Hi,
I was looking for updating to 3.0 and checked the new TrainData class and how it works with the SVM TrainAuto. As far as I can see during cross validation, the temp_test_samples matrix is never filled with data or am I getting anything wrong?
Mat temp_test_samples(test_sample_count, var_count, CV_32F);
predict(temp_test_samples, temp_test_responses, 0);
int test_sample_count = (sample_count + k_fold/2)/k_fold; int train_sample_count = sample_count - test_sample_count;
SvmParams best_params = params;
double min_error = FLT_MAX;
int rtype = responses.type();
Mat temp_train_samples(train_sample_count, var_count, CV_32F);
Mat temp_test_samples(test_sample_count, var_count, CV_32F);
Mat temp_train_responses(train_sample_count, 1, rtype);
Mat temp_test_responses;
// If grid.minVal == grid.maxVal, this will allow one and only one pass through the loop with params.var = grid.minVal.
#define FOR_IN_GRID(var, grid) \
for( params.var = grid.minVal; params.var == grid.minVal || params.var < grid.maxVal; params.var = (grid.minVal == grid.maxVal) ? grid.maxVal + 1 : params.var * grid.logStep )
FOR_IN_GRID(C, C_grid)
FOR_IN_GRID(gamma, gamma_grid)
FOR_IN_GRID(p, p_grid)
FOR_IN_GRID(nu, nu_grid)
FOR_IN_GRID(coef0, coef_grid)
FOR_IN_GRID(degree, degree_grid)
{
// make sure we updated the kernel and other parameters
setParams(params);
double error = 0;
for( k = 0; k < k_fold; k++ )
{
int start = (k*sample_count + k_fold/2)/k_fold;
for( i = 0; i < train_sample_count; i++ )
{
j = sidx[(i+start)%sample_count];
memcpy(temp_train_samples.ptr(i), samples.ptr(j), sample_size);
if( is_classification )
temp_train_responses.at<int>(i) = responses.at<int>(j);
else if( !responses.empty() )
temp_train_responses.at<float>(i) = responses.at<float>(j);
}
// Train SVM on <train_size> samples
if( !do_train( temp_train_samples, temp_train_responses ))
continue;
for( i = 0; i < train_sample_count; i++ )
{
j = sidx[(i+start+train_sample_count) % sample_count];
memcpy(temp_train_samples.ptr(i), samples.ptr(j), sample_size);
}
predict(temp_test_samples, temp_test_responses, 0);
for( i = 0; i < test_sample_count; i++ )
{
float val = temp_test_responses.at<float>(i);
j = sidx[(i+start+train_sample_count) % sample_count];
if( is_classification )
error += (float)(val != responses.at<int>(j));
else
{
val -= responses.at<float>(j);
error += val*val;
}
}
}
if( min_error > error )
{
min_error = error;
best_params = params;
}
}
params = best_params;
return do_train( samples, responses );
Best regards,
chris
