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Warning fixes continued

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This commit is contained in:
Andrey Kamaev
2012-06-09 15:00:04 +00:00
parent f6b451c607
commit f2d3b9b4a1
127 changed files with 6298 additions and 6277 deletions
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modules/ml/src/gbt.cpp
+16 -16
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@@ -166,13 +166,13 @@ bool CvGBTrees::problem_type() const
//===========================================================================
bool
CvGBTrees::train( CvMLData* data, CvGBTreesParams params, bool update )
CvGBTrees::train( CvMLData* _data, CvGBTreesParams _params, bool update )
{
bool result;
result = train ( data->get_values(), CV_ROW_SAMPLE,
data->get_responses(), data->get_var_idx(),
data->get_train_sample_idx(), data->get_var_types(),
data->get_missing(), params, update);
result = train ( _data->get_values(), CV_ROW_SAMPLE,
_data->get_responses(), _data->get_var_idx(),
_data->get_train_sample_idx(), _data->get_var_types(),
_data->get_missing(), _params, update);
//update is not supported
return result;
}
@@ -1294,12 +1294,12 @@ CvGBTrees::calc_error( CvMLData* _data, int type, std::vector<float> *resp )
{
float err = 0.0f;
const CvMat* sample_idx = (type == CV_TRAIN_ERROR) ?
const CvMat* _sample_idx = (type == CV_TRAIN_ERROR) ?
_data->get_train_sample_idx() :
_data->get_test_sample_idx();
const CvMat* response = _data->get_responses();
int n = sample_idx ? get_len(sample_idx) : 0;
int n = _sample_idx ? get_len(_sample_idx) : 0;
n = (type == CV_TRAIN_ERROR && n == 0) ? _data->get_values()->rows : n;
if (!n)
@@ -1315,7 +1315,7 @@ CvGBTrees::calc_error( CvMLData* _data, int type, std::vector<float> *resp )
pred_resp = new float[n];
Sample_predictor predictor = Sample_predictor(this, pred_resp, _data->get_values(),
_data->get_missing(), sample_idx);
_data->get_missing(), _sample_idx);
//#ifdef HAVE_TBB
// tbb::parallel_for(cv::BlockedRange(0,n), predictor, tbb::auto_partitioner());
@@ -1323,7 +1323,7 @@ CvGBTrees::calc_error( CvMLData* _data, int type, std::vector<float> *resp )
cv::parallel_for(cv::BlockedRange(0,n), predictor);
//#endif
int* sidx = sample_idx ? sample_idx->data.i : 0;
int* sidx = _sample_idx ? _sample_idx->data.i : 0;
int r_step = CV_IS_MAT_CONT(response->type) ?
1 : response->step / CV_ELEM_SIZE(response->type);
@@ -1357,7 +1357,7 @@ CvGBTrees::CvGBTrees( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx,
const cv::Mat& sampleIdx, const cv::Mat& varType,
const cv::Mat& missingDataMask,
CvGBTreesParams params )
CvGBTreesParams _params )
{
data = 0;
weak = 0;
@@ -1371,14 +1371,14 @@ CvGBTrees::CvGBTrees( const cv::Mat& trainData, int tflag,
clear();
train(trainData, tflag, responses, varIdx, sampleIdx, varType, missingDataMask, params, false);
train(trainData, tflag, responses, varIdx, sampleIdx, varType, missingDataMask, _params, false);
}
bool CvGBTrees::train( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx,
const cv::Mat& sampleIdx, const cv::Mat& varType,
const cv::Mat& missingDataMask,
CvGBTreesParams params,
CvGBTreesParams _params,
bool update )
{
CvMat _trainData = trainData, _responses = responses;
@@ -1387,13 +1387,13 @@ bool CvGBTrees::train( const cv::Mat& trainData, int tflag,
return train( &_trainData, tflag, &_responses, varIdx.empty() ? 0 : &_varIdx,
sampleIdx.empty() ? 0 : &_sampleIdx, varType.empty() ? 0 : &_varType,
missingDataMask.empty() ? 0 : &_missingDataMask, params, update);
missingDataMask.empty() ? 0 : &_missingDataMask, _params, update);
}
float CvGBTrees::predict( const cv::Mat& sample, const cv::Mat& missing,
float CvGBTrees::predict( const cv::Mat& sample, const cv::Mat& _missing,
const cv::Range& slice, int k ) const
{
CvMat _sample = sample, _missing = missing;
return predict(&_sample, missing.empty() ? 0 : &_missing, 0,
CvMat _sample = sample, miss = _missing;
return predict(&_sample, _missing.empty() ? 0 : &miss, 0,
slice==cv::Range::all() ? CV_WHOLE_SEQ : cvSlice(slice.start, slice.end), k);
}