pseudodynamics.functions.eval_funs.W_distance

pseudodynamics.functions.eval_funs.W_distance(u_b, u_simulate, p=2, log_transform=False)

Normalize the density and compute the Wasserstein distance between observation and prediction. Log-density is supported, pass log_transform = True

Parameters:

• u_b (ndarray, [n_time, n_cell] , observed density)

• u_simulate (ndarray, [n_time, n_cell], inferred desity)

• p (int, degree of the distance, default W-2 distance)

• sanity_check (bool, whether check shape and positivity)

Returns:

Wasserstein distance : ndarry, [n_time,]