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+## A Simple Linear System
+
+```python
+import sys
+sys.path.append("../..")
+
+from autokoopman import SymbolicContinuousSystem, auto_koopman
+import sympy as sp
+import numpy as np
+
+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)
+```
+
+```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
+)
+```
+
+```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
+)
+```
+
+```python
+experiment_results['tuned_model'].A
+```
+
+```python
+
+```