转自：http://blog.youkuaiyun.com/lanxuecc/article/details/52689008
接着上一个博客http://blog.youkuaiyun.com/lanxuecc/article/details/52688605在弱分类器训练的主体函数cvCreateCARTClassifier中我们看到主要是调用cvCreateMTStumpClassifier函数来训练得到弱分类器的结点，下面注释下这个函数
/*
* cvCreateMTStumpClassifier
*
* Multithreaded stump classifier constructor
* Includes huge train data support through callback function
*/
CV_BOOST_IMPL
CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, //训练样本HAAR特征值矩阵
int flags, // 1.按行排列，0.按列排列
CvMat* trainClasses, // 样本类别{-1,1}
CvMat* /*typeMask*/, // 为了便于回调函数统一格式
CvMat* missedMeasurementsMask, // 未知，很少用到
CvMat* compIdx, // 特征序列(必须为NULL)(行向量)
CvMat* sampleIdx, // 实际训练样本序列(行向量)
CvMat* weights, // 实际训练样本样本权重(行向量)
CvClassifierTrainParams* trainParams ) //这个结构体中指明一些参数和数据，比如分类误差计算方法，特征总数以及多线程运行时每个线程处理的特征数
{
CvStumpClassifier* stump = NULL; // 弱分类器（桩）
int m = 0; // 样本总数
int n = 0; // 所有特征个数
uchar* data = NULL; // trainData数据指针
size_t cstep = 0; // trainData一行字节数
size_t sstep = 0; // trainData元素字节数
int datan = 0; // 预计算特征个数
uchar* ydata = NULL; // trainClasses数据指针
size_t ystep = 0; // trainClasses元素字节数
uchar* idxdata = NULL; // sampleIdx数据指针
size_t idxstep = 0; // sampleIdx单个元素字节数
int l = 0; // 实际训练样本个数
uchar* wdata = NULL; // weights数据指针
size_t wstep = 0; // weights元素字节数
/*sortedIdx为事先计算好的特征值-样本矩阵，包含有预计算的所有HAAR特征对应于所有样本的特征值(按大小排列) */
uchar* sorteddata = NULL; // sortedIdx数据指针
int sortedtype = 0; // sortedIdx元素类型
size_t sortedcstep = 0; // sortedIdx一行字节数
size_t sortedsstep = 0; // sortedIdx元素字节数
int sortedn = 0; // sortedIdx行数(预计算特征个数)
int sortedm = 0; // sortedIdx列数(实际训练样本个数)
char* filter = NULL; // 样本存在标示(行向量)，如果样本存在则为1，否则为0
int i = 0;
int compidx = 0; // 每组特征的起始序号
int stumperror; // 计算阈值方法:1.misclass 2.gini 3.entropy 4.least sum of squares
int portion; // 每组特征个数，对所有特征n进行分组处理，每组portion个
/* private variables */
CvMat mat; // 补充特征-样本矩阵
CvValArray va;
float lerror; // 阈值左侧误差
float rerror; // 阈值右侧误差
float left; // 置信度（左分支）
float right; // 置信度（右分支）
float threshold; // 阈值
int optcompidx; // 最优特征
float sumw;
float sumwy;
float sumwyy;
/*临时变量，循环用*/
int t_compidx;
int t_n;
int ti;
int tj;
int tk;
uchar* t_data; // 指向data
size_t t_cstep; // cstep
size_t t_sstep; // sstep
size_t matcstep; // mat一行字节数
size_t matsstep; // mat元素字节数
int* t_idx; // 样本序列
/* end private variables */
CV_Assert( trainParams != NULL );
CV_Assert( trainClasses != NULL );
CV_Assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
CV_Assert( missedMeasurementsMask == NULL );
CV_Assert( compIdx == NULL );
// 计算阈值方法:1.misclass 2.gini 3.entropy 4.least sum of squares
stumperror = (int) ((CvMTStumpTrainParams*) trainParams)->error;
//样本类别
ydata = trainClasses->data.ptr;
if( trainClasses->rows == 1 )
{
m = trainClasses->cols;
ystep = CV_ELEM_SIZE( trainClasses->type );
}
else
{
m = trainClasses->rows;
ystep = trainClasses->step;
}
//样本权重
wdata = weights->data.ptr;
if( weights->rows == 1 )
{
CV_Assert( weights->cols == m );
wstep = CV_ELEM_SIZE( weights->type );
}
else
{
CV_Assert( weights->rows == m );
wstep = weights->step;
}
//事先计算好的排序好的所有样本的所有特征值排序好的序号
//sortedIdx为空，trainData为行向量（1*m）；sortedIdx不为空，trainData为矩阵(m*datan)；
if( ((CvMTStumpTrainParams*) trainParams)->sortedIdx != NULL )
{
sortedtype =
CV_MAT_TYPE( ((CvMTStumpTrainParams*) trainParams)->sortedIdx->type );
assert( sortedtype == CV_16SC1 || sortedtype == CV_32SC1
|| sortedtype == CV_32FC1 );
sorteddata = ((CvMTStumpTrainParams*) trainParams)->sortedIdx->data.ptr;
sortedsstep = CV_ELEM_SIZE( sortedtype );
sortedcstep = ((CvMTStumpTrainParams*) trainParams)->sortedIdx->step;
sortedn = ((CvMTStumpTrainParams*) trainParams)->sortedIdx->rows;
sortedm = ((CvMTStumpTrainParams*) trainParams)->sortedIdx->cols;
}
//事先计算好的排序好的所有样本的所有特征值
if( trainData == NULL ) //为空的情况没有遇到
{
assert( ((CvMTStumpTrainParams*) trainParams)->getTrainData != NULL );
n = ((CvMTStumpTrainParams*) trainParams)->numcomp;
assert( n > 0 );
}
else
{
assert( CV_MAT_TYPE( trainData->type ) == CV_32FC1 );
data = trainData->data.ptr;
if( CV_IS_ROW_SAMPLE( flags ) ) //trainData为矩阵
{
cstep = CV_ELEM_SIZE( trainData->type );
sstep = trainData->step;
assert( m == trainData->rows );
datan = n = trainData->cols;
}
else //trainData为向量
{
sstep = CV_ELEM_SIZE( trainData->type );
cstep = trainData->step;
assert( m == trainData->cols );
datan = n = trainData->rows;
}
// trainData为矩阵,当trainData为向量时，datan = n = 1
if( ((CvMTStumpTrainParams*) trainParams)->getTrainData != NULL )
{
n = ((CvMTStumpTrainParams*) trainParams)->numcomp; // 总特征个数
}
}
//预计算特征个数一定要小于特征总数
assert( datan <= n );
if( sampleIdx != NULL ) // 已经剔除小权值样本
{
assert( CV_MAT_TYPE( sampleIdx->type ) == CV_32FC1 );
idxdata = sampleIdx->data.ptr;
idxstep = ( sampleIdx->rows == 1 )
? CV_ELEM_SIZE( sampleIdx->type ) : sampleIdx->step;
l = ( sampleIdx->rows == 1 ) ? sampleIdx->cols : sampleIdx->rows;
// sorteddata中存放的是所有训练样本，需要筛选出实际训练样本
if( sorteddata != NULL )
{
filter = (char*) cvAlloc( sizeof( char ) * m );
memset( (void*) filter, 0, sizeof( char ) * m );
for( i = 0; i < l; i++ )
{
filter[(int) *((float*) (idxdata + i * idxstep))] = (char) 1; // 存在则为1，不存在则为0
}
}
}
else // 未剔除小权值样本
{
l = m;
}
//桩,分配一个结点的内存空间，用来存储
stump = (CvStumpClassifier*) cvAlloc( sizeof( CvStumpClassifier) );
memset( (void*) stump, 0, sizeof( CvStumpClassifier ) );
//每组特征个数,个从理解是为多线程计算，为提高性能将所有特征分成很多组
portion = ((CvMTStumpTrainParams*)trainParams)->portion;
if( portion < 1 )
{
/* auto portion */
portion = n;
#ifdef _OPENMP
portion /= omp_get_max_threads();
#endif /* _OPENMP */
}
stump->eval = cvEvalStumpClassifier;
stump->tune = NULL;
stump->save = NULL;
stump->release = cvReleaseStumpClassifier;
stump->lerror = FLT_MAX;
stump->rerror = FLT_MAX;
stump->left = 0.0F;
stump->right = 0.0F;
compidx = 0;
// 并行计算，默认为关闭的
#ifdef _OPENMP
#pragma omp parallel private(mat, va, lerror, rerror, left, right, threshold, \
optcompidx, sumw, sumwy, sumwyy, t_compidx, t_n, \
ti, tj, tk, t_data, t_cstep, t_sstep, matcstep, \
matsstep, t_idx)
#endif /* _OPENMP */
{
lerror = FLT_MAX;
rerror = FLT_MAX;
left = 0.0F;
right = 0.0F;
threshold = 0.0F;
optcompidx = 0;
sumw = FLT_MAX;
sumwy = FLT_MAX;
sumwyy = FLT_MAX;
t_compidx = 0;
t_n = 0;
ti = 0;
tj = 0;
tk = 0;
t_data = NULL;
t_cstep = 0;
t_sstep = 0;
matcstep = 0;
matsstep = 0;
t_idx = NULL;
mat.data.ptr = NULL;
// 预计算特征个数小于特征总数，则说明存在新特征，用于计算样本的新特征，存放在mat中
if( datan < n )
{
if( CV_IS_ROW_SAMPLE( flags ) )
{
mat = cvMat( m, portion, CV_32FC1, 0 );
matcstep = CV_ELEM_SIZE( mat.type );
matsstep = mat.step;
}
else
{
mat = cvMat( portion, m, CV_32FC1, 0 );
matcstep = mat.step;
matsstep = CV_ELEM_SIZE( mat.type );
}
mat.data.ptr = (uchar*) cvAlloc( sizeof( float ) * mat.rows * mat.cols );
}
// 将实际训练样本序列存放进t_idx
if( filter != NULL || sortedn < n )
{
t_idx = (int*) cvAlloc( sizeof( int ) * m );
if( sortedn == 0 || filter == NULL )
{
if( idxdata != NULL )
{
for( ti = 0; ti < l; ti++ )
{
t_idx[ti] = (int) *((float*) (idxdata + ti * idxstep));
}
}
else
{
for( ti = 0; ti < l; ti++ )
{
t_idx[ti] = ti;
}
}
}
}
#ifdef _OPENMP
#pragma omp critical(c_compidx)
#endif /* _OPENMP */
// 初始化计算特征范围
{
t_compidx = compidx;
compidx += portion;
}
// 寻找最优弱分类器
while( t_compidx < n )
{
t_n = portion; // 每组特征个数
if( t_compidx < datan ) // 已经计算过的特征
{
t_n = ( t_n < (datan - t_compidx) ) ? t_n : (datan - t_compidx);
t_data = data;
t_cstep = cstep;
t_sstep = sstep;
}
else // 新特征
{
t_n = ( t_n < (n - t_compidx) ) ? t_n : (n - t_compidx);
t_cstep = matcstep;
t_sstep = matsstep;
t_data = mat.data.ptr - t_compidx * ((size_t) t_cstep );
// 计算每个新特征对应于每个训练样本的特征值
((CvMTStumpTrainParams*)trainParams)->getTrainData( &mat,
sampleIdx, compIdx, t_compidx, t_n,
((CvMTStumpTrainParams*)trainParams)->userdata );
}
/* 预计算特征部分，直接寻找最优特征，也就是传说中的最优弱分类器 */
if( sorteddata != NULL )
{
if( filter != NULL ) //需要提取实际训练样本
{
switch( sortedtype )
{
case CV_16SC1: // 这里重复度很高，只注释一个分支，剩下的都一个道理
// 从一组特征(datan个预计算特征)中寻找最优特征
for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
{
tk = 0;
// 提取实际训练样本
for( tj = 0; tj < sortedm; tj++ )
{
int curidx = (int) ( *((short*) (sorteddata
+ ti * sortedcstep + tj * sortedsstep)) );
if( filter[curidx] != 0 )
{
t_idx[tk++] = curidx;
}
}
// 如果findStumpThreshold_32s返回值为1, 则更新最优特征
if( findStumpThreshold_32s[stumperror](
t_data + ti * t_cstep, t_sstep,
wdata, wstep, ydata, ystep,
(uchar*) t_idx, sizeof( int ), tk,
&lerror, &rerror,
&threshold, &left, &right,
&sumw, &sumwy, &sumwyy ) )
{
optcompidx = ti;
}
}
break;
case CV_32SC1:
for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
{
tk = 0;
for( tj = 0; tj < sortedm; tj++ )
{
int curidx = (int) ( *((int*) (sorteddata
+ ti * sortedcstep + tj * sortedsstep)) );
if( filter[curidx] != 0 )
{
t_idx[tk++] = curidx;
}
}
if( findStumpThreshold_32s[stumperror](
t_data + ti * t_cstep, t_sstep,
wdata, wstep, ydata, ystep,
(uchar*) t_idx, sizeof( int ), tk,
&lerror, &rerror,
&threshold, &left, &right,
&sumw, &sumwy, &sumwyy ) )
{
optcompidx = ti;
}
}
break;
case CV_32FC1:
for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
{
tk = 0;
for( tj = 0; tj < sortedm; tj++ )
{
int curidx = (int) ( *((float*) (sorteddata
+ ti * sortedcstep + tj * sortedsstep)) );
if( filter[curidx] != 0 )
{
t_idx[tk++] = curidx;
}
}
if( findStumpThreshold_32s[stumperror](
t_data + ti * t_cstep, t_sstep,
wdata, wstep, ydata, ystep,
(uchar*) t_idx, sizeof( int ), tk,
&lerror, &rerror,
&threshold, &left, &right,
&sumw, &sumwy, &sumwyy ) )
{
optcompidx = ti;
}
}
break;
default:
assert( 0 );
break;
}
}
else //所有训练样本均参与计算
{
switch( sortedtype )
{
case CV_16SC1:/*遍历特征寻找使左右误差最小的特征*/
for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
{
/*
t_data + ti * t_cstep:第ti个特征模版
t_sstep:特征模版存储的跨距
wdata:样本的权重
wstep:样本权重数组的跨距
ydata:样本的类别标签
ystep:样本的类别标签数组的跨距
sorteddata + ti * sortedcstep:第ti个样本排序好的特征值的序号
sortedsstep:跨距
sortedm：序号的列数也就是实际样本列数
lerror：阈值左侧误差
rerror：阈值右侧误差
threshold：阈值
left：左分支置信度
right：右分支置信度
optcompidx：最优特征
*/
if( findStumpThreshold_16s[stumperror](
t_data + ti * t_cstep, t_sstep,
wdata, wstep, ydata, ystep,
sorteddata + ti * sortedcstep, sortedsstep, sortedm,
&lerror, &rerror,
&threshold, &left, &right,
&sumw, &sumwy, &sumwyy ) )
{
optcompidx = ti;
}
}
break;
case CV_32SC1:
for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
{
if( findStumpThreshold_32s[stumperror](
t_data + ti * t_cstep, t_sstep,
wdata, wstep, ydata, ystep,
sorteddata + ti * sortedcstep, sortedsstep, sortedm,
&lerror, &rerror,
&threshold, &left, &right,
&sumw, &sumwy, &sumwyy ) )
{
optcompidx = ti;
}
}
break;
case CV_32FC1:
for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
{
if( findStumpThreshold_32f[stumperror](
t_data + ti * t_cstep, t_sstep,
wdata, wstep, ydata, ystep,
sorteddata + ti * sortedcstep, sortedsstep, sortedm,
&lerror, &rerror,
&threshold, &left, &right,
&sumw, &sumwy, &sumwyy ) )
{
optcompidx = ti;
}
}
break;
default:
assert( 0 );
break;
}
}
}
/* 新特征部分，要对样本特征值进行排序，然后再寻找最优特征 */
ti = MAX( t_compidx, MIN( sortedn, t_compidx + t_n ) );
for( ; ti < t_compidx + t_n; ti++ )
{
va.data = t_data + ti * t_cstep;
va.step = t_sstep;
// 对样本特征值进行排序
icvSortIndexedValArray_32s( t_idx, l, &va );
// 继续寻找最优特征
if( findStumpThreshold_32s[stumperror](
t_data + ti * t_cstep, t_sstep,
wdata, wstep, ydata, ystep,
(uchar*)t_idx, sizeof( int ), l,
&lerror, &rerror,
&threshold, &left, &right,
&sumw, &sumwy, &sumwyy ) )
{
optcompidx = ti;
}
}
#ifdef _OPENMP
#pragma omp critical(c_compidx)
#endif /* _OPENMP */
// 更新特征计算范围
{
t_compidx = compidx;
compidx += portion;
}
}
#ifdef _OPENMP
#pragma omp critical(c_beststump)
#endif /* _OPENMP */
// 设置最优弱分类器
{
if( lerror + rerror < stump->lerror + stump->rerror )
{
stump->lerror = lerror;
stump->rerror = rerror;
stump->compidx = optcompidx;
stump->threshold = threshold;
stump->left = left;
stump->right = right;
}
}
/* free allocated memory */
if( mat.data.ptr != NULL )
{
cvFree( &(mat.data.ptr) );
}
if( t_idx != NULL )
{
cvFree( &t_idx );
}
} /* end of parallel region */
/* END */
/* free allocated memory */
if( filter != NULL )
{
cvFree( &filter );
}
// 如果设置为离散型，置信度应为1或者-1
if( ((CvMTStumpTrainParams*) trainParams)->type == CV_CLASSIFICATION_CLASS ) /*要满足这个条件才转成离散*/
{
stump->left = 2.0F * (stump->left >= 0.5F) - 1.0F; /*在这里将计算出来的左右置信度浮点数转成1或-1*/
stump->right = 2.0F * (stump->right >= 0.5F) - 1.0F;
}
return (CvClassifier*) stump;
}
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从上面函数的代码中观察到在遍历特征时会调用findStumpThreshold_16s、findStumpThreshold_32s、findStumpThreshold_32f数组中定义了的总共12个函数指针，根据参数的不同调用不同的函数，例如findStumpThreshold_16s中的四个函数指针如下：
/*这个数组的类型是一个函数指针*/
CvFindThresholdFunc findStumpThreshold_16s[4] = {
icvFindStumpThreshold_misc_16s,
icvFindStumpThreshold_gini_16s,
icvFindStumpThreshold_entropy_16s,
icvFindStumpThreshold_sq_16s
};
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例如函数指针icvFindStumpThreshold_misc_16s我并未找到函数实现在哪，
在这些数组声明的上面声明的一些宏，其实实现这些函数的
举个例子
宏1
/* misclassification error
* err = MIN( wpos, wneg );
*/
#define ICV_DEF_FIND_STUMP_THRESHOLD_MISC( suffix, type ) \
ICV_DEF_FIND_STUMP_THRESHOLD( misc_##suffix, type, \
wposl = 0.5F * ( wl + wyl ); \
wposr = 0.5F * ( wr + wyr ); \
curleft = 0.5F * ( 1.0F + curleft ); \
curright = 0.5F * ( 1.0F + curright ); \
curlerror = MIN( wposl, wl - wposl ); \
currerror = MIN( wposr, wr - wposr ); \
)
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宏2
#define ICV_DEF_FIND_STUMP_THRESHOLD( suffix, type, error ) \
CV_BOOST_IMPL int icvFindStumpThreshold_##suffix( \
uchar* data, size_t datastep, \
uchar* wdata, size_t wstep, \
uchar* ydata, size_t ystep, \
uchar* idxdata, size_t idxstep, int num, \
float* lerror, \
float* rerror, \
float* threshold, float* left, float* right, \
float* sumw, float* sumwy, float* sumwyy )
{
。。。。。。。。。。。。。。。。
}
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根据宏在预编译阶段的解析原理，在预编译阶段声明：
ICV_DEF_FIND_STUMP_THRESHOLD_MISC( 16s, short )时会被上述第一个宏代替，变成
ICV_DEF_FIND_STUMP_THRESHOLD( misc_16s, short, \
wposl = 0.5F * ( wl + wyl ); \
wposr = 0.5F * ( wr + wyr ); \
curleft = 0.5F * ( 1.0F + curleft ); \
curright = 0.5F * ( 1.0F + curright ); \
curlerror = 2.0F * wposl * ( 1.0F - curleft ); \
currerror = 2.0F * wposr * ( 1.0F - curright ); \
)
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接着上述ICV_DEF_FIND_STUMP_THRESHOLD宏解析后被CV_BOOST_IMPL int icvFindStumpThreshold_##suffix替代变成：
CV_BOOST_IMPL int icvFindStumpThreshold_misc_16s( \
uchar* data, size_t datastep, \
uchar* wdata, size_t wstep, \
uchar* ydata, size_t ystep, \
uchar* idxdata, size_t idxstep, int num, \
float* lerror, \
float* rerror, \
float* threshold, float* left, float* right, \
float* sumw, float* sumwy, float* sumwyy )
{
。。。。。。。。。。
/*后面函数体中的所有"type"都被替换成"short"*/
/*函数体中的"error"被*/
wposl = 0.5F * ( wl + wyl ); \
wposr = 0.5F * ( wr + wyr ); \
curleft = 0.5F * ( 1.0F + curleft ); \
curright = 0.5F * ( 1.0F + curright ); \
curlerror = 2.0F * wposl * ( 1.0F - curleft ); \
currerror = 2.0F * wposr * ( 1.0F - curright );
/*替换*/
}
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这样就实现了icvFindStumpThreshold_misc_16s的函数定义，其他的声明也是同样的方法，这就很巧妙的将12个很相似的函数，用宏声明的方式给分别定义了，而不用重复写很多代码。
所以findStumpThreshold_16s、findStumpThreshold_32s、findStumpThreshold_32f数组中函数指针指定的函数都是由下述宏实现的，只是要改下参数和error的计算方法：：：：
#define ICV_DEF_FIND_STUMP_THRESHOLD( suffix, type, error ) \
CV_BOOST_IMPL int icvFindStumpThreshold_##suffix( \
uchar* data, size_t datastep, \
uchar* wdata, size_t wstep, \
uchar* ydata, size_t ystep, \
uchar* idxdata, size_t idxstep, int num, \
float* lerror, \
float* rerror, \
float* threshold, float* left, float* right, \
float* sumw, float* sumwy, float* sumwyy )
{ \
int found = 0; \
float wyl = 0.0F; \
float wl = 0.0F; \
float wyyl = 0.0F; \
float wyr = 0.0F; \
float wr = 0.0F; \
\
float curleft = 0.0F; \
float curright = 0.0F; \
float* prevval = NULL; \
float* curval = NULL; \
float curlerror = 0.0F; \
float currerror = 0.0F; \
float wposl; \
float wposr; \
\
int i = 0; \
int idx = 0; \
\
wposl = wposr = 0.0F; \
if( *sumw == FLT_MAX ) \
{ \
/* calculate sums */ \
float *y = NULL; \
float *w = NULL; \
float wy = 0.0F; \
\
*sumw = 0.0F; \
*sumwy = 0.0F; \
*sumwyy = 0.0F; \
for( i = 0; i < num; i++ ) \
{ \
idx = (int) ( *((type*) (idxdata + i*idxstep)) ); \
w = (float*) (wdata + idx * wstep); \
*sumw += *w; \
y = (float*) (ydata + idx * ystep); \
wy = (*w) * (*y); \
*sumwy += wy; \
*sumwyy += wy * (*y); \
} \
}
/*num:实际样本个数，遍历样本找到使左右误差最小的阈值curval位置*/ \
for( i = 0; i < num; i++ ) \
{ \
idx = (int) ( *((type*) (idxdata + i*idxstep)) ); \
curval = (float*) (data + idx * datastep); \
/* for debug purpose */ \
if( i > 0 ) assert( (*prevval) <= (*curval) ); \
\
wyr = *sumwy - wyl; \
wr = *sumw - wl; \
\
if( wl > 0.0 ) curleft = wyl / wl; \
else curleft = 0.0F; \
\
if( wr > 0.0 ) curright = wyr / wr; \
else curright = 0.0F; \
\
error \
\
if( curlerror + currerror < (*lerror) + (*rerror) ) \
{ \
(*lerror) = curlerror; \
(*rerror) = currerror; \
*threshold = *curval; \
if( i > 0 ) { \
*threshold = 0.5F * (*threshold + *prevval); \
} \
*left = curleft; \
*right = curright; \
found = 1; \
} \
\
do \
{ \
wl += *((float*) (wdata + idx * wstep)); \
wyl += (*((float*) (wdata + idx * wstep))) \
* (*((float*) (ydata + idx * ystep))); \
wyyl += *((float*) (wdata + idx * wstep)) \
* (*((float*) (ydata + idx * ystep))) \
* (*((float*) (ydata + idx * ystep))); \
} \
while( (++i) < num && \
( *((float*) (data + (idx = \
(int) ( *((type*) (idxdata + i*idxstep))) ) * datastep)) \
== *curval ) ); \
--i; \
prevval = curval; \
} /* for each value */ \
\
return found; \
}
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这段代码的逻辑一句话概括就是：：：遍历某特征的所有样本找到使分类的左右误差最小的阈值。
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