问Python 3中的MIT 6.00牛顿法

EN

def evaluate_poly(poly, x):
""" Computes the polynomial function for a given value x. Returns that value."""
answer = poly[0]
for i in range (1, len(poly)):
    answer = answer + poly[i] * x**i
return answer


def compute_deriv(poly):
"""
#Computes and returns the derivative of a polynomial function. If the
#derivative is 0, returns (0.0,)."""
dpoly = ()
for i in range(1,len(poly)):
    dpoly = dpoly + (poly[i]*i,)

return dpoly

def df(poly, x):
"""Computes and returns the solution as a float to the derivative of a polynomial function
"""
dx = evaluate_poly(compute_deriv(poly), x)
#dpoly = compute_deriv(poly)
#dx = evaluate_poly(dpoly, x)
return dx




def compute_root(poly, x_0, epsilon):
"""
Uses Newton's method to find and return a root of a polynomial function.
Returns a float containing the root"""
iteration = 0
fguess = evaluate_poly(poly, x_0) #evaluates poly for first guess
print(fguess)
x_guess = x_0 #initialize x_guess
if fguess > 0 and fguess < epsilon: #if solution for first guess is close enough to root return first guess
    return x_guess
else: 
    while fguess > 0 and fguess > epsilon:
        iteration+=1
        x_guess = x_0 - (evaluate_poly(poly,x_0)/df(poly, x_0))
        fguess = evaluate_poly(poly, x_guess)
        if fguess > 0 and fguess < epsilon:
            break #fguess where guess is close enough to root, breaks while loop, skips else, return x_guess
        else:
            x_0 = x_guess #guess again with most recent guess as x_0 next time through while loop
print(iteration)
return x_guess




#Example:
poly = (-13.39, 0.0, 17.5, 3.0, 1.0)    #x^4 + 3x^3 + 17.5x^2 - 13.39
x_0 = 0.1
epsilon = .0001
print (compute_root(poly, x_0, epsilon))
#answer should be 0.80679075379635201