# Solve this puzzle – @memo and dynamic programming

Here’s the situation.  You have a puzzle with 15 round coloured plates arranged in an equilateral triangle, and 14 coloured pebbles on top of them.  One plate does not have a pebble – it is called the hole.  Your goal is to rearrange the pebbles so that they are on the matching coloured plates, in the minimum number of moves possible.  For each move, you can only move one pebble in a straight line to the hole, possibly leaping over other pebbles on the way.

The question is – can you design an algorithm to calculate, for any starting board, the minimum number of moves to solve it?

In fact this describes the game Panguru, recently produced by Second Nature Games and available as a board game and online.  In Panguru, there are two pebbles and plates of each colour, and one additional legal move down the centreline of the triangle.  If my quick description is too wordy, the online game will give you a much better feel for it; there are rules available too.

## A dynamic programming solution, with memoization

Here’s the approach I came up with, which I implemented in python. This was informed by the excellent book Python Algorithms, Mastering Basic Algorithms in the Python Language, by Magnus Lie Hetland, which I highly recommend.

Think of all the possible moves used to solve the puzzle as a tree. Number the positions 0 to 14.  The root node of the tree is the hole position, and child nodes give the position of the pebble that is being moved. So with the hole starting at the top of the triangle (position 0), one path through the tree might be 0-3-5.  (If you think about it, the last move always tells you where the hole finishes up.)

It is easy to measure how close we are to the solved board: we just count how many pebbles have the same colour as their plates.  When we hit 14, we are done!

In fact we can turn it around.  Let’s assume we have a solution (14 matching pebbles) after m moves. How did we get here?  Clearly, we must have had 13 matching pebbles the move before. (Because you only move one pebble at a time, each move can only increase or decrease the number of matching pebbles by one, or leave the number unchanged.)

And the move before that, we must have had either 12 or 13 matching pebbles. And so on.

This sounds like induction and lends itself to a recursive solution, like this. First define the tree structure via the `Node` class. The core of this is the `__init__` method which keeps track of the node’s parent. I’ve also added `original_pos` to help us later, and two extra methods to display nodes nicely on the command line (`__repr__`) and to make it easier to access the node’s ancestry (`__getitem__`).

```class Node():
def __init__(self, parent, move, num_matching):
self.parent = parent
self.move = move
self.num_matching = num_matching

def original_pos(self, pos):
"""
Return the original position of the pebble now at position pos.
"""
if not self.parent:
return pos
if pos==self.move:
prev_pos = self.parent.move
elif pos==self.parent.move:
prev_pos = self.move
else:
prev_pos = pos
return self.parent.original_pos(prev_pos)

def __getitem__(self, key):
"""
If m is a node which you think of as the last move in a sequence,
m[-3] is the third last move as a Node.
m[-3].move is the third last move as an integer (which position was moved)
"""
if key>=0:
raise IndexError, "You must index moves from the end, e.g. m[-1] for the last move."
if key==-1:
return self
else:
try:
return self.parent[key+1]
except TypeError:
raise IndexError, "Out of range."

def __repr__(self):
return "%s%d" % ((self.parent and ("%s-" % str(self.parent)) or ""),
self.move)
```

Then the puzzle-solving approach could be implemented like this:

```class Puzzle():
def __init__(self, plates, pebbles, allowed_moves):
"""
Set up a puzzle instance for solving.
Args:
plates is a string of 15 chars representing colours
e.g. "WCBRBOGYGPCORPY"
pebbles is the same, with "-" for the hole
e.g. "-PCBRYOGYGBCORP"
allowed_moves is a list s.t.
allowed_moves[i] = list of positions that a pebble at pos i can move to
= list of positions that can move to this position, if it is the hole
e.g. [[1, 2, 3, 4, 5, 6, 9, 10, 12, 14],
[0, 2, 3, 4, 6, 8, 10, 13],
[0, 1, 4, 5, 7, 9, 11, 14],
[0, 1, 4, 5, 6, 7, 10, 12],
[0, 1, 2, 3, 5, 7, 8, 11, 12, 13],
[0, 2, 3, 4, 8, 9, 12, 14],
[0, 1, 3, 7, 8, 9, 10, 11],
[2, 3, 4, 6, 8, 9, 11, 12],
[1, 4, 5, 6, 7, 9, 12, 13],
[0, 2, 5, 6, 7, 8, 13, 14],
[0, 1, 3, 6, 11, 12, 13, 14],
[2, 4, 6, 7, 10, 12, 13, 14],
[0, 3, 4, 5, 7, 8, 10, 11, 13, 14],
[1, 4, 8, 9, 10, 11, 12, 14],
[0, 2, 5, 9, 10, 11, 12, 13]]
"""
self.plates = plates
self.pebbles = pebbles
self.allowed_moves = allowed_moves
self.num_pebbles = len(filter(lambda x: x not in ["-"], self.pebbles))
self.num_matching = sum(plates[i] == pebbles[i] for i in range(len(pebbles)))
hole_pos = self.pebbles.find('-')
self.root = Node(None, hole_pos, self.num_matching)

def matching_nodes(self, turn, num_matching):
"""
Return all the series of moves (as BoardNodes with parents)
that have 'num_matching' matching spots after 'turn' turns.
"""
if turn==0:
if num_matching==self.num_matching:
return [self.root]
else:
return []
result = []
for change in (-1,0,1):
for prev_node in self.matching_nodes(turn-1, num_matching+change):
for move in self.allowed_moves[prev_node.move]:
pebble_colour = self.pebbles[prev_node.original_pos(move)]
# was the moved pebble on a matching plate already?
old_pos_match = (self.plates[move]==pebble_colour)
# does the prev board's hole plate match the moved pebble?
new_pos_match = (self.plates[prev_node.move]==pebble_colour)
# did the move change how many positions were matching,
# by exactly the number we're looking at?
if (old_pos_match-new_pos_match)==change:
result += [Node(prev_node, move, num_matching)]
return result

```

The interesting recursion here is going on in the `matching_nodes` method.  It just implements the idea that the solutions that have, say, 10 matching positions at turn 10, must have had either 9,10 or 11 matching positions at turn 9. It then works back to turn 0, at which point we know how many matching positions there were.

On top of this, we need a further method which finds the right question to ask. It could start by saying – give me all the solutions after 0 moves. If there are none, find all the solutions after 1 move. And keep trying until you find some solutions, e.g.:

```    def optimal_moves(self, stop_at=20):
"""
Return a tuple (fewest possible number of moves, [optimal moves for this board]).
"""
num = 0
result = []
while not result and num<=stop_at:
result = self.matching_nodes(num, self.num_pebbles)
num += 1
return (num, result)

```

The code above will work, but it will be slow and inefficient, because `matching_nodes` will often recurse through territory it has already covered. And that’s where Hetland’s memo decorator comes to the rescue. (This is only a few lines of code which you can find by searching at Google books.) This will cache the results of the decorated method, so that it does not need to be recalculated.  To use it, simply apply `@memo` to the `def matching_nodes` line, like so:

```@memo
def matching_nodes(self, turn, num_matching):```

And that’s it!

You can see this code in action when you ask for a hint in the online game. Each time you press the hint button, the javascript sends off an ajax query which triggers the server to run the above code on the player’s board, and return which moves would come next if the player plays optimally.

In fact, to get around annoying ever-expanding memory problems on the server, I’m running this asynchronously using Celery and Redis (as covered in an earlier blog post), and restarting the celery worker after every request.  But that’s another story…

I hope that has been of interest, and please let me know if you have any comments or improvements.