Ime Essien
Ime Essien
I got the same issue!
I see now. My original hyper parameters were: ```python measurement="gaussian_nan", structural="categorical", n_steps=3, verbose=1, max_iter=100000, #Run it more times because had an random_state=123, n_init=10, #assignment= 'soft', #preserves the uncertainty in class...
I resolved my initial issue with StepMix by keeping my longitudinal data in long format rather than wide format. The wide format was problematic because each instance had a different...
Thank you for the feedback. I have 7 to 10 measurement points for each instance but they're all different because it's based around time to event for each instance so...
I got the same issue!
Tinygrad is great!