def mutation(self, reproducedNW):
ctm = 0.05 # chance to mutate
for nw in self.population:
if random() > ctm:
for repNW in reproducedNW:
if random() > ctm:
continue
if len(repNW) <= 1:
print("Shoudln t happen, len: " + str(len(repNW)))
continue
index = np.random.randint(0, len(repNW))
if random() < 0.5:
repNW[index] = -repNW[index]
else:
repNW[index] = 2 * repNW[index]
return reproducedNW
def crossover(self, reproducedNW):
resultArray = []
# Not sure if this works correctly
np.random.shuffle(reproducedNW)
# this needs to be randomized, as it is always the same
for i in range(len(reproducedNW)):
crossoverType = random()
# crossoverType = 0.9
# print("Crossover: " + str(crossoverType))
crossedNWs = []
# if multiple weights
mum: np.array = reproducedNW[i]
dad: np.array = reproducedNW[0]
if i < len(reproducedNW) - 1:
dad = reproducedNW[i + 1]
if crossoverType < 0.3:
# onepoint mutation
# print("ONEPOINT")
cutIndex = np.random.randint(0, len(mum))
crossedNWs.extend(mum[0:cutIndex])
crossedNWs.extend(dad[cutIndex:])
elif crossoverType < 0.6:
# print("other")
for j in range(len(mum)):
if random() < 0.5:
crossedNWs.append(mum[j])
else:
crossedNWs.append(dad[j])
else:
# print("ari")
blaa = (mum + dad) / 2
crossedNWs.extend(blaa.tolist())
resultArray.append(crossedNWs)
return resultArray
def test_FlexMaster(self):
"""TrajFlexMaster test"""
from Biskit.MatrixPlot import MatrixPlot
from numpy.random.mtrand import random_sample as random
assert len(hosts.cpus_all) > 0,\
'Master requires at least 1 PVM node for initialisation.'
traj_1 = T.load(T.testRoot() + '/lig_pcr_00/traj.dat')
traj_1 = traj2ensemble(traj_1)
## create fake second trajectory by adding
## increasing noise to first
frames = []
for i in range(len(traj_1)):
f = traj_1.frames[i]
d = N0.zeros(N0.shape(f), N0.Float32)
if i > 0:
d = random(N0.shape(f)) * ((i / 10) + 1)
frames += [f + d]
traj_2 = traj_1.clone()
traj_2.frames = frames
master = TrajFlexMaster(traj_1,
traj_2,
hosts=hosts.cpus_all,
show_output=self.local,
add_hosts=1,
log=None,
slaveLog=None,
verbose=self.local,
only_cross_member=0)
r = master.calculateResult(mirror=0)
if self.local:
p = MatrixPlot(r, palette='plasma2', legend=1)
p.show()
def test_FlexMaster(self):
"""TrajFlexMaster test"""
from Biskit.MatrixPlot import MatrixPlot
from numpy.random.mtrand import random_sample as random
assert len(hosts.cpus_all) > 0,\
'Master requires at least 1 PVM node for initialisation.'
traj_1 = T.load( T.testRoot() + '/lig_pcr_00/traj.dat' )
traj_1 = traj2ensemble( traj_1 )
## create fake second trajectory by adding
## increasing noise to first
frames = []
for i in range( len( traj_1 ) ):
f = traj_1.frames[i]
d = N0.zeros( N0.shape( f ), N0.Float32)
if i > 0:
d = random( N0.shape( f ) ) * ((i / 10) + 1)
frames += [f + d]
traj_2 = traj_1.clone()
traj_2.frames = frames
master = TrajFlexMaster( traj_1, traj_2,
hosts=hosts.cpus_all,
show_output= self.local,
add_hosts=1,
log=None,
slaveLog=None,
verbose= self.local,
only_cross_member=0 )
r = master.calculateResult( mirror=0 )
if self.local:
p = MatrixPlot( r, palette='plasma2', legend=1 )
p.show()
def build_gamma() -> sm.GammaStochasticModel:
return sm.GammaStochasticModel(shape=random(), scale=random())