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13 edited

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  • Orange/testing/regression/results_reference/svm-linear-weights.py.txt

    r9954 r9971  
    1 defaultdict(<type 'float'>, {FloatVariable 'alpha 0': 0.19198054903386352, FloatVariable 'Elu 300': 0.15913983311663107, FloatVariable 'spo- mid': 3.2086605964825132, FloatVariable 'Elu 330': 0.11474308886955724, FloatVariable 'alpha 14': 0.18310901108005986, FloatVariable 'alpha 98': 0.21754881357923167, FloatVariable 'Elu 360': 0.16577258493775038, FloatVariable 'Elu 180': 0.42425268429856355, FloatVariable 'alpha 21': 0.030539018557891578, FloatVariable 'Elu 30': 0.4786304184632838, FloatVariable 'Elu 390': 0.1820761083768519, FloatVariable 'spo- early': 1.9466556509082333, FloatVariable 'alpha 28': 0.04645238275160125, FloatVariable 'cdc15 10': 0.11428450056762224, FloatVariable 'alpha 35': 0.21379911334384863, FloatVariable 'cdc15 30': 0.18270335600911874, FloatVariable 'alpha 42': 0.13641650791763626, FloatVariable 'cdc15 50': 0.24968263583989325, FloatVariable 'alpha 70': 0.26459268873021585, FloatVariable 'alpha 49': 0.16085715739160683, FloatVariable 'cdc15 70': 0.13876265882583333, FloatVariable 'alpha 105': 0.14088060621674625, FloatVariable 'diau b': 0.23473821977067888, FloatVariable 'alpha 56': 0.3367416107117914, FloatVariable 'cdc15 90': 0.32729758144823035, FloatVariable 'alpha 63': 0.18433878873311124, FloatVariable 'cdc15 110': 0.564756618474929, FloatVariable 'Elu 60': 0.36698713537474476, FloatVariable 'dtt 60': 0.5951914850021424, FloatVariable 'cdc15 130': 0.3658301477295572, FloatVariable 'alpha 77': 0.20088381949723239, FloatVariable 'heat 80': 0.38909905042009185, FloatVariable 'cdc15 150': 0.693249161777514, FloatVariable 'alpha 84': 0.1308234316119738, FloatVariable 'cdc15 170': 0.44789694844623534, FloatVariable 'cold 20': 0.4097605043285248, FloatVariable 'cdc15 190': 0.16956982427965123, FloatVariable 'cold 40': 0.3092287272528724, FloatVariable 'alpha 112': 0.19329749923741518, FloatVariable 'cdc15 210': 0.15183429673463036, FloatVariable 'cold 160': 0.6947037871090163, FloatVariable 'diau f': 1.4452997087693935, FloatVariable 'cdc15 230': 0.5474715182870784, FloatVariable 'heat 0': 0.19091815990337407, FloatVariable 'diau a': 0.14935761521655416, FloatVariable 'heat 160': 0.3192185000510415, FloatVariable 'cdc15 250': 0.3573777070361102, FloatVariable 'heat 40': 0.4580618143377812, FloatVariable 'cdc15 270': 0.21951931922594184, FloatVariable 'spo5 2': 0.40417556232809326, FloatVariable 'Elu 0': 0.8466167037587657, FloatVariable 'alpha 7': 0.06557659077448137, FloatVariable 'cold 0': 0.27980454530046744, FloatVariable 'diau d': 0.44932601596409105, FloatVariable 'spo 0': 0.13024372486415015, FloatVariable 'alpha 119': 0.1699365258012951, FloatVariable 'diau e': 0.864767371223623, FloatVariable 'spo 2': 0.7078062232168753, FloatVariable 'heat 10': 1.000320207469925, FloatVariable 'spo 5': 1.0683498605674933, FloatVariable 'Elu 120': 0.5939764872379445, FloatVariable 'diau g': 2.248793727194904, FloatVariable 'spo 7': 0.8081568079276176, FloatVariable 'Elu 150': 0.5965599387054419, FloatVariable 'Elu 90': 0.3834942300173535, FloatVariable 'spo 9': 0.2498282401589412, FloatVariable 'dtt 30': 0.5838556086306895, FloatVariable 'alpha 91': 0.1905674816738207, FloatVariable 'spo 11': 0.20615575833508282, FloatVariable 'Elu 210': 0.12396520046361383, FloatVariable 'cdc15 290': 0.24965577080121784, FloatVariable 'dtt 15': 0.49451797411099035, FloatVariable 'Elu 240': 0.2093390824178926, FloatVariable 'diau c': 0.13741762346585432, FloatVariable 'spo5 7': 0.26780459416067937, FloatVariable 'dtt 120': 0.55305024494988, FloatVariable 'Elu 270': 0.33471969574325466, FloatVariable 'spo5 11': 1.200079459496442, FloatVariable 'heat 20': 0.9867456006798212}) 
     1defaultdict(<type 'float'>, {FloatVariable 'Elu 0': 0.8466167037587657, FloatVariable 'Elu 30': 0.4786304184632838, FloatVariable 'spo 0': 0.13024372486415015, FloatVariable 'Elu 60': 0.36698713537474476, FloatVariable 'spo 2': 0.7078062232168753, FloatVariable 'alpha 63': 0.18433878873311124, 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 'Elu 180': 0.42425268429856355, FloatVariable 'alpha 77': 0.20088381949723239, FloatVariable 'spo 11': 0.20615575833508282, FloatVariable 'alpha 70': 0.26459268873021585, FloatVariable 'Elu 210': 0.12396520046361383, FloatVariable 'spo5 2': 0.40417556232809326, FloatVariable 'alpha 98': 0.21754881357923167, 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 'alpha 119': 0.1699365258012951, FloatVariable 'spo- early': 1.9466556509082333, FloatVariable 'alpha 112': 0.19329749923741518, FloatVariable 'Elu 330': 0.11474308886955724, FloatVariable 'alpha 42': 0.13641650791763626, FloatVariable 'spo- mid': 3.2086605964825132, FloatVariable 'alpha 91': 0.1905674816738207, FloatVariable 'Elu 360': 0.16577258493775038, FloatVariable 'alpha 14': 0.18310901108005986, FloatVariable 'alpha 105': 0.14088060621674625, 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 'heat 160': 0.3192185000510415, FloatVariable 'alpha 49': 0.16085715739160683, FloatVariable 'alpha 56': 0.3367416107117914, FloatVariable 'alpha 84': 0.1308234316119738, FloatVariable 'cdc15 110': 0.564756618474929, FloatVariable 'dtt 30': 0.5838556086306895, FloatVariable 'cdc15 130': 0.3658301477295572, FloatVariable 'dtt 60': 0.5951914850021424, FloatVariable 'cdc15 90': 0.32729758144823035, FloatVariable 'cdc15 150': 0.693249161777514, FloatVariable 'dtt 120': 0.55305024494988, FloatVariable 'heat 10': 1.000320207469925, FloatVariable 'diau g': 2.248793727194904, FloatVariable 'cold 0': 0.27980454530046744, FloatVariable 'cdc15 190': 0.16956982427965123, FloatVariable 'dtt 15': 0.49451797411099035, FloatVariable 'cold 20': 0.4097605043285248, FloatVariable 'Elu 300': 0.15913983311663107, FloatVariable 'cdc15 210': 0.15183429673463036, FloatVariable 'cold 40': 0.3092287272528724, FloatVariable 'cdc15 230': 0.5474715182870784, FloatVariable 'alpha 35': 0.21379911334384863, FloatVariable 'cold 160': 0.6947037871090163, FloatVariable 'cdc15 250': 0.3573777070361102, FloatVariable 'diau a': 0.14935761521655416, 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, FloatVariable 'alpha 0': 0.19198054903386352}) 
  • Orange/testing/regression/results_tests_20/modules_ensemble.py.txt

    r9951 r9972  
    11Classification Accuracy: 
    2            tree: 0.804 
    3    boosted tree: 0.811 
    4     bagged tree: 0.797 
     2           tree: 0.764 
     3   boosted tree: 0.770 
     4    bagged tree: 0.790 
  • Orange/testing/regression/results_tests_20/modules_kmeans-cmp-init.py.txt

    r9951 r9968  
    11           Rnd Div  HC 
    2       iris  11   2  10 
    3    housing  13   5   3 
    4    vehicle  10   3   2 
     2  iris.tab  11   2  10 
     3housing.tab  13   5   3 
     4vehicle.tab  10   3   2 
  • Orange/testing/regression/results_tests_20/modules_logreg2.py.txt

    r9956 r9965  
    44>50K >50K 
    55<=50K >50K 
    6  
    7 class attribute = y 
    8 class values = <>50K, <=50K> 
    9  
    10                              Feature       beta  st. error     wald Z          P OR=exp(beta) 
    11  
    12                            Intercept       6.62       0.00        inf       0.00 
    13                                  age      -0.04       0.00       -inf       0.00       0.96 
    14                               fnlwgt      -0.00       0.00       -inf       0.00       1.00 
    15                        education-num      -0.28       0.00       -inf       0.00       0.76 
    16              marital-status=Divorced       4.29       0.00        inf       0.00      72.62 
    17         marital-status=Never-married       3.79       0.00        inf       0.00      44.45 
    18             marital-status=Separated       3.46        nan        nan        nan      31.95 
    19               marital-status=Widowed       3.85       0.00        inf       0.00      46.96 
    20 marital-status=Married-spouse-absent       3.98       0.00        inf       0.00      53.63 
    21     marital-status=Married-AF-spouse       4.01        nan        nan        nan      55.19 
    22              occupation=Tech-support      -0.32       0.00       -inf       0.00       0.72 
    23              occupation=Craft-repair       0.37       0.00        inf       0.00       1.45 
    24             occupation=Other-service       2.68        nan        nan        nan      14.61 
    25                     occupation=Sales       0.22       0.00        inf       0.00       1.24 
    26            occupation=Prof-specialty       0.18       0.00        inf       0.00       1.19 
    27         occupation=Handlers-cleaners       1.29        nan        nan        nan       3.64 
    28         occupation=Machine-op-inspct       0.86       0.00        inf       0.00       2.37 
    29              occupation=Adm-clerical       0.30       0.00        inf       0.00       1.35 
    30           occupation=Farming-fishing       1.12        nan        nan        nan       3.06 
    31          occupation=Transport-moving       0.62       0.00        inf       0.00       1.85 
    32           occupation=Priv-house-serv       3.46       0.00        inf       0.00      31.87 
    33           occupation=Protective-serv       0.11        nan        nan        nan       1.12 
    34              occupation=Armed-Forces       0.59       0.00        inf       0.00       1.81 
    35                    relationship=Wife      -1.06       0.00       -inf       0.00       0.35 
    36               relationship=Own-child      -1.04        nan        nan        nan       0.35 
    37           relationship=Not-in-family      -1.94       0.00       -inf       0.00       0.14 
    38          relationship=Other-relative      -2.42       0.00       -inf       0.00       0.09 
    39               relationship=Unmarried      -1.92        nan        nan        nan       0.15 
    40              race=Asian-Pac-Islander      -0.19       0.00       -inf       0.00       0.83 
    41              race=Amer-Indian-Eskimo       2.88       0.00        inf       0.00      17.78 
    42                           race=Other       3.93        nan        nan        nan      51.07 
    43                           race=Black       0.11       0.00        inf       0.00       1.12 
    44                           sex=Female       0.30       0.00        inf       0.00       1.36 
    45                         capital-gain      -0.00        nan        nan        nan       1.00 
    46                         capital-loss      -0.00       0.00       -inf       0.00       1.00 
    47                       hours-per-week      -0.04       0.00       -inf       0.00       0.96 
     6age -0.0365046039224 
     7fnlwgt -1.13033081561e-06 
     8education-num -0.278378069401 
     9marital-status=Divorced 4.28520584106 
     10marital-status=Never-married 3.79432463646 
     11marital-status=Separated 3.4642136097 
     12marital-status=Widowed 3.84919857979 
     13marital-status=Married-spouse-absent 3.98207736015 
     14marital-status=Married-AF-spouse 4.01079034805 
     15occupation=Tech-support -0.324787259102 
     16occupation=Craft-repair 0.371972113848 
     17occupation=Other-service 2.68194651604 
     18occupation=Sales 0.215603515506 
     19occupation=Prof-specialty 0.176146954298 
     20occupation=Handlers-cleaners 1.29317069054 
     21occupation=Machine-op-inspct 0.8613935709 
     22occupation=Adm-clerical 0.301324903965 
     23occupation=Farming-fishing 1.11930930614 
     24occupation=Transport-moving 0.616262614727 
     25occupation=Priv-house-serv 3.46170806885 
     26occupation=Protective-serv 0.113764844835 
     27occupation=Armed-Forces 0.593791663647 
     28relationship=Wife -1.0589966774 
     29relationship=Own-child -1.03764116764 
     30relationship=Not-in-family -1.93763542175 
     31relationship=Other-relative -2.420140028 
     32relationship=Unmarried -1.92468094826 
     33race=Asian-Pac-Islander -0.191510245204 
     34race=Amer-Indian-Eskimo 2.87814831734 
     35race=Other 3.93312478065 
     36race=Black 0.111131064594 
     37sex=Female 0.304161816835 
     38capital-gain -0.000317209050991 
     39capital-loss -0.000606149493251 
     40hours-per-week -0.0415332503617 
  • Orange/testing/regression/results_tests_20/modules_misc_bestOnTheFly.py.txt

    r9951 r9968  
    1 0.565: EnumVariable 'lym_dimin' 
    2 0.565: EnumVariable 'lym_dimin' 
    3 0.565: EnumVariable 'lym_dimin' 
     11.000: EnumVariable 'milk' 
     21.000: EnumVariable 'milk' 
     31.000: EnumVariable 'milk' 
  • Orange/testing/regression/results_tests_20/modules_statExamples.py.txt

    r9951 r9968  
    1717 
    1818Confusion matrix for naive Bayes for 'van': 
    19 TP: 192, FP: 151, FN: 7.0, TN: 496 
     19TP: 192, FP: 152, FN: 7.0, TN: 495 
    2020 
    2121Confusion matrix for naive Bayes for 'opel': 
    22 TP: 79, FP: 75, FN: 133.0, TN: 559 
     22TP: 80, FP: 71, FN: 132.0, TN: 563 
    2323 
    24     bus van saab    opel 
    25 bus 156 19  17  26 
    26 van 4   192 2   1 
    27 saab    8   68  93  48 
    28 opel    8   64  61  79 
     24    bus opel    saab    van 
     25bus 156 25  17  20 
     26opel    6   80  61  65 
     27saab    7   46  97  67 
     28van 4   0   3   192 
    2929 
    3030Sensitivity and specificity for 'voting' 
     
    3636Sensitivity and specificity for 'vehicle=van' 
    3737method  sens    spec 
    38 bayes   0.965   0.767 
    39 tree    0.834   0.966 
     38bayes   0.965   0.765 
     39tree    0.794   0.969 
    4040majrty  0.000   1.000 
    4141 
     
    4747AUC for vehicle using weighted single-out method 
    4848bayes   tree    majority 
    49 0.840   0.816   0.500 
     490.841   0.795   0.500 
    5050 
    5151AUC for vehicle, using different methods 
    5252                            bayes   tree    majority 
    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 
     53       by pairs, weighted:  0.858   0.869   0.500 
     54                 by pairs:  0.859   0.870   0.500 
     55    one vs. all, weighted:  0.841   0.795   0.500 
     56              one vs. all:  0.841   0.795   0.500 
    5757 
    5858AUC for detecting class 'van' in 'vehicle' 
    59 0.923   0.900   0.500 
     590.924   0.881   0.500 
    6060 
    6161AUCs for detecting various classes in 'vehicle' 
    62 bus (218.000) vs others:    0.952   0.936   0.500 
    63 van (199.000) vs others:    0.923   0.900   0.500 
    64 saab (217.000) vs others:   0.737   0.707   0.500 
    65 opel (212.000) vs others:   0.749   0.718   0.500 
     62bus (218.000) vs others:    0.954   0.943   0.500 
     63opel (212.000) vs others:   0.749   0.685   0.500 
     64saab (217.000) vs others:   0.739   0.672   0.500 
     65van (199.000) vs others:    0.924   0.881   0.500 
    6666 
    67     bus van saab 
    68 van 0.987 
    69 saab    0.927   0.860 
    70 opel    0.921   0.894   0.587 
     67    bus opel    saab 
     68opel    0.922 
     69saab    0.927   0.561 
     70van 0.991   0.898   0.857 
    7171 
    7272AUCs for detecting various pairs of classes in 'vehicle' 
    73 van vs bus:     0.987   0.976   0.500 
    74 saab vs bus:    0.927   0.936   0.500 
    75 saab vs van:    0.860   0.906   0.500 
    76 opel vs bus:    0.921   0.951   0.500 
    77 opel vs van:    0.894   0.915   0.500 
    78 opel vs saab:   0.587   0.622   0.500 
     73opel vs bus:    0.922   0.949   0.500 
     74saab vs bus:    0.927   0.941   0.500 
     75saab vs opel:   0.561   0.578   0.500 
     76van vs bus:     0.991   0.977   0.500 
     77van vs opel:    0.898   0.902   0.500 
     78van vs saab:    0.857   0.872   0.500 
    7979 
    8080AUC and SE for voting 
  • Orange/testing/regression/results_tests_20/reference_example2.py.txt

    r9951 r9968  
    1 ['young', 'myope', 'no', 'reduced', 'none'], {-42:0.84} 
     1['young', 'myope', 'no', 'reduced', 'none'], {-42:0.64} 
    22<15.000, 4.000, 5.000> 
    3 <9.691, 1.969, 3.232> 
    4 ['young', 'myope', 'no', 'reduced', 'none'], {"w":0.844} 
    5 0.844 
    6 0.844 
    7 0.844 
     3<7.326, 0.822, 1.628> 
     4['young', 'myope', 'no', 'reduced', 'none'], {"w":0.639} 
     50.639 
     60.639 
     70.639 
  • Orange/testing/regression/results_tests_20/reference_example3.py.txt

    r9951 r9968  
    88['young', 'myope', 'yes', 'normal', 'hard'], {"ok?":'no'} 
    99['young', 'hypermetrope', 'no', 'reduced', 'none'], {"ok?":'yes'} 
    10 ['young', 'hypermetrope', 'no', 'normal', 'soft'], {"ok?":'no'} 
     10['young', 'hypermetrope', 'no', 'normal', 'soft'], {"ok?":'yes'} 
    1111 
    1212 
     
    1717['young', 'myope', 'yes', 'normal', 'hard'], {"ok?":'no'} 
    1818['young', 'hypermetrope', 'no', 'reduced', 'none'], {"ok?":'yes'} 
    19 ['young', 'hypermetrope', 'no', 'normal', 'soft'], {"ok?":'no'} 
     19['young', 'hypermetrope', 'no', 'normal', 'soft'], {"ok?":'yes'} 
  • Orange/testing/regression/tests_20/modules_logreg2.py

    r9952 r9965  
    1111for ex in data[:5]: 
    1212    print ex.getclass(), lr(ex) 
    13      
    14 orngLR.printOUT(lr)  
     13 
     14out = [''] 
     15 
     16# get the longest attribute name 
     17longest=0 
     18for at in lr.continuized_domain.features: 
     19    if len(at.name)>longest: 
     20        longest=len(at.name) 
     21 
     22# print out the head 
     23for i in range(len(lr.continuized_domain.features)): 
     24    print lr.continuized_domain.features[i].name, lr.beta[i+1] 
  • Orange/testing/regression/tests_20/modules_misc_bestOnTheFly.py

    r9952 r9969  
    11import orange, orngMisc 
    22 
    3 data = orange.ExampleTable("lymphography") 
     3data = orange.ExampleTable("zoo") 
    44 
    55findBest = orngMisc.BestOnTheFly(orngMisc.compare2_firstBigger) 
     
    1111 
    1212 
    13 findBest = orngMisc.BestOnTheFly(callCompareOn1st = True) 
     13findBest = orngMisc.BestOnTheFly(callCompareOn1st=True) 
    1414for attr in data.domain.attributes: 
    1515    findBest.candidate((orange.MeasureAttribute_gainRatio(attr, data), attr)) 
  • Orange/testing/regression/xtest.py

    r9949 r9972  
    144144                    p.kill() 
    145145                    result2 = "timedout" 
    146                     print "timedout" 
     146                    print "timedout (use: --timeout #)" 
    147147                    # remove output file and change it for *.timedout.* 
    148148                    for state in states: 
     
    154154                    timeoutname = "%s/%s.%s.%s.%s.txt" % (outputsdir, name, sys.platform, sys.version[:3], "timedout") 
    155155                    open(timeoutname, "wt").close() 
     156                    result = "timedout" 
    156157                else: 
    157158                    stdout, stderr = p.communicate() 
  • docs/reference/rst/Orange.data.continuization.rst

    r9941 r9966  
    1111variable separately. 
    1212 
    13 .. class DomainContinuizer 
     13.. class:: DomainContinuizer 
    1414 
    1515    Returns a new domain containing only continuous attributes given a 
     
    2929      ``multinomial_treatment``. 
    3030 
    31     .. attribute zero_based 
     31    The typical use of the class is as follows:: 
     32 
     33        continuizer = orange.DomainContinuizer() 
     34        continuizer.multinomialTreatment = continuizer.LowestIsBase 
     35        domain0 = continuizer(data) 
     36        data0 = data.translate(domain0) 
     37 
     38    .. attribute:: zero_based 
    3239 
    3340        Determines the value used as the "low" value of the variable. When 
     
    3845        following text assumes the default case. 
    3946 
    40     .. attribute multinomial_treatment 
     47    .. attribute:: multinomial_treatment 
    4148 
    4249       Decides the treatment of multinomial variables. Let N be the 
     
    5461           used (directly) in, for instance, linear or logistic regression. 
    5562 
     63           For example, data set "bridges" has feature "RIVER" with 
     64           values "M", "A", "O" and "Y", in that order. Its value for 
     65           the 15th row is "M". Continuization replaces the variable 
     66           with variables "RIVER=M", "RIVER=A", "RIVER=O" and 
     67           "RIVER=Y". For the 15th row, the first has value 1 and 
     68           others are 0. 
     69 
    5670       DomainContinuizer.LowestIsBase 
    5771           Similar to the above except that it creates only N-1 
     
    6377           specified value is used as base instead of the lowest one. 
    6478 
     79           Continuizing the variable "RIVER" gives similar results as 
     80           above except that it would omit "RIVER=M"; all three 
     81           variables would be zero for the 15th data instance. 
     82 
    6583       DomainContinuizer.FrequentIsBase 
    66  
    6784           Like above, except that the most frequent value is used as the 
    6885           base (this can again be overidden by setting the descriptor's 
     
    7188           extracted from data, so this option cannot be used if constructor 
    7289           is given only a domain. 
     90 
     91           Variable "RIVER" would be continuized similarly to above 
     92           except that it omits "RIVER=A", which is the most frequent value. 
    7393            
    7494       DomainContinuizer.Ignore 
     
    87107           variable. 
    88108 
    89     .. attribute normalize_continuous 
     109    .. attribute:: normalize_continuous 
    90110 
    91111        If ``False`` (default), continues variables are left unchanged. If 
  • docs/reference/rst/code/transformvalue-d2c.py

    r9945 r9970  
    22import Orange.feature 
    33 
    4 data = Orange.data.Table("monk1") 
     4data = Orange.data.Table("monks-1") 
    55 
    66e1 = Orange.feature.Continuous("e=1") 
    7 e1.getValueFrom = Orange.core.ClassifierFromVar(whichVar = data.domain["e"]) 
     7e1.getValueFrom = Orange.core.ClassifierFromVar(whichVar=data.domain["e"]) 
    88e1.getValueFrom.transformer = Orange.core.Discrete2Continuous() 
    9 e1.getValueFrom.transformer.value = int(Orange.data.Value(e, "1")) 
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