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Mastermind-style code-breaking (Python recipe) by Raymond Hettinger
ActiveState Code (http://code.activestate.com/recipes/496907/)

Framework for experimenting with guessing strategies in Master-mind style games.

Python, 187 lines
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import random
from itertools import izip, imap
from math import log

digits = 4
trials = 100
fmt = '%0' + str(digits) + 'd'
searchspace = tuple([tuple(map(int,fmt % i)) for i in range(0,10**digits)])

def compare(a, b, imap=imap, sum=sum, izip=izip, min=min):
    count1 = [0] * 10
    count2 = [0] * 10
    strikes = 0
    for dig1, dig2 in izip(a,b):
        if dig1 == dig2:
            strikes += 1
        count1[dig1] += 1
        count2[dig2] += 1
    balls = sum(imap(min, count1, count2)) - strikes
    return (strikes, balls)

def rungame(target, strategy, verbose=True, maxtries=15):
    possibles = list(searchspace)
    for i in xrange(maxtries):
        g = strategy(i, possibles)
        if verbose:
            print "Out of %7d possibilities.  I'll guess %r" % (len(possibles), g),
        score = compare(g, target)
        if verbose:
            print ' ---> ', score
        if score[0] == digits:
            if verbose:
                print "That's it.  After %d tries, I won." % (i+1,)
            break
        possibles = [n for n in possibles if compare(g, n) == score]
    return i+1

############################################### Strategy support

def info(seqn):
    bits = 0
    s = float(sum(seqn))
    for i in seqn:
        p = i / s
        bits -= p * log(p, 2)
    return bits

def utility(play, possibles):
    b = {}
    for poss in possibles:
        score = compare(play, poss)
        b[score] = b.get(score, 0) + 1
    return info(b.values())

def hasdup(play, set=set, digits=digits):
    return len(set(play)) != digits

def nodup(play, set=set, digits=digits):
    return len(set(play)) == digits

################################################ Strategies

def s_allrand(i, possibles):
    return random.choice(possibles)

def s_trynodup(i, possibles):
    for j in xrange(20):
        g = random.choice(possibles)
        if nodup(g):
            break
    return g

def s_bestinfo(i, possibles):
    if i == 0:
        return s_trynodup(i, possibles)
    plays = random.sample(possibles, min(20, len(possibles)))
    _, play = max([(utility(play, possibles), play) for play in plays])
    return play

def s_worstinfo(i, possibles):
    if i == 0:
        return s_trynodup(i, possibles)
    plays = random.sample(possibles, min(20, len(possibles)))
    _, play = min([(utility(play, possibles), play) for play in plays])
    return play

def s_samplebest(i, possibles):
    if i == 0:
        return s_trynodup(i, possibles)
    if len(possibles) > 150:
        possibles = random.sample(possibles, 150)
        plays = possibles[:20]
    elif len(possibles) > 20:
        plays = random.sample(possibles, 20)
    else:
        plays = possibles
    _, play = max([(utility(play, possibles), play) for play in plays])
    return play

## Evaluate Strategies

def average(seqn):
    return sum(seqn) / float(len(seqn))

def counts(seqn):
    limit = max(10, max(seqn)) + 1
    tally = [0] * limit
    for i in seqn:
        tally[i] += 1
    return tuple(tally[1:])

from time import clock
print '-' * 60

##for ss in searchspace, filter(hasdup, searchspace), filter(nodup, searchspace):
##    print 'spacesize:', len(ss), ' = ', log(len(ss),2), 'bits'
##for play in [(1,2,3,4,5), (1,1,2,3,4), (1,1,1,2,3), (1,1,2,2,3), (1,1,1,2,2)]:
##    print 'First play:', play, 'has info content', utility(play, searchspace)
##    print 'First play:', play, 'has info content', utility(play, filter(hasdup, searchspace)), 'against dups'
##    print 'First play:', play, 'has info content', utility(play, filter(nodup, searchspace)), 'against nodups'
##    print

for strategy in (s_bestinfo, s_samplebest, s_worstinfo, s_allrand, s_trynodup, s_bestinfo):
    start = clock()
    data = [rungame(random.choice(searchspace), strategy, verbose=False) for i in xrange(trials)]
    #print '[%d  %d| median %.2f    mean %.2f |%d %d]  dig=%d  n=%d   %s' % (min(data), lq(data), median(data),average(data), uq(data), max(data), digits, len(data), strategy.__name__)
    print 'mean=%.2f %r  %s n=%d dig=%d' % (average(data), counts(data), strategy.__name__, len(data), digits)
    print 'Time elapsed %.2f' % (clock() - start,)

""" Analysis of strategies with four digit targets

s_worstinfo             [3 | median 7.00       mean 6.76 | 10]  n=120
s_allrand               [3 | median 6.00       mean 6.19 | 9]  n=120
s_firstnodup            [3 | median 6.00       mean 6.24 | 9]  n=120
s_trynodup              [3 | median 6.00       mean 5.99 | 9]  n=120
s_bestinfo              [2 | median 6.00       mean 5.75 | 10]  n=120

s_worstinfo             [3 | median 7.00       mean 6.86 | 10]  n=200
s_allrand               [4 | median 6.00       mean 6.52 | 10]  n=200
s_firstnodup            [1 | median 6.00       mean 6.19 | 10]  n=200
s_firstdup_restnodup    [3 | median 6.00       mean 6.13 | 10]  n=200
s_trynodup              [3 | median 6.00       mean 6.11 | 10]  n=200
s_bestinfo              [2 | median 6.00       mean 5.73 | 9]  n=200

[3  6| median 7.00    mean 6.78 |8 11]  dig=4  n=200   s_worstinfo            6.77
[3  5| median 6.00    mean 6.24 |7 10]  dig=4  n=200   s_allrand              6.38
[2  6| median 6.00    mean 6.26 |7 9]   dig=4  n=200   s_firstnodup             6.22
[2  5| median 6.00    mean 5.93 |7 9]   dig=4  n=200   s_firstdup_restnodup   6.03
[4  5| median 6.00    mean 6.14 |7 9]   dig=4  n=200   s_trynodup           6.12
[3  5| median 6.00    mean 5.87 |6 9]   dig=4  n=200   s_bestinfo       5.74

[1  6| median 7.00    mean 6.93 |8 11]  dig=4  n=500   s_worstinfo
[2  5| median 6.00    mean 6.11 |7 10]  dig=4  n=500   s_allrand
[3  5| median 6.00    mean 6.12 |7 10]  dig=4  n=500   s_firstnodup
[2  5| median 6.00    mean 6.07 |7 10]  dig=4  n=500   s_firstdup_restnodup
[2  5| median 6.00    mean 6.03 |7 9]  dig=4  n=500   s_trynodup
[2  5| median 6.00    mean 5.82 |6 9]  dig=4  n=500   s_bestinfo
[2  5| median 6.00    mean 5.82 |6 9]  dig=4  n=500   s_bestinfo_randfirst

spacesize: 100000  =  16.6096404744 bits
spacesize: 69760  =  16.0901124197 bits
spacesize: 30240  =  14.8841705191 bits

First play: (1, 2, 3, 4, 5) has info content 2.72731327592
First play: (1, 2, 3, 4, 5) has info content 2.54363363003 against dups
First play: (1, 2, 3, 4, 5) has info content 2.77115216576 against nodups

First play: (1, 1, 2, 3, 4) has info content 2.57287940403
First play: (1, 1, 2, 3, 4) has info content 2.51059435464 against dups
First play: (1, 1, 2, 3, 4) has info content 2.56508152257 against nodups

First play: (1, 1, 1, 2, 3) has info content 2.16683284403
First play: (1, 1, 1, 2, 3) has info content 2.11526590293 against dups
First play: (1, 1, 1, 2, 3) has info content 2.13291456398 against nodups

First play: (1, 1, 2, 2, 3) has info content 2.28656723529
First play: (1, 1, 2, 2, 3) has info content 2.28606417747 against dups
First play: (1, 1, 2, 2, 3) has info content 2.14424289741 against nodups

First play: (1, 1, 1, 2, 2) has info content 2.16683284403
First play: (1, 1, 1, 2, 2) has info content 2.11526590293 against dups
First play: (1, 1, 1, 2, 2) has info content 2.13291456398 against nodups

mean=6.22 (0, 0, 0, 4, 33, 130, 72, 9, 2, 0)  s_samplebest n=250 dig=5
Time elapsed 1395.75

"""

The basic part of the framework takes only about 30 lines. They rest is an implementation of various search strategies and an engine to gather statistics on how well each strategy performs:

  • Randomly pick any one of the remaining possibilities
  • If possible, only make guesses that do not have duplicated digits.
  • Make the guess that provides the most information (the one that most evenly divides-up the remaining possibilities).

To make it run fast, use psyco. To make it even faster, implement the compare function in C:

 /* ------------- C version of the compare() function ----------------*/

#include "Python.h"

PyObject *
compare(PyObject *self, PyObject *args)
{
        PyObject *t1, *t2, *elem;
        int len, i, j, c1, c2;
        int balls=0, strikes=0;

        if (!PyArg_ParseTuple(args, "O!O!:compare", &PyTuple_Type, &t1, &PyTuple_Type, &t2))
                return NULL;

        len = PyTuple_Size(t1);
        if (PyTuple_Size(t2) != len)
                return NULL;
        for (i=0 ; i<len ; i++) {
                elem = PyTuple_GET_ITEM(t1, i);
                for (c1=0, j=0 ; j<len ; j++) {
                        if (j<i && PyTuple_GET_ITEM(t1, j) == elem)
                                break;
                        if (PyTuple_GET_ITEM(t1, j) == elem)
                                c1 += 1;
                }
                for (c2=0, j=0 ; j<len ; j++) {
                        if (PyTuple_GET_ITEM(t2, j) == elem)
                                c2 += 1;
                }
                balls += (c1 < c2) ? c1 : c2;
                if (PyTuple_GetItem(t2, i) == elem)
                        strikes += 1;
        }

        return Py_BuildValue("(ii)", strikes, balls-strikes);
}

static PyMethodDef mmfuncs[] = {
        {"compare", (PyCFunction)compare,       METH_VARARGS, "score two positions"},
        {NULL}
};

void
initmm(void)
{
        Py_InitModule3("mm", mmfuncs, "mastermind helper functions module");
}
Tags: algorithms