trainsm.c

nnToolKit 神经网络工具包是基于 MATLAB 神经网络工具箱自行开发的一组神经网络算法函数库

/*
 * MATLAB Compiler: 3.0
 * Date: Sun May 13 16:47:40 2007
 * Arguments: "-B" "macro_default" "-O" "all" "-O" "fold_scalar_mxarrays:on"
 * "-O" "fold_non_scalar_mxarrays:on" "-O" "optimize_integer_for_loops:on" "-O"
 * "array_indexing:on" "-O" "optimize_conditionals:on" "-M" "-silentsetup" "-d"
 * "d:/MATLAB6p5/work/nnToolKit/src" "-B" "csglcom:nnToolKit,nnToolKit,2.0"
 * "-B" "sgl" "-m" "-W" "main" "-L" "C" "-t" "-T" "link:exe" "-h"
 * "libmmfile.mlib" "-W" "mainhg" "libmwsglm.mlib" "-t" "-W"
 * "comhg:nnToolKit,nnToolKit,2.0" "-T" "link:lib" "-h" "libmmfile.mlib" "-i"
 * "-i" "D:/MATLAB6p5/work/nnToolKit/lmnet/LmSimu.m"
 * "D:/MATLAB6p5/work/nnToolKit/lmnet/LmTrain.m"
 * "D:/MATLAB6p5/work/nnToolKit/sofm/SofmSimu.m"
 * "D:/MATLAB6p5/work/nnToolKit/sofm/SofmTrain.m" 
 */
#include "trainsm.h"
#include "mwservices.h"
#include "learnis.h"
#include "libmatlbm.h"
#include "nndef.h"
#include "nntobsf.h"
#include "plotsm.h"
#include "simusm.h"

static mxChar _array1_[7] = { 't', 'r', 'a', 'i', 'n', 's', 'm' };
static mxArray * _mxarray0_;

static mxChar _array3_[55] = { 'U', 's', 'e', ' ', 'N', 'N', 'T', '2', 'S', 'O',
                               'M', ' ', 'a', 'n', 'd', ' ', 'T', 'R', 'A', 'I',
                               'N', ' ', 't', 'o', ' ', 'u', 'p', 'd', 'a', 't',
                               'e', ' ', 'a', 'n', 'd', ' ', 't', 'r', 'a', 'i',
                               'n', ' ', 'y', 'o', 'u', 'r', ' ', 'n', 'e', 't',
                               'w', 'o', 'r', 'k', '.' };
static mxArray * _mxarray2_;

static mxChar _array5_[21] = { 'N', 'o', 't', ' ', 'e', 'n', 'o',
                               'u', 'g', 'h', ' ', 'a', 'r', 'g',
                               'u', 'm', 'e', 'n', 't', 's', '.' };
static mxArray * _mxarray4_;
static mxArray * _mxarray6_;

static double _array8_[3] = { 25.0, 100.0, 1.0 };
static mxArray * _mxarray7_;

static mxChar _array10_[55] = { 'T', 'R', 'A', 'I', 'N', 'S', 'M', ':',
                                ' ', '%', '%', 'g', '/', '%', 'g', ' ',
                                'e', 'p', 'o', 'c', 'h', 's', ',', ' ',
                                'n', 'e', 'i', 'g', 'h', 'b', 'o', 'r',
                                'h', 'o', 'o', 'd', ' ', '=', ' ', '%',
                                '%', 'g', ',', ' ', 'l', 'r', ' ', '=',
                                ' ', '%', '%', 'g', '.', 0x005c, 'n' };
static mxArray * _mxarray9_;
static mxArray * _mxarray11_;
static mxArray * _mxarray12_;
static mxArray * _mxarray13_;
static mxArray * _mxarray14_;
static mxArray * _mxarray15_;

void InitializeModule_trainsm(void) {
    _mxarray0_ = mclInitializeString(7, _array1_);
    _mxarray2_ = mclInitializeString(55, _array3_);
    _mxarray4_ = mclInitializeString(21, _array5_);
    _mxarray6_ = mclInitializeDoubleVector(0, 0, (double *)NULL);
    _mxarray7_ = mclInitializeDoubleVector(1, 3, _array8_);
    _mxarray9_ = mclInitializeString(55, _array10_);
    _mxarray11_ = mclInitializeDouble(0.0);
    _mxarray12_ = mclInitializeDouble(100.0);
    _mxarray13_ = mclInitializeDouble(1.0);
    _mxarray14_ = mclInitializeDouble(1000.0);
    _mxarray15_ = mclInitializeDouble(10.0);
}

void TerminateModule_trainsm(void) {
    mxDestroyArray(_mxarray15_);
    mxDestroyArray(_mxarray14_);
    mxDestroyArray(_mxarray13_);
    mxDestroyArray(_mxarray12_);
    mxDestroyArray(_mxarray11_);
    mxDestroyArray(_mxarray9_);
    mxDestroyArray(_mxarray7_);
    mxDestroyArray(_mxarray6_);
    mxDestroyArray(_mxarray4_);
    mxDestroyArray(_mxarray2_);
    mxDestroyArray(_mxarray0_);
}

static mxArray * Mtrainsm(int nargout_,
                          mxArray * w_in,
                          mxArray * m,
                          mxArray * p,
                          mxArray * tp);

_mexLocalFunctionTable _local_function_table_trainsm
  = { 0, (mexFunctionTableEntry *)NULL };

/*
 * The function "mlfTrainsm" contains the normal interface for the "trainsm"
 * M-function from file "d:\matlab6p5\toolbox\nnet\nnobsolete\trainsm.m" (lines
 * 1-78). This function processes any input arguments and passes them to the
 * implementation version of the function, appearing above.
 */
mxArray * mlfTrainsm(mxArray * w_in, mxArray * m, mxArray * p, mxArray * tp) {
    int nargout = 1;
    mxArray * w = NULL;
    mlfEnterNewContext(0, 4, w_in, m, p, tp);
    w = Mtrainsm(nargout, w_in, m, p, tp);
    mlfRestorePreviousContext(0, 4, w_in, m, p, tp);
    return mlfReturnValue(w);
}

/*
 * The function "mlxTrainsm" contains the feval interface for the "trainsm"
 * M-function from file "d:\matlab6p5\toolbox\nnet\nnobsolete\trainsm.m" (lines
 * 1-78). The feval function calls the implementation version of trainsm
 * through this function. This function processes any input arguments and
 * passes them to the implementation version of the function, appearing above.
 */
void mlxTrainsm(int nlhs, mxArray * plhs[], int nrhs, mxArray * prhs[]) {
    mxArray * mprhs[4];
    mxArray * mplhs[1];
    int i;
    if (nlhs > 1) {
        mlfError(
          mxCreateString(
            "Run-time Error: File: trainsm Line: 1 Column: "
            "1 The function \"trainsm\" was called with mor"
            "e than the declared number of outputs (1)."),
          NULL);
    }
    if (nrhs > 4) {
        mlfError(
          mxCreateString(
            "Run-time Error: File: trainsm Line: 1 Column:"
            " 1 The function \"trainsm\" was called with m"
            "ore than the declared number of inputs (4)."),
          NULL);
    }
    for (i = 0; i < 1; ++i) {
        mplhs[i] = NULL;
    }
    for (i = 0; i < 4 && i < nrhs; ++i) {
        mprhs[i] = prhs[i];
    }
    for (; i < 4; ++i) {
        mprhs[i] = NULL;
    }
    mlfEnterNewContext(0, 4, mprhs[0], mprhs[1], mprhs[2], mprhs[3]);
    mplhs[0] = Mtrainsm(nlhs, mprhs[0], mprhs[1], mprhs[2], mprhs[3]);
    mlfRestorePreviousContext(0, 4, mprhs[0], mprhs[1], mprhs[2], mprhs[3]);
    plhs[0] = mplhs[0];
}

/*
 * The function "Mtrainsm" is the implementation version of the "trainsm"
 * M-function from file "d:\matlab6p5\toolbox\nnet\nnobsolete\trainsm.m" (lines
 * 1-78). It contains the actual compiled code for that M-function. It is a
 * static function and must only be called from one of the interface functions,
 * appearing below.
 */
/*
 * function w = trainsm(w,m,p,tp)
 */
static mxArray * Mtrainsm(int nargout_,
                          mxArray * w_in,
                          mxArray * m,
                          mxArray * p,
                          mxArray * tp) {
    mexLocalFunctionTable save_local_function_table_
      = mclSetCurrentLocalFunctionTable(&_local_function_table_trainsm);
    int nargin_ = mclNargin(4, w_in, m, p, tp, NULL);
    mxArray * w = NULL;
    mxArray * dw = NULL;
    mxArray * a = NULL;
    mxArray * P = NULL;
    mxArray * j = NULL;
    mxArray * lr = NULL;
    mxArray * nb = NULL;
    mxArray * i = NULL;
    mxArray * z = NULL;
    mxArray * nb_x = NULL;
    mxArray * base_lr = NULL;
    mxArray * lr_x = NULL;
    mxArray * message = NULL;
    mxArray * S = NULL;
    mxArray * Q = NULL;
    mxArray * R = NULL;
    mxArray * max_nb = NULL;
    mxArray * init_lr = NULL;
    mxArray * max_pres = NULL;
    mxArray * df = NULL;
    mxArray * ans = NULL;
    mclCopyInputArg(&w, w_in);
    mclCopyArray(&m);
    mclCopyArray(&p);
    mclCopyArray(&tp);
    /*
     * %TRAINSM Train self-organizing map with Kohonen rule.
     * %  
     * %  This function is obselete.
     * %  Use NNT2SOM and TRAIN to update and train your network.
     * 
     * nntobsf('trainsm','Use NNT2SOM and TRAIN to update and train your network.')
     */
    mlfNntobsf(_mxarray0_, _mxarray2_, NULL);
    /*
     * 
     * %  TRAINSM(W,M,P,TP)
     * %    W  - SxR weight matrix.
     * %    M  - Neighborhood matrix.
     * %    P  - RxQ matrix of input vectors.
     * %    TP - Training parameters (optional).
     * %  Returns new weights.
     * %  
     * %  Training parameters are:
     * %    TP(1) - Presentations between updating display, default = 25.
     * %    TP(2) - Number of presentations, default = 100.
     * %    TP(3) - Initial learning rate, default = 1.
     * %  Missing parameters and NaN's are replaced with defaults.
     * 
     * % Mark Beale, 12-15-93
     * % Copyright 1992-2002 The MathWorks, Inc.
     * % $Revision: 1.12 $  $Date: 2002/03/25 16:54:31 $
     * 
     * if nargin < 3, error('Not enough arguments.'); end
     */
    if (nargin_ < 3) {
        mlfError(_mxarray4_, NULL);
    }
    /*
     * 
     * % TRAINING PARAMETERS
     * if nargin == 3, tp = []; end
     */
    if (nargin_ == 3) {
        mlfAssign(&tp, _mxarray6_);
    }
    /*
     * tp = nndef(tp,[25 100 1]);
     */
    mlfAssign(&tp, mlfNndef(mclVa(tp, "tp"), _mxarray7_));
    /*
     * df = tp(1);
     */
    mlfAssign(&df, mclIntArrayRef1(mclVa(tp, "tp"), 1));
    /*
     * max_pres = tp(2);
     */
    mlfAssign(&max_pres, mclIntArrayRef1(mclVa(tp, "tp"), 2));
    /*
     * init_lr = tp(3);
     */
    mlfAssign(&init_lr, mclIntArrayRef1(mclVa(tp, "tp"), 3));
    /*