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fixed GBTrees build on NetBSD & Android; fixed GPU brute force matcher test build on MacOSX

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This commit is contained in:
Vadim Pisarevsky
2010-12-06 19:42:16 +00:00
parent 652fb1212e
commit ed934ed6fc
2 changed files with 20 additions and 23 deletions
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26
modules/ml/src/gbt.cpp
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@@ -11,10 +11,6 @@ using namespace std;
#define CV_CMP_FLOAT(a,b) ((a) < (b))
static CV_IMPLEMENT_QSORT_EX( icvSortFloat, float, CV_CMP_FLOAT, float)
#if ANDROID
#define expl(x) exp(x)
#endif
//===========================================================================
string ToString(int i)
{
@@ -461,17 +457,17 @@ void CvGBTrees::find_gradient(const int k)
{
for (int i=0; i<get_len(subsample_train); ++i)
{
long double exp_fk = 0;
long double exp_sfi = 0;
double exp_fk = 0;
double exp_sfi = 0;
int s_step = (sample_idx->cols > sample_idx->rows) ? 1
: sample_idx->step/CV_ELEM_SIZE(sample_idx->type);
int idx = *(sample_data + subsample_data[i]*s_step);
for (int j=0; j<class_count; ++j)
{
long double res;
double res;
res = current_data[idx + j*sum_response->cols];
res = expl(res);
res = exp(res);
if (j == k) exp_fk = res;
exp_sfi += res;
}
@@ -625,7 +621,7 @@ void CvGBTrees::change_values(CvDTree* tree, const int _k)
float CvGBTrees::find_optimal_value( const CvMat* _Idx )
{
long double gamma = (long double)0.0;
double gamma = (double)0.0;
int* idx = _Idx->data.i;
float* resp_data = orig_response->data.fl;
@@ -639,7 +635,7 @@ float CvGBTrees::find_optimal_value( const CvMat* _Idx )
{
for (int i=0; i<n; ++i)
gamma += resp_data[idx[i]] - cur_data[idx[i]];
gamma /= (long double)n;
gamma /= (double)n;
}; break;
case ABSOLUTE_LOSS:
@@ -671,7 +667,7 @@ float CvGBTrees::find_optimal_value( const CvMat* _Idx )
float dif = residuals[i] - r_median;
gamma += (delta < fabs(dif)) ? Sign(dif)*delta : dif;
}
gamma /= (long double)n;
gamma /= (double)n;
gamma += r_median;
delete[] residuals;
@@ -680,9 +676,9 @@ float CvGBTrees::find_optimal_value( const CvMat* _Idx )
case DEVIANCE_LOSS:
{
float* grad_data = data->responses->data.fl;
long double tmp1 = 0;
long double tmp2 = 0;
long double tmp = 0;
double tmp1 = 0;
double tmp2 = 0;
double tmp = 0;
for (int i=0; i<n; ++i)
{
tmp = grad_data[idx[i]];
@@ -694,7 +690,7 @@ float CvGBTrees::find_optimal_value( const CvMat* _Idx )
tmp2 = 1;
}
gamma = ((long double)(class_count-1)) / (long double)class_count * (tmp1/tmp2);
gamma = ((double)(class_count-1)) / (double)class_count * (tmp1/tmp2);
}; break;
default: break;