def track_execution(): LOGGER.info('Starting training.') timer = Time() yield timer.stop() LOGGER.info('Training completed, took {0:.2f}s.'.format( timer.time_diff_sec()))
def track_execution(): LOGGER.info('Starting training.') timer = Time() yield timer.stop() LOGGER.info('Training completed, took {0:.2f}s.'.format(timer.time_diff_sec()))
args = parse_arguments() print 'Loading training data...' sparse_data = load_sparse_data(args.dataset,args.dimension) kernel_params = array([args.width], dtype=float) rf_feats = RandomFourierDotFeatures(sparse_data['data'], args.D, GAUSSIAN, kernel_params) svm = SVMOcas(args.C, rf_feats, sparse_data['labels']) svm.set_epsilon(args.epsilon) print 'Starting training.' timer = Time() svm.train() timer.stop() print 'Training completed, took {0:.2f}s.'.format(timer.time_diff_sec()) predicted_labels = svm.apply() evaluate(predicted_labels, sparse_data['labels'], 'Training results') if args.testset!=None: random_coef = rf_feats.get_random_coefficients() # removing current dataset from memory in order to load the test dataset, # to avoid running out of memory rf_feats = None svm.set_features(None) svm.set_labels(None) sparse_data = None print 'Loading test data...'
args = parse_arguments() print 'Loading training data...' sparse_data = load_sparse_data(args.dataset, args.dimension) kernel_params = array([args.width], dtype=float) rf_feats = RandomFourierDotFeatures(sparse_data['data'], args.D, GAUSSIAN, kernel_params) svm = SVMOcas(args.C, rf_feats, sparse_data['labels']) svm.set_epsilon(args.epsilon) print 'Starting training.' timer = Time() svm.train() timer.stop() print 'Training completed, took {0:.2f}s.'.format(timer.time_diff_sec()) predicted_labels = svm.apply() evaluate(predicted_labels, sparse_data['labels'], 'Training results') if args.testset != None: random_coef = rf_feats.get_random_coefficients() # removing current dataset from memory in order to load the test dataset, # to avoid running out of memory rf_feats = None svm.set_features(None) svm.set_labels(None) sparse_data = None print 'Loading test data...'