Art2神经网络源程序

1、'* ADAPTIVE RESONANCE THEORY (ARTN) ETWORK*#include <stdio.h>#include <stdlib.h> #include <string.h> #include <conio.h> #include <math.h>/ DEFINES#define MAXCNEURONS 75/ MAX COMPARISON LAYER NEURONS#define MAXRNEURONS 30/ MAX RECOGNITION LAYER NEURONS#define MAXPATT

2、ERNS 30#define VERBOSE 1/ MAX NUMBER OF PATTERNS IN A TRAINING SETclass ARTNET private:double WbMAXCNEURONSMAXRNEURONS; / Bottom up weight matrixintWtMAXRNEURONSMAXCNEURONS; / Top down weight matrixintInDataMAXPATTERNSMAXCNEURONS;/ Array of input vectors to be/ presented to the networkintNumPatterns

3、;/ Number of input patternsdouble VigilThresh;/ Vigilence threshold valuedouble L;/ ART training const (see text)intM;/ # of neurons in C-layerintN;/ # of neurons in R-layerintXVectMAXCNEURONS;/ Current in vect at C-CVectMAXCNEURONS;/ Output vector from C-layerintBestNeuron;/ Current best R

4、-layer NeuronintReset;/ Active when vigilence has/ disabled someoneintRVectMAXCNEURONS;/ Output vector from R-layerintPVectMAXCNEURONS;/ WeightedOutput vector from R-layerintDisabledMAXRNEURONS;/ Resets way of disqualifying neuronsintTrainedMAXRNEURONS;/ To identify allocated R-NeuronsvoidClearPvect

5、();voidClearDisabled();voidRecoPhase();/ Recognition phasevoidCompPhase();/ Comparison phasevoidSearchPhase();/ Search PhasevoidRunCompLayer();/ Calc comparison layer by 2/3 rulevoidRunRecoLayer();/ Calc recognition layers R-vectvoidRvect2Pvect(int);/ Distribute winners resultintGain1();/ Comp layer

6、 gainintGain2();/ Reco layer gaindouble Vigilence();/ Calc vigilence metricvoidInitWeights();/ Initialize weightsvoidTrain();/ Weight adjustment is done herepublic:ARTNET(void);/ Constructor/initializationsintLoadInVects(char *Fname);/ load all data vectorsvoidRun(int i);/ Run net w/ ith patternvoid

7、ShowWeights();/ display top down and/ bottom up weightsvoidShowInVect();/ Display current input patternvoidShowOutVect();/ P-vector from Reco layer(see text);/ / METHOD DEFINITIONSARTNET:ARTNET()int i;L=2.0;N=MAXRNEURONS;for (i=0; i<N; i+) /Set all neurons to untrained and enabledTrainedi=0;Disab

8、ledi=0; /* endfor */int ARTNET:LoadInVects(char *Fname)FILE *PFILE;int i,j,k;PFILE = fopen(Fname,"r");if (PFILE=NULL) printf("nUnable to open file %sn",Fname);exit(0); fscanf(PFILE,"%d",&NumPatterns); fscanf(PFILE,"%d",&M); fscanf(PFILE,"%lf"

9、,&VigilThresh); for (i=0; i<NumPatterns; i+) for (j=0; j<M; j+) fscanf(PFILE,"%d",&k);/How many patterns/get width of input vector/Read all the pattern data and.InDataij=k; /* endfor */ /* endfor */ InitWeights(); return NumPatterns; int ARTNET:Gain2() int i;for (i=0; i<M;

10、 i+) if (XVecti=1) return 1; /* endfor */ / .save it for later.void ARTNET:Rvect2Pvect(int best) int i;for (i=0; i<M; i+) PVecti= Wtbesti; /* endfor */int ARTNET:Gain1()int i,G;G=Gain2();for (i=0; i<M; i+) if (RVecti=1)return 0; /* endfor */return G;void ARTNET:RunCompLayer()int i,x;for (i=0;

11、i<M; i+) x=XVecti+Gain1()+PVecti;if (x>=2) CVecti=1;else CVecti=0; /* endif */ /* endfor */double ARTNET:Vigilence()int i;double S,K,D;/ count # of 1's in p-vect & x-vectK=0.0;D=0.0;for (i=0; i<M; i+) K+=CVecti;D+=XVecti; /* endfor */S=K/D;return S;void ARTNET:RunRecoLayer()int i,j,

12、k;double NetMAXRNEURONS;int BestNeruon=-1;double NetMax=-1;for (i=0; i<N; i+) /Traverse all R-layer NeuronsNeti=0;for (j=0; j<M; j+) / Do the productNeti +=Wbij*CVectj; /* endfor */if (Neti>NetMax) && (Disabledi=0) /disabled neurons cant win! BestNeuron=i;NetMax=Neti; /* endfor */fo

13、r (k=0; k<N; k+) if (k=BestNeuron)RVectk=1;/ Winner gets 1elseRVectk=0; / lateral inhibition kills the rest /* endfor */void ARTNET:RecoPhase()int i;/First force all R-layer outputs to zerofor (i=0; i<N; i+) RVecti=0; /* endfor */ for (i=0; i<M; i+) PVecti=0; /* endfor */Now Calculate C-lay

14、er outputsRunCompLayer(); /C-vector now has the result RunRecoLayer();/Calc dot prod w/ bot up weight & CRvect2Pvect(BestNeuron);void ARTNET:CompPhase()double S;RunCompLayer();/Cvector<-dif between x & pS=Vigilence();if (S<VigilThresh)Reset=1;RVectBestNeuron=0;DisabledBestNeuron=1;else

15、Reset=0;void ARTNET:SearchPhase() double S;while (Reset) ClearPvect();RunCompLayer(); /Xvect -> CvectRunRecoLayer();/Find a new winner with prev winners disabledRvect2Pvect(BestNeuron); /new pvect based on new winner S=Vigilence(); /calc vigilence for the new guy if (S<VigilThresh) /check if h

16、e did okReset=1; / if not disable him too RVectBestNeuron=0;DisabledBestNeuron=1;elseReset=0;/Current Best neuron is a good winner.Train him /* endwhile */if (BestNeuron!=-1) Train();else /Failed to allocate a neuron for current pattern.printf("Out of neurons in F2n"); /* endif */ClearDisa

17、bled();void ARTNET:ClearDisabled() int i;for (i=0; i<M; i+) Disabledi=0; /* endfor */void ARTNET:ClearPvect() int i;for (i=0; i<M; i+) PVecti=0; /* endfor */void ARTNET:Train()int i,z=0;for (i=0; i<M; i+) z+=CVecti; /* endfor */for (i=0; i<M; i+) WbBestNeuroni=L*CVecti/(L-1+z);WtBestNeur

18、oni=CVecti; /* endfor */TrainedBestNeuron=1;void ARTNET:Run(int tp)int i,j;ClearPvect();for (i=0; i<M; i+) XVecti=InDatatpi; /* endfor */RecoPhase();CompPhase(); SearchPhase();/ Initialize weights/ from R-neuron i/ to C-neuron j/ All init'd to 1/ from C-neuron i / to R-neuron jvoid ARTNET:Ini

19、tWeights() int i,j;double b;for (i=0; i<N; i+) for (j=0; j<M; j+) Wtij= 1; /* endfor */ /* endfor */ b=L/(L-1+M); for (i=0; i<N; i+) for (j=0; j<M; j+) Wbij= b; /* endfor */ /* endfor */void ARTNET:ShowWeights() int i,j;printf("nTop Down weights:n"); for (i=0; i<N; i+) if(Tr

20、ainedi=1) for (j=0; j<M; j+) printf("%d ",Wtij); /* endfor */ printf("n"); /* endif */ /* endfor */ printf("nBottom up weights:n"); for (i=0; i<N; i+) if(Trainedi=1) for (j=0; j<M; j+) printf("%f ",Wbij); /* endfor */ printf("n"); /* endif */ /* endfor */void ARTNET:ShowInVe