Writing Signed Trinary: or, Back To the Four Weights Problem
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Recently on Puzzle Corner, we looked at the Four Weights problem: what are the four weights that can weigh any object in the (integer) range 1 to 40 on a balance scale?
The puzzle just asks you to find the values of the weights, which are . But how do you weigh —that is, with (a known) in the right-hand pan of the scale, how do you determine the distribution of weights so that the scale balances?
This isn’t a practical question, since if I know , then I don’t have to weigh the object. But thinking about the solution brings up a cute little observation about modular arithmetic, and so it felt worth a short writeup. The solution is based on a standard procedure for converting decimal values to a base- representation. If you are familiar with that procedure, you can skim the next section and/or skip to this section. Otherwise, read on.
Converting to a base-n representation
Assume you want to represent a nonnegative integer with up to digits in binary. Recall that binary digits can represent any number in the range . This is the pseudo-code.
i = 1 # initialize the index r = zeros(m) # all-zeros vector of length m if x >= 2^m then throw("x is out of range") while x > 0 d = floor(x/2) r[i] = x mod 2 x = d i = i+1 return reverse(r) # so lowest value digit is rightmost
Let’s do it by hand for , using 4 digits (which can represent up to the value ).
13/2 = 6 rem 1 (d = 6, r[1] = 1) 6/2 = 3 rem 0 3/2 = 1 rem 1 1/2 = 0 rem 1
So we have 13 = 1101 in base-2, or (1*8) + (1*4) + (0*2) + (1*1)
.
For an unsigned trinary representation, the idea is the same, only you divide by 3 instead of 2. Let’s try it with 18, using 4 digits (which can represent up to the value , which is 80).
18/3 = 6 rem 0 6/3 = 2 rem 0 2/3 = 0 rem 2
So 18 = 0200 in base-3, or (0*27) + (2*9) + (0*3) + (0*1)
.
We can write a general function to convert a decimal number x
to base-n representation.
# convert x to its unsigned base-n representation # n: the base # ndigits: the maximum number of digits base_n = function(x, n, ndigits) { r = numeric(ndigits) if (x >= n ^ ndigits) stop("x is out of range") i = 1 while (x > 0) { d = floor(x / n) r[i] = x %% n x = d i = i + 1 } rev(r) } # convert from unsigned base-n back to decimal to_decimal = function(r, n) { r = rev(r) # put the lowest digit to the leftmost ndigits = length(r) x = 0 for (i in 1:ndigits) x = x + r[i] * n ^ (i - 1) x }
# convert 13 to binary r = base_n(13, 2, 4) # confirm this is 13 stopifnot(to_decimal(r, 2) == 13) r
[1] 1 1 0 1
# convert 18 to trinary r = base_n(18, 3, 4) # confirm this is 18 stopifnot(to_decimal(r, 3) == 18) r
[1] 0 2 0 0
Converting to signed trinary
Recall that when solving the Four Weights problem, we established that any nonnegative integer value in the range 1:40 could be represented as
where the weights are (1, 3, 9, 27)
—we write it smallest-first, consistently with the previous post on this problem—and , rather than the of unsigned trinary. A positive coefficient means the weight goes in the left pan, a negative coefficient means the weight goes in the right pan with , and 0 means the weight isn’t used.
The four digits still represent unique values, but some of them are now negative. For the Four Weights problem, we are only concerned with nonnegative , of which we can represent zero, plus values—the numbers 1:40. So we’ll concentrate on just expressing these nonnegative integers.
To modify the unsigned trinary conversion to a signed one, we use the observation that
- The equation
x/3 = d rem 2
is equivalent tox/3 = (d+1) rem -1
.
This is a bit of an abuse of the notation; but here’s an example of what we are trying to say. We know that
8/3 = 2 rem 2
, which is the same as saying8 = 2*3 + 2
.
Another way of writing this is
8 = (2+1)*3 - 1 = 3*3 - 1
which we’ll write as:8/3 = 3 rem -1
So (when considering only nonnegative ) the pseudo-code for the algorithm becomes:
i = 1 r = zeros(m) maxval = (3^m - 1)/2 if x > maxval then throw("x is out of range") while x > 0 d = floor(x/3) r[i] = x mod 3 if r[i]==2 d = d+1 r[i] = -1 x = d i = i+1 return r # we won't reverse it, to be consistent with our puzzle solution
Let’s convert 18 to signed trinary.
18/3 = 6 rem 0 6/3 = 2 rem 0 2/3 = 0 rem 2 = 1 rem -1 1/3 = 0 rem 1
So 18 codes to [0 0 -1 1] = (0*1) + (0*3) + (-1*9) + (1*27)
. In the scale notation that we used while solving the puzzle, we would write this as , meaning the 27-weight is in the left pan, and the 9-weight is in the right pan with .
Here’s the code.
# convert nonnegative x to signed trinary weigh = function(x) { if (x > 40) stop("x out of range") r = numeric(4) i = 1 while (x > 0) { d = floor(x / 3) r[i] = x %% 3 if (r[i] == 2) { d = d + 1 r[i] = -1 } x = d i = i + 1 } r } # write the signed trinary representation in our scale notation scale_notation = function(signs) { w = c(1, 3, 9, 27) lefti = which(signs > 0) righti = which(signs < 0) leftset = paste(w[lefti], collapse = ", ") if (length(righti) > 0) rightset = paste("x,", paste(w[righti], collapse = ", ")) else rightset = "x" notation = paste("[ {", leftset, "} | {", rightset, "} ]") notation } # convert signed trinary back to decimal to_x = function(signs) { w = c(1, 3, 9, 27) x = sum(w * signs) # dot product of w and signs }
Let’s try a few.
x = 18 signs = weigh(x) # convert back and check it's the same number stopifnot(x == to_x(signs)) scale_notation(signs)
[1] "[ { 27 } | { x, 9 } ]"
x = 35 signs = weigh(x) stopifnot(x == to_x(signs)) scale_notation(signs)
[1] "[ { 9, 27 } | { x, 1 } ]"
x = 15 signs = weigh(x) stopifnot(x == to_x(signs)) scale_notation(signs)
[1] "[ { 27 } | { x, 3, 9 } ]"
x = 30 signs = weigh(x) stopifnot(x == to_x(signs)) scale_notation(signs)
[1] "[ { 3, 27 } | { x } ]"
x = 4 signs = weigh(x) stopifnot(x == to_x(signs)) scale_notation(signs)
[1] "[ { 1, 3 } | { x } ]"
So now we can weigh objects we know the weight of. Hurrah? But I still think it’s cute.
Nina Zumel is a data scientist based in San Francisco, with 20+ years of experience in machine learning, statistics, and analytics. She is the co-founder of the data science consulting firm Win-Vector LLC, and (with John Mount) the co-author of Practical Data Science with R, now in its second edition.
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