I do agree.  It's a good solution too.  I'll keep it in mind.
This problem was very narrow,  
Given two numbers return a value between -1 and 1 representing similarity. 
That's all. 
Thanks to all respondents Ben, Jim, John, Aaron, etc. (sorry if I left anyone 
out).
Crowdsourcing works.  
Collaboration works too.
Michael. 
Date: Sat, 22 Feb 2014 12:05:51 +0800
Subject: Re: [agi] Numeric Similarity
From: [email protected]
To: [email protected]


It's still important that the little cogs in the big machine are not broken, 
though ;)
Anyway we have similar issues in OpenCog and deal with them via 
context-specific quantile normalization as I noted above.  The heuristic I 
suggested to you earlier works but you need to specify a reference context 
which is associated with some distribution of numbers.  I don't see how to 
avoid this relativity to a reference context without sinking into 
meaninglessness...

ben

On Sat, Feb 22, 2014 at 12:02 PM, Piaget Modeler <[email protected]> 
wrote:




You are correct context is very important, and it is meaningless to say 
1000 ~ 100 =  0.0011098

Numeric similarity is a small cog in a larger machine.
We can talk about it later if you want. 
Cheers,


~PM

Date: Sat, 22 Feb 2014 11:48:14 +0800
Subject: Re: [agi] Numeric Similarity
From: [email protected]
To: [email protected]



But that is almost meaningless
For instance, it says
similarity(1000000, 1001000) =  1/ 1001
which is very small


yet, in many contexts (e.g. city populations), those numbers would be 
considered very similar
You need to consider the context in order to have a useful, general numeric 
similarity measure


Unless you already have some implicit context you are not mentioning..
ben


On Sat, Feb 22, 2014 at 11:44 AM, Piaget Modeler <[email protected]> 
wrote:





Two measures:
The first  measure   1 - | a - b |  where a = 1000, and b = 100    .:   -900. 
I said that I wasn't quite happy with this measure but the value is still less 
than 1. 

So I was willing to work with it. 
The second measure   1 / ( 1 + | a - b |)  where a = 1000, and b = 100   .:  
0.0011098
The second measure is better because the value falls within the range -1 to +1. 
 


So I'm happier with Aaron Hosford's recommendation.

Michael.

Date: Sat, 22 Feb 2014 10:59:23 +0800
Subject: Re: [agi] Numeric Similarity


From: [email protected]
To: [email protected]


I don't understand...


if a=1000 and b = 100, then
a-b=900
so
1 - | a - b | = 1-900 = -899

That doesn't make sense as a similarity score...
Your posited measure seems only applicable when |a-b|<=1, which indeed would be 
the case if a and b lie in [-1.1]



-- Ben


On Sat, Feb 22, 2014 at 10:56 AM, Piaget Modeler <[email protected]> 
wrote:






A and B are any two numbers that can be represented computationally. 
We are looking for the similarity of any two numbers as part of a larger 
similarity based matching scheme. The function Aaron provided will allow 


the similarity to be computed in the range 0 .. +1 which is fine.  As longas 
it's between -1 and +1 it will do. 
Cheers,
Michael.



Date: Sat, 22 Feb 2014 09:54:20 +0800
Subject: Re: [agi] Numeric Similarity
From: [email protected]
To: [email protected]





I assume your numbers a and b are between -1 and 1?  you didn't mention that in 
your original email ! ...

On Fri, Feb 21, 2014 at 10:02 PM, Piaget Modeler <[email protected]> 
wrote:







Thanks to all respondents. 
In the end I found a classic numeric similarity metric:    1 - | a - b |
It's not ideal since numeric scores can dominate other attribute scores.



Ergo, I have to devise a good weighting scheme.
Nothing's perfect I suppose.




Cheers,
~ PM


                                          


  
    
      
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