def get_dataset(size, partition, label_category=None, skip=1): """ :param size: 0:mini, 1:small, 2:medium, 3:large :param label_category: 0:moving, 1:position, 2:in/outdoor """ train_x, train_y = load_data.load_prepared_data(load_data.load_sample_expId(size, partition), skip=skip) if label_category is not None: train_y = numpy.asarray(train_y[:,label_category], dtype='int32') else: train_y = numpy.asarray(train_y, dtype='int32') return [train_x, train_y]
def eval_exps(self, exp_ids): preparedset = load_prepared_data(exp_ids) prepared_x = preparedset[0] prepared_y = numpy.asarray(preparedset[1][:,0], 'int32') mv_logreg_error = 1-self.mv_logreg.score(prepared_x, prepared_y) # dataset = load_data(exp_ids, window=self.window, sliding_step=self.sliding_step) # if dataset is None: # return 0, 0, 0, 0 # sharedset = shared_dataset(dataset, self.convnet.static_sensor) # convnet_inst_num = dataset[1].shape[0] dataset = cc_preprocessor.load_data(exp_ids, window=self.window, sliding_step=self.sliding_step) if dataset.start_idx < 1: return 0, 0, 0, 0 sharedset = cc_multimodal_conv.shared_dataset(dataset) convnet_inst_num = dataset.start_idx """ shared_x = theano.shared(prepared_x, borrow=True) shared_y = theano.shared(prepared_y, borrow=True) sharedset = [shared_x, shared_y] """ if self.mv_rf is None: convnet_scores = self.convnet.score(sharedset) print convnet_scores else: repr_x, repr_y = self.convnet.input_to_feature(sharedset, layer=self.layer) print repr_x.shape convnet_scores = self.mv_rf.score(repr_x, repr_y[:,0]) convnet_errors = 1-convnet_scores logreg_inst_num = len(preparedset[1]) return logreg_inst_num, mv_logreg_error, convnet_inst_num, convnet_errors