# could you give an example calling the 3 functions.

``````# 6.00 Problem Set 2
#
# Successive Approximation: Newton's Method
#
# Name          : Solutions
# Time spent    : <total time>
#

def evaluate_poly(poly, x):
"""
Computes the value of a polynomial function at given value x. Returns that value.

Example:
>>> poly = [0.0, 0.0, 5.0, 9.3, 7.0]    # f(x) = 5x^2 + 9.3x^3 + 7x^4
>>> x = -13
>>> print evaluate_poly(poly, x)  # f(-13) = 5(-13)^2 + 9.3(-13)^3 + 7(-13)^4
180339.9

poly: list of numbers, length > 0
x: number
returns: float
"""
total = 0.0
for i in xrange(len(poly)):
total += poly[i] * (x ** i)

def compute_deriv(poly):
"""
Computes and returns the derivative of a polynomial function. If the
derivative is 0, returns [0.0].

Example:
>>> poly = [-13.39, 0.0, 17.5, 3.0, 1.0]    # - 13.39 + 17.5x^2 + 3x^3 + x^4
>>> print compute_deriv(poly)        # 35^x + 9x^2 + 4x^3
[0.0, 35.0, 9.0, 4.0]

poly: list of numbers, length > 0
returns: list of numbers
"""
poly_deriv = []
if len(poly) < 2:
return [0.0]
for j in xrange(1, len(poly)):
poly_deriv.append(float(j * poly[j]))
return poly_deriv

def compute_root(poly, x_0, epsilon):
"""

Uses Newton's method to find and return a root of a polynomial function.
Returns a list containing the root and the number of iterations required
to get to the root.

Example:
>>> poly = [-13.39, 0.0, 17.5, 3.0, 1.0]    # - 13.39 + 17.5x^2 + 3x^3 + x^4
>>> x_0 = 0.1
>>> epsilon = .0001
>>> print compute_root(poly, x_0, epsilon)
[0.80679075379635201, 8]

poly: list of numbers, length > 1.
Represents a polynomialfunction containing at least one real root.
The derivative of this polynomial function at x_0 is not 0.
x_0: float
epsilon: float > 0
returns: list [float, int]
"""
root = x_0
counter = 1
while abs(evaluate_poly(poly, root)) >= epsilon:
root = (root - evaluate_poly(poly, root) /
evaluate_poly(compute_deriv(poly), root))
counter += 1
return [root, counter]
``````

how is this function called

note: this is an old class from mit in 2011
LVL 1
###### Who is Participating?

Commented:
Here is a screen shot of my version of the evaluate_poly function.
Some of the lines of code are taken directly from the problem definition, but I did have to write code for the actual function definition.  You should not need any special libraries for these problems.

I typed the code in a .py file and saved it.  Then I ran it, which opened the Python Shell and executed the code properly.  At this point, the function was available interactively.  So I could change the value of x, and the values of the polynomial coefficients.

I don't have or use Portable Python, but if you post screen shots of your work and error messages I can probably help.

Here is the evaluate_poly function definition in pastable form:
def evaluate_poly(poly, x):
value = (    0 + poly[4])*x
value = (value + poly[3])*x
value = (value + poly[2])*x
value = (value + poly[1])*x
value = (value + poly[0])
return value
0

Commented:
Have you installed the Python and the Idle GUI on your computer yet?

The Problem Set includes example calls and results for each function:

def evaluate_poly(poly, x):

Example:
>>> poly = [0.0, 0.0, 5.0, 9.3, 7.0]    # f(x) = 5x^2 + 9.3x^3 + 7x^4
>>> x = -13
>>> print evaluate_poly(poly, x) # f(-13) = 5(-13)^2 + 9.3(-13)^3 + 7(-13)^4
180339.9

def compute_deriv(poly):

Example:
>>> poly = [-13.39, 0.0, 17.5, 3.0, 1.0]    # - 13.39 + 17.5x^2 + 3x^3 + x^4
>>> print compute_deriv(poly)        # 35^x + 9x^2 + 4x^3
[0.0, 35.0, 9.0, 4.0]

def compute_root(poly, x_0, epsilon):

Example:
>>> poly = [-13.39, 0.0, 17.5, 3.0, 1.0]    # - 13.39 + 17.5x^2 + 3x^3 + x^4
>>> x_0 = 0.1
>>> epsilon = .0001
>>> print compute_root(poly, x_0, epsilon)
[0.80679075379635201, 8]
0

Author Commented:
Have you installed the Python and the Idle GUI on your computer yet?
Hours ago and I will learn how to use it for python 2.5

I am currently using python 3 portable for this problem

Message      File Name      Line      Position
Traceback
<module>      C:\Users\Acer\Documents\portable-python\myfiles\ps2_sol\ps2_newton_sol1.py      84
evaluate_poly      C:\Users\Acer\Documents\portable-python\myfiles\ps2_sol\ps2_newton_sol1.py      25
NameError: global name 'xrange' is not defined

``````# 6.00 Problem Set 2
#
# Successive Approximation: Newton's Method
#
# Name          : Solutions
# Time spent    : <total time>
#

def evaluate_poly(poly, x):
"""
Computes the value of a polynomial function at given value x. Returns that value.

Example:
>>> poly = [0.0, 0.0, 5.0, 9.3, 7.0]    # f(x) = 5x^2 + 9.3x^3 + 7x^4
>>> x = -13
>>> print evaluate_poly(poly, x)  # f(-13) = 5(-13)^2 + 9.3(-13)^3 + 7(-13)^4
180339.9

poly: list of numbers, length > 0
x: number
returns: float
"""
total = 0.0
for i in xrange(len(poly)):
total += poly[i] * (x ** i)

def compute_deriv(poly):
"""
Computes and returns the derivative of a polynomial function. If the
derivative is 0, returns [0.0].

Example:
>>> poly = [-13.39, 0.0, 17.5, 3.0, 1.0]    # - 13.39 + 17.5x^2 + 3x^3 + x^4
>>> print compute_deriv(poly)        # 35^x + 9x^2 + 4x^3
[0.0, 35.0, 9.0, 4.0]

poly: list of numbers, length > 0
returns: list of numbers
"""
poly_deriv = []
if len(poly) < 2:
return [0.0]
for j in xrange(1, len(poly)):
poly_deriv.append(float(j * poly[j]))
return poly_deriv

def compute_root(poly, x_0, epsilon):
"""

Uses Newton's method to find and return a root of a polynomial function.
Returns a list containing the root and the number of iterations required
to get to the root.

Example:
>>> poly = [-13.39, 0.0, 17.5, 3.0, 1.0]    # - 13.39 + 17.5x^2 + 3x^3 + x^4
>>> x_0 = 0.1
>>> epsilon = .0001
>>> print compute_root(poly, x_0, epsilon)
[0.80679075379635201, 8]

poly: list of numbers, length > 1.
Represents a polynomialfunction containing at least one real root.
The derivative of this polynomial function at x_0 is not 0.
x_0: float
epsilon: float > 0
returns: list [float, int]
"""
root = x_0
counter = 1
while abs(evaluate_poly(poly, root)) >= epsilon:
root = (root - evaluate_poly(poly, root) /
evaluate_poly(compute_deriv(poly), root))
counter += 1
return [root, counter]

#def evaluate_poly(poly, x):
poly = [0.0, 0.0, 5.0, 9.3, 7.0]    # f(x) = 5x^2 + 9.3x^3 + 7x^4
x = -13
print (evaluate_poly(poly, x)) # f(-13) = 5(-13)^2 + 9.3(-13)^3 + 7(-13)^4

#def evaluate_poly(poly, x):
poly = [0.0, 0.0, 5.0, 9.3, 7.0]    # f(x) = 5x^2 + 9.3x^3 + 7x^4
x = -13
print (evaluate_poly(poly, x)) # f(-13) = 5(-13)^2 + 9.3(-13)^3 + 7(-13)^4

#def compute_deriv(poly):
poly = [-13.39, 0.0, 17.5, 3.0, 1.0]    # - 13.39 + 17.5x^2 + 3x^3 + x^4
print (compute_deriv(poly))        # 35^x + 9x^2 + 4x^3

#def compute_root(poly, x_0, epsilon):
poly = [-13.39, 0.0, 17.5, 3.0, 1.0]    # - 13.39 + 17.5x^2 + 3x^3 + x^4
x_0 = 0.1
epsilon = .0001
print (compute_root(poly, x_0, epsilon))
poly = [-13.39, 0.0, 17.5, 3.0, 1.0]    # - 13.39 + 17.5x^2 + 3x^3 + x^4
print (compute_deriv(poly))        # 35^x + 9x^2 + 4x^3)

#def compute_root(poly, x_0, epsilon):
poly = [-13.39, 0.0, 17.5, 3.0, 1.0]    # - 13.39 + 17.5x^2 + 3x^3 + x^4
x_0 = 0.1
epsilon = .0001
print (compute_root(poly, x_0, epsilon))
``````
0

Author Commented:
this worked for the function

thankls
0
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