Correction to the algorithm below. . .



Yes, I don't have that yet since I have only part of an algorithm to test it 
with.
This much I have, I'm writing this in the Premise language.

function numeric-similarity {?a ?b}
  (put (mean  ?a ?b)             ?m)  (put (sigma  ?a ?b)            ?s)   (put 
(z-score ?a ?mean ?sigma) ?za)   (put (z-score ?b ?mean ?sigma) ?zb)
  ; hmmm... scratch my head ... what's next? 
end

Any idea what comes next? 
~PM
Date: Thu, 20 Feb 2014 11:46:11 -0600
From: [email protected]
To: [email protected]
Subject: Re: [agi] Numeric Similarity


  
    
  
  
    Well, all of your examples have a
      positive value. 

      Can you describe examples of where the result is 0, and where the
      result is negative?

      

      Thanks,

      Dimitry

      

      

      On 2/20/2014 10:57 AM, Piaget Modeler wrote:

    
    
      
      Thanks for your response Boris. 
        

        
        My aim at the moment is to define a function for any two
          numbers a b.
        

        
        Similarity(a, b) ::=  c | c in [-1 .. +1].  
        

        
        Examples:
        

        
        Similarity(0, 0) = 1.0
        

        
        Similarity(239420,  239420) = 1.0
        

        
        Similarity(3.1415926, 3.14) = 0.9995      /* or something
          close to but less than one */ 
        

        
        Similarity(-7123456789098765, -7123456789098765) = 1.0
        

          
        And so forth. 
        

        
        

        
        From it I gather, your suggestion, not algorithm, is 
        

        
        "initial
                comparison between integers is by subtraction, which
                compresses miss from !AND to difference by cancelling
                opposite-sign bits, & increases match because it’s a
                complimentary of that reduced difference.
        
            Division
              will further reduce magnitude of miss by converting it
              from difference to ratio, which can then be reduced again
              by converting it to logarithm, & so on. By reducing
              miss, higher power of comparison will also increase
              complimentary match. But the costs may grow even faster,
              for both operations & incremental syntax to record
              incidental sign, fraction, & irrational fraction. The
              power of comparison is increased if current-power match
              plus miss predict an improvement, as indicated by
              higher-order comparison between results from different
              powers of comparison. Such “meta-comparison” can discover
              algorithms, or meta-patterns."
        

          Similarity(number a, number b) ::= log( (a-b) / ????) 
        

        
        This seems a bit confusing for me.  
        

        
        Your thoughts? 
        

        
        ~PM. 

          

            

                                          


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