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Code repository for the book 'Machine Learning in Python for Dynamic Process Systems'

Home Page: https://mlforpse.com/books/

License: Apache License 2.0

Jupyter Notebook 96.36% Python 3.64%
data-science engineering learning-python machine-learning processes

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machine_learning_for_dps's Issues

About example code :

Dear Dr. Kumar

I am a student who is studying System Identification based on your latest book.

There is a error in example code, but I cannot fix it.

I would appreciate you if you let me know how to tackle this error.

ValueError Traceback (most recent call last)
Cell In[71], line 4
1 # get model's 1-step ahead and 5-step ahead predictions, and simulation responses on validation dataset
2 from sippy import functionset as fset
----> 4 TO_val_predicted_1step_centered = np.transpose(fset.validation(ARXmodel, FG_val_centered, TO_val_centered, np.linspace(0,299,300), k=1))
5 TO_val_predicted_5step_centered = np.transpose(fset.validation(ARXmodel, FG_val_centered, TO_val_centered, np.linspace(0,299,300), k=5))
6 TO_val_simulated_centered, _, _ = control.matlab.lsim(ARXmodel.G, FG_val_centered[:,0], np.linspace(0,299,300))

File ~\Desktop\Python\SysID\Chapter_InputOutputModels_Part1\sippy\functionset.py:192, in validation(SYS, u, y, Time, k, centering)
189 for i in range(ydim):
190 # one-step ahead predictor
191 if k == 1:
--> 192 T, Y_u = cnt.forced_response((1/SYS.H[i,0])*SYS.G[i,:], Time, u)
193 T, Y_y = cnt.forced_response(1 - (1/SYS.H[i,0]), Time, y[i,:] - y_rif[i])
194 Yval[i,:] = (Y_u + np.atleast_2d(Y_y) + y_rif[i])

ValueError: too many values to unpack (expected 2)

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