IRC Graphs July 24th, 2013
Patrick Stein

For some time, I’ve been wanting to find some big data source to dig around in and make plots of. Yesterday, I realized that I have access to #lisp logs from IRC going back several years.

The first question that I wanted to look at was: How well does talkativeness on IRC follow a Power Law?

It looks pretty close when you’re looking at the raw data if you limit yourself to the top 100 to 300 people. Once you get up near the top 500 people, the best-fit curve really skyrockets way through the roof. There are just tons of speakers who have said one or two lines in the given time period. And, I made no effort to track lurkers so I have no zeros in my data set.

Here is a plot of the top 250 speakers (ranked by lines spoken). stassats is the leader, followed by pjb, then H4ns, then Xach. I made a best-effort to collate different handles for the same person (e.g. Xach_ vs. Xach). The least-squares, best-fit power-law curve here is 68435 k^{-1.0638}. So, if we’re going to match the curve exactly, we’ll need stassats to talk more than twice as much. If you’d like to know how much more (or less) you should talk, drop me a note. :)

IRC Top 250 Talkers on #lisp by lines spoken

Click on the image above for the full-size version. I used optima.ppcre to read the log files and vecto to draw the graph. Here is the relevant source code: package.lisp, read.lisp, and power.lisp.

Inverse functions with fixed-points July 18th, 2013
Patrick Stein


SICP has a few sections devoted to using a general, damped fixed-point iteration to solve square roots and then nth-roots. The Functional Programming In Scala course that I did on Coursera did the same exercise (at least as far as square roots go).

The idea goes like this. Say that I want to find the square root of five. I am looking then for some number s so that s^2 = 5. This means that I’m looking for some number s so that s = \frac{5}{s}. So, if I had a function f(x) = \frac{5}{x} and I could find some point s where s = f(s), I’d be done. Such a point is called a fixed point of f.

There is a general method by which one can find a fixed point of an arbitrary function. If you type some random number into a calculator and hit the “COS” button over and over, your calculator is eventually going to get stuck at 0.739085…. What happens is that you are doing a recurrence where x_{n+1} = \cos(x_n). Eventually, you end up at a point where x_{n+1} = x_{n} (to the limits of your calculator’s precision/display). After that, your stuck. You’ve found a fixed point. No matter how much you iterate, you’re going to be stuck in the same spot.

Now, there are some situations where you might end up in an oscillation where x_{n+1} \ne x_n, but x_{n+1} = x_{n-k} for some k \ge 1. To avoid that, one usually does the iteration x_{n+1} = \mathsf{avg}(x_n,f(x_n)) for some averaging function \mathsf{avg}. This “damps” the oscillation.

The Fixed Point higher-order function

In languages with first-class functions, it is easy to write a higher-order function called fixed-point that takes a function and iterates (with damping) to find a fixed point. In SICP and the Scala course mentioned above, the fixed-point function was written recursively.

(defun fixed-point (fn &optional (initial-guess 1) (tolerance 1e-8))
  (labels ((close-enough? (v1 v2)
             (<= (abs (- v1 v2)) tolerance))
           (average (v1 v2)
             (/ (+ v1 v2) 2))
           (try (guess)
             (let ((next (funcall fn guess)))
                ((close-enough? guess next) next)
                (t (try (average guess next)))))))
    (try (* initial-guess 1d0))))

It is easy to express the recursion there iteratively instead if that’s easier for you to see/think about.

(defun fixed-point (fn &optional (initial-guess 1) (tolerance 1e-8))
  (flet ((close-enough? (v1 v2)
            (<= (abs (- v1 v2)) tolerance))
         (average (v1 v2)
            (/ (+ v1 v2) 2)))
    (loop :for guess = (* initial-guess 1d0) :then (average guess next)
          :for next = (funcall fn guess)
          :until (close-enough? guess next)
          :finally (return next))))

Using the Fixed Point function to find k-th roots

Above, we showed that the square root of n is a fixed point of the function f(x) = \frac{n}{x}. Now, we can use that to write our own square root function:

(defun my-sqrt (n)
  (fixed-point (lambda (x) (/ n x)))

By the same argument we used with the square root, we can find the k-th root of 5 by finding the fixed point of f(x) = \frac{5}{x^{k-1}}. We can make a function that returns a function that does k-th roots:

(defun kth-roots (k)
  (lambda (n)
    (fixed-point (lambda (x) (/ n (expt x (1- k)))))))

(setf (symbol-function 'cbrt) (kth-root 3))

Inverting functions

I found myself wanting to find inverses of various complicated functions. All that I knew about the functions was that if you restricted their domain to the unit interval, they were one-to-one and their domain was also the unit interval. What I needed was the inverse of the function.

For some functions (like f(x) = x^2), the inverse is easy enough to calculate. For other functions (like f(x) = 6x^5 - 15x^4 + 10x^3), the inverse seems possible but incredibly tedious to calculate.

Could I use fixed points to find inverses of general functions? We’ve already used them to find inverses for f(x) = x^k. Can we extend it further?

After flailing around Google for quite some time, I found this article by Chen, Lu, Chen, Ruchala, and Olivera about using fixed-point iteration to find inverses for deformation fields.

There, the approach to inverting f(x) was to formulate u(x) = f(x) - x and let v(x) = f^{-1}(x) - x. Then, because

x = f(f^{-1}(x)) =  f^{-1}(x) + u(f^{-1}(x)) = x + v(x) + u(x + v(x))

That leaves the relationship that v(x) = -u(x + v(x)). The goal then is to find a fixed point of v(x).

I messed this up a few times by conflating f and u so I abandoned it in favor of the tinkering that follows in the next section. Here though, is a debugged version based on the cited paper:

(defun pseudo-inverse (fn &optional (tolerance 1d-10))
  (lambda (x)
    (let ((iterant (lambda (v)
                     (flet ((u (x)
                               (- (funcall fn x) x)))
                       (- (u (+ x v)))))))
      (+ x (fixed-point iterant 0d0 tolerance)))))

Now, I can easily check the average variance over some points in the unit interval:

(defun check-pseudo-inverse (fn &optional (steps 100))
  (flet ((sqr (x) (* x x)))
    (/ (loop :with dx = (/ (1- steps))
             :with inverse = (pseudo-inverse fn)
             :repeat steps
             :for x :from 0 :by dx
             :summing (sqr (- (funcall fn (funcall inverse x)) x)))

(check-pseudo-inverse #'identity) => 0.0d0
(check-pseudo-inverse #'sin)      => 2.8820112095939962D-12
(check-pseudo-inverse #'sqrt)     => 2.7957469632748447D-19                                                          
(check-pseudo-inverse (lambda (x) (* x x x (+ (* x (- (* x 6) 15)) 10))))
                                  => 1.3296561385041381D-21

A tinkering attempt when I couldn’t get the previous to work

When I had abandoned the above, I spent some time tinkering on paper. To find f^{-1}(x), I need to find y so that f(y) = x. Multiplying both sides by y and dividing by f(y), I get y = \frac{xy}{f(y)}. So, to find f^{-1}(x), I need to find a y that is a fixed point for \frac{xy}{f(y)}:

(defun pseudo-inverse (fn &optional (tolerance 1d-10))
  (lambda (x)
    (let ((iterant (lambda (y)
                     (/ (* x y) (funcall fn y)))))
      (fixed-point iterant 1 tolerance))))

This version, however, has the disadvantage of using division. Division is more expensive and has obvious problems if you bump into zero on your way to your goal. Getting rid of the division also allows the above algorithms to be generalized for inverting endomorphisms of vector spaces (the \mathsf{avg} function being the only slightly tricky part).


I finally found a use of the fixed-point function that goes beyond k-th roots. Wahoo!

Track-Best Library Updated July 8th, 2013
Patrick Stein

I updated my track-best library to allow you to keep all of the the things tied for best. The WITH-TRACK-BEST macro now accepts the :KEEP-TIES keyword parameter.

Here are some examples of using the :KEEP-TIES option. For all of the examples, we will use the same sequence of TRACK calls:

(defun track-numbers ()
  (track :one 1)
  (track :uno 1)
  (track :two 2)
  (track :dos 2)

Here are some calls with :KEEP-TIES as NIL (the default):

(with-track-best (:keep 1 :keep-ties nil) (track-numbers))
=> (values :TWO 2)

(with-track-best (:keep 3 :keep-ties nil) (track-numbers))
=> (values (:TWO :DOS :ONE) (2 2 1))

Here are some calls with :KEEP-TIES as T:

(with-track-best (:keep 1 :keep-ties t) (track-numbers))
=> (values (:TWO :DOS) (2 2))

(with-track-best (:keep 3 :keep-ties t) (track-numbers))
=> (values (:TWO :DOS :ONE :UNO) (2 2 1 1))

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