Skip to content

Instantly share code, notes, and snippets.

@kennytm
Created August 18, 2010 19:58
Show Gist options
  • Save kennytm/535970 to your computer and use it in GitHub Desktop.
Save kennytm/535970 to your computer and use it in GitHub Desktop.
#!/usr/bin/env python2.7
#
# parrondo_optimizer.py ... Parrondo's paradox which optimizes the sequence.
# Copyright (C) 2010 KennyTM~ <[email protected]>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from __future__ import division, print_function, unicode_literals
from collections import defaultdict
from math import fsum
# the global configuration variables.
aCos = [0.5**0.5] * 4
aSin = [0.5**0.5 * 1j] * 4
bCos = aCos[:]
bSin = aSin[:]
coinsCount = 3
coinsMask = 7
seq1 = []
seq2 = []
length = 0
save = None
optimize = 0
optimize_method = 'hill'
write_mean = False
game_type = 'qhd'
observeInterval = 0
def computeWeight(probs):
# compute total weight of a single sequence.
return fsum(w for _, w in probs)
def computeMean(probs):
# compute unnormalized mean of a single sequence.
from itertools import starmap
from operator import mul
return fsum(starmap(mul, probs))
def computeVar(probs, mean):
# compute unnormalized variance of a single sequence.
return fsum( (x - mean)**2 * w for x, w in probs )
def writeSeq(seq):
from itertools import imap
write('#', ''.join(imap("01".__getitem__, seq)))
class Parrondo(object):
def reset(self):
assert False
def __init__(self):
self.reset()
def flip(self, coss, sins):
assert False
def translate(self):
assert False
def rotate(self):
assert False
def observe(self):
pass
def step(self, whichGame):
(coss, sins) = ((aCos, aSin), (bCos, bSin))[whichGame]
self.flip(coss, sins)
self.translate()
self.rotate()
def writeMean(self, i, game):
probs = self.prob()
weight = computeWeight(probs)
mean = computeMean(probs)/weight
var = computeVar(probs, mean)/weight
write(i, "\t", game, "\t", mean, "\t", var)
return (mean, var)
def performOneSeq(self, seq):
# evaluate the sequence.
self.reset()
writeSeq(seq)
if write_mean:
self.writeMean(0, '-')
for i, whichGame in enumerate(seq):
self.step(whichGame)
if observeInterval and i % observeInterval == observeInterval-1:
self.observe()
if write_mean:
self.writeMean(i+1, "AB"[whichGame])
(mean, var) = self.writeMean(length, "Last")
return mean
def performOptimizeMean(self, seq):
curMax = self.performOneSeq(seq)
theSeq = None
for j in xrange(optimize):
for i, n in enumerate(seq):
newSeq = seq[:]
newSeq[i] = 1-n
value = self.performOneSeq(newSeq)
if value > curMax:
curMax = value
theSeq = newSeq
if theSeq:
write("# ({0}/{1}) Found new maximum = {2}".format(j, optimize, curMax))
writeSeq(theSeq)
seq = theSeq
theSeq = None
else:
break
write("# Found local maximum = {0}".format(curMax))
writeSeq(seq)
def performBrute(self, seqlen):
from itertools import product
curMax = float("-inf")
theSeq = None
for seq0 in product([0, 1], repeat=seqlen-5):
seq = (0, 0, 1, 1, 1) + seq0
value = self.performOneSeq(seq)
if value > curMax:
curMax = value
theSeq = seq
write("# Found maximum = {0}".format(curMax))
writeSeq(theSeq)
################################################################################
#
# OK, so the ket looks like this.
# the entries are pairs of integers (indices) and complex numbers (amplitudes).
# each integer can be split into two parts by modulo 2^h.
# e.g. 12773 = (1596)*8 + (5)
# the quotient is the walker's position and the remainder is the coins.
# with 0 = ---
# 1 = --+ etc
# the most current coin is at the least significant digit.
# so the (5) above = +-+ means
# the current coin is |+>, and previous coin is |->,
# and the oldest coin is |+>
#
def cfsum(lst):
real = fsum(c.real for c in lst)
imag = fsum(c.imag for c in lst)
return complex(real, imag)
class QHDParrondoFast(Parrondo):
def reset(self):
sqrt12 = complex(0.5**0.5)
self.ket = [[0, sqrt12], [coinsMask, -sqrt12]]
def flip(self, coss, sins):
# Apply the coin operator.
resDict = defaultdict(list)
for x, a in self.ket:
coinHistory = (x & coinsMask) >> 1
resDict[x].append(a * coss[coinHistory])
resDict[x^1].append(a * sins[coinHistory])
self.ket = [[x, cfsum(a)] for x, a in resDict.iteritems()]
def translate(self):
coinsModulo = coinsMask + 1
for i, (x, _) in enumerate(self.ket):
self.ket[i][0] = x + (coinsModulo if x & 1 else -coinsModulo)
def rotate(self):
# recycle the coins.
# rotation won't cause superposition.
for i, (x, _) in enumerate(self.ket):
self.ket[i][0] = x & ~coinsMask | (x << 1) & coinsMask | (x >> (coinsCount-1)) & 1
def prob(self):
# convert ket to probability.
probs = defaultdict(list)
for x, a in self.ket:
probs[x >> coinsCount].append(abs(a)**2)
return [(x, fsum(a)) for x, a in probs.iteritems()]
def observe(self):
assert False, "Error: Cannot perform observation with kets. Please use '-g qhddm' instead."
# This uses
class QHDParrondo(Parrondo):
def reset(self):
self.rho = [
[(0, 0), 0.5],
[(coinsMask, 0), -0.5],
[(0, coinsMask), -0.5],
[(coinsMask, coinsMask), 0.5]
]
def flip(self, coss, sins):
# Apply the coin operator.
resDict = defaultdict(list)
for (x, y), a in self.rho:
coinHistoryX = (x & coinsMask) >> 1
coinHistoryY = (y & coinsMask) >> 1
# C|X> = cos1|X> + isin1|~X>
# C|Y> = cos2|Y> + isin2|~Y>
# i.e. <Y|C* = <Y|cos2 - <~Y|isin2
# thus,
# C|X><Y|C* = cos1cos2|X><Y| + isin1cos2|~X><Y| - icos1sin2|X><~Y| - i^2 sin1sin2|~X><~Y|
cos1 = coss[coinHistoryX]
cos2 = coss[coinHistoryY]
sin1 = sins[coinHistoryX]
sin2 = sins[coinHistoryY].conjugate()
resDict[x,y].append(a * cos1 * cos2)
resDict[x^1,y].append(a * sin1 * cos2)
resDict[x,y^1].append(a * cos1 * sin2)
resDict[x^1,y^1].append(a * sin1 * sin2)
self.rho = [[p, cfsum(a)] for p, a in resDict.iteritems()]
def translate(self):
coinsModulo = coinsMask + 1
for i, ((x, y), _) in enumerate(self.rho):
x += coinsModulo if x & 1 else -coinsModulo
y += coinsModulo if y & 1 else -coinsModulo
self.rho[i][0] = (x, y)
def rotate(self):
# recycle the coins.
# rotation won't cause superposition.
for i, ((x, y), _) in enumerate(self.rho):
x = x & ~coinsMask | (x << 1) & coinsMask | (x >> (coinsCount-1)) & 1
y = y & ~coinsMask | (y << 1) & coinsMask | (y >> (coinsCount-1)) & 1
self.rho[i][0] = (x, y)
def prob(self):
# convert ket to probability.
probs = defaultdict(list)
for (x, y), a in self.rho:
if x == y:
probs[x >> coinsCount].append(abs(a))
return [(x, fsum(a)) for x, a in probs.iteritems()]
def observe(self):
# block-diagonalize the density matrix.
self.rho = [[(x, y), a] for (x, y), a in self.rho if (x >> coinsCount) == (y >> coinsCount)]
################################################################################
#
# So, this is classical history-dependent walk.
#
class CHDParrondo(Parrondo):
def reset(self):
self.probs = [[0, 0.5], [coinsMask, 0.5]]
def flip(self, coss, sins):
resDict = defaultdict(list)
coss2 = [abs(x)**2 for x in coss]
sins2 = [abs(x)**2 for x in sins]
for x, a in self.probs:
coinHistory = (x & coinsMask) >> 1
resDict[x].append(a * coss2[coinHistory]**2)
resDict[x^1].append(a * sins2[coinHistory]**2)
self.probs = [[x, fsum(a)] for x, a in resDict.iteritems()]
def translate(self):
coinsModulo = coinsMask + 1
for i, (x, _) in enumerate(self.probs):
self.probs[i][0] = x + (coinsModulo if x & 1 else -coinsModulo)
def rotate(self):
# recycle the coins.
# rotation won't cause superposition.
for i, (x, _) in enumerate(self.probs):
self.probs[i][0] = x & ~coinsMask | (x << 1) & coinsMask | (x >> (coinsCount-1)) & 1
def prob(self):
probs = defaultdict(list)
for x, a in self.probs:
probs[x >> coinsCount].append(a)
return [(x, fsum(a)) for x, a in probs.iteritems()]
################################################################################
def retrieve():
'Retrieve result from command line and parse it'
from argparse import ArgumentParser, FileType, RawTextHelpFormatter
from sys import stdin
parser = ArgumentParser(description='History-Dependent Quantum Parrondo Analyzer', formatter_class=RawTextHelpFormatter)
# i/o.
parser.add_argument('--seq1', '-1', metavar='FILE', type=FileType('r'), default=stdin, help=
"File name of first game sequence. Read from stdin if omitted.\n"
"The file should contain line-separated integers of 0 or 1 \n"
"indicating which game to play in sequence. 0 means play game A, \n"
"and 1 means play game B.")
parser.add_argument('--seq2', '-2', metavar='FILE', type=FileType('r'), help='File name of second game sequence.')
parser.add_argument('--save', '-o', metavar='FILE', type=FileType('w'), help='File name to fork the output into.')
# game parameters.
parser.add_argument('-g', choices=['qhd', 'qhddm', 'chd', 'qpd', 'cpd'], default='qhd', help=
"Game type, where \n"
" qhd = Quantum history-dependent, \n"
" qhddm = Quantum history-dependent with density matrix, \n"
" chd = Classical history-dependent, \n"
" qpd = Quantum position-dependent, \n"
" cpd = Classical position-dependent.")
parser.add_argument('-a', metavar='NUM', default=45, type=float, help='Angle of game A, in degrees')
parser.add_argument('-c', metavar='COUNT', default=3, type=int, help='Number of coins to use.')
parser.add_argument('-b', nargs='+', default=[42], type=float, help=
"Angle of game B with given history.\n"
"This argument should be passed like '-b 42 45 45 ...', which denotes \n"
"the angle at history |-->, |-+>, |+-> etc. If a value is missing, it \n"
"will be padded by 45."
)
parser.add_argument('-m', default=0, type=int, help='Intervals to collapse the wavefunction.')
# advanced option.
parser.add_argument('-l', metavar='LENGTH', default=133, type=int, help='Length of the game. If longer than the given sequence, it will be repeated.')
parser.add_argument('-v', action='store_true', help='Record mean and variance vs. time.')
parser.add_argument('-O', metavar='STEPS', default=0, type=int, help='How many steps to optimize.')
parser.add_argument('--om', choices=['hill', 'brute'], default='hill', help='How to optimize.')
analyze(parser.parse_args())
def analyze(result):
'Analyze the result from command line.'
from math import radians, cos, sin
from itertools import cycle, islice
global coinsMask, aCos, aSin, bCos, bSin, seq1, seq2, length, save, optimize, write_mean, game_type, optimize_method, observeInterval
game_type = result.g
# convert angle into sin & cos form.
coinsCount = result.c
coinsModulo = 1 << coinsCount
coinsMask = coinsModulo - 1
coinsModulo //= 2
angle = radians(result.a)
bAngles = map(radians, result.b + [45] * (coinsModulo - len(result.b)))
aCos = [cos(angle)] * coinsModulo
aSin = [sin(angle)*1j] * coinsModulo
bCos = map(cos, bAngles)
bSin = [sin(q)*1j for q in bAngles]
# read game sequence.
seq1_list = map(int, result.seq1)
seq2_list = [False]
if result.seq2:
seq2_list = map(int, result.seq2)
length = result.l or max(len(seq1_list), len(seq2_list))
seq1 = list(islice(cycle(seq1_list), length))
seq2 = list(islice(cycle(seq2_list), length))
save = result.save
optimize = result.O
optimize_method = result.om
write_mean = result.v
observeInterval = result.m
def write(*args, **kwargs):
'Convenient print statement that forks to our destinated file.'
print(*args, **kwargs)
if save:
print(file=save, *args, **kwargs)
################################################################################
retrieve()
parr = {
'qhd': QHDParrondoFast,
'chd': CHDParrondo,
'qhddm': QHDParrondo,
}[game_type]()
if optimize_method == 'brute':
parr.performBrute(len(seq1))
else:
parr.performOptimizeMean(seq1)
print('\a')
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment