mlp.zip_MLP_Visual Perception_precepetion program

/*---------------------------------------------------------------------- File : mlp.c Contents: multilayer perceptron management Author : Christian Borgelt History : 2001.02.26 file created 2001.03.10 first version completed 2001.03.18 other weight update methods added 2001.03.19 tabulated logistic function added 2001.04.15 tangens hyperbolicus added as a #define option 2001.04.16 output scaling added 2001.05.20 extended functions added (attribute set/table) 2001.07.17 adapted to modified module scan 2001.09.11 some assertions added 2002.06.04 input scaling/normalization added 2002.06.18 sensitivity analysis functions added 2002.06.19 bug in function mlp_result fixed 2002.09.12 scaling bug concerning numeric targets fixed 2003.01.30 functionality of mlp_reg extended 2003.02.01 bug in mapping binary attributes fixed 2003.02.07 reinitialization added to function mlp_range 2003.03.11 weight parameter added to function mlp_reg 2003.04.01 bug in function mlp_createx fixed 2003.08.06 another bug in mapping binary attributes fixed 2003.10.24 bug in output range determination fixed 2004.02.23 check of parabola orientation added to _quick 2004.08.10 function mlp_deletex added 2004.08.11 adapted to new module attmap 2004.08.12 adapted to new modules parse and nstats 2007.01.10 usage of attribute maps changed 2007.02.14 adapted to modified module attset 2008.04.14 bug in function _quick fixed (parabola orient.) ----------------------------------------------------------------------*/ #include <stdio.h> #include <stdlib.h> #include <string.h> #include <limits.h> #include <float.h> #include <math.h> #include <assert.h> #include "mlp.h" #ifdef STORAGE #include "storage.h" #endif /*---------------------------------------------------------------------- Preprocessor Definitions ----------------------------------------------------------------------*/ /* --- activation function and its derivative --- */ #ifdef MLP_TABFN /* if to use a tabulated function */ #define TABSIZE 1024 /* size of interpolation table */ #define TABMAX 16.0 /* maximum (absolute) argument value */ #define TABSCALE (TABSIZE/TABMAX) #endif /* scale factor for table indices */ #ifndef MLP_TANH /* default: logistic function */ #define MACTFN(x) (1/(1 +exp(-(x)))) #ifdef MLP_TABFN /* tabulated function */ #define ACTFN(x) _logistic(x) #else /* direct computation */ #define ACTFN(x) MACTFN(x) #endif #define DERIV(x) ((x)*(1-(x))) #define ACTMIN 0.0 /* minimal and maximal value */ #define ACTMAX 1.0 /* of the activation function */ #define ACTMID 0.5 /* middle value */ #else /* alternative: tangens hyperbolicus */ #define MACTFN(x) (2/(1 +exp(-2*(x))) -1) #ifdef MLP_TABFN /* tabulated function */ #define ACTFN(x) _tanh(x) #else /* direct computation */ #define ACTFN(x) MACTFN(x) #endif #define DERIV(x) ((1+(x))*(1-(x))) #define ACTMIN -1.0 /* minimal and maximal value */ #define ACTMAX 1.0 /* of the activation function */ #define ACTMID 0.0 /* middle value */ #endif #define NAMELEN 255 /* maximum target name length */ /*---------------------------------------------------------------------- Type Definitions ----------------------------------------------------------------------*/ typedef void UPDATEFN (MLP* mlp); /* a weight update function */ /*---------------------------------------------------------------------- Global Variables ----------------------------------------------------------------------*/ #ifdef MLP_TABFN static double _tab[TABSIZE+1]; /* tabulated activation function */ static int _init = 0; /* whether table is initialized */ #endif /*---------------------------------------------------------------------- Activation Function ----------------------------------------------------------------------*/ #ifdef MLP_TABFN #ifndef MLP_TANH static double _logistic (double x) { /* --- table-based logistic function */ int i; /* table index */ double y = fabs(x); /* argument of tabulated function */ if (y >= TABMAX) /* if beyond the table range, */ return (x > 0) ? 1 : 0; /* return asymptotic values */ i = (int)(y *= TABSCALE); /* otherwise compute the table index */ y = _tab[i] + (y -i) * (_tab[i+1] -_tab[i]); return (x < 0) ? 1-y : y; /* interpolate between table values */ } /* _logistic() */ #else /*--------------------------------------------------------------*/ static double _tanh (double x) { /* --- table-based tanh function */ int i; /* table index */ double y = fabs(x); /* argument of tabulated function */ if (y >= TABMAX) /* if beyond the table range, */ return (x > 0) ? 1 : -1; /* return asymptotic values */ i = (int)(y *= TABSCALE); /* otherwise compute the table index */ y = _tab[i] + (y -i) * (_tab[i+1] -_tab[i]); return (x < 0) ? -y : y; /* interpolate between table values */ } /* _tanh() */ #endif /* #ifndef MLP_TANH */ #endif /* #ifdef MLP_TABFN */ /*---------------------------------------------------------------------- Weight Update Functions ----------------------------------------------------------------------*/ static void _standard (MLP *mlp) { /* --- standard backpropagation */ int k; /* loop variable */ double *w, *c, *g; /* to traverse the vectors */ double lrate = mlp->lrate; /* learning rate */ double moment = mlp->moment; /* momentum coefficient */ w = mlp->wgts; /* get the necessary vectors and */ g = mlp->grds; /* traverse the connection weights */ if (moment <= 0) /* if standard backpropagation */ for (k = mlp->wgtcnt; --k >= 0; ) { w[k] -= lrate *g[k]; g[k] = 0; } else { /* if backpropagation with momentum */ c = mlp->chgs; /* get the vector of old changes */ for (k = mlp->wgtcnt; --k >= 0; ) { w[k] += c[k] = moment *c[k] - lrate *g[k]; g[k] = 0; } } /* update the connection weights */ } /* _standard() */ /*--------------------------------------------------------------------*/ static void _adaptive (MLP *mlp) { /* --- super self-adaptive backprop. */ int k; /* loop variable */ double *w, *c, *g, *p; /* to traverse the vectors */ double t; /* temporary buffer */ w = mlp->wgts; c = mlp->chgs; /* get the necessary vectors and */ g = mlp->grds; p = mlp->bufs; /* traverse the connection weights */ for (k = mlp->wgtcnt; --k >= 0; ) { if (g[k] > 0) t = p[k]; else if (g[k] < 0) t = -p[k]; else t = 0; /* check directions of the changes */ if (t > 0) { /* if gradients have the same sign, */ c[k] *= mlp->growth; /* increase the learning rate */ if (c[k] > mlp->maxchg) c[k] = mlp->maxchg; p[k] = g[k]; } /* note the current gradient */ else if (t < 0) { /* if gradients have opposite signs, */ c[k] *= mlp->shrink; /* decrease the learning rate */ if (c[k] < mlp->minchg) c[k] = mlp->minchg; p[k] = 0; } /* suppress a change in the next step */ else { /* if one gra