def run_trials():
numTrials = 3000
gens = 1000
from multiprocessing.pool import ThreadPool as Pool
pool = Pool(50)
jids = pool.map(f,[gens]*numTrials)
print "Done spawning trials. Retrieving results..."
results = pool.map(cloud_result, jids)
firstLocusFreqsHists = zeros((numTrials,gens+1), dtype='float')
lastLocusFreqsHists = zeros((numTrials,gens+1), dtype='float')
print "Done retrieving results. Press Enter to serialize..."
raw_input()
for i, result in enumerate(results):
firstLocusFreqsHists[i, :], lastLocusFreqsHists[i, :] = result
with closing(FileStorage("soda_results.durus")) as durus:
conn = Connection(durus)
conn.get_root()[str(int(floor(time.time())))] = (firstLocusFreqsHists, lastLocusFreqsHists)
conn.commit()
pool.close()
pool.join()
def run_trials():
numTrials = 3000
gens = 1000
from multiprocessing.pool import ThreadPool as Pool
pool = Pool(50)
jids = pool.map(f, [gens] * numTrials)
print "Done spawning trials. Retrieving results..."
results = pool.map(cloud_result, jids)
firstLocusFreqsHists = zeros((numTrials, gens + 1), dtype='float')
lastLocusFreqsHists = zeros((numTrials, gens + 1), dtype='float')
print "Done retrieving results. Press Enter to serialize..."
raw_input()
for i, result in enumerate(results):
firstLocusFreqsHists[i, :], lastLocusFreqsHists[i, :] = result
with closing(FileStorage("soda_results.durus")) as durus:
conn = Connection(durus)
conn.get_root()[str(int(floor(time.time())))] = (firstLocusFreqsHists,
lastLocusFreqsHists)
conn.commit()
pool.close()
pool.join()
def neap_uga(m,
n,
gens,
probMutation,
effectiveAttrIndices,
probMisclassification,
bitFreqVisualizer=None):
""" neap = "noisy effective attribute parity"
"""
pop = rand(m, n) < 0.5
bitFreqHist = zeros((n, gens + 1))
for t in range(gens + 1):
print "Generation %s" % t
bitFreqs = pop.astype('float').sum(axis=0) / m
bitFreqHist[:, t] = transpose(bitFreqs)
if bitFreqVisualizer:
bitFreqVisualizer(bitFreqs, t)
fitnessVals = mod(
pop[:, effectiveAttrIndices].astype('byte').sum(axis=1) +
(rand(m) < probMisclassification).astype('byte'), 2)
totalFitness = sum(fitnessVals)
cumNormFitnessVals = cumsum(fitnessVals).astype('float') / totalFitness
parentIndices = zeros(2 * m, dtype='int16')
markers = sort(rand(2 * m))
ctr = 0
for idx in xrange(2 * m):
while markers[idx] > cumNormFitnessVals[ctr]:
ctr += 1
parentIndices[idx] = ctr
shuffle(parentIndices)
crossoverMasks = rand(m, n) < 0.5
newPop = zeros((m, n), dtype='bool')
newPop[crossoverMasks] = pop[parentIndices[:m], :][crossoverMasks]
newPop[logical_not(crossoverMasks)] = pop[parentIndices[m:], :][
logical_not(crossoverMasks)]
mutationMasks = rand(m, n) < probMutation
pop = logical_xor(newPop, mutationMasks)
return bitFreqHist[0, :], bitFreqHist[-1, :]
def neap_uga(m, n, gens, probMutation, effectiveAttrIndices, probMisclassification, bitFreqVisualizer=None):
""" neap = "noisy effective attribute parity"
"""
pop = rand(m,n)<0.5
bitFreqHist= zeros((n,gens+1))
for t in range(gens+1):
print "Generation %s" % t
bitFreqs = pop.astype('float').sum(axis=0)/m
bitFreqHist[:,t] = transpose(bitFreqs)
if bitFreqVisualizer:
bitFreqVisualizer(bitFreqs,t)
fitnessVals = mod(pop[:, effectiveAttrIndices].astype('byte').sum(axis=1) +
(rand(m) < probMisclassification).astype('byte'),2)
totalFitness = sum (fitnessVals)
cumNormFitnessVals = cumsum(fitnessVals).astype('float')/totalFitness
parentIndices = zeros(2*m, dtype='int16')
markers = sort(rand(2*m))
ctr = 0
for idx in xrange(2*m):
while markers[idx]>cumNormFitnessVals[ctr]:
ctr += 1
parentIndices[idx] = ctr
shuffle(parentIndices)
crossoverMasks = rand(m, n) < 0.5
newPop = zeros((m, n), dtype='bool')
newPop[crossoverMasks] = pop[parentIndices[:m], :][crossoverMasks]
newPop[logical_not(crossoverMasks)] = pop[parentIndices[m:], :][logical_not(crossoverMasks)]
mutationMasks = rand(m, n)<probMutation
pop = logical_xor(newPop,mutationMasks)
return bitFreqHist[0, :], bitFreqHist[-1, :]