Merge pull request #66 from MatthewSZhang/narx-jac

FEAT add jac for narx

Author: MatthewSZhang

.github/workflows/ci.yml

@@ -22,7 +22,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Build wheels
-        uses: pypa/[email protected].1
+        uses: pypa/[email protected].2
         env:
           CIBW_BUILD: cp3*-*
           CIBW_SKIP: pp* *i686* *musllinux* *-macosx_universal2 *-manylinux_ppc64le *-manylinux_s390x

.github/workflows/lint.yml

@@ -9,14 +9,14 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
-      - uses: prefix-dev/[email protected].3
+      - uses: prefix-dev/[email protected].4
         with:
           environments: default
           cache: true
 
       - name: Re-install local
         run: |
-          pixi run rebuild
+          pixi reinstall --frozen fastcan
 
       - name: Lint with ruff
         run: |

.github/workflows/test.yml

@@ -28,7 +28,7 @@ jobs:
 
       - name: Re-install local
         run: |
-          pixi run rebuild
+          pixi reinstall --frozen fastcan
 
       - name: Test with pytest
         run: |

examples/plot_narx.py

@@ -125,11 +125,14 @@
 # In the printed NARX model, it is found that :class:`FastCan` selects the correct
 # terms and the coefficients are close to the true values.
 
-from fastcan.narx import NARX, print_narx
+from fastcan.narx import NARX, print_narx, tp2fd
+
+# Convert poly_ids and time_shift_ids to feat_ids and delay_ids
+feat_ids, delay_ids = tp2fd(time_shift_ids, selected_poly_ids)
 
 narx_model = NARX(
-    time_shift_ids=time_shift_ids,
-    poly_ids=selected_poly_ids,
+    feat_ids=feat_ids,
+    delay_ids=delay_ids,
 )
 
 narx_model.fit(X, y)

examples/plot_narx_msa.py

@@ -15,7 +15,7 @@
 # Nonlinear system
 # ----------------
 #
-# `Duffing equation <https://en.wikipedia.org/wiki/Duffing_equation>` is used to
+# `Duffing equation <https://en.wikipedia.org/wiki/Duffing_equation>`_ is used to
 # generate simulated data. The mathematical model is given by
 #
 # .. math::
@@ -82,15 +82,18 @@ def auto_duffing_equation(y, t):
 dur = 10
 n_samples = 1000
 
+rng = np.random.default_rng(12345)
+e_train = rng.normal(0, 0.0002, n_samples)
+e_test = rng.normal(0, 0.0002, n_samples)
 t = np.linspace(0, dur, n_samples)
 
 sol = odeint(duffing_equation, [0.6, 0.8], t)
 u_train = 2.5 * np.cos(2 * np.pi * t).reshape(-1, 1)
-y_train = sol[:, 0]
+y_train = sol[:, 0] + e_train
 
-sol = odeint(auto_duffing_equation, [0.6, -0.8], t)
+sol = odeint(duffing_equation, [0.6, -0.8], t)
 u_test = 2.5 * np.cos(2 * np.pi * t).reshape(-1, 1)
-y_test = sol[:, 0]
+y_test = sol[:, 0]+ e_test
 
 # %%
 # One-step-head VS. multi-step-ahead NARX
@@ -105,12 +108,12 @@ def auto_duffing_equation(y, t):
 
 from fastcan.narx import make_narx
 
-max_delay = 2
+max_delay = 3
 
 narx_model = make_narx(
     X=u_train,
     y=y_train,
-    n_terms_to_select=10,
+    n_terms_to_select=5,
     max_delay=max_delay,
     poly_degree=3,
     verbose=0,
@@ -130,7 +133,7 @@ def plot_prediction(ax, t, y_true, y_pred, title):
 y_train_osa_pred = narx_model.predict(u_train, y_init=y_train[:max_delay])
 y_test_osa_pred = narx_model.predict(u_test, y_init=y_test[:max_delay])
 
-narx_model.fit(u_train, y_train, coef_init="one_step_ahead", method="Nelder-Mead")
+narx_model.fit(u_train, y_train, coef_init="one_step_ahead")
 y_train_msa_pred = narx_model.predict(u_train, y_init=y_train[:max_delay])
 y_test_msa_pred = narx_model.predict(u_test, y_init=y_test[:max_delay])
 
@@ -159,7 +162,7 @@ def plot_prediction(ax, t, y_true, y_pred, title):
 narx_model = make_narx(
     X=u_all,
     y=y_all,
-    n_terms_to_select=10,
+    n_terms_to_select=5,
     max_delay=max_delay,
     poly_degree=3,
     verbose=0,
@@ -169,7 +172,7 @@ def plot_prediction(ax, t, y_true, y_pred, title):
 y_train_osa_pred = narx_model.predict(u_train, y_init=y_train[:max_delay])
 y_test_osa_pred = narx_model.predict(u_test, y_init=y_test[:max_delay])
 
-narx_model.fit(u_all, y_all, coef_init="one_step_ahead", method="Nelder-Mead")
+narx_model.fit(u_all, y_all, coef_init="one_step_ahead")
 y_train_msa_pred = narx_model.predict(u_train, y_init=y_train[:max_delay])
 y_test_msa_pred = narx_model.predict(u_test, y_init=y_test[:max_delay])