Complete the code to compute the error signal as the difference between the reference and output signals.
error = reference [1] outputThe error signal is calculated by subtracting the output from the reference signal.
Complete the code to compute the squared error signal.
squared_error = (error [1] 2)
Squaring the error signal is done by raising it to the power of 2 using the '**' operator.
Fix the error in the code to compute the absolute error signal.
abs_error = abs([1])The absolute error is the absolute value of the error signal, so use the variable 'error' inside abs().
Fill both blanks to create a dictionary that maps signal names to their error computations.
error_signals = {"[1]": reference [2] output, "squared_error": (reference - output)**2}The dictionary key 'error' maps to the difference between reference and output, which is reference - output.
Fill both blanks to compute mean squared error from a list of errors.
mse = sum([e[1] for e in errors]) [2] len(errors)
Each error is squared (e**2), summed, then divided by the number of errors to get the mean squared error. The last blank is empty because no operator is needed after len(errors).
