def run_customization(image_loader, feature_extractor):
logging.info("Start customize svm")
logging.info("Generate sample")
data = get_class_data(params.first_class_params, params.sample_size/2) + get_class_data(params.second_class_params, params.sample_size/2)
random.shuffle(data)
trainer = Trainer(image_loader, feature_extractor)
c_range = [10 ** i for i in xrange(-5, 10)]
gamma_range = [10 ** i for i in xrange(-5, 5)]
results = trainer.svm_params_customization(data, params.svm_params, c_range, gamma_range)
return results
