Geopup and I found quite easily while out on a walk. The excitable doggo isn’t so keen on stopping and searching for caches when there are so many new and exciting smells just over her
visual horizon, so today’s expedition might only give me a couple of minutes to hunt for each: we’ll have to see if that’s enough to log any further finds this morning.
Found left out in clear with lid open but otherwise not vandalised. Closed and re-hid deeper into hiding place. Will add a geocaching information card on my next visit to better explain
its presence to any muggles passing by.
Dropped by for a routine check during an autumnal shower this afternoon. Hiding position had drifted slightly, so moved back where it belongs. All well and ready to find!
Routine maintenance visit. Looks like a “Reggie” comes by this way with his dogs and signs the logbook, sometimes! :-) Not such a forgotten footbridge after all?
I’ve been working in Witney one day every week or two lately, but somehow I’ve never managed to sync up my work times with the hours that this building is accessible! Or, when I do, I’m
in a hurry and don’t have time to stop and hunt!
This morning, though, the stars aligned and I was able to get to the GZ. The cache was pretty much where I expected based on the
coordinates and the hint, but still took a minute out two to lay hands on. Soon, though, I was quietly sitting and reading past log entries.
The geopup and I took a slightly inelegant route down to the valley bottom after she insisted we try a steep route atop a carpet of dry, dusty leaves. Made it down intact, though, and
found this cache in the very second hiding place we tried. TFTC!
A pair of walkers who’d stopped at the GZ for a snack made searching difficult, plus the geodog isn’t very good at stealth, so we had to
give up on our search for this one. Maybe on the way back. (Although as I write this I see they’re coming the same direction as us; might need stealth again yet!)
QEF for the geohound and I while out for a walk. Not convinced we’ll do the entire trail in a single run (the pooch only has little
legs!) but we’ll see how we get on. SL. TFTC.
That’s a really useful thing to have in this new age of the web, where Refererer: headers are no-longer commonly passed cross-domain and Google Search no longer provides the link: operator. If you want to know if I’ve ever
linked to your site, it’s a bit of a drag to find out.
So, obviously, I’ve written an implementation for WordPress. It’s really basic right now, but the source code can be
found here if you want it. Install it as a plugin and run wp outbound-links to kick it off. It’s fast: it takes 3-5 seconds to parse the entirety of danq.me,
and I’ve got somewhere in the region of 5,000 posts to parse.
You can see the results at https://danq.me/.well-known/links – if you’ve ever wondered “has Dan ever linked to my site?”, now you can find the
answer.
If this could be useful to you, let’s collaborate on making this into an actually-useful plugin! Otherwise it’ll just languish “as-is”, which is good enough for my purposes.
I swear that I used to be good at Mastermind when I was a kid. But now, when it’s my turn to break
the code that one of our kids has chosen, I fail more often than I succeed. That’s no good!
Mastermind and me
Maybe it’s because I’m distracted; multitasking doesn’t help problem-solving. Or it’s because we’re “Super” Mastermind, which differs from the one I had as a child in that
eight (not six) peg colours are available and secret codes are permitted to have duplicate peg colours. These changes increase the possible permutations from 360 to 4,096, but the
number of guesses allowed only goes up from 8 to 10. That’s hard.
Hey, that’s an idea. Let’s crack the code… by writing some code!
Representing a search space
The search space for Super Mastermind isn’t enormous, and it lends itself to some highly-efficient computerised storage.
There are 8 different colours of peg. We can express these colours as a number between 0 and 7, in three bits of binary, like this:
Decimal
Binary
Colour
0
000
Red
1
001
Orange
2
010
Yellow
3
011
Green
4
100
Blue
5
101
Pink
6
110
Purple
7
111
White
There are four pegs in a row, so we can express any given combination of coloured pegs as a 12-bit binary number. E.g. 100 110 111 010 would represent the
permutation blue (100), purple (110), white (111), yellow (010). The total search space, therefore, is the range of numbers from
000000000000 through 111111111111… that is: decimal 0 through 4,095:
Decimal
Binary
Colours
0
000000000000
Red, red, red, red
1
000000000001
Red, red, red, orange
2
000000000010
Red, red, red, yellow
…………
4092
111111111100
White, white, white, blue
4093
111111111101
White, white, white, pink
4094
111111111110
White, white, white, purple
4095
111111111111
White, white, white, white
Whenever we make a guess, we get feedback in the form of two variables: each peg that is in the right place is a bull; each that represents a peg in the secret code but
isn’t in the right place is a cow (the names come from Mastermind’s precursor, Bulls & Cows). Four bulls
would be an immediate win (lucky!), any other combination of bulls and cows is still valuable information. Even a zero-score guess is valuable- potentially very valuable! – because it
tells the player that none of the pegs they’ve guessed appear in the secret code.
Solving with Javascript
The latest versions of Javascript support binary literals and bitwise operations, so we can encode and decode between arrays of four coloured pegs (numbers 0-7) and the number 0-4,095
representing the guess as shown below. Decoding uses an AND bitmask to filter to the requisite digits then divides by the order of magnitude. Encoding is just a reduce
function that bitshift-concatenates the numbers together.
/** * Decode a candidate into four peg values by using binary bitwise operations. */function decodeCandidate(candidate){
return [
(candidate &0b111000000000) /0b001000000000,
(candidate &0b000111000000) /0b000001000000,
(candidate &0b000000111000) /0b000000001000,
(candidate &0b000000000111) /0b000000000001
];
}
/** * Given an array of four integers (0-7) to represent the pegs, in order, returns a single-number * candidate representation. */function encodeCandidate(pegs) {
return pegs.reduce((a, b)=>(a <<3) + b);
}
With this, we can simply:
Produce a list of candidate solutions (an array containing numbers 0 through 4,095).
Choose one candidate, use it as a guess, and ask the code-maker how it scores.
Eliminate from the candidate solutions list all solutions that would not score the same number of bulls and cows for the guess that was made.
Repeat from step #2 until you win.
Step 3’s the most important one there. Given a function getScore( solution, guess ) which returns an array of [ bulls, cows ] a given guess would
score if faced with a specific solution, that code would look like this (I’m convined there must be a more-performant way to eliminate candidates from the list with XOR
bitmasks, but I haven’t worked out what it is yet):
/** * Given a guess (array of four integers from 0-7 to represent the pegs, in order) and the number * of bulls (number of pegs in the guess that are in the right place) and cows (number of pegs in the * guess that are correct but in the wrong place), eliminates from the candidates array all guesses * invalidated by this result. Return true if successful, false otherwise. */function eliminateCandidates(guess, bulls, cows){
const newCandidatesList = data.candidates.filter(candidate=>{
const score = getScore(candidate, guess);
return (score[0] == bulls) && (score[1] == cows);
});
if(newCandidatesList.length ==0) {
alert('That response would reduce the candidate list to zero.');
returnfalse;
}
data.candidates = newCandidatesList;
chooseNextGuess();
returntrue;
}
I continued in this fashion to write a full solution (source code). It uses ReefJS for
component rendering and state management, and you can try it for yourself right in your web browser. If you play against the online version I mentioned you’ll need to transpose the colours in your head: the physical version I play with the kids has pink and
purple pegs, but the online one replaces these with brown and black.
Testing the solution
Let’s try it out against the online version:
As expected, my code works well-enough to win the game every time I’ve tried, both against computerised and in-person opponents. So – unless you’ve been actively thinking about the
specifics of the algorithm I’ve employed – it might surprise you to discover that… my solution is very-much a suboptimal one!
My solution is suboptimal
A couple of games in, the suboptimality of my solution became pretty visible. Sure, it still won every game, but it was a blunt instrument, and anybody who’s seriously thought about
games like this can tell you why. You know how when you play e.g. Wordle (but not in “hard mode”) you sometimes want to type in a word that can’t possibly be the
solution because it’s the best way to rule in (or out) certain key letters? This kind of strategic search space bisection reduces the mean number of guesses you need to solve the
puzzle, and the same’s true in Mastermind. But because my solver will only propose guesses from the list of candidate solutions, it can’t make this kind of improvement.
Search space bisection is also used in my adverserial hangman game, but in this case the aim is to split the search space in such a way that no
matter what guess a player makes, they always find themselves in the larger remaining portion of the search space, to maximise the number of guesses they have to make. Y’know, because
it’s evil.
There are mathematically-derived heuristics to optimise Mastermind strategy. The first
of these came from none other than Donald Knuth (legend of computer science, mathematics, and pipe organs) back in 1977. His solution,
published at probably the height of the game’s popularity in the amazingly-named Journal of Recreational Mathematics, guarantees a solution to the six-colour version of the
game within five guesses. Ville [2013] solved an
optimal solution for a seven-colour variant, but demonstrated how rapidly the tree of possible moves grows and the need for early pruning – even with powerful modern computers – to
conserve memory. It’s a very enjoyable and readable paper.
But for my purposes, it’s unnecessary. My solver routinely wins within six, maybe seven guesses, and by nonchalantly glancing at my phone in-between my guesses I can now reliably guess
our children’s codes quickly and easily. In the end, that’s what this was all about.