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  • Orange/testing/regression/results_modules/statExamples.py.txt

    r9790 r9825  
    11 
    22method  CA  AP  Brier   IS 
    3 bayes   0.903   0.902   0.175    0.759 
    4 tree    0.846   0.845   0.286    0.641 
     3bayes   0.903   0.902   0.176    0.758 
     4tree    0.825   0.824   0.326    0.599 
    55majrty  0.614   0.526   0.474   -0.000 
    66 
    77method  CA  AP  Brier   IS 
    8 bayes   0.903+-0.019    0.902+-0.019    0.175+-0.036     0.759+-0.039 
    9 tree    0.846+-0.016    0.845+-0.015    0.286+-0.030     0.641+-0.032 
     8bayes   0.903+-0.008    0.902+-0.008    0.176+-0.016     0.758+-0.017 
     9tree    0.825+-0.016    0.824+-0.016    0.326+-0.033     0.599+-0.034 
    1010majrty  0.614+-0.003    0.526+-0.001    0.474+-0.001    -0.000+-0.000 
    1111 
     
    1414 
    1515Confusion matrix for naive Bayes: 
    16 TP: 239, FP: 18, FN: 28.0, TN: 150 
     16TP: 240, FP: 18, FN: 27.0, TN: 150 
    1717 
    1818Confusion matrix for naive Bayes for 'van': 
    19 TP: 189, FP: 241, FN: 10.0, TN: 406 
     19TP: 192, FP: 151, FN: 7.0, TN: 496 
    2020 
    2121Confusion matrix for naive Bayes for 'opel': 
    22 TP: 86, FP: 112, FN: 126.0, TN: 522 
     22TP: 79, FP: 75, FN: 133.0, TN: 559 
    2323 
    2424    bus van saab    opel 
    25 bus 56  95  21  46 
    26 van 6   189 4   0 
    27 saab    3   75  73  66 
    28 opel    4   71  51  86 
     25bus 156 19  17  26 
     26van 4   192 2   1 
     27saab    8   68  93  48 
     28opel    8   64  61  79 
    2929 
    3030Sensitivity and specificity for 'voting' 
    3131method  sens    spec 
    3232bayes   0.891   0.923 
    33 tree    0.816   0.893 
     33tree    0.801   0.863 
    3434majrty  1.000   0.000 
    3535 
    3636Sensitivity and specificity for 'vehicle=van' 
    3737method  sens    spec 
    38 bayes   0.950   0.628 
    39 tree    0.809   0.966 
     38bayes   0.965   0.767 
     39tree    0.834   0.966 
    4040majrty  0.000   1.000 
    4141 
    4242AUC (voting) 
    4343     bayes: 0.974 
    44       tree: 0.930 
     44      tree: 0.926 
    4545    majrty: 0.500 
    4646 
    4747AUC for vehicle using weighted single-out method 
    4848bayes   tree    majority 
    49 0.783   0.800   0.500 
     490.840   0.816   0.500 
    5050 
    5151AUC for vehicle, using different methods 
    5252                            bayes   tree    majority 
    53        by pairs, weighted:  0.789   0.870   0.500 
    54                  by pairs:  0.791   0.871   0.500 
    55     one vs. all, weighted:  0.783   0.800   0.500 
    56               one vs. all:  0.783   0.800   0.500 
     53       by pairs, weighted:  0.861   0.883   0.500 
     54                 by pairs:  0.863   0.884   0.500 
     55    one vs. all, weighted:  0.840   0.816   0.500 
     56              one vs. all:  0.840   0.816   0.500 
    5757 
    5858AUC for detecting class 'van' in 'vehicle' 
    59 0.858   0.888   0.500 
     590.923   0.900   0.500 
    6060 
    6161AUCs for detecting various classes in 'vehicle' 
    62 bus (218.000) vs others:    0.894   0.932   0.500 
    63 van (199.000) vs others:    0.858   0.888   0.500 
    64 saab (217.000) vs others:   0.699   0.687   0.500 
    65 opel (212.000) vs others:   0.682   0.694   0.500 
     62bus (218.000) vs others:    0.952   0.936   0.500 
     63van (199.000) vs others:    0.923   0.900   0.500 
     64saab (217.000) vs others:   0.737   0.707   0.500 
     65opel (212.000) vs others:   0.749   0.718   0.500 
    6666 
    6767    bus van saab 
    68 van 0.933 
    69 saab    0.820   0.828 
    70 opel    0.822   0.825   0.519 
     68van 0.987 
     69saab    0.927   0.860 
     70opel    0.921   0.894   0.587 
    7171 
    7272AUCs for detecting various pairs of classes in 'vehicle' 
    73 van vs bus:     0.933   0.978   0.500 
    74 saab vs bus:    0.820   0.938   0.500 
    75 saab vs van:    0.828   0.879   0.500 
    76 opel vs bus:    0.822   0.932   0.500 
    77 opel vs van:    0.825   0.903   0.500 
    78 opel vs saab:   0.519   0.599   0.500 
     73van vs bus:     0.987   0.976   0.500 
     74saab vs bus:    0.927   0.936   0.500 
     75saab vs van:    0.860   0.906   0.500 
     76opel vs bus:    0.921   0.951   0.500 
     77opel vs van:    0.894   0.915   0.500 
     78opel vs saab:   0.587   0.622   0.500 
    7979 
    8080AUC and SE for voting 
    81 bayes: 0.968+-0.015 
    82 tree: 0.924+-0.022 
     81bayes: 0.982+-0.008 
     82tree: 0.888+-0.025 
    8383majrty: 0.500+-0.045 
    8484 
    85 Difference between naive Bayes and tree: 0.014+-0.062 
     85Difference between naive Bayes and tree: 0.065+-0.066 
    8686 
    8787ROC (first 20 points) for bayes on 'voting' 
    88881.000   1.000 
    89890.970   1.000 
     900.940   1.000 
    90910.910   1.000 
     920.896   1.000 
    91930.881   1.000 
     940.836   1.000 
    92950.821   1.000 
    93960.806   1.000 
    94 0.791   1.000 
    95970.761   1.000 
    96980.746   1.000 
     
    991010.687   1.000 
    1001020.672   1.000 
    101 0.672   0.991 
    102 0.657   0.991 
    103 0.642   0.991 
    104 0.552   0.991 
    105 0.537   0.991 
    106 0.522   0.991 
    107 0.507   0.991 
     1030.627   1.000 
     1040.612   1.000 
     1050.597   1.000 
     1060.582   1.000 
     1070.567   1.000 
  • Orange/testing/regression/results_modules/tree8.py.txt

    r9689 r9834  
    11m = 0.000: 239 nodes, 134 leaves 
    2 m = 0.000: 228 nodes, 128 leaves 
     2m = 0.000: 183 nodes, 104 leaves 
    33m = 0.100: 173 nodes, 99 leaves 
    44m = 0.500: 179 nodes, 102 leaves 
  • Orange/testing/regression/results_modules/tuning1.py.txt

    r9689 r9834  
    1 *** optimization  1: [0.97058829757682885]: 
    2 *** optimization  2: [0.97642948164590782]: 
    3 *** optimization  3: [0.98338801755375926]: 
    4 *** optimization  4: [0.98788177744892502]: 
    5 *** optimization  5: [0.98894238973780235]: 
    6 *** optimization  10: [0.98692031923626455]: 
    7 *** optimization  15: [0.98842239138206578]: 
    8 *** optimization  20: [0.97804067310171638]: 
     1*** optimization  1: [0.9706992853681718]: 
     2*** optimization  2: [0.9743207136103917]: 
     3*** optimization  3: [0.9833880175537593]: 
     4*** optimization  4: [0.987881777448925]: 
     5*** optimization  5: [0.9889423897378024]: 
     6*** optimization  10: [0.9869203192362646]: 
     7*** optimization  15: [0.9884223913820658]: 
     8*** optimization  20: [0.9780406731017164]: 
    99*** Optimal parameter: minSubset = 5 
    1010Optimal setting:  5 
    11 *** optimization  1: [0.98321908602150532]: 
    12 *** optimization  2: [0.97819892473118264]: 
     11*** optimization  1: [0.9832190860215053]: 
     12*** optimization  2: [0.9781989247311826]: 
    1313*** optimization  3: [0.9912679211469535]: 
    1414*** optimization  4: [0.9937656810035842]: 
    15 *** optimization  5: [0.99075044802867385]: 
     15*** optimization  5: [0.9907504480286738]: 
    1616*** optimization  10: [0.9872647849462366]: 
    17 *** optimization  15: [0.98976926523297493]: 
    18 *** optimization  20: [0.99105062724014337]: 
     17*** optimization  15: [0.9897692652329749]: 
     18*** optimization  20: [0.9910506272401434]: 
    1919*** Optimal parameter: minSubset = 4 
    20 *** optimization  1: [0.97296370967741941]: 
    21 *** optimization  2: [0.97278673835125462]: 
    22 *** optimization  3: [0.98086245519713267]: 
    23 *** optimization  4: [0.98209901433691749]: 
    24 *** optimization  5: [0.98543682795698928]: 
    25 *** optimization  10: [0.98856854838709673]: 
    26 *** optimization  15: [0.99162634408602157]: 
    27 *** optimization  20: [0.98600806451612899]: 
     20*** optimization  1: [0.9729637096774194]: 
     21*** optimization  2: [0.9727867383512546]: 
     22*** optimization  3: [0.9808624551971327]: 
     23*** optimization  4: [0.9820990143369175]: 
     24*** optimization  5: [0.9854368279569893]: 
     25*** optimization  10: [0.9885685483870967]: 
     26*** optimization  15: [0.9916263440860216]: 
     27*** optimization  20: [0.986008064516129]: 
    2828*** Optimal parameter: minSubset = 15 
    29 *** optimization  1: [0.98023073476702494]: 
    30 *** optimization  2: [0.98306899641577061]: 
    31 *** optimization  3: [0.98245295698924728]: 
     29*** optimization  1: [0.9802307347670249]: 
     30*** optimization  2: [0.9830689964157706]: 
     31*** optimization  3: [0.9824529569892473]: 
    3232*** optimization  4: [0.9896012544802868]: 
    33 *** optimization  5: [0.98472670250896055]: 
    34 *** optimization  10: [0.98965277777777783]: 
    35 *** optimization  15: [0.98743503584229386]: 
    36 *** optimization  20: [0.97437948028673826]: 
     33*** optimization  5: [0.9847267025089605]: 
     34*** optimization  10: [0.9896527777777778]: 
     35*** optimization  15: [0.9874350358422939]: 
     36*** optimization  20: [0.9743794802867383]: 
    3737*** Optimal parameter: minSubset = 10 
    38 *** optimization  1: [0.96825044802867388]: 
    39 *** optimization  2: [0.97539202508960576]: 
    40 *** optimization  3: [0.97483422939068098]: 
    41 *** optimization  4: [0.98026657706093201]: 
    42 *** optimization  5: [0.97956765232974918]: 
     38*** optimization  1: [0.9682504480286739]: 
     39*** optimization  2: [0.9763328853046596]: 
     40*** optimization  3: [0.974834229390681]: 
     41*** optimization  4: [0.980266577060932]: 
     42*** optimization  5: [0.9795676523297492]: 
    4343*** optimization  10: [0.9769332437275986]: 
    44 *** optimization  15: [0.97734543010752684]: 
     44*** optimization  15: [0.9773454301075268]: 
    4545*** optimization  20: [0.9740815412186381]: 
    4646*** Optimal parameter: minSubset = 4 
    47 *** optimization  1: [0.96364247311827955]: 
    48 *** optimization  2: [0.974209229390681]: 
    49 *** optimization  3: [0.97841621863799277]: 
    50 *** optimization  4: [0.98721102150537632]: 
    51 *** optimization  5: [0.98688396057347672]: 
    52 *** optimization  10: [0.98780689964157697]: 
    53 *** optimization  15: [0.98020833333333335]: 
    54 *** optimization  20: [0.97671370967741944]: 
     47*** optimization  1: [0.9640591397849462]: 
     48*** optimization  2: [0.9741397849462365]: 
     49*** optimization  3: [0.9783467741935483]: 
     50*** optimization  4: [0.9872110215053763]: 
     51*** optimization  5: [0.9868839605734767]: 
     52*** optimization  10: [0.987806899641577]: 
     53*** optimization  15: [0.9802083333333333]: 
     54*** optimization  20: [0.9767137096774194]: 
    5555*** Optimal parameter: minSubset = 10 
    56 *** optimization  1: [0.97435707885304657]: 
    57 *** optimization  2: [0.97433705471435883]: 
    58 *** optimization  3: [0.97782107197717805]: 
    59 *** optimization  4: [0.97559295223465736]: 
    60 *** optimization  5: [0.98345644978421476]: 
    61 *** optimization  10: [0.98451827774120404]: 
    62 *** optimization  15: [0.98160682283666145]: 
     56*** optimization  1: [0.9743570788530466]: 
     57*** optimization  2: [0.9743370547143588]: 
     58*** optimization  3: [0.977821071977178]: 
     59*** optimization  4: [0.9755929522346574]: 
     60*** optimization  5: [0.9834564497842148]: 
     61*** optimization  10: [0.984518277741204]: 
     62*** optimization  15: [0.9816068228366615]: 
    6363*** optimization  20: [0.9802781892326824]: 
    6464*** Optimal parameter: minSubset = 10 
    6565*** optimization  1: [0.9764701740911419]: 
    66 *** optimization  2: [0.98519868700168234]: 
    67 *** optimization  3: [0.98775030173359668]: 
    68 *** optimization  4: [0.98942574610489364]: 
    69 *** optimization  5: [0.98909228476336764]: 
    70 *** optimization  10: [0.98262768817204293]: 
    71 *** optimization  15: [0.98151337685611872]: 
    72 *** optimization  20: [0.98251312083973374]: 
     66*** optimization  2: [0.9851986870016823]: 
     67*** optimization  3: [0.9877503017335967]: 
     68*** optimization  4: [0.9894257461048936]: 
     69*** optimization  5: [0.9890922847633676]: 
     70*** optimization  10: [0.9826276881720429]: 
     71*** optimization  15: [0.9815133768561187]: 
     72*** optimization  20: [0.9825131208397337]: 
    7373*** Optimal parameter: minSubset = 4 
    74 *** optimization  1: [0.98090168056469895]: 
    75 *** optimization  2: [0.99166556945358786]: 
    76 *** optimization  3: [0.98834444261575594]: 
    77 *** optimization  4: [0.98837159863945589]: 
    78 *** optimization  5: [0.99087895911052581]: 
    79 *** optimization  10: [0.98963769109794453]: 
    80 *** optimization  15: [0.98902969790066564]: 
     74*** optimization  1: [0.980901680564699]: 
     75*** optimization  2: [0.9916655694535879]: 
     76*** optimization  3: [0.9883444426157559]: 
     77*** optimization  4: [0.9884388029405311]: 
     78*** optimization  5: [0.9908789591105258]: 
     79*** optimization  10: [0.9896376910979445]: 
     80*** optimization  15: [0.9890296979006656]: 
    8181*** optimization  20: [0.9841350760734402]: 
    8282*** Optimal parameter: minSubset = 2 
    83 *** optimization  1: [0.97177195340501799]: 
    84 *** optimization  2: [0.97940412186379944]: 
    85 *** optimization  3: [0.98024641577060934]: 
    86 *** optimization  4: [0.98027105734767028]: 
    87 *** optimization  5: [0.98310707885304649]: 
    88 *** optimization  10: [0.98413978494623655]: 
    89 *** optimization  15: [0.98651209677419371]: 
    90 *** optimization  20: [0.97995519713261658]: 
     83*** optimization  1: [0.971771953405018]: 
     84*** optimization  2: [0.9794041218637994]: 
     85*** optimization  3: [0.9802464157706093]: 
     86*** optimization  4: [0.9802710573476703]: 
     87*** optimization  5: [0.9831070788530465]: 
     88*** optimization  10: [0.9841397849462366]: 
     89*** optimization  15: [0.9865120967741937]: 
     90*** optimization  20: [0.9799551971326166]: 
    9191*** Optimal parameter: minSubset = 15 
    92 *** optimization  1: [0.97084229390681009]: 
    93 *** optimization  2: [0.97555107526881724]: 
    94 *** optimization  3: [0.98384184587813628]: 
    95 *** optimization  4: [0.97722222222222244]: 
    96 *** optimization  5: [0.98428539426523298]: 
     92*** optimization  1: [0.9708422939068101]: 
     93*** optimization  2: [0.9755510752688172]: 
     94*** optimization  3: [0.9838418458781363]: 
     95*** optimization  4: [0.9772222222222224]: 
     96*** optimization  5: [0.984285394265233]: 
    9797*** optimization  10: [0.989247311827957]: 
    9898*** optimization  15: [0.987780017921147]: 
    99 *** optimization  20: [0.98074148745519696]: 
     99*** optimization  20: [0.980741487455197]: 
    100100*** Optimal parameter: minSubset = 10 
    101 Untuned tree: 0.925 
     101Untuned tree: 0.926 
    102102Tuned tree: 0.983 
    103 *** optimization  1: [0.97058829757682885]: 
    104 *** optimization  2: [0.97642948164590782]: 
    105 *** optimization  3: [0.98338801755375926]: 
    106 *** optimization  4: [0.98788177744892502]: 
    107 *** optimization  5: [0.98894238973780235]: 
    108 *** optimization  10: [0.98692031923626455]: 
    109 *** optimization  15: [0.98842239138206578]: 
    110 *** optimization  20: [0.97804067310171638]: 
     103*** optimization  1: [0.9706992853681718]: 
     104*** optimization  2: [0.9743207136103917]: 
     105*** optimization  3: [0.9833880175537593]: 
     106*** optimization  4: [0.987881777448925]: 
     107*** optimization  5: [0.9889423897378024]: 
     108*** optimization  10: [0.9869203192362646]: 
     109*** optimization  15: [0.9884223913820658]: 
     110*** optimization  20: [0.9780406731017164]: 
    111111*** Optimal parameter: ['split.continuousSplitConstructor.minSubset', 'split.discreteSplitConstructor.minSubset'] = 5 
    112112Optimal setting:  5.0 
  • Orange/testing/regression/results_ofb/bagging_test.py.linux2.txt

    r9795 r9833  
    1 tree: 0.795 
    2 bagged classifier: 0.802 
     1tree: 0.733 
     2bagged classifier: 0.733 
  • Orange/testing/regression/results_ofb/data_characteristics.py.txt

    r9689 r9833  
    11Classes: 2 
    22Attributes: 14 , 6 continuous, 8 discrete 
    3 Instances:  977 total , 236 with class >50K , 741 with class <=50K 
     3Instances:  30 total , 8 with class >50K , 22 with class <=50K 
  • Orange/testing/regression/results_ofb/data_characteristics2.py.txt

    r9689 r9833  
    11Classes: 2 
    22Attributes: 14 , 6 continuous, 8 discrete 
    3 Instances:  977 total , 236(24.2%) with class >50K , 741(75.8%) with class <=50K 
     3Instances:  30 total , 8(26.7%) with class >50K , 22(73.3%) with class <=50K 
  • Orange/testing/regression/results_ofb/data_characteristics3.py.txt

    r9689 r9833  
    11Continuous attributes: 
    2   age, mean=38.91 
    3   fnlwgt, mean=187280.26 
    4   education-num, mean=9.98 
    5   capital-gain, mean=1041.83 
    6   capital-loss, mean=96.18 
    7   hours-per-week, mean=39.91 
     2  age, mean=39.63 
     3  fnlwgt, mean=198892.33 
     4  education-num, mean=10.63 
     5  capital-gain, mean=378.40 
     6  capital-loss, mean=207.73 
     7  hours-per-week, mean=39.87 
    88 
    99Nominal attributes (contingency matrix for classes: <>50K, <=50K> ) 
    1010  workclass: 
    11     Private, total 685, <147.000, 538.000> 
    12     Self-emp-not-inc, total 72, <23.000, 49.000> 
    13     Self-emp-inc, total 28, <11.000, 17.000> 
    14     Federal-gov, total 29, <15.000, 14.000> 
    15     Local-gov, total 59, <18.000, 41.000> 
    16     State-gov, total 43, <15.000, 28.000> 
    17     Without-pay, total 2, <0.000, 2.000> 
     11    Private, total 22, <4.000, 18.000> 
     12    Self-emp-not-inc, total 1, <1.000, 0.000> 
     13    Self-emp-inc, total 1, <0.000, 1.000> 
     14    Federal-gov, total 1, <1.000, 0.000> 
     15    Local-gov, total 1, <1.000, 0.000> 
     16    State-gov, total 1, <1.000, 0.000> 
     17    Without-pay, total 0, <0.000, 0.000> 
    1818    Never-worked, total 0, <0.000, 0.000> 
    1919 
    2020  education: 
    21     Bachelors, total 152, <69.000, 83.000> 
    22     Some-college, total 193, <33.000, 160.000> 
    23     11th, total 34, <2.000, 32.000> 
    24     HS-grad, total 327, <61.000, 266.000> 
    25     Prof-school, total 9, <7.000, 2.000> 
    26     Assoc-acdm, total 39, <13.000, 26.000> 
    27     Assoc-voc, total 41, <5.000, 36.000> 
    28     9th, total 18, <1.000, 17.000> 
    29     7th-8th, total 30, <2.000, 28.000> 
    30     12th, total 22, <0.000, 22.000> 
    31     Masters, total 57, <33.000, 24.000> 
    32     1st-4th, total 8, <1.000, 7.000> 
    33     10th, total 24, <1.000, 23.000> 
    34     Doctorate, total 15, <7.000, 8.000> 
    35     5th-6th, total 8, <1.000, 7.000> 
     21    Bachelors, total 6, <2.000, 4.000> 
     22    Some-college, total 6, <1.000, 5.000> 
     23    11th, total 0, <0.000, 0.000> 
     24    HS-grad, total 7, <0.000, 7.000> 
     25    Prof-school, total 0, <0.000, 0.000> 
     26    Assoc-acdm, total 3, <2.000, 1.000> 
     27    Assoc-voc, total 1, <0.000, 1.000> 
     28    9th, total 1, <0.000, 1.000> 
     29    7th-8th, total 2, <0.000, 2.000> 
     30    12th, total 0, <0.000, 0.000> 
     31    Masters, total 3, <3.000, 0.000> 
     32    1st-4th, total 0, <0.000, 0.000> 
     33    10th, total 0, <0.000, 0.000> 
     34    Doctorate, total 1, <0.000, 1.000> 
     35    5th-6th, total 0, <0.000, 0.000> 
    3636    Preschool, total 0, <0.000, 0.000> 
    3737 
    3838  marital-status: 
    39     Married-civ-spouse, total 452, <201.000, 251.000> 
    40     Divorced, total 132, <11.000, 121.000> 
    41     Never-married, total 297, <16.000, 281.000> 
    42     Separated, total 41, <3.000, 38.000> 
    43     Widowed, total 37, <4.000, 33.000> 
    44     Married-spouse-absent, total 17, <1.000, 16.000> 
    45     Married-AF-spouse, total 1, <0.000, 1.000> 
     39    Married-civ-spouse, total 12, <7.000, 5.000> 
     40    Divorced, total 4, <0.000, 4.000> 
     41    Never-married, total 8, <0.000, 8.000> 
     42    Separated, total 3, <0.000, 3.000> 
     43    Widowed, total 3, <1.000, 2.000> 
     44    Married-spouse-absent, total 0, <0.000, 0.000> 
     45    Married-AF-spouse, total 0, <0.000, 0.000> 
    4646 
    4747  occupation: 
    48     Tech-support, total 33, <13.000, 20.000> 
    49     Craft-repair, total 117, <30.000, 87.000> 
    50     Other-service, total 103, <1.000, 102.000> 
    51     Sales, total 92, <31.000, 61.000> 
    52     Exec-managerial, total 125, <53.000, 72.000> 
    53     Prof-specialty, total 110, <44.000, 66.000> 
    54     Handlers-cleaners, total 40, <3.000, 37.000> 
    55     Machine-op-inspct, total 62, <7.000, 55.000> 
    56     Adm-clerical, total 115, <20.000, 95.000> 
    57     Farming-fishing, total 29, <5.000, 24.000> 
    58     Transport-moving, total 61, <16.000, 45.000> 
    59     Priv-house-serv, total 10, <0.000, 10.000> 
    60     Protective-serv, total 20, <6.000, 14.000> 
    61     Armed-Forces, total 1, <0.000, 1.000> 
     48    Tech-support, total 1, <0.000, 1.000> 
     49    Craft-repair, total 3, <1.000, 2.000> 
     50    Other-service, total 3, <0.000, 3.000> 
     51    Sales, total 4, <1.000, 3.000> 
     52    Exec-managerial, total 7, <3.000, 4.000> 
     53    Prof-specialty, total 5, <3.000, 2.000> 
     54    Handlers-cleaners, total 0, <0.000, 0.000> 
     55    Machine-op-inspct, total 0, <0.000, 0.000> 
     56    Adm-clerical, total 2, <0.000, 2.000> 
     57    Farming-fishing, total 0, <0.000, 0.000> 
     58    Transport-moving, total 2, <0.000, 2.000> 
     59    Priv-house-serv, total 0, <0.000, 0.000> 
     60    Protective-serv, total 0, <0.000, 0.000> 
     61    Armed-Forces, total 0, <0.000, 0.000> 
    6262 
    6363  relationship: 
    64     Wife, total 63, <33.000, 30.000> 
    65     Own-child, total 146, <3.000, 143.000> 
    66     Husband, total 384, <166.000, 218.000> 
    67     Not-in-family, total 249, <25.000, 224.000> 
    68     Other-relative, total 27, <2.000, 25.000> 
    69     Unmarried, total 108, <7.000, 101.000> 
     64    Wife, total 1, <1.000, 0.000> 
     65    Own-child, total 3, <0.000, 3.000> 
     66    Husband, total 11, <6.000, 5.000> 
     67    Not-in-family, total 8, <0.000, 8.000> 
     68    Other-relative, total 0, <0.000, 0.000> 
     69    Unmarried, total 7, <1.000, 6.000> 
    7070 
    7171  race: 
    72     White, total 825, <214.000, 611.000> 
    73     Asian-Pac-Islander, total 32, <8.000, 24.000> 
    74     Amer-Indian-Eskimo, total 8, <0.000, 8.000> 
    75     Other, total 12, <1.000, 11.000> 
    76     Black, total 100, <13.000, 87.000> 
     72    White, total 27, <7.000, 20.000> 
     73    Asian-Pac-Islander, total 0, <0.000, 0.000> 
     74    Amer-Indian-Eskimo, total 0, <0.000, 0.000> 
     75    Other, total 0, <0.000, 0.000> 
     76    Black, total 3, <1.000, 2.000> 
    7777 
    7878  sex: 
    79     Female, total 353, <51.000, 302.000> 
    80     Male, total 624, <185.000, 439.000> 
     79    Female, total 13, <2.000, 11.000> 
     80    Male, total 17, <6.000, 11.000> 
    8181 
    8282  native-country: 
    83     United-States, total 864, <215.000, 649.000> 
    84     Cuba, total 1, <0.000, 1.000> 
    85     Jamaica, total 2, <1.000, 1.000> 
    86     India, total 3, <1.000, 2.000> 
    87     Mexico, total 25, <2.000, 23.000> 
    88     South, total 1, <0.000, 1.000> 
    89     Puerto-Rico, total 1, <0.000, 1.000> 
     83    United-States, total 29, <8.000, 21.000> 
     84    Cuba, total 0, <0.000, 0.000> 
     85    Jamaica, total 0, <0.000, 0.000> 
     86    India, total 0, <0.000, 0.000> 
     87    Mexico, total 0, <0.000, 0.000> 
     88    South, total 0, <0.000, 0.000> 
     89    Puerto-Rico, total 0, <0.000, 0.000> 
    9090    Honduras, total 0, <0.000, 0.000> 
    91     England, total 4, <0.000, 4.000> 
    92     Canada, total 5, <1.000, 4.000> 
    93     Germany, total 7, <2.000, 5.000> 
    94     Iran, total 1, <0.000, 1.000> 
    95     Philippines, total 4, <1.000, 3.000> 
    96     Italy, total 3, <1.000, 2.000> 
    97     Poland, total 2, <0.000, 2.000> 
    98     Columbia, total 2, <1.000, 1.000> 
    99     Cambodia, total 1, <0.000, 1.000> 
     91    England, total 1, <0.000, 1.000> 
     92    Canada, total 0, <0.000, 0.000> 
     93    Germany, total 0, <0.000, 0.000> 
     94    Iran, total 0, <0.000, 0.000> 
     95    Philippines, total 0, <0.000, 0.000> 
     96    Italy, total 0, <0.000, 0.000> 
     97    Poland, total 0, <0.000, 0.000> 
     98    Columbia, total 0, <0.000, 0.000> 
     99    Cambodia, total 0, <0.000, 0.000> 
    100100    Thailand, total 0, <0.000, 0.000> 
    101101    Ecuador, total 0, <0.000, 0.000> 
    102     Laos, total 1, <0.000, 1.000> 
    103     Taiwan, total 4, <1.000, 3.000> 
    104     Haiti, total 2, <1.000, 1.000> 
     102    Laos, total 0, <0.000, 0.000> 
     103    Taiwan, total 0, <0.000, 0.000> 
     104    Haiti, total 0, <0.000, 0.000> 
    105105    Portugal, total 0, <0.000, 0.000> 
    106     Dominican-Republic, total 1, <0.000, 1.000> 
    107     El-Salvador, total 3, <0.000, 3.000> 
     106    Dominican-Republic, total 0, <0.000, 0.000> 
     107    El-Salvador, total 0, <0.000, 0.000> 
    108108    France, total 0, <0.000, 0.000> 
    109     Guatemala, total 1, <0.000, 1.000> 
    110     China, total 3, <1.000, 2.000> 
    111     Japan, total 1, <1.000, 0.000> 
     109    Guatemala, total 0, <0.000, 0.000> 
     110    China, total 0, <0.000, 0.000> 
     111    Japan, total 0, <0.000, 0.000> 
    112112    Yugoslavia, total 0, <0.000, 0.000> 
    113     Peru, total 1, <0.000, 1.000> 
    114     Outlying-US(Guam-USVI-etc), total 1, <0.000, 1.000> 
    115     Scotland, total 1, <0.000, 1.000> 
     113    Peru, total 0, <0.000, 0.000> 
     114    Outlying-US(Guam-USVI-etc), total 0, <0.000, 0.000> 
     115    Scotland, total 0, <0.000, 0.000> 
    116116    Trinadad&Tobago, total 0, <0.000, 0.000> 
    117     Greece, total 1, <0.000, 1.000> 
    118     Nicaragua, total 2, <0.000, 2.000> 
    119     Vietnam, total 3, <0.000, 3.000> 
     117    Greece, total 0, <0.000, 0.000> 
     118    Nicaragua, total 0, <0.000, 0.000> 
     119    Vietnam, total 0, <0.000, 0.000> 
    120120    Hong, total 0, <0.000, 0.000> 
    121     Ireland, total 3, <1.000, 2.000> 
     121    Ireland, total 0, <0.000, 0.000> 
    122122    Hungary, total 0, <0.000, 0.000> 
    123123    Holand-Netherlands, total 0, <0.000, 0.000> 
  • Orange/testing/regression/results_ofb/data_characteristics4.py.txt

    r9689 r9833  
    11Average values and mean square errors: 
    2 age, mean=38.91 +-  0.44 
    3 fnlwgt, mean=187280.29 +- 3464.37 
    4 education-num, mean= 9.98 +-  0.08 
    5 capital-gain, mean=1041.83 +- 218.78 
    6 capital-loss, mean=96.18 +- 13.68 
    7 hours-per-week, mean=39.91 +-  0.41 
     2age, mean=39.63 +-  2.75 
     3fnlwgt, mean=198892.33 +- 19629.05 
     4education-num, mean=10.63 +-  0.53 
     5capital-gain, mean=378.40 +- 201.02 
     6capital-loss, mean=207.73 +- 156.10 
     7hours-per-week, mean=39.87 +-  2.34 
    88 
    99Frequencies for values of discrete attributes: 
    1010workclass: 
    11   Private: 685 
    12   Self-emp-not-inc: 72 
    13   Self-emp-inc: 28 
    14   Federal-gov: 29 
    15   Local-gov: 59 
    16   State-gov: 43 
    17   Without-pay: 2 
     11  Private: 22 
     12  Self-emp-not-inc: 1 
     13  Self-emp-inc: 1 
     14  Federal-gov: 1 
     15  Local-gov: 1 
     16  State-gov: 1 
     17  Without-pay: 0 
    1818  Never-worked: 0 
    1919education: 
    20   Bachelors: 152 
    21   Some-college: 193 
    22   11th: 34 
    23   HS-grad: 327 
    24   Prof-school: 9 
    25   Assoc-acdm: 39 
    26   Assoc-voc: 41 
    27   9th: 18 
    28   7th-8th: 30 
    29   12th: 22 
    30   Masters: 57 
    31   1st-4th: 8 
    32   10th: 24 
    33   Doctorate: 15 
    34   5th-6th: 8 
     20  Bachelors: 6 
     21  Some-college: 6 
     22  11th: 0 
     23  HS-grad: 7 
     24  Prof-school: 0 
     25  Assoc-acdm: 3 
     26  Assoc-voc: 1 
     27  9th: 1 
     28  7th-8th: 2 
     29  12th: 0 
     30  Masters: 3 
     31  1st-4th: 0 
     32  10th: 0 
     33  Doctorate: 1 
     34  5th-6th: 0 
    3535  Preschool: 0 
    3636marital-status: 
    37   Married-civ-spouse: 452 
    38   Divorced: 132 
    39   Never-married: 297 
    40   Separated: 41 
    41   Widowed: 37 
    42   Married-spouse-absent: 17 
    43   Married-AF-spouse: 1 
     37  Married-civ-spouse: 12 
     38  Divorced: 4 
     39  Never-married: 8 
     40  Separated: 3 
     41  Widowed: 3 
     42  Married-spouse-absent: 0 
     43  Married-AF-spouse: 0 
    4444occupation: 
    45   Tech-support: 33 
    46   Craft-repair: 117 
    47   Other-service: 103 
    48   Sales: 92 
    49   Exec-managerial: 125 
    50   Prof-specialty: 110 
    51   Handlers-cleaners: 40 
    52   Machine-op-inspct: 62 
    53   Adm-clerical: 115 
    54   Farming-fishing: 29 
    55   Transport-moving: 61 
    56   Priv-house-serv: 10 
    57   Protective-serv: 20 
    58   Armed-Forces: 1 
     45  Tech-support: 1 
     46  Craft-repair: 3 
     47  Other-service: 3 
     48  Sales: 4 
     49  Exec-managerial: 7 
     50  Prof-specialty: 5 
     51  Handlers-cleaners: 0 
     52  Machine-op-inspct: 0 
     53  Adm-clerical: 2 
     54  Farming-fishing: 0 
     55  Transport-moving: 2 
     56  Priv-house-serv: 0 
     57  Protective-serv: 0 
     58  Armed-Forces: 0 
    5959relationship: 
    60   Wife: 63 
    61   Own-child: 146 
    62   Husband: 384 
    63   Not-in-family: 249 
    64   Other-relative: 27 
    65   Unmarried: 108 
     60  Wife: 1 
     61  Own-child: 3 
     62  Husband: 11 
     63  Not-in-family: 8 
     64  Other-relative: 0 
     65  Unmarried: 7 
    6666race: 
    67   White: 825 
    68   Asian-Pac-Islander: 32 
    69   Amer-Indian-Eskimo: 8 
    70   Other: 12 
    71   Black: 100 
     67  White: 27 
     68  Asian-Pac-Islander: 0 
     69  Amer-Indian-Eskimo: 0 
     70  Other: 0 
     71  Black: 3 
    7272sex: 
    73   Female: 353 
    74   Male: 624 
     73  Female: 13 
     74  Male: 17 
    7575native-country: 
    76   United-States: 864 
    77   Cuba: 1 
    78   Jamaica: 2 
    79   India: 3 
    80   Mexico: 25 
    81   South: 1 
    82   Puerto-Rico: 1 
     76  United-States: 29 
     77  Cuba: 0 
     78  Jamaica: 0 
     79  India: 0 
     80  Mexico: 0 
     81  South: 0 
     82  Puerto-Rico: 0 
    8383  Honduras: 0 
    84   England: 4 
    85   Canada: 5 
    86   Germany: 7 
    87   Iran: 1 
    88   Philippines: 4 
    89   Italy: 3 
    90   Poland: 2 
    91   Columbia: 2 
    92   Cambodia: 1 
     84  England: 1 
     85  Canada: 0 
     86  Germany: 0 
     87  Iran: 0 
     88  Philippines: 0 
     89  Italy: 0 
     90  Poland: 0 
     91  Columbia: 0 
     92  Cambodia: 0 
    9393  Thailand: 0 
    9494  Ecuador: 0 
    95   Laos: 1 
    96   Taiwan: 4 
    97   Haiti: 2 
     95  Laos: 0 
     96  Taiwan: 0 
     97  Haiti: 0 
    9898  Portugal: 0 
    99   Dominican-Republic: 1 
    100   El-Salvador: 3 
     99  Dominican-Republic: 0 
     100  El-Salvador: 0 
    101101  France: 0 
    102   Guatemala: 1 
    103   China: 3 
    104   Japan: 1 
     102  Guatemala: 0 
     103  China: 0 
     104  Japan: 0 
    105105  Yugoslavia: 0 
    106   Peru: 1 
    107   Outlying-US(Guam-USVI-etc): 1 
    108   Scotland: 1 
     106  Peru: 0 
     107  Outlying-US(Guam-USVI-etc): 0 
     108  Scotland: 0 
    109109  Trinadad&Tobago: 0 
    110   Greece: 1 
    111   Nicaragua: 2 
    112   Vietnam: 3 
     110  Greece: 0 
     111  Nicaragua: 0 
     112  Vietnam: 0 
    113113  Hong: 0 
    114   Ireland: 3 
     114  Ireland: 0 
    115115  Hungary: 0 
    116116  Holand-Netherlands: 0 
     
    118118Number of instances where attribute is not defined: 
    119119   0 age 
    120   59 workclass 
     120   3 workclass 
    121121   0 fnlwgt 
    122122   0 education 
    123123   0 education-num 
    124124   0 marital-status 
    125   59 occupation 
     125   3 occupation 
    126126   0 relationship 
    127127   0 race 
     
    130130   0 capital-loss 
    131131   0 hours-per-week 
    132   23 native-country 
     132   0 native-country 
  • Orange/testing/regression/results_ofb/domain4.py.txt

    r9689 r9833  
    1 Size of original data set: 977 instances 
    2 p(>50K)=0.242 
    3 Size of data1: 624 instances 
    4 p(>50K)=0.296 
    5 Size of data2: 38 instances 
    6 p(>50K)=0.632 
     1Size of original data set: 30 instances 
     2p(>50K)=0.267 
     3Size of data1: 17 instances 
     4p(>50K)=0.353 
     5Size of data2: 1 instances 
     6p(>50K)=1.000 
  • Orange/testing/regression/results_ofb/domain5.py.txt

    r9689 r9833  
    1 Size of data: 977 instances;  p(>50K)=0.242 
    2 Size of data1 (conjunction): 38 instances;  p(>50K)=0.632 
    3 Size of data1 (disjunction): 643 instances;  p(>50K)=0.302 
    4 Size of data2 (select, conjuction): 38 instances;  p(>50K)=0.632 
     1Size of data: 30 instances;  p(>50K)=0.267 
     2Size of data1 (conjunction): 1 instances;  p(>50K)=1.000 
     3Size of data1 (disjunction): 19 instances;  p(>50K)=0.421 
     4Size of data2 (select, conjuction): 1 instances;  p(>50K)=1.000 
  • Orange/testing/regression/results_ofb/domain6.py.txt

    r9689 r9833  
    1 Size of data: 977 instances;  p(>50K)=0.242 
    2 Size of data1, age from 30 to 40: 301 instances;  p(>50K)=0.312 
    3 Size of data2, younger than 30 or older than 40: 676 instances;  p(>50K)=0.210 
     1Size of data: 30 instances;  p(>50K)=0.267 
     2Size of data1, age from 30 to 40: 13 instances;  p(>50K)=0.231 
     3Size of data2, younger than 30 or older than 40: 17 instances;  p(>50K)=0.294 
  • Orange/testing/regression/results_orange25/simple_tree_random_forest.py.txt

    r9802 r9830  
    44 
    55Runtimes: 
    6 for_gain 0.0934960842133 
    7 for_simp 0.0251078605652 
     6for_gain 0.0932638645172 
     7for_simp 0.0247650146484 
  • Orange/testing/regression/results_orange25/svm-linear-weights.py.txt

    r9804 r9831  
    1 defaultdict(<type 'float'>, {FloatVariable 'Elu 30': 0.4786304184632838, FloatVariable 'spo 0': 0.13024372486415015, FloatVariable 'Elu 60': 0.36698713537474476, FloatVariable 'spo 2': 0.7078062232168753, FloatVariable 'Elu 90': 0.3834942300173535, FloatVariable 'spo 5': 1.0683498605674933, FloatVariable 'alpha 7': 0.06557659077448137, FloatVariable 'Elu 120': 0.5939764872379445, FloatVariable 'spo 7': 0.8081568079276176, FloatVariable 'diau d': 0.44932601596409105, FloatVariable 'Elu 150': 0.5965599387054419, FloatVariable 'alpha 119': 0.1699365258012951, FloatVariable 'spo 9': 0.2498282401589412, FloatVariable 'Elu 180': 0.42425268429856355, FloatVariable 'spo 11': 0.20615575833508282, FloatVariable 'alpha 70': 0.26459268873021585, FloatVariable 'Elu 210': 0.12396520046361383, FloatVariable 'spo5 2': 0.40417556232809326, FloatVariable 'Elu 240': 0.2093390824178926, FloatVariable 'spo5 7': 0.26780459416067937, FloatVariable 'Elu 270': 0.33471969574325466, FloatVariable 'alpha 84': 0.1308234316119738, FloatVariable 'spo5 11': 1.200079459496442, FloatVariable 'diau e': 0.864767371223623, FloatVariable 'Elu 300': 0.15913983311663107, FloatVariable 'spo- early': 1.9466556509082333, FloatVariable 'Elu 330': 0.11474308886955724, FloatVariable 'alpha 42': 0.13641650791763626, FloatVariable 'spo- mid': 3.2086605964825132, FloatVariable 'Elu 360': 0.16577258493775038, FloatVariable 'alpha 14': 0.18310901108005986, FloatVariable 'Elu 390': 0.1820761083768519, FloatVariable 'alpha 21': 0.030539018557891578, FloatVariable 'cdc15 10': 0.11428450056762224, FloatVariable 'alpha 28': 0.04645238275160125, FloatVariable 'alpha 91': 0.1905674816738207, FloatVariable 'cdc15 30': 0.18270335600911874, FloatVariable 'alpha 35': 0.21379911334384863, FloatVariable 'heat 0': 0.19091815990337407, FloatVariable 'cdc15 50': 0.24968263583989325, FloatVariable 'cdc15 170': 0.44789694844623534, FloatVariable 'heat 80': 0.38909905042009185, FloatVariable 'diau f': 1.4452997087693935, FloatVariable 'cdc15 70': 0.13876265882583333, FloatVariable 'alpha 49': 0.16085715739160683, FloatVariable 'cdc15 90': 0.32729758144823035, FloatVariable 'alpha 56': 0.3367416107117914, FloatVariable 'cold 0': 0.27980454530046744, FloatVariable 'cdc15 110': 0.564756618474929, FloatVariable 'Elu 0': 0.8466167037587657, FloatVariable 'alpha 63': 0.18433878873311124, FloatVariable 'cdc15 130': 0.3658301477295572, FloatVariable 'dtt 60': 0.5951914850021424, FloatVariable 'alpha 105': 0.14088060621674625, FloatVariable 'cdc15 150': 0.693249161777514, FloatVariable 'dtt 120': 0.55305024494988, FloatVariable 'alpha 112': 0.19329749923741518, FloatVariable 'diau g': 2.248793727194904, FloatVariable 'heat 10': 1.000320207469925, FloatVariable 'cdc15 190': 0.16956982427965123, FloatVariable 'heat 160': 0.3192185000510415, FloatVariable 'dtt 15': 0.49451797411099035, FloatVariable 'cold 20': 0.4097605043285248, FloatVariable 'alpha 0': 0.19198054903386352, FloatVariable 'cdc15 210': 0.15183429673463036, FloatVariable 'cold 40': 0.3092287272528724, FloatVariable 'alpha 98': 0.21754881357923167, FloatVariable 'cdc15 230': 0.5474715182870784, FloatVariable 'cold 160': 0.6947037871090163, FloatVariable 'heat 40': 0.4580618143377812, FloatVariable 'cdc15 250': 0.3573777070361102, FloatVariable 'dtt 30': 0.5838556086306895, FloatVariable 'diau a': 0.14935761521655416, FloatVariable 'alpha 77': 0.20088381949723239, FloatVariable 'cdc15 270': 0.21951931922594184, FloatVariable 'diau b': 0.23473821977067888, FloatVariable 'heat 20': 0.9867456006798212, FloatVariable 'cdc15 290': 0.24965577080121784, FloatVariable 'diau c': 0.13741762346585432}) 
     1defaultdict(<type 'float'>, {FloatVariable 'Elu 30': 0.4786304184632838, FloatVariable 'spo 0': 0.13024372486415015, FloatVariable 'Elu 60': 0.36698713537474476, FloatVariable 'spo 2': 0.7078062232168753, FloatVariable 'alpha 98': 0.21754881357923167, FloatVariable 'Elu 90': 0.3834942300173535, FloatVariable 'spo 5': 1.0683498605674933, FloatVariable 'alpha 7': 0.06557659077448137, FloatVariable 'Elu 120': 0.5939764872379445, FloatVariable 'spo 7': 0.8081568079276176, FloatVariable 'diau d': 0.44932601596409105, FloatVariable 'Elu 150': 0.5965599387054419, FloatVariable 'spo 9': 0.2498282401589412, FloatVariable 'alpha 35': 0.21379911334384863, FloatVariable 'Elu 180': 0.42425268429856355, FloatVariable 'spo 11': 0.20615575833508282, FloatVariable 'alpha 70': 0.26459268873021585, FloatVariable 'Elu 210': 0.12396520046361383, FloatVariable 'spo5 2': 0.40417556232809326, FloatVariable 'alpha 63': 0.18433878873311124, FloatVariable 'Elu 240': 0.2093390824178926, FloatVariable 'spo5 7': 0.26780459416067937, FloatVariable 'Elu 270': 0.33471969574325466, FloatVariable 'spo5 11': 1.200079459496442, FloatVariable 'diau e': 0.864767371223623, FloatVariable 'spo- early': 1.9466556509082333, FloatVariable 'Elu 330': 0.11474308886955724, FloatVariable 'alpha 42': 0.13641650791763626, FloatVariable 'spo- mid': 3.2086605964825132, FloatVariable 'Elu 360': 0.16577258493775038, FloatVariable 'cdc15 270': 0.21951931922594184, FloatVariable 'alpha 14': 0.18310901108005986, FloatVariable 'Elu 390': 0.1820761083768519, FloatVariable 'alpha 21': 0.030539018557891578, FloatVariable 'cdc15 10': 0.11428450056762224, FloatVariable 'alpha 28': 0.04645238275160125, FloatVariable 'cdc15 30': 0.18270335600911874, FloatVariable 'heat 40': 0.4580618143377812, FloatVariable 'heat 0': 0.19091815990337407, FloatVariable 'cdc15 50': 0.24968263583989325, FloatVariable 'cdc15 170': 0.44789694844623534, FloatVariable 'heat 80': 0.38909905042009185, FloatVariable 'diau f': 1.4452997087693935, FloatVariable 'cdc15 70': 0.13876265882583333, FloatVariable 'alpha 49': 0.16085715739160683, FloatVariable 'cdc15 90': 0.32729758144823035, FloatVariable 'alpha 56': 0.3367416107117914, FloatVariable 'alpha 84': 0.1308234316119738, FloatVariable 'cdc15 110': 0.564756618474929, FloatVariable 'dtt 30': 0.5838556086306895, FloatVariable 'alpha 112': 0.19329749923741518, FloatVariable 'cdc15 130': 0.3658301477295572, FloatVariable 'heat 10': 1.000320207469925, FloatVariable 'dtt 60': 0.5951914850021424, FloatVariable 'cdc15 150': 0.693249161777514, FloatVariable 'dtt 120': 0.55305024494988, FloatVariable 'diau g': 2.248793727194904, FloatVariable 'cold 0': 0.27980454530046744, FloatVariable 'Elu 0': 0.8466167037587657, FloatVariable 'cdc15 190': 0.16956982427965123, FloatVariable 'alpha 77': 0.20088381949723239, FloatVariable 'cold 20': 0.4097605043285248, FloatVariable 'alpha 0': 0.19198054903386352, FloatVariable 'cdc15 210': 0.15183429673463036, FloatVariable 'cold 40': 0.3092287272528724, FloatVariable 'alpha 119': 0.1699365258012951, FloatVariable 'dtt 15': 0.49451797411099035, FloatVariable 'cdc15 230': 0.5474715182870784, FloatVariable 'diau a': 0.14935761521655416, FloatVariable 'cold 160': 0.6947037871090163, FloatVariable 'cdc15 250': 0.3573777070361102, FloatVariable 'diau c': 0.13741762346585432, FloatVariable 'heat 160': 0.3192185000510415, FloatVariable 'alpha 105': 0.14088060621674625, FloatVariable 'diau b': 0.23473821977067888, FloatVariable 'heat 20': 0.9867456006798212, FloatVariable 'cdc15 290': 0.24965577080121784, FloatVariable 'Elu 300': 0.15913983311663107, FloatVariable 'alpha 91': 0.1905674816738207}) 
  • docs/reference/rst/Orange.data.domain.rst

    r9704 r9840  
    425425         keys and variables (:obj:`~Orange.data.variable.Variable`) as the 
    426426         corresponding values. The following example shows how to add all 
    427           meta attributes from another domain:: 
     427         meta attributes from another domain:: 
    428428 
    429429              >>> newdomain.add_metas(domain.get_metas()) 
  • docs/reference/rst/code/exclude-from-regression.txt

    r9801 r9824  
    44serverfiles2.py 
    55statistics-contingency6.py 
     6correspondence.py 
  • docs/reference/rst/code/mds-scatterplot.py

    r9823 r9838  
    1111 
    1212# Construct a distance matrix using Euclidean distance 
    13 euclidean = Orange.distance.instances.EuclideanConstructor(iris) 
     13euclidean = Orange.distance.Euclidean(iris) 
    1414distance = Orange.core.SymMatrix(len(iris)) 
    1515for i in range(len(iris)): 
    16    for j in range(i+1): 
     16   for j in range(i + 1): 
    1717       distance[i, j] = euclidean(iris[i], iris[j]) 
    1818 
  • docs/reference/rst/code/network-optimization.py

    r9823 r9839  
    99for i in range(4): 
    1010    for j in range(i + 1, 5): 
    11         net[i,j] = 1 
     11        net[i, j] = 1 
    1212 
    1313# vertices are placed randomly in NetworkOptimization constructor 
    1414networkOptimization = Orange.network.NetworkOptimization(net) 
     15 
    1516 
    1617# read all edges and plot a line 
  • docs/tutorial/rst/code/sample_adult.py

    r9747 r9832  
    99selection = orange.MakeRandomIndices2(data, 0.03) 
    1010sample = data.select(selection, 0) 
    11 sample.save("adult_sample.tab") 
     11sample.save("adult_sample_sampled.tab") 
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