Simulate a Predefined ModelΒΆ
Example created by Wilson Rocha Lacerda Junior
pip install sysidentpy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sysidentpy.metrics import root_relative_squared_error
from sysidentpy.utils.generate_data import get_miso_data, get_siso_data
from sysidentpy.polynomial_basis.simulation import SimulatePolynomialNarmax
Generating 1 input 1 output sample dataΒΆ
The data is generated by simulating the following model:ΒΆ
\(y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-2} + e_{k}\)
If colored_noise is set to True:
\(e_{k} = 0.8\nu_{k-1} + \nu_{k}\)
where \(x\) is a uniformly distributed random variable and \(\nu\) is a gaussian distributed variable with \(\mu=0\) and \(\sigma=0.1\)
In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model.
x_train, x_test, y_train, y_test = get_siso_data(n=1000,
colored_noise=False,
sigma=0.001,
train_percentage=90)
Defining the modelΒΆ
We already know that the generated data is a result of the model \(π¦_π=0.2π¦_{πβ1}+0.1π¦_{πβ1}π₯_{πβ1}+0.9π₯_{πβ2}+π_π\) . Thus, we can create a model with those regressors follwing a codification pattern:
\(0\) is the constant term,
\([1001] = y_{k-1}\)
\([100n] = y_{k-n}\)
\([200n] = x1_{k-n}\)
\([300n] = x2_{k-n}\)
\([1011, 1001] = y_{k-11} \times y_{k-1}\)
\([100n, 100m] = y_{k-n} \times y_{k-m}\)
\([12001, 1003, 1001] = x11_{k-1} \times y_{k-3} \times y_{k-1}\)
and so on
Importante NoteΒΆ
The order of the arrays matter.
If you use [2001, 1001], it will work, but [1001, 2001] will not (the regressor will be ignored). Always put the highest value first:
\([2003, 2001]\) works
\([2001, 2003]\) do not work
We will handle this limitation in upcoming update.
s = SimulatePolynomialNarmax()
# the model must be a numpy array
model = np.array(
[
[1001, 0], # y(k-1)
[2001, 1001], # x1(k-1)y(k-1)
[2002, 0], # x1(k-2)
]
)
# theta must be a numpy array of shape (n, 1) where n is the number of regressors
theta = np.array([[0.2, 0.9, 0.1]]).T
Simulating the modelΒΆ
After defining the model and theta we just need to use the simulate method.
The simulate method returns the predicted values and the results where we can look at regressors, parameters and ERR values.
yhat, results = s.simulate(
X_test=x_test,
y_test=y_test,
model_code=model,
theta=theta,
plot=True)
results = pd.DataFrame(results, columns=['Regressors', 'Parameters', 'ERR'])
results
Regressors | Parameters | ERR | |
---|---|---|---|
0 | y(k-1) | 0.2000 | 0.00000000 |
1 | x1(k-2) | 0.9000 | 0.00000000 |
2 | x1(k-1)y(k-1) | 0.1000 | 0.00000000 |
OptionsΒΆ
You can set the steps_ahead
to run the prediction/simulation:
yhat, results = s.simulate(
X_test=x_test,
y_test=y_test,
model_code=model,
theta=theta,
plot=False,
steps_ahead=1)
rrse = root_relative_squared_error(y_test, yhat)
rrse
0.001695176840746673
yhat, results = s.simulate(
X_test=x_test,
y_test=y_test,
model_code=model,
theta=theta,
plot=False,
steps_ahead=21)
rrse = root_relative_squared_error(y_test, yhat)
rrse
0.0017301972652496206
Estimating the parametersΒΆ
If you have only the model strucuture, you can create an object with estimate_parameter=True
and
choose the methed for estimation using estimator
. In this case, you have to pass the training data
for parameters estimation.
When estimate_parameter=True
, we also computate the ERR considering only the regressors defined by the user.
s2 = SimulatePolynomialNarmax(estimate_parameter=True, estimator='recursive_least_squares')
yhat, results = s2.simulate(
X_train=x_train,
y_train=y_train,
X_test=x_test,
y_test=y_test,
model_code=model,
# theta will be estimated using the defined estimator
plot=True)
results = pd.DataFrame(results, columns=['Regressors', 'Parameters', 'ERR'])
results
Regressors | Parameters | ERR | |
---|---|---|---|
0 | y(k-1) | 0.2003 | 0.95597023 |
1 | x1(k-2) | 0.9000 | 0.04072471 |
2 | x1(k-1)y(k-1) | 0.1002 | 0.00330137 |
yhat, results = s2.simulate(
X_train=x_train,
y_train=y_train,
X_test=x_test,
y_test=y_test,
model_code=model,
# theta will be estimated using the defined estimator
plot=True,
steps_ahead=8)
results = pd.DataFrame(results, columns=['Regressors', 'Parameters', 'ERR'])
results
Regressors | Parameters | ERR | |
---|---|---|---|
0 | y(k-1) | 0.2003 | 0.95597023 |
1 | x1(k-2) | 0.9000 | 0.04072471 |
2 | x1(k-1)y(k-1) | 0.1002 | 0.00330137 |
yhat, results = s2.simulate(
X_train=x_train,
y_train=y_train,
X_test=x_test,
y_test=y_test,
model_code=model,
# theta will be estimated using the defined estimator
plot=True,
steps_ahead=8)