# params for autoencoder
n_cate: 1                       # (int) Category num of items
reparam: True                   # (bool) Autoencoder with variational inference or not
in_dims: [300]                  # (list of int) The dims for the encoder
out_dims: []                    # (list of int) The hidden dims for the decoder
ae_act_func: 'tanh'             # (str) Activation function for AutoEncoder
lamda: 0.03                     # (float) Hyper-parameter of multinomial log-likelihood for AE
anneal_cap: 0.005               # (float) The upper bound of the annealing weight
anneal_steps: 1000              # (int) The steps of annealing
vae_anneal_cap: 0.3             # (float) The upper bound of the VAE annealing weight
vae_anneal_steps: 200           # (int) The steps of VAE annealing

# params for the diffusion
noise_schedule: 'linear'        # (str) The schedule for noise generating: [linear, linear-var, cosine, binomial]
noise_scale: 0.1                # (int) The scale for noise generating
noise_min: 0.001                # (int) Noise lower bound for noise generating
noise_max: 0.005                # (int) Noise upper bound for noise generating
sampling_noise: False           # (bool) Whether to use sampling noise
sampling_steps: 0               # (int) Steps of the forward process during inference
reweight: True                  # (bool) Assign different weight to different timestep or not
mean_type: 'x0'                 # (str) MeanType for diffusion: [x0, eps]
steps: 5                        # (int) Diffusion steps
history_num_per_term: 10        # (int) The number of history items needed to calculate loss weight
beta_fixed: True                # (bool) Whether to fix the variance of the first step to prevent overfitting

# params for the model
dims_dnn: [300]                 # (list of int) The dims for the DNN
embedding_size: 10              # (int) Timestep embedding size
mlp_act_func: 'tanh'            # (str) Activation function for MLP
time-aware: False               # (bool) T-DiffRec or not
w_max: 1                        # (int) The upper bound of the time-aware interaction weight
w_min: 0.1                      # (int) The lower bound of the time-aware interaction weight