learnlm.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 16:47:41 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 "learnlm.h"
#include "libmatlbm.h"
#include "nncpy.h"
#include "nncpyi.h"
#include "nntobsf.h"

static mxChar _array1_[7] = { 'l', 'e', 'a', 'r', 'n', 'l', 'm' };
static mxArray * _mxarray0_;

static mxChar _array3_[54] = { 'U', 's', 'e', ' ', 'N', 'N', 'T', '2', 'F',
                               'F', ' ', '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_[26] = { 'W', 'r', 'o', 'n', 'g', ' ', 'n', 'u', 'm',
                               'b', 'e', 'r', ' ', 'o', 'f', ' ', 'a', 'r',
                               'g', 'u', 'm', 'e', 'n', 't', 's', '.' };
static mxArray * _mxarray4_;

void InitializeModule_learnlm(void) {
    _mxarray0_ = mclInitializeString(7, _array1_);
    _mxarray2_ = mclInitializeString(54, _array3_);
    _mxarray4_ = mclInitializeString(26, _array5_);
}

void TerminateModule_learnlm(void) {
    mxDestroyArray(_mxarray4_);
    mxDestroyArray(_mxarray2_);
    mxDestroyArray(_mxarray0_);
}

static mxArray * Mlearnlm(int nargout_, mxArray * p, mxArray * d);

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

/*
 * The function "mlfLearnlm" contains the normal interface for the "learnlm"
 * M-function from file "d:\matlab6p5\toolbox\nnet\nnobsolete\learnlm.m" (lines
 * 1-26). This function processes any input arguments and passes them to the
 * implementation version of the function, appearing above.
 */
mxArray * mlfLearnlm(mxArray * p, mxArray * d) {
    int nargout = 1;
    mxArray * j = NULL;
    mlfEnterNewContext(0, 2, p, d);
    j = Mlearnlm(nargout, p, d);
    mlfRestorePreviousContext(0, 2, p, d);
    return mlfReturnValue(j);
}

/*
 * The function "mlxLearnlm" contains the feval interface for the "learnlm"
 * M-function from file "d:\matlab6p5\toolbox\nnet\nnobsolete\learnlm.m" (lines
 * 1-26). The feval function calls the implementation version of learnlm
 * through this function. This function processes any input arguments and
 * passes them to the implementation version of the function, appearing above.
 */
void mlxLearnlm(int nlhs, mxArray * plhs[], int nrhs, mxArray * prhs[]) {
    mxArray * mprhs[2];
    mxArray * mplhs[1];
    int i;
    if (nlhs > 1) {
        mlfError(
          mxCreateString(
            "Run-time Error: File: learnlm Line: 1 Column: "
            "1 The function \"learnlm\" was called with mor"
            "e than the declared number of outputs (1)."),
          NULL);
    }
    if (nrhs > 2) {
        mlfError(
          mxCreateString(
            "Run-time Error: File: learnlm Line: 1 Column:"
            " 1 The function \"learnlm\" was called with m"
            "ore than the declared number of inputs (2)."),
          NULL);
    }
    for (i = 0; i < 1; ++i) {
        mplhs[i] = NULL;
    }
    for (i = 0; i < 2 && i < nrhs; ++i) {
        mprhs[i] = prhs[i];
    }
    for (; i < 2; ++i) {
        mprhs[i] = NULL;
    }
    mlfEnterNewContext(0, 2, mprhs[0], mprhs[1]);
    mplhs[0] = Mlearnlm(nlhs, mprhs[0], mprhs[1]);
    mlfRestorePreviousContext(0, 2, mprhs[0], mprhs[1]);
    plhs[0] = mplhs[0];
}

/*
 * The function "Mlearnlm" is the implementation version of the "learnlm"
 * M-function from file "d:\matlab6p5\toolbox\nnet\nnobsolete\learnlm.m" (lines
 * 1-26). 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 j = learnlm(p,d)
 */
static mxArray * Mlearnlm(int nargout_, mxArray * p, mxArray * d) {
    mexLocalFunctionTable save_local_function_table_
      = mclSetCurrentLocalFunctionTable(&_local_function_table_learnlm);
    int nargin_ = mclNargin(2, p, d, NULL);
    mxArray * j = NULL;
    mxArray * S = NULL;
    mxArray * Q = NULL;
    mxArray * R = NULL;
    mxArray * ans = NULL;
    mclCopyArray(&p);
    mclCopyArray(&d);
    /*
     * %LEARNLM Levenberg-Marquardt learning rule.
     * %  
     * %  This function is obselete.
     * %  Use NNT2FF and TRAIN to update and train your network.
     * 
     * nntobsf('learnlm','Use NNT2FF and TRAIN to update and train your network.')
     */
    mlfNntobsf(_mxarray0_, _mxarray2_, NULL);
    /*
     * 
     * %  LEARNLM(P,D)
     * %    P  - RxQ matrix of input (column) vectors.
     * %    D  - SxQ matrix of delta (column) vectors.
     * %  Returns:
     * %    Partial jacobian matrix.
     * %  
     * %  See also NNLEARN, BACKPROP, INITFF, SIMFF, TRAINLM.
     * 
     * % Mark Beale, 12-15-93
     * % Copyright 1992-2002 The MathWorks, Inc.
     * % $Revision: 1.11 $  $Date: 2002/03/25 16:53:55 $
     * 
     * if nargin < 2, error('Wrong number of arguments.'),end
     */
    if (nargin_ < 2) {
        mlfError(_mxarray4_, NULL);
    }
    /*
     * 
     * [R,Q]=size(p);
     */
    mlfSize(mlfVarargout(&R, &Q, NULL), mclVa(p, "p"), NULL);
    /*
     * [S,Q]=size(d);
     */
    mlfSize(mlfVarargout(&S, &Q, NULL), mclVa(d, "d"), NULL);
    /*
     * j = nncpy(d',R) .* nncpyi(p',S);
     */
    mlfAssign(
      &j,
      mclTimes(
        mlfNncpy(mlfCtranspose(mclVa(d, "d")), mclVv(R, "R")),
        mlfNncpyi(mlfCtranspose(mclVa(p, "p")), mclVv(S, "S"))));
    mclValidateOutput(j, 1, nargout_, "j", "learnlm");
    mxDestroyArray(ans);
    mxDestroyArray(R);
    mxDestroyArray(Q);
    mxDestroyArray(S);
    mxDestroyArray(d);
    mxDestroyArray(p);
    mclSetCurrentLocalFunctionTable(save_local_function_table_);
    return j;
}