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Timestamp:
02/06/12 10:56:22 (2 years ago)
Author:
markotoplak
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default
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Orange.distance renames.

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  • docs/reference/rst/Orange.distance.rst

    r9663 r9719  
     1.. py:currentmodule:: Orange.distance 
     2 
    13.. automodule:: Orange.distance 
    24 
     
    57########################################## 
    68 
    7 This page describes a bunch of classes for different metrics for measure 
    8 distances (dissimilarities) between instances. 
     9Distance measures typically have to be adjusted to the data. For instance, 
     10when the data set contains continuous features, the distances between 
     11continuous values should be normalized to ensure that all features have 
     12similar impats, e.g. by dividing the distance with the range. 
    913 
    10 Typical (although not all) measures of distance between instances require 
    11 some "learning" - adjusting the measure to the data. For instance, when 
    12 the dataset contains continuous features, the distances between continuous 
    13 values should be normalized, e.g. by dividing the distance with the range 
    14 of possible values or with some interquartile distance to ensure that all 
    15 features have, in principle, similar impacts. 
    16  
    17 Different measures of distance thus appear in pairs - a class that measures 
    18 the distance and a class that constructs it based on the data. The abstract 
    19 classes representing such a pair are `ExamplesDistance` and 
    20 `ExamplesDistanceConstructor`. 
     14Distance measures thus appear in pairs - a class that measures 
     15the distance (:obj:`Distance`) and a class that constructs it based on the 
     16data (:obj:`DistanceConstructor`). 
    2117 
    2218Since most measures work on normalized distances between corresponding 
    23 features, there is an abstract intermediate class 
    24 `ExamplesDistance_Normalized` that takes care of normalizing. 
    25 The remaining classes correspond to different ways of defining the distances, 
    26 such as Manhattan or Euclidean distance. 
     19features, an abstract class `DistanceNormalized` takes care of 
     20normalizing. 
    2721 
    28 Unknown values are treated correctly only by Euclidean and Relief distance. 
    29 For other measure of distance, a distance between unknown and known or between 
    30 two unknown values is always 0.5. 
     22Unknown values are treated correctly only by Euclidean and Relief 
     23distance.  For other measures, a distance between unknown and known or 
     24between two unknown values is always 0.5. 
    3125 
    32 .. class:: ExamplesDistance 
     26.. class:: Distance 
    3327 
    3428    .. method:: __call__(instance1, instance2) 
    3529 
    36         Returns a distance between the given instances as floating point number. 
     30        Return a distance between the given instances (as a floating point number). 
    3731 
    38 .. class:: ExamplesDistanceConstructor 
     32.. class:: DistanceConstructor 
    3933 
    4034    .. method:: __call__([instances, weightID][, distributions][, basic_var_stat]) 
    4135 
    42         Constructs an instance of ExamplesDistance. 
    43         Not all the data needs to be given. Most measures can be constructed 
    44         from basic_var_stat; if it is not given, they can help themselves 
    45         either by instances or distributions. 
    46         Some (e.g. ExamplesDistance_Hamming) even do not need any arguments. 
     36        Constructs an :obj:`Distance`.  Not all the data needs to be 
     37        given. Most measures can be constructed from basic_var_stat; 
     38        if it is not given, they can help themselves either by instances 
     39        or distributions. Some do not need any arguments. 
    4740 
    48 .. class:: ExamplesDistance_Normalized 
     41.. class:: DistanceNormalized 
    4942 
    5043    This abstract class provides a function which is given two instances 
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