats) + for i=1:N % N=size(cache,2)-3; + if ~isempty(candidats) + [tmp2 y]=find(cache(tmp,i)==parents(i)); + candidats=intersect(candidats,tmp(tmp2)); + end + end +end + +%Tpre=toc + +if ~isempty(candidats) + bool=1; +else + bool=0; +end + +if bool + score=cache(candidats(1),N+2); +else + score=0; +end \ No newline at end of file diff --git a/sourcecodes/bnt-master/SLP/scoring/score_init_cache.m b/sourcecodes/bnt-master/SLP/scoring/score_init_cache.m new file mode 100644 index 00000000..fcbca268 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/score_init_cache.m @@ -0,0 +1,29 @@ +function cache = score_init_cache(N,L) +% SCORE_INIT_CACHE generate an empty cache for local computation in structure learning +% cache = score_init_cache(number_of_nodes,cache_size) +% +% For 2 nodes with cache of size 5 : +% +% cache = +% Nw b 0 0 0 --> Nw=number of writings in cache (+1) and b==1 iff the cache is full +% 0 0 1 -239.12 1 --> 1st familly in the cache (node 1 without parents) calculate with bic +% 0 0 2 -318.98 1 +% 1 0 2 -189.23 2 --> 3rd familly in the cache (node 2 with 1 as parent) calculate with bayesian +% 0 1 1 -251.09 1 +% 0 0 0 0 0 --> empty entry +% | | | | | +% | | | | |___> scoring function : 1 for 'bic', 2 for 'bayesian', ... +% | | | |__________> local score of the familly +% | | |_________________> son node of the familly +% | |__________________________> ==1 iff node 2 is parent of son node +% |______________________________> ==1 iff node 1 is parent of son node +% +% +% V1.1 : 6 may 2003 (O. Francois - francois.olivier.c.h@gmail.com, Ph. Leray - philippe.leray@univ-nantes.fr) +% +% + +cache=zeros(L+1,N+3); +cache(1,1)=2; + +% using a sparse matrix does not improve performances -- cgit 1.4.1