Lesson 9/41_find_best_fit_line_polynomial.py
def error_poly(C, data):  # error function
    """Ciompute error between given polynomial and observed data.

    Parameters:
        C: numpy.poly1d object or equivalent array representing polynomial coefficients
        data: 2D array where each row is a point (x, y)

    Returns error as a single real value.
    """
    # Metric: sum of squared Y-axis differences
    err = np.sum((data[:, 1] - np.polyval(C, data[:, 0]))**2)
    return err
Ciompute error between given polynomial and observed data.

    Parameters:
        C: numpy.poly1d object or equivalent array representing polynomial coefficients
        data: 2D array where each row is a point (x, y)

    Returns error as a single real value.
    
Ciompute error between given polynomial and observed data.
Ciompute error between given polynomial and observed data .
def error_poly(C, data):  
    err = np.sum((data[:, 1] - np.polyval(C, data[:, 0]))**2)
    return err
def error_poly ( C , data ) : err = np . sum ( ( data [ : , 1 ] - np . polyval ( C , data [ : , 0 ] ) ) ** 2 ) return err
Ciompute error between given polynomial and observed data.
Ciompute error between given polynomial and observed data .
# error function """Ciompute error between given polynomial and observed data.

    Parameters:
        C: numpy.poly1d object or equivalent array representing polynomial coefficients
        data: 2D array where each row is a point (x, y)

    Returns error as a single real value.
    """ # Metric: sum of squared Y-axis differences
C
numpy.poly1d object or equivalent array representing polynomial coefficients
numpy . poly1d object or equivalent array representing polynomial coefficients
data
2D array where each row is a point (x, y)
2D array where each row is a point ( x y )