Ignore:
Timestamp:
02/05/12 23:11:21 (2 years ago)
Author:
anze <anze.staric@…>
Branch:
default
rebase_source:
f4c83a7c75ad24158d5d59e4689566b8f63423e2
Message:

Updated measure formulas to "recent" python pseudo code.

File:
1 edited

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

    r9640 r9641  
    113113 
    114114    Manhattan distance between two instances is a sum of absolute values 
    115     of distances between pairs of features, e.g. ``apply(add, [abs(x) for x in dist])`` 
     115    of distances between pairs of features, e.g. ``sum(abs(x) for x in dist)`` 
    116116    where dist is the result of ExamplesDistance_Normalized.attributeDistances. 
    117117 
     
    120120 
    121121    Euclidean distance is a square root of sum of squared per-feature distances, 
    122     i.e. ``sqrt(apply(add, [x*x for x in dist]))``, where dist is the result of 
     122    i.e. ``sqrt(sum(x*x for x in dist))``, where dist is the result of 
    123123    ExamplesDistance_Normalized.attributeDistances. 
    124124 
     
    142142          between the known and the average, plus variance 
    143143        - Two unknown continuous attributes equals double variance 
    144         - A known and unknown discrete attribute equals the probabilit 
     144        - A known and unknown discrete attribute equals the probability 
    145145          that the unknown attribute has different value than the known 
    146146          (i.e., 1 - probability of the known value) 
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