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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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79
modules/ml/src/svm.cpp
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@@ -88,10 +88,6 @@ using namespace cv;
#include <stdarg.h>
#include <ctype.h>
#if _MSC_VER >= 1200
#pragma warning( disable: 4514 ) /* unreferenced inline functions */
#endif
#if 1
typedef float Qfloat;
#define QFLOAT_TYPE CV_32F
@@ -1065,10 +1061,10 @@ bool CvSVMSolver::solve_eps_svr( int _sample_count, int _var_count, const float*
CvSVMKernel* _kernel, double* _alpha, CvSVMSolutionInfo& _si )
{
int i;
double p = _kernel->params->p, C = _kernel->params->C;
double p = _kernel->params->p, kernel_param_c = _kernel->params->C;
if( !create( _sample_count, _var_count, _samples, 0,
_sample_count*2, 0, C, C, _storage, _kernel, &CvSVMSolver::get_row_svr,
_sample_count*2, 0, kernel_param_c, kernel_param_c, _storage, _kernel, &CvSVMSolver::get_row_svr,
&CvSVMSolver::select_working_set, &CvSVMSolver::calc_rho ))
return false;
@@ -1101,7 +1097,7 @@ bool CvSVMSolver::solve_nu_svr( int _sample_count, int _var_count, const float**
CvSVMKernel* _kernel, double* _alpha, CvSVMSolutionInfo& _si )
{
int i;
double C = _kernel->params->C, sum;
double kernel_param_c = _kernel->params->C, sum;
if( !create( _sample_count, _var_count, _samples, 0,
_sample_count*2, 0, 1., 1., _storage, _kernel, &CvSVMSolver::get_row_svr,
@@ -1110,11 +1106,11 @@ bool CvSVMSolver::solve_nu_svr( int _sample_count, int _var_count, const float**
y = (schar*)cvMemStorageAlloc( storage, sample_count*2*sizeof(y[0]) );
alpha = (double*)cvMemStorageAlloc( storage, alpha_count*sizeof(alpha[0]) );
sum = C * _kernel->params->nu * sample_count * 0.5;
sum = kernel_param_c * _kernel->params->nu * sample_count * 0.5;
for( i = 0; i < sample_count; i++ )
{
alpha[i] = alpha[i + sample_count] = MIN(sum, C);
alpha[i] = alpha[i + sample_count] = MIN(sum, kernel_param_c);
sum -= alpha[i];
b[i] = -_y[i];
@@ -1593,7 +1589,7 @@ bool CvSVM::train( const CvMat* _train_data, const CvMat* _responses,
return ok;
}
struct indexedratio
struct indexedratio
{
double val;
int ind;
@@ -1628,12 +1624,11 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
int svm_type, sample_count, var_count, sample_size;
int block_size = 1 << 16;
double* alpha;
int i, k;
RNG* rng = &theRNG();
// all steps are logarithmic and must be > 1
double degree_step = 10, g_step = 10, coef_step = 10, C_step = 10, nu_step = 10, p_step = 10;
double gamma = 0, C = 0, degree = 0, coef = 0, p = 0, nu = 0;
double gamma = 0, curr_c = 0, degree = 0, coef = 0, p = 0, nu = 0;
double best_degree = 0, best_gamma = 0, best_coef = 0, best_C = 0, best_nu = 0, best_p = 0;
float min_error = FLT_MAX, error;
@@ -1760,7 +1755,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
cvZero( responses_local );
// randomly permute samples and responses
for( i = 0; i < sample_count; i++ )
for(int i = 0; i < sample_count; i++ )
{
int i1 = (*rng)(sample_count);
int i2 = (*rng)(sample_count);
@@ -1774,25 +1769,25 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
else
CV_SWAP( responses->data.i[i1], responses->data.i[i2], y );
}
if (!is_regression && class_labels->cols==2 && balanced)
{
// count class samples
int num_0=0,num_1=0;
for (i=0; i<sample_count; ++i)
for (int i=0; i<sample_count; ++i)
{
if (responses->data.i[i]==class_labels->data.i[0])
++num_0;
else
++num_1;
}
int label_smallest_class;
int label_biggest_class;
if (num_0 < num_1)
{
label_biggest_class = class_labels->data.i[1];
label_smallest_class = class_labels->data.i[0];
label_smallest_class = class_labels->data.i[0];
}
else
{
@@ -1875,10 +1870,10 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
}
int* cls_lbls = class_labels ? class_labels->data.i : 0;
C = C_grid.min_val;
curr_c = C_grid.min_val;
do
{
params.C = C;
params.C = curr_c;
gamma = gamma_grid.min_val;
do
{
@@ -1906,7 +1901,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
int train_size = trainset_size;
error = 0;
for( k = 0; k < k_fold; k++ )
for(int k = 0; k < k_fold; k++ )
{
memcpy( samples_local, samples, sizeof(samples[0])*test_size*k );
memcpy( samples_local + test_size*k, test_samples_ptr + test_size,
@@ -1930,7 +1925,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
EXIT;
// Compute test set error on <test_size> samples
for( i = 0; i < test_size; i++, true_resp += resp_elem_size, test_samples_ptr++ )
for(int i = 0; i < test_size; i++, true_resp += resp_elem_size, test_samples_ptr++ )
{
float resp = predict( *test_samples_ptr, var_count );
error += is_regression ? powf( resp - *(float*)true_resp, 2 )
@@ -1943,7 +1938,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
best_degree = degree;
best_gamma = gamma;
best_coef = coef;
best_C = C;
best_C = curr_c;
best_nu = nu;
best_p = p;
}
@@ -1962,9 +1957,9 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
gamma *= gamma_grid.step;
}
while( gamma < gamma_grid.max_val );
C *= C_grid.step;
curr_c *= C_grid.step;
}
while( C < C_grid.max_val );
while( curr_c < C_grid.max_val );
}
min_error /= (float) sample_count;
@@ -2001,7 +1996,7 @@ float CvSVM::predict( const float* row_sample, int row_len, bool returnDFVal ) c
int var_count = get_var_count();
assert( row_len == var_count );
(void)row_len;
(void)row_len;
int class_count = class_labels ? class_labels->cols :
params.svm_type == ONE_CLASS ? 1 : 0;
@@ -2072,7 +2067,7 @@ float CvSVM::predict( const CvMat* sample, bool returnDFVal ) const
__BEGIN__;
int class_count;
if( !kernel )
CV_ERROR( CV_StsBadArg, "The SVM should be trained first" );
@@ -2082,7 +2077,7 @@ float CvSVM::predict( const CvMat* sample, bool returnDFVal ) const
CV_CALL( cvPreparePredictData( sample, var_all, var_idx,
class_count, 0, &row_sample ));
result = predict( row_sample, get_var_count(), returnDFVal );
__END__;
if( sample && (!CV_IS_MAT(sample) || sample->data.fl != row_sample) )
@@ -2099,12 +2094,12 @@ struct predict_body_svm {
samples = _samples;
results = _results;
}
const CvSVM* pointer;
float* result;
const CvMat* samples;
CvMat* results;
void operator()( const cv::BlockedRange& range ) const
{
for(int i = range.begin(); i < range.end(); i++ )
@@ -2116,15 +2111,15 @@ struct predict_body_svm {
results->data.fl[i] = (float)r;
if (i == 0)
*result = (float)r;
}
}
}
};
float CvSVM::predict(const CvMat* samples, CV_OUT CvMat* results) const
{
float result = 0;
cv::parallel_for(cv::BlockedRange(0, samples->rows),
predict_body_svm(this, &result, samples, results)
cv::parallel_for(cv::BlockedRange(0, samples->rows),
predict_body_svm(this, &result, samples, results)
);
return result;
}
@@ -2141,7 +2136,7 @@ CvSVM::CvSVM( const Mat& _train_data, const Mat& _responses,
kernel = 0;
solver = 0;
default_model_name = "my_svm";
train( _train_data, _responses, _var_idx, _sample_idx, _params );
}
@@ -2166,7 +2161,7 @@ bool CvSVM::train_auto( const Mat& _train_data, const Mat& _responses,
float CvSVM::predict( const Mat& _sample, bool returnDFVal ) const
{
CvMat sample = _sample;
CvMat sample = _sample;
return predict(&sample, returnDFVal);
}
@@ -2648,11 +2643,11 @@ cvTrainSVM_CrossValidation( const CvMat* train_data, int tflag,
__BEGIN__;
double degree_step = 7,
g_step = 15,
coef_step = 14,
C_step = 20,
nu_step = 5,
p_step = 7; // all steps must be > 1
g_step = 15,
coef_step = 14,
C_step = 20,
nu_step = 5,
p_step = 7; // all steps must be > 1
double degree_begin = 0.01, degree_end = 2;
double g_begin = 1e-5, g_end = 0.5;
double coef_begin = 0.1, coef_end = 300;
@@ -2662,12 +2657,12 @@ cvTrainSVM_CrossValidation( const CvMat* train_data, int tflag,
double rate = 0, gamma = 0, C = 0, degree = 0, coef = 0, p = 0, nu = 0;
double best_rate = 0;
double best_rate = 0;
double best_degree = degree_begin;
double best_gamma = g_begin;
double best_coef = coef_begin;
double best_C = C_begin;
double best_nu = nu_begin;
double best_C = C_begin;
double best_nu = nu_begin;
double best_p = p_begin;
CvSVMModelParams svm_params, *psvm_params;