Could you code a function to calculate the root mean squared error between two arrays?

Question

Anonymous · Accepted Answer

let's consider two vectors y and y_bis, the root mean square error between these
two vectors is equal to the L2-norm between y and y_bis. in another words, let's
consider z = y - y_bis. the root mean squared error corresponds to the square
root of the sum square element of z.
I will code it in Python :

first thing to have in mind is to code the sqrt function to compute square root
of a number. for this purpose we use newton method.

def sqrt(x: float, epsilon: float = 1e-10) -> float:
    if x < 0:
        raise ValueError("Cannot compute square root of negative number.")
    if x == 0 or x == 1:
        return x

guess = x / 2.0
    while abs(guess * guess - x) > epsilon:
        guess = (guess + x / guess) / 2.0
    return guess

now that we have the sqrt function we can define the root mean squared error
function as :
def rmse(y: List[float], y_bis:List[float]) -> float:
    return sqrt(sum((y-y_bis)**2))

sum() is a native function in python.

Anonymous · Answer

def rms(array1,array2):
"""
Calculate the root mean square error between two arrays.
Args:
array1: A numpy array.
array2: A numpy array.
Returns:
The root mean square error between the two arrays.
"""
return np.sqrt(np.mean((array1 - array2,axis=0) ** 2)'''

Could you code a function to calculate the root mean squared error between two arrays?

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