random.shuffle

All Samples(2292) | Call(1933) | Derive(0) | Import(359)

x, random=random.random -> shuffle list x in place; return None.

Optional arg random is a 0-argument function returning a random
float in [0.0, 1.0); by default, the standard random.random.

src/s/h/shedskin-HEAD/examples/rdb.py shedskin(Download)

  seq=[]
  last_domain=-1
  for slice in slices:
    random.shuffle(slice)
    if len(slice)>2 and slice[0][1]==last_domain:
      slice.append(slice.pop(0))
    seq+=[x[0] for x in slice]

  else:
    log("Generating shuffle sequence ...",False)
    seq=range(count)
    random.shuffle(seq)
  try:
    file("iPod_Control/iTunes/iTunesShuffle","wb").write("".join([stringval(x) for x in seq]))
  except IOError:

src/b/r/brian-HEAD/trunk/examples/misc/pulsepacket.py brian(Download)

'''
This example basically replicates what the Brian PulsePacket object does,
and then compares to that object.
'''
 
from brian import *
from random import gauss, shuffle

def pulse_packet(t, n, sigma):
    # generate a list of n times with Gaussian distribution, sort them in time, and
    # then randomly assign the neuron numbers to them
    times = [gauss(t, sigma) for i in range(n)]
    times.sort()
    neuron = range(n)
    shuffle(neuron)

src/b/r/brian-HEAD/examples/misc/pulsepacket.py brian(Download)

'''
This example basically replicates what the Brian PulsePacket object does,
and then compares to that object.
'''
 
from brian import *
from random import gauss, shuffle

def pulse_packet(t, n, sigma):
    # generate a list of n times with Gaussian distribution, sort them in time, and
    # then randomly assign the neuron numbers to them
    times = [gauss(t, sigma) for i in range(n)]
    times.sort()
    neuron = range(n)
    shuffle(neuron)

src/r/h/rhnsqa_python-HEAD/src/PyUnit_Sample.py rhnsqa_python(Download)

    def testshuffle(self):
        # make sure the shuffled sequence does not lose any elements
        random.shuffle(self.seq)
        self.seq.sort()
        self.assertEqual(self.seq, range(10))
 
    def testchoice(self):

src/p/y/pytoaster-HEAD/tests/sample/sampletest.py pytoaster(Download)

    def test_shuffle(self):
        # make sure the shuffled sequence does not lose any elements
        random.shuffle(self.seq)
        self.seq.sort()
        self.assertEqual(self.seq, range(10))
 
        # should raise an exception for an immutable sequence

src/p/y/python-ldapdict-HEAD/trunk/test/scripts/testexample.py python-ldapdict(Download)

    def testshuffle(self):
        # make sure the shuffled sequence does not lose any elements
        random.shuffle(self.seq)
        self.seq.sort()
        self.assertEqual(self.seq, range(10))
 
    def testchoice(self):

src/p/y/python-ldapdict-HEAD/test/scripts/testexample.py python-ldapdict(Download)

    def testshuffle(self):
        # make sure the shuffled sequence does not lose any elements
        random.shuffle(self.seq)
        self.seq.sort()
        self.assertEqual(self.seq, range(10))
 
    def testchoice(self):

src/p/y/pyevolve-HEAD/trunk/examples/pyevolve_ex12_tsp.py pyevolve(Download)

def G1DListTSPInitializator(genome, **args):
   """ The initializator for the TSP """
   lst = [i for i in xrange(genome.getListSize())]
   random.shuffle(lst)
   genome.setInternalList(lst)
 
# This is to make a video of best individuals along the evolution

src/p/y/pyevolve-HEAD/trunk/examples/pyevolve_ex21_nqueens.py pyevolve(Download)

from pyevolve import G1DList
from pyevolve import Mutators, Crossovers
from pyevolve import Consts, GSimpleGA
from pyevolve import DBAdapters
from random import shuffle
 
# The "n" in n-queens

def queens_init(genome, **args):
   genome.genomeList = range(0, BOARD_SIZE)
   shuffle(genome.genomeList)
 
def run_main():
   genome = G1DList.G1DList(BOARD_SIZE)
   genome.setParams(bestrawscore=BOARD_SIZE, rounddecimal=2)

src/p/y/pyevolve-HEAD/examples/pyevolve_ex12_tsp.py pyevolve(Download)

def G1DListTSPInitializator(genome, **args):
   """ The initializator for the TSP """
   lst = [i for i in xrange(genome.getListSize())]
   random.shuffle(lst)
   genome.setInternalList(lst)
 
# This is to make a video of best individuals along the evolution

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