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rhlawek wrote:I've been looking for some old source code to prove it to myself but this looks very similar to what I was taught as Predictor/Corrector methods back in the mid-80s

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#include "FiveWin.ch"

function Main()

   local oNet := TNet():New( { 1, 2, 1 } ), n
   local x

   while oNet:nRecentAverageError < 0.95
      oNet:FeedForward( { x := nRandom( 1000 ) } )
      oNet:Backprop( { If( x % 5 == 0, 5, 1 ) } )
   end  

   oNet:FeedForward( { 15 } )
   
   XBROWSER ArrTranspose( { "Layer 1 1st neuron" + CRLF + "Input:" + Str( oNet:aLayers[ 1 ][ 1 ]:nOutput ) + ;
                                                   CRLF + "Weigth 1:" + Str( oNet:aLayers[ 1 ][ 1 ]:aWeights[ 1 ], 4, 2 ), ;
                            { "Layer 2, 1st neuron" + CRLF + "Weigth 1: " + Str( oNet:aLayers[ 2 ][ 1 ]:aWeights[ 1 ] ) + ;
                                                      CRLF + "Output: " + Str( oNet:aLayers[ 2 ][ 1 ]:nOutput ),;
                            "Layer 2, 2nd neuron" + CRLF + "Weight 1: " + Str( oNet:aLayers[ 2 ][ 2 ]:aWeights[ 1 ] ) + ;
                                                    CRLF + "Output: " + Str( oNet:aLayers[ 2 ][ 2 ]:nOutput ) },;
                            "Layer 3 1st neuron" + CRLF + "Weigth 1: " + Str( oNet:aLayers[ 3 ][ 1 ]:aWeights[ 1 ] ) + ;
                                                   CRLF + "Weigth 2: " + Str( oNet:aLayers[ 3 ][ 1 ]:aWeights[ 2 ] ) + ;
                                                   CRLF + "Output: " + Str( oNet:aLayers[ 2 ][ 2 ]:nOutput ) } ) ;
      SETUP ( oBrw:nDataLines := 4,;
              oBrw:aCols[ 1 ]:nWidth := 180,;
              oBrw:aCols[ 2 ]:nWidth := 180,;
              oBrw:aCols[ 3 ]:nWidth := 180,;
              oBrw:nMarqueeStyle := 3 )                      
   
return nil

Code: Select all  Expand view

#include "FiveWin.ch"

function Main()

   local oNeuron := TPerceptron():New( 1 )
   local n, nValue

   for n = 1 to 50
      oNeuron:Learn( { nValue := nRandom( 1000 ) }, ExpectedResult( nValue ) )
   next

   MsgInfo( oNeuron:aWeights[ 1 ] )
   
   MsgInfo( oNeuron:Calculate( { 5 } ) )

return nil  

function ExpectedResult( nValue )

return nValue * 2

CLASS TPerceptron

   DATA aWeights

   METHOD New( nInputs )

   METHOD Learn( aInputs, nExpectedResult )

   METHOD Calculate( aInputs )

ENDCLASS

METHOD New( nInputs ) CLASS TPerceptron

   local n

   ::aWeights = Array( nInputs )

   for n = 1 to nInputs
      ::aWeights[ n ] = 0
   next

return Self

METHOD Learn( aInputs, nExpectedResult ) CLASS TPerceptron

   local nSum := ::Calculate( aInputs )

   if nSum < nExpectedResult
      ::aWeights[ 1 ] += 0.1
   endif

   if nSum > nExpectedResult
      ::aWeights[ 1 ] -= 0.1
   endif

return nil        

METHOD Calculate( aInputs ) CLASS TPerceptron

   local n, nSum := 0

   for n = 1 to Len( aInputs )
      nSum += aInputs[ n ] * ::aWeights[ n ]
   next

return nSum