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 --- .../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 | 9 + .../SLP/examples/UCI_DataSets/diabetes.names | 75 + .../SLP/examples/UCI_DataSets/diabetesD.m | 22 + .../bnt-master/SLP/examples/UCI_DataSets/german | 25 + .../SLP/examples/UCI_DataSets/german.doc | 175 + .../bnt-master/SLP/examples/UCI_DataSets/germanD.m | 22 + .../bnt-master/SLP/examples/UCI_DataSets/heart | 14 + .../bnt-master/SLP/examples/UCI_DataSets/heart.doc | 44 + .../bnt-master/SLP/examples/UCI_DataSets/heart2 | 270 + .../SLP/examples/UCI_DataSets/heart2.mat | Bin 0 -> 154408 bytes .../bnt-master/SLP/examples/UCI_DataSets/heartD.m | 22 + .../SLP/examples/UCI_DataSets/hepatitis.dat | 155 + .../SLP/examples/UCI_DataSets/hepatitisL.m | 35 + .../bnt-master/SLP/examples/UCI_DataSets/horse.dat | 300 + .../bnt-master/SLP/examples/UCI_DataSets/horseD | 28 + .../bnt-master/SLP/examples/UCI_DataSets/horseL.m | 41 + .../bnt-master/SLP/examples/UCI_DataSets/horseTD | 28 + .../SLP/examples/UCI_DataSets/horse_test.dat | 68 + .../bnt-master/SLP/examples/UCI_DataSets/house.dat | 435 + .../bnt-master/SLP/examples/UCI_DataSets/houseL.m | 22 + .../SLP/examples/UCI_DataSets/letterD.data | 20000 +++++++++++++++++++ .../bnt-master/SLP/examples/UCI_DataSets/letterD.m | 22 + .../SLP/examples/UCI_DataSets/letterD.names | 62 + .../bnt-master/SLP/examples/UCI_DataSets/letter_A | 17 + .../bnt-master/SLP/examples/UCI_DataSets/letter_T | 17 + .../SLP/examples/UCI_DataSets/monks.names | 78 + .../SLP/examples/UCI_DataSets/monks1.dat | 124 + .../bnt-master/SLP/examples/UCI_DataSets/monks1D.m | 21 + .../SLP/examples/UCI_DataSets/monks2.dat | 169 + .../bnt-master/SLP/examples/UCI_DataSets/monks2D.m | 21 + .../SLP/examples/UCI_DataSets/monks3.dat | 122 + .../bnt-master/SLP/examples/UCI_DataSets/monks3D.m | 20 + .../bnt-master/SLP/examples/UCI_DataSets/monks_A1 | 7 + .../bnt-master/SLP/examples/UCI_DataSets/monks_A2 | 7 + .../bnt-master/SLP/examples/UCI_DataSets/monks_A3 | 7 + .../bnt-master/SLP/examples/UCI_DataSets/monks_T | 7 + .../SLP/examples/UCI_DataSets/monks_test.dat | 432 + .../SLP/examples/UCI_DataSets/mushrooms.dat | 8124 ++++++++ .../SLP/examples/UCI_DataSets/mushroomsL.m | 34 + .../bnt-master/SLP/examples/UCI_DataSets/nursery | 9 + .../SLP/examples/UCI_DataSets/nursery.names | 101 + .../SLP/examples/UCI_DataSets/nurseryD.m | 22 + .../bnt-master/SLP/examples/UCI_DataSets/penD.m | 22 + .../bnt-master/SLP/examples/UCI_DataSets/pen_A | 17 + .../bnt-master/SLP/examples/UCI_DataSets/pen_T | 17 + .../SLP/examples/UCI_DataSets/pendigits.names | 136 + .../bnt-master/SLP/examples/UCI_DataSets/segment | 20 + .../SLP/examples/UCI_DataSets/segment.doc | 68 + .../SLP/examples/UCI_DataSets/segmentD.m | 22 + .../SLP/examples/UCI_DataSets/soybean.dat | 307 + .../SLP/examples/UCI_DataSets/soybeanL.m | 27 + .../SLP/examples/UCI_DataSets/soybean_test.dat | 376 + .../bnt-master/SLP/examples/UCI_DataSets/spect_a | 23 + .../bnt-master/SLP/examples/UCI_DataSets/spect_t | 23 + .../bnt-master/SLP/examples/UCI_DataSets/tae | 6 + .../bnt-master/SLP/examples/UCI_DataSets/tae.names | 39 + .../bnt-master/SLP/examples/UCI_DataSets/taeD.m | 26 + .../SLP/examples/UCI_DataSets/thyroidL.m | 21 + .../SLP/examples/UCI_DataSets/thyroid_app.mat | Bin 0 -> 123400 bytes .../SLP/examples/UCI_DataSets/thyroid_test.mat | Bin 0 -> 42968 bytes .../bnt-master/SLP/examples/UCI_DataSets/wine | 14 + .../SLP/examples/UCI_DataSets/wine.names | 100 + .../bnt-master/SLP/examples/UCI_DataSets/wineD.m | 26 + .../bnt-master/SLP/examples/UCI_DataSets/zoo | 17 + .../bnt-master/SLP/examples/UCI_DataSets/zoo.names | 68 + .../bnt-master/SLP/examples/UCI_DataSets/zooD.m | 26 + sourcecodes/bnt-master/SLP/examples/test_MWSTEM.m | 154 + sourcecodes/bnt-master/SLP/examples/test_bnpc.m | 51 + sourcecodes/bnt-master/SLP/examples/test_bnpc2.m | 44 + sourcecodes/bnt-master/SLP/examples/test_cache.m | 177 + .../bnt-master/SLP/examples/test_chisquare.m | 61 + sourcecodes/bnt-master/SLP/examples/test_cpdag.m | 22 + .../bnt-master/SLP/examples/test_data_generation.m | 46 + sourcecodes/bnt-master/SLP/examples/test_gener.m | 123 + sourcecodes/bnt-master/SLP/examples/test_ges.m | 45 + sourcecodes/bnt-master/SLP/examples/test_ges_em.m | 25 + sourcecodes/bnt-master/SLP/examples/test_gs2.m | 54 + sourcecodes/bnt-master/SLP/examples/test_kldiv.m | 22 + sourcecodes/bnt-master/SLP/examples/test_knn.m | 83 + sourcecodes/bnt-master/SLP/examples/test_mwst.m | 33 + .../bnt-master/SLP/examples/test_netica_export.m | 85 + sourcecodes/bnt-master/SLP/examples/test_pc.m | 39 + sourcecodes/bnt-master/SLP/examples/test_sem1.m | 56 + sourcecodes/bnt-master/SLP/examples/test_sem2.m | 59 + 98 files changed, 34394 insertions(+) create mode 100644 sourcecodes/bnt-master/SLP/examples/UCI_DataSets/SPECT.names create mode 100644 sourcecodes/bnt-master/SLP/examples/UCI_DataSets/SPECTD.m create mode 100644 sourcecodes/bnt-master/SLP/examples/UCI_DataSets/abalone create mode 100644 sourcecodes/bnt-master/SLP/examples/UCI_DataSets/abalone.names create mode 100644 sourcecodes/bnt-master/SLP/examples/UCI_DataSets/abaloneD.m create mode 100644 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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 3.0000000e+00 3.0000000e+00 7.0000000e+00 1.7000000e+01 1.5000000e+01 1.9000000e+01 2.3000000e+01 2.1000000e+01 1.5000000e+01 3.0000000e+00 2.7000000e+01 1.1000000e+01 1.1000000e+01 7.0000000e+00 7.0000000e+00 1.3000000e+01 2.1000000e+01 9.0000000e+00 2.3000000e+01 1.9000000e+01 5.0000000e+00 9.0000000e+00 7.0000000e+00 1.5000000e+01 1.5000000e+01 1.9000000e+01 1.5000000e+01 1.0000000e+00 3.0000000e+00 5.0000000e+00 1.5000000e+01 1.5000000e+01 3.0000000e+00 3.0000000e+00 1.1000000e+01 1.7000000e+01 1.5000000e+01 3.0000000e+00 1.5000000e+01 1.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.7000000e+01 1.1000000e+01 5.0000000e+00 1.5000000e+01 1.1000000e+01 1.0000000e+00 5.0000000e+00 3.0000000e+00 9.0000000e+00 5.0000000e+00 1.1000000e+01 2.7000000e+01 9.0000000e+00 3.0000000e+00 3.0000000e+00 1.5000000e+01 1.1000000e+01 1.0000000e+00 5.0000000e+00 7.0000000e+00 5.0000000e+00 1.5000000e+01 1.0000000e+00 1.1000000e+01 5.0000000e+00 2.7000000e+01 5.0000000e+00 2.8000000e+01 3.0000000e+00 3.0000000e+00 9.0000000e+00 1.5000000e+01 9.0000000e+00 5.0000000e+00 1.3000000e+01 5.0000000e+00 3.0000000e+00 1.3000000e+01 3.0000000e+00 3.0000000e+00 3.0000000e+00 1.0000000e+00 3.0000000e+00 5.0000000e+00 3.0000000e+00 3.0000000e+00 9.0000000e+00 7.0000000e+00 1.0000000e+00 7.0000000e+00 1.7000000e+01 3.0000000e+00 9.0000000e+00 1.5000000e+01 9.0000000e+00 1.1000000e+01 1.1000000e+01 9.0000000e+00 9.0000000e+00 1.0000000e+00 1.3000000e+01 5.0000000e+00 1.1000000e+01 1.0000000e+00 3.0000000e+00 7.0000000e+00 3.0000000e+00 3.0000000e+00 1.0000000e+00 9.0000000e+00 1.9000000e+01 7.0000000e+00 1.7000000e+01 5.0000000e+00 5.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.1000000e+01 7.0000000e+00 1.1000000e+01 5.0000000e+00 2.1000000e+01 9.0000000e+00 1.0000000e+00 1.9000000e+01 5.0000000e+00 1.1000000e+01 5.0000000e+00 3.0000000e+00 9.0000000e+00 1.9000000e+01 3.0000000e+00 1.7000000e+01 1.5000000e+01 5.0000000e+00 3.0000000e+00 5.0000000e+00 2.8000000e+01 9.0000000e+00 1.5000000e+01 1.0000000e+00 5.0000000e+00 1.0000000e+00 1.3000000e+01 7.0000000e+00 9.0000000e+00 9.0000000e+00 7.0000000e+00 1.3000000e+01 1.3000000e+01 5.0000000e+00 3.0000000e+00 5.0000000e+00 1.7000000e+01 1.3000000e+01 1.0000000e+00 1.1000000e+01 1.1000000e+01 1.3000000e+01 1.0000000e+00 3.0000000e+00 1.1000000e+01 9.0000000e+00 1.5000000e+01 1.7000000e+01 3.0000000e+00 1.7000000e+01 1.1000000e+01 7.0000000e+00 1.9000000e+01 1.5000000e+01 2.3000000e+01 1.7000000e+01 1.1000000e+01 3.0000000e+00 7.0000000e+00 9.0000000e+00 9.0000000e+00 1.0000000e+00 3.0000000e+00 1.0000000e+00 5.0000000e+00 1.3000000e+01 1.1000000e+01 1.7000000e+01 1.1000000e+01 3.0000000e+00 1.5000000e+01 5.0000000e+00 1.0000000e+00 1.5000000e+01 1.0000000e+00 1.9000000e+01 2.5000000e+01 1.1000000e+01 1.3000000e+01 1.1000000e+01 1.1000000e+01 1.0000000e+00 5.0000000e+00 1.5000000e+01 1.5000000e+01 3.0000000e+00 3.0000000e+00 1.0000000e+00 7.0000000e+00 9.0000000e+00 1.0000000e+00 9.0000000e+00 1.3000000e+01 3.0000000e+00 9.0000000e+00 7.0000000e+00 9.0000000e+00 1.5000000e+01 1.0000000e+00 1.9000000e+01 1.0000000e+00 3.0000000e+00 9.0000000e+00 7.0000000e+00 1.3000000e+01 5.0000000e+00 1.9000000e+01 2.1000000e+01 1.0000000e+00 1.9000000e+01 3.0000000e+00 1.9000000e+01 5.0000000e+00 5.0000000e+00 1.0000000e+00 2.5000000e+01 3.0000000e+00 7.0000000e+00 5.0000000e+00 3.0000000e+00 2.3000000e+01 7.0000000e+00 7.0000000e+00 9.0000000e+00 7.0000000e+00 9.0000000e+00 1.1000000e+01 1.0000000e+00 5.0000000e+00 1.0000000e+00 5.0000000e+00 2.1000000e+01 5.0000000e+00 7.0000000e+00 3.0000000e+00 2.7000000e+01 5.0000000e+00 1.5000000e+01 1.0000000e+00 1.1000000e+01 5.0000000e+00 1.0000000e+00 2.1000000e+01 1.5000000e+01 1.5000000e+01 5.0000000e+00 1.5000000e+01 1.1000000e+01 3.0000000e+00 9.0000000e+00 1.1000000e+01 1.0000000e+00 1.0000000e+00 5.0000000e+00 3.0000000e+00 1.0000000e+00 1.3000000e+01 5.0000000e+00 1.0000000e+00 2.8000000e+01 1.7000000e+01 1.0000000e+00 5.0000000e+00 1.1000000e+01 1.1000000e+01 7.0000000e+00 5.0000000e+00 2.1000000e+01 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.3000000e+01 1.0000000e+00 5.0000000e+00 7.0000000e+00 1.5000000e+01 5.0000000e+00 7.0000000e+00 7.0000000e+00 7.0000000e+00 1.3000000e+01 9.0000000e+00 7.0000000e+00 1.0000000e+00 2.7000000e+01 5.0000000e+00 3.0000000e+00 3.0000000e+00 2.1000000e+01 5.0000000e+00 1.3000000e+01 1.7000000e+01 5.0000000e+00 3.0000000e+00 2.5000000e+01 3.0000000e+00 1.0000000e+00 1.0000000e+00 1.1000000e+01 1.9000000e+01 1.5000000e+01 3.0000000e+00 3.0000000e+00 3.0000000e+00 1.1000000e+01 1.7000000e+01 1.7000000e+01 3.0000000e+00 7.0000000e+00 7.0000000e+00 1.1000000e+01 9.0000000e+00 9.0000000e+00 7.0000000e+00 3.0000000e+00 7.0000000e+00 1.9000000e+01 3.0000000e+00 2.7000000e+01 2.5000000e+01 3.0000000e+00 1.1000000e+01 1.1000000e+01 1.1000000e+01 9.0000000e+00 9.0000000e+00 1.1000000e+01 1.3000000e+01 1.0000000e+00 7.0000000e+00 3.0000000e+00 7.0000000e+00 1.0000000e+00 1.0000000e+00 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9.0000000e+00 1.0000000e+00 3.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 3.0000000e+00 5.0000000e+00 1.0000000e+00 5.0000000e+00 5.0000000e+00 2.8000000e+01 3.0000000e+00 1.1000000e+01 2.1000000e+01 1.9000000e+01 1.9000000e+01 3.0000000e+00 1.7000000e+01 1.1000000e+01 2.1000000e+01 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.7000000e+01 1.3000000e+01 3.0000000e+00 1.0000000e+00 1.0000000e+00 1.5000000e+01 9.0000000e+00 1.0000000e+00 5.0000000e+00 1.5000000e+01 1.7000000e+01 9.0000000e+00 7.0000000e+00 1.0000000e+00 9.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 3.0000000e+00 1.0000000e+00 9.0000000e+00 1.7000000e+01 5.0000000e+00 5.0000000e+00 9.0000000e+00 9.0000000e+00 7.0000000e+00 1.3000000e+01 1.1000000e+01 5.0000000e+00 1.5000000e+01 1.3000000e+01 5.0000000e+00 7.0000000e+00 1.3000000e+01 1.5000000e+01 7.0000000e+00 2.1000000e+01 1.0000000e+00 3.0000000e+00 5.0000000e+00 1.7000000e+01 2.5000000e+01 1.0000000e+00 1.9000000e+01 5.0000000e+00 7.0000000e+00 7.0000000e+00 1.9000000e+01 1.5000000e+01 2.7000000e+01 1.3000000e+01 5.0000000e+00 7.0000000e+00 1.3000000e+01 1.9000000e+01 7.0000000e+00 7.0000000e+00 3.0000000e+00 7.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e+00 1.0000000e+00 3.0000000e+00 1.3000000e+01 3.0000000e+00 9.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 7.0000000e+00 1.7000000e+01 7.0000000e+00 2.1000000e+01 9.0000000e+00 3.0000000e+00 1.7000000e+01 1.1000000e+01 9.0000000e+00 3.0000000e+00 9.0000000e+00 3.0000000e+00 7.0000000e+00 1.3000000e+01 3.0000000e+00 3.0000000e+00 1.5000000e+01 3.0000000e+00 1.7000000e+01 2.3000000e+01 2.3000000e+01 1.3000000e+01 1.0000000e+00 3.0000000e+00 1.3000000e+01 1.0000000e+00 5.0000000e+00 3.0000000e+00 1.3000000e+01 9.0000000e+00 1.0000000e+00 7.0000000e+00 5.0000000e+00 7.0000000e+00 5.0000000e+00 3.0000000e+00 3.0000000e+00 1.3000000e+01 5.0000000e+00 2.1000000e+01 5.0000000e+00 1.0000000e+00 1.3000000e+01 2.5000000e+01 1.7000000e+01 1.7000000e+01 3.0000000e+00 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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 } -> phenomène du a une taille de cache trop petite +% 5000 exemples : 127 } ca se stabilise à 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 à partir de la taille 500 environ... +% Au final on arrive presque à 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, à 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 à jour, c'était comme ca au debut maintenant le prétraitement est passé à ~~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 +% | +% |__> à 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); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%� +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); + -- cgit 1.4.1