+
+
+ 💬 Get Help
+
+
Need assistance? Explore various options for getting help, including discussion forums, GitHub issues, and community support channels.
+
+
+
+ 🤝 Meetups
+
+
Connect with other SysIdentPy users through meetups, online events, and community discussions. Stay updated on upcoming gatherings and share your experience.
+
+
-
+
🔗 Basis Function
Choose from 8+ basis functions, combining linear and nonlinear types for custom NARMAX models.
-
+
🎯 Parameter Estimation
Over 15 parameter estimation methods for exploring various structure selection scenarios.
-
+
🔄 Model Simulation
Reproduce paper results easily with SimulateNARMAX. Test and compare published models effortlessly.
-
+
🤖 Neural NARX (PyTorch)
Integrate with PyTorch for custom neural NARX architectures using all PyTorch optimizers and loss functions.
-
+
🛠️ General Estimators
Compatible with scikit-learn, Catboost, and more for creating NARMAX models.
@@ -158,22 +158,22 @@ the help box. Install with:
diff --git a/docs/getting_started/license.md b/docs/getting-started/license.md
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@@ -103,7 +103,7 @@ As you can see, SysIdentPy supports numerous model combinations, each tailored t
This book provides in-depth guidance to support your work with
SysIdentPy.
- 🛠️ You can also explore the tutorials in the documentation for practical, hands-on examples.
+ 🛠️ You can also explore the tutorials in the documentation for practical, hands-on examples.
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diff --git a/docs/user_guide/tutorials/your-first-model.ipynb b/docs/user-guide/tutorials/your-first-model.ipynb
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diff --git a/mkdocs.yml b/mkdocs.yml
index a677eb8b..33d7db5d 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -149,62 +149,62 @@ markdown_extensions:
nav:
- Home: index.md
- Getting started:
- - Overview: getting_started/getting-started.md
- - Quickstart Guide: getting_started/quickstart-guide.md
- - Introduction to NARMAX: getting_started/narmax-intro.md
- - License: getting_started/license.md
+ - Overview: getting-started/getting-started.md
+ - Quickstart Guide: getting-started/quickstart-guide.md
+ - Introduction to NARMAX: getting-started/narmax-intro.md
+ - License: getting-started/license.md
- User guide:
- - Overview: user_guide/overview.md
+ - Overview: user-guide/overview.md
- Tutorials:
- Features:
- - your-first-model: user_guide/tutorials/your-first-model.ipynb
- - model-with-multiple-inputs: user_guide/tutorials/model-with-multiple-inputs.ipynb
- - parameter-estimation-overview: user_guide/tutorials/parameter-estimation-overview.ipynb
- - basis-function-overview: user_guide/tutorials/basis-function-overview.ipynb
- - information-criteria-overview: user_guide/tutorials/information-criteria-overview.ipynb
- - general-NARX-models: user_guide/tutorials/general-NARX-models.ipynb
- - fourier-NARX-overview: user_guide/tutorials/fourier-NARX-overview.ipynb
- - aols-overview: user_guide/tutorials/aols-overview.ipynb
- - nfir-model-overview: user_guide/tutorials/NFIR-model-overview.ipynb
- - importance-of-extended-least-squares: user_guide/tutorials/importance-of-extended-least-squares.ipynb
+ - your-first-model: user-guide/tutorials/your-first-model.ipynb
+ - model-with-multiple-inputs: user-guide/tutorials/model-with-multiple-inputs.ipynb
+ - parameter-estimation-overview: user-guide/tutorials/parameter-estimation-overview.ipynb
+ - basis-function-overview: user-guide/tutorials/basis-function-overview.ipynb
+ - information-criteria-overview: user-guide/tutorials/information-criteria-overview.ipynb
+ - general-NARX-models: user-guide/tutorials/general-NARX-models.ipynb
+ - fourier-NARX-overview: user-guide/tutorials/fourier-NARX-overview.ipynb
+ - aols-overview: user-guide/tutorials/aols-overview.ipynb
+ - nfir-model-overview: user-guide/tutorials/NFIR-model-overview.ipynb
+ - importance-of-extended-least-squares: user-guide/tutorials/importance-of-extended-least-squares.ipynb
- Electrical and Mechanical Systems:
- - modeling-a-magneto-rheological-damper-device: user_guide/tutorials/modeling-a-magneto-rheological-damper-device.ipynb
- - silver-box-system: user_guide/tutorials/silver-box-system.ipynb
- - coupled-eletric-device: user_guide/tutorials/coupled-eletric-device.ipynb
- - wiener-hammerstein-system: user_guide/tutorials/wiener-hammerstein-system.ipynb
- - f16-aircraft: user_guide/tutorials/f-16-aircraft.ipynb
- - f-16-aircraft-n-steps-ahead-prediction: user_guide/tutorials/f-16-aircraft-n-steps-ahead-prediction.ipynb
- - electromechanical-system-identification-overview: user_guide/tutorials/electromechanical-system-identification-overview.ipynb
- - electromechanical-system-identification-metamss: user_guide/tutorials/electromechanical-system-identification-metamss.ipynb
- - electromechanical-system-identification-entropic-regression: user_guide/tutorials/electromechanical-system-identification-entropic-regression.ipynb
- - multiobjective-parameter-estimation-overview: user_guide/tutorials/multiobjective-parameter-estimation-overview.ipynb
+ - modeling-a-magneto-rheological-damper-device: user-guide/tutorials/modeling-a-magneto-rheological-damper-device.ipynb
+ - silver-box-system: user-guide/tutorials/silver-box-system.ipynb
+ - coupled-eletric-device: user-guide/tutorials/coupled-eletric-device.ipynb
+ - wiener-hammerstein-system: user-guide/tutorials/wiener-hammerstein-system.ipynb
+ - f16-aircraft: user-guide/tutorials/f-16-aircraft.ipynb
+ - f-16-aircraft-n-steps-ahead-prediction: user-guide/tutorials/f-16-aircraft-n-steps-ahead-prediction.ipynb
+ - electromechanical-system-identification-overview: user-guide/tutorials/electromechanical-system-identification-overview.ipynb
+ - electromechanical-system-identification-metamss: user-guide/tutorials/electromechanical-system-identification-metamss.ipynb
+ - electromechanical-system-identification-entropic-regression: user-guide/tutorials/electromechanical-system-identification-entropic-regression.ipynb
+ - multiobjective-parameter-estimation-overview: user-guide/tutorials/multiobjective-parameter-estimation-overview.ipynb
- Industrial and Corporate:
- - m4-benchmark: user_guide/tutorials/m4-benchmark.ipynb
- - air-passenger-benchmark: user_guide/tutorials/air-passenger-benchmark.ipynb
- - load-forecasting-benchmark: user_guide/tutorials/load-forecasting-benchmark.ipynb
- - pv-forecasting-benchmark: user_guide/tutorials/PV-forecasting-benchmark.ipynb
+ - m4-benchmark: user-guide/tutorials/m4-benchmark.ipynb
+ - air-passenger-benchmark: user-guide/tutorials/air-passenger-benchmark.ipynb
+ - load-forecasting-benchmark: user-guide/tutorials/load-forecasting-benchmark.ipynb
+ - pv-forecasting-benchmark: user-guide/tutorials/PV-forecasting-benchmark.ipynb
- How to:
- - create-a-narx-neural-network: user_guide/how_to/create-a-narx-neural-network.ipynb
- - save-and-load-models: user_guide/how_to/save-and-load-models.ipynb
- - set-specific-lags: user_guide/how_to/set-specific-lags.ipynb
- - simulating-existing-models: user_guide/how_to/simulating-existing-models.ipynb
- - use-extended-least-squares: user_guide/how_to/use-extended-least-squares.ipynb
+ - create-a-narx-neural-network: user-guide/how-to/create-a-narx-neural-network.ipynb
+ - save-and-load-models: user-guide/how-to/save-and-load-models.ipynb
+ - set-specific-lags: user-guide/how-to/set-specific-lags.ipynb
+ - simulating-existing-models: user-guide/how-to/simulating-existing-models.ipynb
+ - use-extended-least-squares: user-guide/how-to/use-extended-least-squares.ipynb
- API:
- - NARMAX Base: user_guide/API/narmax-base.md
- - Basis Functions: user_guide/API/basis-function.md
- - FROLS: user_guide/API/frols.md
- - MetaMSS: user_guide/API/metamss.md
- - AOLS: user_guide/API/aols.md
- - Entropic Regression: user_guide/API/entropic-regression.md
- - Neural NARX: user_guide/API/neural-narx.md
- - General Estimators: user_guide/API/general-estimators.md
- - Parameter Estimation: user_guide/API/parameter-estimation.md
- - Multiobjective Parameter Estimation: user_guide/API/multiobjective-parameter-estimation.md
- - Simulation: user_guide/API/simulation.md
- - Residual Analysis: user_guide/API/residues.md
- - Metaheuristics: user_guide/API/metaheuristics.md
- - Metrics: user_guide/API/metrics.md
- - Utils: user_guide/API/utils.md
+ - NARMAX Base: user-guide/API/narmax-base.md
+ - Basis Functions: user-guide/API/basis-function.md
+ - FROLS: user-guide/API/frols.md
+ - MetaMSS: user-guide/API/metamss.md
+ - AOLS: user-guide/API/aols.md
+ - Entropic Regression: user-guide/API/entropic-regression.md
+ - Neural NARX: user-guide/API/neural-narx.md
+ - General Estimators: user-guide/API/general-estimators.md
+ - Parameter Estimation: user-guide/API/parameter-estimation.md
+ - Multiobjective Parameter Estimation: user-guide/API/multiobjective-parameter-estimation.md
+ - Simulation: user-guide/API/simulation.md
+ - Residual Analysis: user-guide/API/residues.md
+ - Metaheuristics: user-guide/API/metaheuristics.md
+ - Metrics: user-guide/API/metrics.md
+ - Utils: user-guide/API/utils.md
- Companion Book:
- Preface: book/0-Preface.md
- Contents: book/0.1-Contents.md
@@ -219,20 +219,20 @@ nav:
- 9. Validation: book/9-Validation.md
- 10. Case Studies: book/10-Case-Studies.md
- Developer guide:
- - How to contribute: developer_guide/contribute.md
+ - How to contribute: developer-guide/contribute.md
- Documentation:
- - Documentation Overview: developer_guide/documentation-overview.md
- - How To Write a Tutorial: developer_guide/how-to-write-a-tutorial.md
- - How To Write a How-to Guide: developer_guide/how-to-write-a-how-to-guide.md
+ - Documentation Overview: developer-guide/documentation-overview.md
+ - How To Write a Tutorial: developer-guide/how-to-write-a-tutorial.md
+ - How To Write a How-to Guide: developer-guide/how-to-write-a-how-to-guide.md
- Community & Support:
- - Get Help: community_support/get-help.md
- - Meetups: community_support/meetups/meetup.md
+ - community-overview: community-support/community-overview.md
+ - Get Help: community-support/get-help.md
- Meetups:
- - AI Networks Meetup: community_support/meetups/ai-networks-meetup.md
- - Nubank Timeseries Meetup: community_support/meetups/nubank-meetup.md
- - Nubank Open Source Talk: community_support/meetups/nubank-meetup-open-source.md
- - GCoM Meetup: community_support/meetups/gcom-meetup.md
- - Estatidados Meetup: community_support/meetups/estatidados.md
+ - AI Networks Meetup: community-support/meetups/ai-networks-meetup.md
+ - Nubank Timeseries Meetup: community-support/meetups/nubank-meetup.md
+ - Nubank Open Source Talk: community-support/meetups/nubank-meetup-open-source.md
+ - GCoM Meetup: community-support/meetups/gcom-meetup.md
+ - Estatidados Meetup: community-support/meetups/estatidados.md
- About: landing-page/about-us.md
- Changelog: changelog/changelog.md
- Sponsors: landing-page/sponsor.md
diff --git a/sysidentpy/general_estimators/tests/test_general_narx.py b/sysidentpy/general_estimators/tests/test_general_narx.py
index cc3b4e7a..93ae064f 100644
--- a/sysidentpy/general_estimators/tests/test_general_narx.py
+++ b/sysidentpy/general_estimators/tests/test_general_narx.py
@@ -25,7 +25,9 @@ def create_test_data(n=1000):
# y = np.reshape(y, (len(y), 1))
# x = np.reshape(x, (len(x), 1))
# data = np.concatenate([x, y], axis=1)
- data = np.loadtxt("examples/datasets/data_for_testing.txt")
+ data = np.loadtxt(
+ "https://raw.githubusercontent.com/wilsonrljr/sysidentpy-data/refs/heads/main/datasets/testing/data_for_testing.txt"
+ )
x = data[:, 0].reshape(-1, 1)
y = data[:, 1].reshape(-1, 1)
return x, y, theta
diff --git a/sysidentpy/model_structure_selection/tests/test_aols.py b/sysidentpy/model_structure_selection/tests/test_aols.py
index 1eafbb89..6800c851 100644
--- a/sysidentpy/model_structure_selection/tests/test_aols.py
+++ b/sysidentpy/model_structure_selection/tests/test_aols.py
@@ -21,7 +21,9 @@ def create_test_data(n=1000):
# y = np.reshape(y, (len(y), 1))
# x = np.reshape(x, (len(x), 1))
# data = np.concatenate([x, y], axis=1)
- data = np.loadtxt("examples/datasets/data_for_testing.txt")
+ data = np.loadtxt(
+ "https://raw.githubusercontent.com/wilsonrljr/sysidentpy-data/refs/heads/main/datasets/testing/data_for_testing.txt"
+ )
x = data[:, 0].reshape(-1, 1)
y = data[:, 1].reshape(-1, 1)
return x, y, theta
diff --git a/sysidentpy/model_structure_selection/tests/test_er.py b/sysidentpy/model_structure_selection/tests/test_er.py
index a31d2129..769ef159 100644
--- a/sysidentpy/model_structure_selection/tests/test_er.py
+++ b/sysidentpy/model_structure_selection/tests/test_er.py
@@ -16,7 +16,9 @@ def create_test_data(n=1000):
# y = np.reshape(y, (len(y), 1))
# x = np.reshape(x, (len(x), 1))
# data = np.concatenate([x, y], axis=1)
- data = np.loadtxt("examples/datasets/data_for_testing.txt")
+ data = np.loadtxt(
+ "https://raw.githubusercontent.com/wilsonrljr/sysidentpy-data/refs/heads/main/datasets/testing/data_for_testing.txt"
+ )
x = data[:, 0].reshape(-1, 1)
y = data[:, 1].reshape(-1, 1)
return x, y, theta
diff --git a/sysidentpy/model_structure_selection/tests/test_frols.py b/sysidentpy/model_structure_selection/tests/test_frols.py
index 4b1f7cfa..fbc34e07 100644
--- a/sysidentpy/model_structure_selection/tests/test_frols.py
+++ b/sysidentpy/model_structure_selection/tests/test_frols.py
@@ -22,7 +22,9 @@ def create_test_data(n=1000):
# y = np.reshape(y, (len(y), 1))
# x = np.reshape(x, (len(x), 1))
# data = np.concatenate([x, y], axis=1)
- data = np.loadtxt("examples/datasets/data_for_testing.txt")
+ data = np.loadtxt(
+ "https://raw.githubusercontent.com/wilsonrljr/sysidentpy-data/refs/heads/main/datasets/testing/data_for_testing.txt"
+ )
x = data[:, 0].reshape(-1, 1)
y = data[:, 1].reshape(-1, 1)
return x, y, theta
diff --git a/sysidentpy/model_structure_selection/tests/test_metamss.py b/sysidentpy/model_structure_selection/tests/test_metamss.py
index a9b74ebd..525ffb52 100644
--- a/sysidentpy/model_structure_selection/tests/test_metamss.py
+++ b/sysidentpy/model_structure_selection/tests/test_metamss.py
@@ -9,7 +9,9 @@
def create_test_data(n=1000):
theta = np.array([[0.6], [-0.5], [0.7], [-0.7], [0.2]])
- data = np.loadtxt("examples/datasets/data_for_testing.txt")
+ data = np.loadtxt(
+ "https://raw.githubusercontent.com/wilsonrljr/sysidentpy-data/refs/heads/main/datasets/testing/data_for_testing.txt"
+ )
x = data[:, 0].reshape(-1, 1)
y = data[:, 1].reshape(-1, 1)
return x, y, theta
diff --git a/sysidentpy/multiobjective_parameter_estimation/tests/test_mo_estimators.py b/sysidentpy/multiobjective_parameter_estimation/tests/test_mo_estimators.py
index 8e012e4f..c15ddeda 100644
--- a/sysidentpy/multiobjective_parameter_estimation/tests/test_mo_estimators.py
+++ b/sysidentpy/multiobjective_parameter_estimation/tests/test_mo_estimators.py
@@ -7,10 +7,14 @@
df_train = np.genfromtxt(
- "examples/datasets/buck_id.csv", delimiter=",", skip_header=True
+ "https://raw.githubusercontent.com/wilsonrljr/sysidentpy-data/refs/heads/main/datasets/buck/buck_id.csv",
+ delimiter=",",
+ skip_header=True,
)
df_valid = np.genfromtxt(
- "examples/datasets/buck_valid.csv", delimiter=",", skip_header=True
+ "https://raw.githubusercontent.com/wilsonrljr/sysidentpy-data/refs/heads/main/datasets/buck/buck_valid.csv",
+ delimiter=",",
+ skip_header=True,
)
Vd = 24
@@ -26,27 +30,31 @@
y_valid = df_valid[:, 2].reshape(-1, 1)
w = set_weights(static_function=True, static_gain=True)
-final_model = np.array([
- [0, 0],
- [1001, 0],
- [1002, 0],
- [2001, 0],
- [1001, 1001],
- [1002, 1001],
- [2001, 1001],
- [1002, 1002],
-])
+final_model = np.array(
+ [
+ [0, 0],
+ [1001, 0],
+ [1002, 0],
+ [2001, 0],
+ [1001, 1001],
+ [1002, 1001],
+ [2001, 1001],
+ [1002, 1002],
+ ]
+)
-final_model = np.array([
- [1001, 0],
- [1002, 0],
- [2001, 1001],
- [0, 0],
- [1001, 1001],
- [2001, 0],
- [1002, 1001],
- [1002, 1002],
-])
+final_model = np.array(
+ [
+ [1001, 0],
+ [1002, 0],
+ [2001, 1001],
+ [0, 0],
+ [1001, 1001],
+ [2001, 0],
+ [1002, 1001],
+ [1002, 1002],
+ ]
+)
def test_default_values():
@@ -95,98 +103,102 @@ def test_build_static_function_information():
)
# those values were taken from matlab implementation to compare
# with this scenario
- static_covariance_mat = np.array([
- [
- 1.72408163e04,
- 1.72408163e04,
- 1.67183673e04,
- 8.00000000e02,
- 4.17959184e05,
- 1.04489796e03,
- 4.17959184e05,
- 4.17959184e05,
- ],
- [
- 1.72408163e04,
- 1.72408163e04,
- 1.67183673e04,
- 8.00000000e02,
- 4.17959184e05,
- 1.04489796e03,
- 4.17959184e05,
- 4.17959184e05,
- ],
+ static_covariance_mat = np.array(
[
- 1.67183673e04,
- 1.67183673e04,
- 2.67605287e04,
- 1.04489796e03,
- 3.20903526e05,
- 2.08979592e03,
- 3.20903526e05,
- 3.20903526e05,
- ],
- [
- 8.00000000e02,
- 8.00000000e02,
- 1.04489796e03,
- 5.00000000e01,
- 1.72408163e04,
- 1.00000000e02,
- 1.72408163e04,
- 1.72408163e04,
- ],
- [
- 4.17959184e05,
- 4.17959184e05,
- 3.20903526e05,
- 1.72408163e04,
- 1.08074657e07,
- 1.67183673e04,
- 1.08074657e07,
- 1.08074657e07,
- ],
- [
- 1.04489796e03,
- 1.04489796e03,
- 2.08979592e03,
- 1.00000000e02,
- 1.67183673e04,
- 2.69387755e02,
- 1.67183673e04,
- 1.67183673e04,
- ],
- [
- 4.17959184e05,
- 4.17959184e05,
- 3.20903526e05,
- 1.72408163e04,
- 1.08074657e07,
- 1.67183673e04,
- 1.08074657e07,
- 1.08074657e07,
- ],
+ [
+ 1.72408163e04,
+ 1.72408163e04,
+ 1.67183673e04,
+ 8.00000000e02,
+ 4.17959184e05,
+ 1.04489796e03,
+ 4.17959184e05,
+ 4.17959184e05,
+ ],
+ [
+ 1.72408163e04,
+ 1.72408163e04,
+ 1.67183673e04,
+ 8.00000000e02,
+ 4.17959184e05,
+ 1.04489796e03,
+ 4.17959184e05,
+ 4.17959184e05,
+ ],
+ [
+ 1.67183673e04,
+ 1.67183673e04,
+ 2.67605287e04,
+ 1.04489796e03,
+ 3.20903526e05,
+ 2.08979592e03,
+ 3.20903526e05,
+ 3.20903526e05,
+ ],
+ [
+ 8.00000000e02,
+ 8.00000000e02,
+ 1.04489796e03,
+ 5.00000000e01,
+ 1.72408163e04,
+ 1.00000000e02,
+ 1.72408163e04,
+ 1.72408163e04,
+ ],
+ [
+ 4.17959184e05,
+ 4.17959184e05,
+ 3.20903526e05,
+ 1.72408163e04,
+ 1.08074657e07,
+ 1.67183673e04,
+ 1.08074657e07,
+ 1.08074657e07,
+ ],
+ [
+ 1.04489796e03,
+ 1.04489796e03,
+ 2.08979592e03,
+ 1.00000000e02,
+ 1.67183673e04,
+ 2.69387755e02,
+ 1.67183673e04,
+ 1.67183673e04,
+ ],
+ [
+ 4.17959184e05,
+ 4.17959184e05,
+ 3.20903526e05,
+ 1.72408163e04,
+ 1.08074657e07,
+ 1.67183673e04,
+ 1.08074657e07,
+ 1.08074657e07,
+ ],
+ [
+ 4.17959184e05,
+ 4.17959184e05,
+ 3.20903526e05,
+ 1.72408163e04,
+ 1.08074657e07,
+ 1.67183673e04,
+ 1.08074657e07,
+ 1.08074657e07,
+ ],
+ ]
+ )
+ static_response_mat = np.array(
[
- 4.17959184e05,
- 4.17959184e05,
- 3.20903526e05,
- 1.72408163e04,
- 1.08074657e07,
- 1.67183673e04,
- 1.08074657e07,
- 1.08074657e07,
- ],
- ])
- static_response_mat = np.array([
- [17240.81632653],
- [17240.81632653],
- [16718.36734694],
- [800.0],
- [417959.18367347],
- [1044.89795918],
- [417959.18367347],
- [417959.18367347],
- ])
+ [17240.81632653],
+ [17240.81632653],
+ [16718.36734694],
+ [800.0],
+ [417959.18367347],
+ [1044.89795918],
+ [417959.18367347],
+ [417959.18367347],
+ ]
+ )
assert_allclose(static_covariance_mat, static_covariance)
assert_allclose(static_response_mat, static_response)
assert_almost_equal(QR.mean(), 136.2933673469388)
@@ -200,98 +212,102 @@ def test_build_static_gain_information():
)
# those values were taken from matlab implementation to compare
# with this scenario
- gain_covariance_mat = np.array([
- [
- 3.20000000e03,
- 3.20000000e03,
- 2.80000000e03,
- 0.00000000e00,
- 1.02400000e05,
- -4.00000000e02,
- 1.02400000e05,
- 1.02400000e05,
- ],
- [
- 3.20000000e03,
- 3.20000000e03,
- 2.80000000e03,
- 0.00000000e00,
- 1.02400000e05,
- -4.00000000e02,
- 1.02400000e05,
- 1.02400000e05,
- ],
- [
- 2.80000000e03,
- 2.80000000e03,
- 2.45000000e03,
- 0.00000000e00,
- 8.96000000e04,
- -3.50000000e02,
- 8.96000000e04,
- 8.96000000e04,
- ],
- [
- 0.00000000e00,
- 0.00000000e00,
- 0.00000000e00,
- 0.00000000e00,
- 0.00000000e00,
- 0.00000000e00,
- 0.00000000e00,
- 0.00000000e00,
- ],
- [
- 1.02400000e05,
- 1.02400000e05,
- 8.96000000e04,
- 0.00000000e00,
- 4.41364898e06,
- -1.28000000e04,
- 4.41364898e06,
- 4.41364898e06,
- ],
+ gain_covariance_mat = np.array(
[
- -4.00000000e02,
- -4.00000000e02,
- -3.50000000e02,
- 0.00000000e00,
- -1.28000000e04,
- 5.00000000e01,
- -1.28000000e04,
- -1.28000000e04,
- ],
- [
- 1.02400000e05,
- 1.02400000e05,
- 8.96000000e04,
- 0.00000000e00,
- 4.41364898e06,
- -1.28000000e04,
- 4.41364898e06,
- 4.41364898e06,
- ],
+ [
+ 3.20000000e03,
+ 3.20000000e03,
+ 2.80000000e03,
+ 0.00000000e00,
+ 1.02400000e05,
+ -4.00000000e02,
+ 1.02400000e05,
+ 1.02400000e05,
+ ],
+ [
+ 3.20000000e03,
+ 3.20000000e03,
+ 2.80000000e03,
+ 0.00000000e00,
+ 1.02400000e05,
+ -4.00000000e02,
+ 1.02400000e05,
+ 1.02400000e05,
+ ],
+ [
+ 2.80000000e03,
+ 2.80000000e03,
+ 2.45000000e03,
+ 0.00000000e00,
+ 8.96000000e04,
+ -3.50000000e02,
+ 8.96000000e04,
+ 8.96000000e04,
+ ],
+ [
+ 0.00000000e00,
+ 0.00000000e00,
+ 0.00000000e00,
+ 0.00000000e00,
+ 0.00000000e00,
+ 0.00000000e00,
+ 0.00000000e00,
+ 0.00000000e00,
+ ],
+ [
+ 1.02400000e05,
+ 1.02400000e05,
+ 8.96000000e04,
+ 0.00000000e00,
+ 4.41364898e06,
+ -1.28000000e04,
+ 4.41364898e06,
+ 4.41364898e06,
+ ],
+ [
+ -4.00000000e02,
+ -4.00000000e02,
+ -3.50000000e02,
+ 0.00000000e00,
+ -1.28000000e04,
+ 5.00000000e01,
+ -1.28000000e04,
+ -1.28000000e04,
+ ],
+ [
+ 1.02400000e05,
+ 1.02400000e05,
+ 8.96000000e04,
+ 0.00000000e00,
+ 4.41364898e06,
+ -1.28000000e04,
+ 4.41364898e06,
+ 4.41364898e06,
+ ],
+ [
+ 1.02400000e05,
+ 1.02400000e05,
+ 8.96000000e04,
+ 0.00000000e00,
+ 4.41364898e06,
+ -1.28000000e04,
+ 4.41364898e06,
+ 4.41364898e06,
+ ],
+ ]
+ )
+ gain_response_mat = np.array(
[
- 1.02400000e05,
- 1.02400000e05,
- 8.96000000e04,
- 0.00000000e00,
- 4.41364898e06,
- -1.28000000e04,
- 4.41364898e06,
- 4.41364898e06,
- ],
- ])
- gain_response_mat = np.array([
- [3200.0],
- [3200.0],
- [2800.0],
- [0.0],
- [102400.0],
- [-400.0],
- [102400.0],
- [102400.0],
- ])
+ [3200.0],
+ [3200.0],
+ [2800.0],
+ [0.0],
+ [102400.0],
+ [-400.0],
+ [102400.0],
+ [102400.0],
+ ]
+ )
assert_allclose(gain_covariance_mat, gain_covariance)
assert_allclose(gain_response_mat, gain_response)
assert_almost_equal(HR.mean(), -98.75)
@@ -303,16 +319,18 @@ def test_estimate():
J, E, theta, _, _, position = mo_estimator.estimate(
y=y_train, gain=gain, y_static=Yo, X_static=Uo, X=x_train, weighing_matrix=w
)
- expected_theta = np.array([
- [1.54048245],
- [0.29686668],
- [0.64693309],
- [-0.41302211],
- [0.27670633],
- [-0.53473868],
- [0.00406244],
- [0.25831577],
- ])
+ expected_theta = np.array(
+ [
+ [1.54048245],
+ [0.29686668],
+ [0.64693309],
+ [-0.41302211],
+ [0.27670633],
+ [-0.53473868],
+ [0.00406244],
+ [0.25831577],
+ ]
+ )
assert_allclose(expected_theta, theta[position, :].reshape(-1, 1), rtol=1e-05)
assert J.shape == (3, 2295)
assert E.shape == (2295,)
diff --git a/sysidentpy/neural_network/tests/test_narxnn.py b/sysidentpy/neural_network/tests/test_narxnn.py
index 1d306781..e70f609b 100644
--- a/sysidentpy/neural_network/tests/test_narxnn.py
+++ b/sysidentpy/neural_network/tests/test_narxnn.py
@@ -40,7 +40,9 @@ def create_test_data(n=1000):
# y = np.reshape(y, (len(y), 1))
# x = np.reshape(x, (len(x), 1))
# data = np.concatenate([x, y], axis=1)
- data = np.loadtxt("examples/datasets/data_for_testing.txt")
+ data = np.loadtxt(
+ "https://raw.githubusercontent.com/wilsonrljr/sysidentpy-data/refs/heads/main/datasets/testing/data_for_testing.txt"
+ )
x = data[:, 0].reshape(-1, 1)
y = data[:, 1].reshape(-1, 1)
return x, y, theta
diff --git a/sysidentpy/tests/test_narmax_base.py b/sysidentpy/tests/test_narmax_base.py
index 6efe287f..f0cb8a80 100644
--- a/sysidentpy/tests/test_narmax_base.py
+++ b/sysidentpy/tests/test_narmax_base.py
@@ -37,7 +37,9 @@ def create_test_data(n=1000):
# y = np.reshape(y, (len(y), 1))
# x = np.reshape(x, (len(x), 1))
# data = np.concatenate([x, y], axis=1)
- data = np.loadtxt("examples/datasets/data_for_testing.txt")
+ data = np.loadtxt(
+ "https://raw.githubusercontent.com/wilsonrljr/sysidentpy-data/refs/heads/main/datasets/testing/data_for_testing.txt"
+ )
x = data[:, 0].reshape(-1, 1)
y = data[:, 1].reshape(-1, 1)
return x, y, theta