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Merged the trunk r8589:8653 - all changes related to build warnings

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This commit is contained in:
Andrey Kamaev
2012-06-15 13:04:17 +00:00
parent 73c152abc4
commit bd0e0b5800
438 changed files with 20374 additions and 19674 deletions
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72
modules/ml/src/rtrees.cpp
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@@ -307,14 +307,14 @@ bool CvRTrees::train( const CvMat* _train_data, int _tflag,
return grow_forest( params.term_crit );
}
bool CvRTrees::train( CvMLData* data, CvRTParams params )
bool CvRTrees::train( CvMLData* _data, CvRTParams params )
{
const CvMat* values = data->get_values();
const CvMat* response = data->get_responses();
const CvMat* missing = data->get_missing();
const CvMat* var_types = data->get_var_types();
const CvMat* train_sidx = data->get_train_sample_idx();
const CvMat* var_idx = data->get_var_idx();
const CvMat* values = _data->get_values();
const CvMat* response = _data->get_responses();
const CvMat* missing = _data->get_missing();
const CvMat* var_types = _data->get_var_types();
const CvMat* train_sidx = _data->get_train_sample_idx();
const CvMat* var_idx = _data->get_var_idx();
return train( values, CV_ROW_SAMPLE, response, var_idx,
train_sidx, var_types, missing, params );
@@ -331,7 +331,7 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit )
const int dims = data->var_count;
float maximal_response = 0;
CvMat* oob_sample_votes = 0;
CvMat* oob_sample_votes = 0;
CvMat* oob_responses = 0;
float* oob_samples_perm_ptr= 0;
@@ -347,7 +347,7 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit )
// initialize these variable to avoid warning C4701
CvMat oob_predictions_sum = cvMat( 1, 1, CV_32FC1 );
CvMat oob_num_of_predictions = cvMat( 1, 1, CV_32FC1 );
nsamples = data->sample_count;
nclasses = data->get_num_classes();
@@ -369,14 +369,14 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit )
cvGetRow( oob_responses, &oob_predictions_sum, 0 );
cvGetRow( oob_responses, &oob_num_of_predictions, 1 );
}
oob_samples_perm_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims );
samples_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims );
missing_ptr = (uchar*)cvAlloc( sizeof(uchar)*nsamples*dims );
true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples );
true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples );
data->get_vectors( 0, samples_ptr, missing_ptr, true_resp_ptr );
double minval, maxval;
CvMat responses = cvMat(1, nsamples, CV_32FC1, true_resp_ptr);
cvMinMaxLoc( &responses, &minval, &maxval );
@@ -536,7 +536,7 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit )
cvFree( &samples_ptr );
cvFree( &missing_ptr );
cvFree( &true_resp_ptr );
cvReleaseMat( &sample_idx_mask_for_tree );
cvReleaseMat( &sample_idx_for_tree );
@@ -592,9 +592,9 @@ float CvRTrees::calc_error( CvMLData* _data, int type , std::vector<float> *resp
{
CvMat sample, miss;
int si = sidx ? sidx[i] : i;
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
float r = (float)predict( &sample, missing ? &miss : 0 );
if( pred_resp )
pred_resp[i] = r;
@@ -610,15 +610,15 @@ float CvRTrees::calc_error( CvMLData* _data, int type , std::vector<float> *resp
CvMat sample, miss;
int si = sidx ? sidx[i] : i;
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
if( missing )
cvGetRow( missing, &miss, si );
float r = (float)predict( &sample, missing ? &miss : 0 );
if( pred_resp )
pred_resp[i] = r;
float d = r - response->data.fl[si*r_step];
err += d*d;
}
err = sample_count ? err / (float)sample_count : -FLT_MAX;
err = sample_count ? err / (float)sample_count : -FLT_MAX;
}
return err;
}
@@ -635,12 +635,12 @@ float CvRTrees::get_train_error()
float *responses_ptr = (float*)cvAlloc( sizeof(float)*sample_count );
data->get_vectors( 0, values_ptr, missing_ptr, responses_ptr);
if (data->is_classifier)
{
int err_count = 0;
float *vp = values_ptr;
uchar *mp = missing_ptr;
uchar *mp = missing_ptr;
for (int si = 0; si < sample_count; si++, vp += var_count, mp += var_count)
{
CvMat sample = cvMat( 1, var_count, CV_32FC1, vp );
@@ -653,10 +653,10 @@ float CvRTrees::get_train_error()
}
else
CV_Error( CV_StsBadArg, "This method is not supported for regression problems" );
cvFree( &values_ptr );
cvFree( &missing_ptr );
cvFree( &responses_ptr );
cvFree( &responses_ptr );
return err;
}
@@ -701,7 +701,7 @@ float CvRTrees::predict( const CvMat* sample, const CvMat* missing ) const
float CvRTrees::predict_prob( const CvMat* sample, const CvMat* missing) const
{
if( nclasses == 2 ) //classification
if( nclasses == 2 ) //classification
{
cv::AutoBuffer<int> _votes(nclasses);
int* votes = _votes;
@@ -711,15 +711,15 @@ float CvRTrees::predict_prob( const CvMat* sample, const CvMat* missing) const
CvDTreeNode* predicted_node = trees[k]->predict( sample, missing );
int class_idx = predicted_node->class_idx;
CV_Assert( 0 <= class_idx && class_idx < nclasses );
++votes[class_idx];
}
return float(votes[1])/ntrees;
return float(votes[1])/ntrees;
}
else // regression
CV_Error(CV_StsBadArg, "This function works for binary classification problems only...");
CV_Error(CV_StsBadArg, "This function works for binary classification problems only...");
return -1;
}
@@ -809,15 +809,15 @@ void CvRTrees::read( CvFileStorage* fs, CvFileNode* fnode )
{
// initialize active variables mask
CvMat submask1;
cvGetCols( active_var_mask, &submask1, 0, nactive_vars );
cvGetCols( active_var_mask, &submask1, 0, nactive_vars );
cvSet( &submask1, cvScalar(1) );
if( nactive_vars < var_count )
{
CvMat submask2;
cvGetCols( active_var_mask, &submask2, nactive_vars, var_count );
cvZero( &submask2 );
}
if( nactive_vars < var_count )
{
CvMat submask2;
cvGetCols( active_var_mask, &submask2, nactive_vars, var_count );
cvZero( &submask2 );
}
}
}