$ mkdir ~/cs170/labs/lab11 $ cd ~/cs170/labs/lab11
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A recursive function is simply a function that contains a function call to itself. People tend to struggle with , with good reason most of the time. As a matter of fact, some of you may have inadvertently (or purposefully on occasion) written recursive functions, it is likey the case that you haven't seen the true power of recursion. Recursive functions allow us to break a problem into more managable pieces. While it might not always be the most efficient mechanism, it usually results in incredibly pretty code.
The most important thing to realize about recursion is that ANY loop can be rewritten to use recursion. Sometimes, this process is easier than others. But understanding how you can convert a loop into a recursive statement can make writing future recursive expressions a lot easier.
Create a file find_min.py, and write a
function find_min(a_list) in this file. This function
should recursively compute the minimum value from a_list.
For simplicity sakes, consult the loop version
of find_min below.
def find_min(a_list):
curr_min = a_list[0]
for index in range(1, len(a_list)):
curr_value = a_list[index]
if curr_value < curr_min:
curr_min = curr_value
return curr_min
> find_min([1, 2, 0, 3, 4]) 0 > find_min([1, 2, 3, 4, 5]) 1 > find_min([2, 3, 1, 4, 5]) 1
One of the oldest algorithms known is the Euclidean algorithm for computing the greatest common divisor of two numbers. GCD is used when simplifying fractions, and sounds like it should be pretty complicated. However, it is one of the most beautiful algorithms in existence.
Luckily, I showed you the loop version of this function last class, during the hand tracing exercises. If you need a jump start on how to get started, consult the function in Lecture 10.
Create a file called euclidean.py, and create a function
gcd(a, b) which computes the greatest common common
divisor of the integers a and b. The logical mechanism for this
function would be to check every integer in the range [1, min(a,
b)]. However, there is an elegant recursive definition that can
compute the gcd:
\[ \begin{eqnarray} gcd(a, 0) &=& a \\ gcd(a, b) &=& gcd(b, a \% b) \end{eqnarray} \]
gcd(a, 0) = a
gcd(a, b) = gcd(b, a mod b)
>>> gcd(3, 6) 3 >>> gcd(6, 3) 3 >>> gcd(1, 9001) 1 >>> gcd(36, 84) 12
Your base case is that the integer b is 0. In this case, you want to return the value a.
Your recursive case is simply the recursive call with the
value of b for parameter a, and the value a % b
for the parameter b. Don't forget to return this value!
Remember the Fraction class you wrote about three
weeks ago? Well, there are some operations that might produce a
non-simplified fraction. If you were to show this to a
mathematician, you would likely give them an aneurism. Add the
Euclidean GCD function to your fraction.py file, and
use the computed GCD to simplify your fractions.
One nice thing about recursive functions is that they can sometimes be more efficient. Last semester, we made you write the power function using a loop. However, this function uses on the order of n multiplications. Using recursion, we can improve things pretty well.
In a file called power.py, create a function
called power(x, y). This function should follow the
following recursive definition for power, which should
perform fewer multiplications:
\[ \begin{eqnarray} x^1 &=& x\\ x^y &=& x ^ {\frac{y}{2}} \times x ^ {\frac{y}{2}} \mbox{ if y is even and greater than 1}\\ x^y &=& x \times x ^ {\frac{y-1}{2}} \times x ^ {\frac{y - 1}{2}} \mbox{ if y is odd and greater than 1} \end{eqnarray}\]
You should also write a function called loop_pow(x, y),
which computes the power using the traditional loop
methodology. For both of these functions, you should return both
the value of the power, and the number of multiplications required
to compute the value. Test both functions, and make sure the
recursive definition performs fewer multiplications.
>>> power(2, 3) (8, ###) >>> loop_power(2, 3) (8, 2) >>> loop_power(2, 10) (1024, 9) >>> power(2, 10) (1024, ###) >>> power(9, 15) (205891132094649, ###) >>> loop_power(9, 15) (205891132094649, 14)
Example censored to not spoil the surprise!
Notice in the above recursive definition that while there are really two recursive calls to power, you only really need to perform one in each recursive case. You can store that value in a variable, and use that variable in your multiplications.
How does this recursive definition compare in run times? Let's
use a timer and plot the results! Try computing the powers of 2
from 1 to 500, and plot the results as you did in the last lab.
If you are mind is not too melted after todays recursion lab, you
can compare the performance of the ** operator to the
above functions.
When you have finished, create a tar file of your lab16
directory. To create a tar file, execute the following commands:
cd ~/cs170/labs tar czvf lab11.tgz lab11/
To submit your activity, go to inquire.roanoke.edu. You should
see an available assignment called Lab Assignment 15.
Make sure you include a header listing the authors of the file.