nndef.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 "nndef.h"
#include "mwservices.h"
#include "libmatlbm.h"
#include "nntobsf.h"

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

static mxChar _array3_[33] = { 'U', 's', 'e', ' ', 'T', 'R', 'A', 'I', 'N',
                               'B', ' ', 't', 'o', ' ', 't', 'r', 'a', 'i',
                               'n', ' ', 'y', 'o', 'u', 'r', ' ', 'n', 'e',
                               't', 'w', 'o', 'r', 'k', '.' };
static mxArray * _mxarray2_;
static mxArray * _mxarray4_;

void InitializeModule_nndef(void) {
    _mxarray0_ = mclInitializeString(7, _array1_);
    _mxarray2_ = mclInitializeString(33, _array3_);
    _mxarray4_ = mclInitializeDouble(1.0);
}

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

static mxArray * Mnndef(int nargout_, mxArray * x, mxArray * d);

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

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

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

/*
 * The function "Mnndef" is the implementation version of the "nndef"
 * M-function from file "d:\matlab6p5\toolbox\nnet\nnobsolete\nndef.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 y=nndef(x,d)
 */
static mxArray * Mnndef(int nargout_, mxArray * x, mxArray * d) {
    mexLocalFunctionTable save_local_function_table_
      = mclSetCurrentLocalFunctionTable(&_local_function_table_nndef);
    mxArray * y = NULL;
    mxArray * i = NULL;
    mxArray * ans = NULL;
    mclCopyArray(&x);
    mclCopyArray(&d);
    /*
     * %NNDEF Replace missing and NaN values with defaults.
     * %  
     * %  This function is obselete.
     * 
     * nntobsf('trainwb','Use TRAINB to train your network.')
     */
    mlfNntobsf(_mxarray0_, _mxarray2_, NULL);
    /*
     * 
     * 
     * % NNDEF(X,D)
     * %   X - Row vector of proposed values.
     * %   D - Row vector of default values.
     * % Returns X with all non-finite and missing values with
     * %   the corresponding values in D.
     * %
     * % EXAMPLE: x = [1 2 NaN 4 5];
     * %          d = [10 20 30 40 50 60];
     * %          y = nndef(x,d)
     * 
     * % Mark Beale, 12-15-93
     * % Copyright 1992-2002 The MathWorks, Inc.
     * % $Revision: 1.14 $  $Date: 2002/03/25 16:54:01 $
     * 
     * y = d;
     */
    mlfAssign(&y, mclVa(d, "d"));
    /*
     * i = find(finite(x(1:min(length(x),length(y)))));
     */
    mlfAssign(
      &i,
      mlfFind(
        NULL,
        NULL,
        mlfFinite(
          mclArrayRef1(
            mclVa(x, "x"),
            mlfColon(
              _mxarray4_,
              mlfScalar(
                svDoubleScalarMin(
                  (double) mclLengthInt(mclVa(x, "x")),
                  (double) mclLengthInt(mclVv(y, "y")))),
              NULL)))));
    /*
     * y(i) = x(i);
     */
    mclArrayAssign1(
      &y, mclArrayRef1(mclVa(x, "x"), mclVv(i, "i")), mclVv(i, "i"));
    mclValidateOutput(y, 1, nargout_, "y", "nndef");
    mxDestroyArray(ans);
    mxDestroyArray(i);
    mxDestroyArray(d);
    mxDestroyArray(x);
    mclSetCurrentLocalFunctionTable(save_local_function_table_);
    return y;
}