- Python (v2.7) - PyTorch (v0.2.20) - scikit-learn 

 $ cat klcpd_py2.7_pt0.2.0_conda.txt 

python klcpd.py [OPTIONS] OPTIONS: --data_path DATA_PATH data path to dataset.mat --trn_ratio TRN_RATIO how much data used for training --val_ratio VAL_RATIO how much data used for validation --gpu GPU gpu device id --cuda CUDA use gpu or not --random_seed RANDOM_SEED random seed --wnd_dim WND_DIM window size (past and future) --sub_dim SUB_DIM dimension of subspace embedding --RNN_hid_dim RNN_HID_DIM number of RNN hidden units --batch_size BATCH_SIZE batch size for training --max_iter MAX_ITER max iteration for pretraining RNN --optim OPTIM sgd|rmsprop|adam for optimization method --lr LR learning rate --weight_decay WEIGHT_DECAY weight decay (L2 regularization) --momentum MOMENTUM momentum for sgd --grad_clip GRAD_CLIP gradient clipping for RNN (both netG and netD) --eval_freq EVAL_FREQ evaluation frequency per generator update --CRITIC_ITERS CRITIC_ITERS number of updates for critic per generator --weight_clip WEIGHT_CLIP weight clipping for crtic --lambda_ae LAMBDA_AE coefficient for the reconstruction loss --lambda_real LAMBDA_REAL coefficient for the real MMD2 loss --save_path SAVE_PATH path to save the final model --save_name SAVE_NAME model/prediction names 

 $ python run_klcpd.py --dataroot ./data --dataset beedance --wnd_dim_list 25 --max_iter 2000 --batch_size 64 

@article{chang2019kernel, title={Kernel change-point detection with auxiliary deep generative models}, author={Chang, Wei-Cheng and Li, Chun-Liang and Yang, Yiming and P{\'o}czos, Barnab{\'a}s}, journal={arXiv preprint arXiv:1901.06077}, year={2019} }