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add basic manual testbench in jupytest for reviewing the autokoopman …
…performance (#82)
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## A Simple Linear System | ||
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```python | ||
import sys | ||
sys.path.append("../..") | ||
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from autokoopman import SymbolicContinuousSystem, auto_koopman | ||
import sympy as sp | ||
import numpy as np | ||
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class SimpleLinear(SymbolicContinuousSystem): | ||
def __init__(self): | ||
x1, x2 = sp.symbols("x1 x2") | ||
xdot = [ | ||
1.2 * x1 + 0.5 * x2, | ||
-0.7 * x1 + 0.1 * x2 | ||
] | ||
super().__init__((x1, x2), xdot) | ||
``` | ||
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```python | ||
sys = SimpleLinear() | ||
training_data = sys.solve_ivps( | ||
initial_states=np.random.uniform(low=-2.0, high=2.0, size=(10, 2)), | ||
tspan=[0.0, 10.0], | ||
sampling_period=0.1 | ||
) | ||
``` | ||
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```python | ||
# learn model from data | ||
experiment_results = auto_koopman( | ||
training_data, # list of trajectories | ||
sampling_period=0.1, # sampling period of trajectory snapshots | ||
learn_continuous=True, | ||
obs_type="id", # use Random Fourier Features Observables | ||
opt="grid", # grid search to find best hyperparameters | ||
n_obs=200, # maximum number of observables to try | ||
max_opt_iter=200, # maximum number of optimization iterations | ||
grid_param_slices=5, # for grid search, number of slices for each parameter | ||
n_splits=5, # k-folds validation for tuning, helps stabilize the scoring | ||
normalize = False, | ||
rank=(1, 200, 40) # rank range (start, stop, step) DMD hyperparameter | ||
) | ||
``` | ||
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```python | ||
experiment_results['tuned_model'].A | ||
``` | ||
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```python | ||
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``` |