Just for some fun and relaxation, I decided to start doing some Lisp. I had set up SLIME a few months backs while I was starting some book but I had not done much. I decided to use SBCL because I had read that is usually fast. The reason I was using SLIME is that the back arrow key does not work when I use the SBCL REPL and that is very annoying to me because I type both opening and closing parentheses together and type the rest of the code in between. To setup SLIME, a lot of documentation is available online so I wont re-iterate. Just for reference, the SLIME section of the .emacs file is as follows:
(setq inferior-lisp-program "/usr/bin/sbcl")
(add-to-list 'load-path "/usr/share/emacs/site-lisp/slime/")
(require 'slime)
(slime-setup)
Once I was in SLIME REPL, I started writing a few s-expression to get a hang of it.
(+ 1 2)
(print 'hello)
(format t "hello world")
After that, I started writing a simple function that returns the nth number in the Fibonacci sequence. I came up with the following.
(defun foo (n)
(cond
((= n 1) 0)
((= n 2) (+ 1 (foo (- n 1))))
(+ (foo (- n 1)) (foo (- n 2)))))
And I started testing it.
(foo 1)
(foo 2)
(foo 3)
The result for
n = 3
was wrong. The reason lay in the last section of cond
. I intended it to be default but I had not specified the condition for it. So, I corrected it as follows.(defun foo (n)
(cond
((= n 1) 0)
((= n 2) (+ 1 (foo (- n 1))))
(t (+ (foo (- n 1)) (foo (- n 2))))))
Now, the method was returning correct values. Clearly, it is a bad implementation. So, I thought the calls to
foo
should be memoized. I could go ahead and try writing my own memoization but I wanted to see what Common Lisp had to offer. However, before doing that I wanted to know whether memoization will be beneficial. So, I wanted to benchmark the memoized and the plain versions. Our good friend Google helped me out. I was able to start profiling using SBCL's built-in package sb-prof
.
(in-package :cl-user)
(require :sb-sprof)
(declaim (optimize speed))
(sb-sprof:with-profiling (:max-samples 1000
:report :flat
:loop nil)
(foo 100))
I was a little doubtful that I should not be trying for the 100th Fibonacci number but still I went ahead with it and even after some minutes, it was going on. So, I now I decided to kill the profiling.
Ctrl+C Ctrl+C
came in handy. I started low this time. From 10, 15, 25, I reached up to 40 at which value it took around 6 seconds.
(sb-sprof:with-profiling (:max-samples 1000
:report :flat
:loop nil)
(foo 40))
Now, I was ready to test a memoized version of the function and see how much benefit I can get. I remembered reading some code by Peter Norvig in Python which used a decorator to achieve generic memoization. So, I thought Lisp ought to have something similar. I found a nice memoization API. However, I had to install the package and I did not want to delve into that because I did not find any packages for Arch. So, I decided to settle for a less robust implementation.
(defun Basic-Memo (Function)
"Takes a normal function object and returns an `equivalent' memoized one"
(let ((Hash-Table (make-hash-table)))
#'(lambda (Arg)
(multiple-value-bind (Value Foundp)
(gethash Arg Hash-Table)
(if
Foundp
Value
(setf (gethash Arg Hash-Table) (funcall Function Arg))))) ))
(defun Basic-Memoize (Function-Name)
"Memoize function associated with Function-Name. Simplified version"
(setf (symbol-function Function-Name)
(Basic-Memo (symbol-function Function-Name))))
(Basic-Memoize 'foo)
Now, the 1000th Fibonacci number was also easily calculated. When I tried to the above profiling code for (foo 1000), I was getting error for the run being too short. Trying for 10000th Fibonacci number, I got 0.01 seconds.
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