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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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31
modules/ml/src/svm.cpp
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@@ -1065,10 +1065,10 @@ 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, _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, _C, _C, _storage, _kernel, &CvSVMSolver::get_row_svr,
&CvSVMSolver::select_working_set, &CvSVMSolver::calc_rho ))
return false;
@@ -1101,7 +1101,7 @@ 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 _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 +1110,11 @@ 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 = _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, _C);
sum -= alpha[i];
b[i] = -_y[i];
@@ -1628,12 +1628,11 @@ 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, _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 +1759,7 @@ 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);
@@ -1779,7 +1778,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
{
// 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;
@@ -1875,10 +1874,10 @@ 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;
_C = C_grid.min_val;
do
{
params.C = C;
params.C = _C;
gamma = gamma_grid.min_val;
do
{
@@ -1906,7 +1905,7 @@ 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 +1929,7 @@ 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 +1942,7 @@ 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 = _C;
best_nu = nu;
best_p = p;
}
@@ -1962,9 +1961,9 @@ 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;
_C *= C_grid.step;
}
while( C < C_grid.max_val );
while( _C < C_grid.max_val );
}
min_error /= (float) sample_count;