source
get_linear_model
get_linear_model (X:numpy.ndarray, X_prime:numpy.ndarray,
sorted:bool=True)
Returns the linear model A such that X’ = AX
X |
ndarray |
|
data matrix (state_vars, time_steps) |
X_prime |
ndarray |
|
shifted data matrix (state_vars, time_steps) |
sorted |
bool |
True |
sort eigenvalues and eigenvectors |
Returns |
Tuple |
|
(eigenvalues, eigenvectors) |
source
sort_eigvals_eigvecs
sort_eigvals_eigvecs (eigvals:numpy.ndarray, eigvecs:numpy.ndarray)
eigvals |
ndarray |
eigenvalues |
eigvecs |
ndarray |
eigenvectors |
Returns |
Tuple |
(eigenvalues, eigenvectors) |
fast_predict
fast_predict (y:jax.Array, inv_modes:jax.Array, fwd_modes:jax.Array,
eigs:jax.Array, lenght:int=None)
y |
Array |
|
gridsize |
inv_modes |
Array |
|
|
fwd_modes |
Array |
|
|
eigs |
Array |
|
|
lenght |
int |
None |
|
source
fit_dmd_to_sample
fit_dmd_to_sample (x:jax.Array, r:int=50)
x |
Array |
|
|
r |
int |
50 |
|
Returns |
DMD |
|
(timesteps, gridpoints) |
source
hankelize
hankelize (u:jax.Array, d:int=2)
Utilities to replace weights
source
set_params_from_linear
set_params_from_linear (params:Dict, single_eigvals:jax.Array,
single_lstvecs:jax.Array, model:str)
params |
Dict |
|
single_eigvals |
Array |
|
single_lstvecs |
Array |
|
model |
str |
“lru” or “koopman” |
Returns |
Dict |
|
source
get_linear_approximation
get_linear_approximation (y:jax.Array, r:int=50, method:str='dmd')
y |
Array |
|
(time_steps, grid_size) |
r |
int |
50 |
|
method |
str |
dmd |
“dmd”, “full”, “analytical” |
Returns |
Tuple |
|
|