Changeset 9696:26fc745b27f6 in orange


Ignore:
Timestamp:
02/06/12 12:31:13 (2 years ago)
Author:
Jure Zbontar <jure.zbontar@…>
Branch:
default
Message:

Renamed RandomGenerator to Random in docs.

Location:
docs
Files:
5 edited

Legend:

Unmodified
Added
Removed
  • docs/reference/rst/Orange.evaluation.testing.rst

    r9506 r9696  
    2727    randomize it. This is opposed to the previous versions where special 
    2828    care needed to be taken to make experiments repeatable. 
    29     See arguments :obj:`randseed` and :obj:`randomGenerator` for the 
    30     explanation. 
     29    See argument obj:`random_generator` for the explanation. 
    3130 
    3231Example scripts in this section suppose that the data is loaded and a 
     
    7271    if the class variable is discrete and has no unknown values. 
    7372 
    74 *randseed (obsolete: indicesrandseed), randomGenerator* 
    75     Random seed (``randseed``) or random generator (``randomGenerator``) for 
     73*randseed (obsolete: indicesrandseed), random_generator* 
     74    Random seed (``randseed``) or random generator (``random_generator``) for 
    7675    random selection of examples. If omitted, random seed of 0 is used and 
    7776    the same test will always select the same examples from the example 
     
    7978 
    8079    * 
    81       Set ``randomGenerator`` to :obj:`orange.globalRandom`. The function's 
     80      Set ``random_generator`` to :obj:`orange.globalRandom`. The function's 
    8281      selection will depend upon Orange's global random generator that 
    8382      is reset (with random seed 0) when Orange is imported. The Script's 
     
    8685 
    8786          res = Orange.evaluation.testing.proportion_test(learners, data, 0.7, 
    88               randomGenerator=orange.globalRandom) 
     87              random_generator=orange.globalRandom) 
    8988 
    9089    * 
    91       Construct a new :obj:`orange.RandomGenerator`. The code below, 
     90      Construct a new :obj:`Orange.misc.Random`. The code below, 
    9291      for instance, will produce different results in each iteration, 
    9392      but overall the same results each time it's run. 
  • docs/reference/rst/SimpleTreeLearner.txt

    r9372 r9696  
    2828    .. attribute:: random_generator 
    2929         
    30         :obj:`Orange.core.RandomGenerator` to use. 
     30        Provide your own :obj:`Orange.misc.Random`. 
  • docs/reference/rst/code/randomindices2.py

    r9372 r9696  
    2222 
    2323print "\nIndices with random generator" 
    24 indices2.random_generator = Orange.core.RandomGenerator(42)     
     24indices2.random_generator = Orange.misc.Random(42)     
    2525for i in range(5): 
    2626    print indices2(data) 
  • docs/reference/rst/code/testing-test.py

    r9372 r9696  
    2424print "\nproportionsTest that will give different results, \ 
    2525but the same each time the script is run" 
    26 myRandom = Orange.core.RandomGenerator() 
     26myRandom = Orange.misc.Random() 
    2727for i in range(3): 
    2828    res = Orange.evaluation.testing.proportion_test(learners, table, 0.7, 
    29         randomGenerator=myRandom) 
     29        random_generator=myRandom) 
    3030    printResults(res) 
    3131# End 
  • docs/tutorial/rst/code/domain8.py

    r9374 r9696  
    1515for noiselevel in (0.2, 0.4, 0.6): 
    1616  add_noise.proportion = noiselevel 
    17   add_noise.randomGenerator = 42 
     17  add_noise.random_generator = 42 
    1818  d = add_noise(data) 
    1919  d.name = "class noise %4.2f" % noiselevel 
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