def __init__(self,sim, final_momentum=0.9, initial_momentum=0.5,momentum_switchover=5,lr_s=1e-6, lr_nu=1e-2 , lr_Yslack=1e-2, maxIter=1000,initS=0.0,initSviaLineSearch=True):
self.initSviaLineSearch=initSviaLineSearch
self.sim=sim
self.initYslack=0
self.n=self.sim.N*2
self.theta=self.sim.theta/(self.sim.L/self.sim.winSize);
self.initC0 = np.ones(self.sim.numReplicates,dtype=floatX)*logit(sim.X0.min())
self.Times=np.tile(sim.getGenerationTimes(),(self.sim.numReplicates,1)).T.astype(np.float32)
self.momentum_ = T.scalar()
self.final_momentum=final_momentum; self.initial_momentum=initial_momentum;self.momentum_switchover=momentum_switchover;self.W=3;self.lr_s=lr_s;self.lr_theta=lr_Yslack;self.lr_nu=lr_nu;self.maxIter=maxIter;self.initS=initS
self.lrS_ = T.scalar();self.lrNu_ = T.scalar();self.lrTheta_ = T.scalar();self.target_ = T.matrix(); self.times_ = T.fmatrix("times"); self.theta_ = T.scalar()
self.Yslack__=theano.shared(np.asarray(0, dtype = floatX), 'theta');self.n_ = T.scalar("n ")
self.S__=theano.shared(np.asarray(self.initS, dtype = floatX))
self.c__=theano.shared(self.initC0, 'c')
self.weightUpdateS__ = theano.shared(np.asarray(0, dtype = floatX))
self.weightUpdatec__ = theano.shared(np.zeros(self.sim.numReplicates, dtype = floatX))
self.weightUpdateYslack__ = theano.shared(np.asarray(0, dtype = floatX))
self.pred_= Z(sig_(0.5*self.S__*self.times_ + self.c__),self.n_,self.theta_) + self.Yslack__
self.Feedforward_ = theano.function(inputs=[self.times_,self.n_,self.theta_], outputs=self.pred_)
self.cost_=0
for j in range(self.sim.numReplicates):
self.cost_ += 0.5*((self.target_[:,j] - self.pred_[:,j])**2).sum()
self.Loss_ = theano.function(inputs=[self.target_,self.pred_], outputs=self.cost_)
self.gS_,self.gc_, self.gYslack_ = T.grad(self.cost_, [self.S__,self.c__, self.Yslack__])
self.updatesS=[(self.weightUpdateS__, self.momentum_ * self.weightUpdateS__ - self.lrS_ * self.gS_),(self.S__, self.S__ + self.momentum_ * self.weightUpdateS__ - self.lrS_ * self.gS_)]
self.updatesc=[(self.weightUpdatec__, self.momentum_ * self.weightUpdatec__ - self.lrNu_ * self.gc_),(self.c__, self.c__ + self.momentum_ * self.weightUpdatec__ - self.lrNu_ * self.gc_)]
self.updatesYslack=[(self.weightUpdateYslack__, self.momentum_ * self.weightUpdateYslack__ - self.lrTheta_ * self.gYslack_),(self.Yslack__, self.Yslack__ + self.momentum_ * self.weightUpdateYslack__ - self.lrTheta_ * self.gYslack_)]
self.updates= self.updatesc +self.updatesS + self.updatesYslack
self.Objective_ = theano.function([ self.target_, self.lrS_, self.lrNu_, self.lrTheta_, self.times_,self.momentum_,self.n_,self.theta_], self.cost_, on_unused_input='warn',updates=self.updates,allow_input_downcast=True)
def __init__(self,initNu0,final_momentum=0.9, initial_momentum=0.5,momentum_switchover=5,times=[10,20,30,40,50],S=3,lr=1e-2,maxIter=10000,initS=0.0, numReplicates=3):
times=np.array(times)
self.numReplicates=numReplicates
self.initC0 = np.ones(self.numReplicates,dtype=floatX)*logit(initNu0)
self.times=times[times!=0].astype(np.float32)
self.times=np.tile(self.times,(self.numReplicates,1)).T.astype(np.float32)
self.momentum_ = T.scalar()
self.final_momentum=final_momentum; self.initial_momentum=initial_momentum;self.momentum_switchover=momentum_switchover;self.W=3;self.lr=lr;
self.maxIter=maxIter;self.initS=initS
self.lr_ = T.scalar();self.target_ =T.matrix(); self.times_ = T.fmatrix("times")
self.S__=theano.shared(np.asarray(self.initS, dtype = floatX), 'S')
self.c__=theano.shared(self.initC0, 'c')
self.weightUpdateS__ = theano.shared(np.asarray(0, dtype = floatX))
self.weightUpdatec__ = theano.shared(np.zeros(self.numReplicates, dtype = floatX))
self.pred_= sig_(0.5*self.S__*self.times_ + self.c__)
self.Feedforward_ = theano.function(inputs=[self.times_], outputs=self.pred_)
self.cost_=0
for j in range(self.numReplicates):
self.cost_ += 0.5*((self.target_[:,j] - self.pred_[:,j])**2).sum()
self.Loss_ = theano.function(inputs=[self.target_,self.pred_], outputs=self.cost_)
self.gS_,self.gc_ = T.grad(self.cost_, [self.S__,self.c__])
self.updatesS=[(self.weightUpdateS__, self.momentum_ * self.weightUpdateS__ - self.lr_ * self.gS_),(self.S__, self.S__ + self.momentum_ * self.weightUpdateS__ - self.lr_ * self.gS_)]
self.updatesc=[(self.weightUpdatec__, self.momentum_ * self.weightUpdatec__ - self.lr_ * self.gc_),(self.c__, self.c__ + self.momentum_ * self.weightUpdatec__ - self.lr_ * self.gc_)]
self.updates= self.updatesS+self.updatesc
self.Objective_ = theano.function([ self.target_, self.lr_,self.times_,self.momentum_], self.cost_, on_unused_input='warn',updates=self.updates,allow_input_downcast=True)
def getErr(i=96, s=0.01):
sim = Simulation.Simulation.load(
s=s,
nu0=0.005,
experimentID=i,
ModelName="TimeSeries",
numReplicates=10,
step=10,
startGeneration=0,
maxGeneration=50,
)
bot = pd.read_pickle(home + "out/BottleneckedNeutrals.NotNormalized.df")[i].loc[[0, 10, 20, 30, 40, 50]]
td = Estimate.Estimate.getEstimate(sim.X, n=200, method="tajimaD", removeFixedSites=True, normalizeTajimaD=False)
td.index = sim.getTrueGenerationTimes()
ctd = td.apply(lambda x: ((x - bot).diff().iloc[1:]))
ctd[leaveOneOut(ctd)] = None
ctd = ctd.apply(regularize)
t = sim.getTrueGenerationTimes()
nu = sig(s * t / 2 + logit(0.1))
a = Estimate.Estimate.getEstimate(sim.X0, n=200, method="pi")
b = -Estimate.Estimate.getEstimate(sim.X0, n=200, method="watterson") / (1.0 / np.arange(1, 201)).sum()
f = pd.Series(b * np.log(1 - nu) - a * nu ** 2, index=t).diff().iloc[1:]
# ctd.mean(1).plot(color='b',legend=False);f.plot(linewidth=3,color='k');
# ctds.mean(1).plot(ax=plt.gca(),color='r',legend=False);
return ((ctd.mean(1).sum() - f.mean())) / neurtrality(i, s)
def setSIM(self, sim):
if sim is not None:
self.sim=sim
self.numReplicates=self.sim.numReplicates
self.n=self.sim.N*2
self.initTheta=self.sim.theta/(self.sim.L/self.sim.winSize);
self.initC0 = np.ones(self.numReplicates,dtype=floatX)*logit(sim.X[0].mean(1).min())
self.Times=np.tile(sim.getGenerationTimes(),(self.numReplicates,1)).T.astype(np.float32)
self.replicateIndex=range(self.numReplicates)
else:
self.initC0 = np.ones(self.numReplicates,dtype=floatX)
try:
self.reset()
except:
pass
def load2(iii=96, s=0.01):
sim = Simulation.Simulation.load(
s=s,
nu0=0.005,
experimentID=iii,
ModelName="TimeSeries",
numReplicates=10,
step=10,
startGeneration=0,
maxGeneration=50,
)
simn = Simulation.Simulation.load(
s=0,
nu0=0.005,
experimentID=iii,
ModelName="TimeSeries",
numReplicates=10,
step=10,
startGeneration=0,
maxGeneration=50,
)
bot = pd.read_pickle(home + "out/BottleneckedNeutrals.NotNormalized.df")[iii].loc[[0, 10, 20, 30, 40, 50]]
td = Estimate.Estimate.getEstimate(
sim.X, n=200, method="tajimaD", removeFixedSites=True, normalizeTajimaD=False
).mean(1)
td.index = sim.getTrueGenerationTimes()
tdn = Estimate.Estimate.getEstimate(
simn.X, n=200, method="tajimaD", removeFixedSites=True, normalizeTajimaD=False
).mean(1)
tdn.index = simn.getTrueGenerationTimes()
ctd = [td - bot, regularize2(td - bot), regularize3(td - bot)]
ctdn = [tdn - bot, regularize2(tdn - bot), regularize3(tdn - bot)]
t = sim.getTrueGenerationTimes()
nu = sig(s * t / 2 + logit(0.005))
a = Estimate.Estimate.getEstimate(sim.X0, n=200, method="pi")
b = -Estimate.Estimate.getEstimate(sim.X0, n=200, method="watterson") / (1.0 / np.arange(1, 201)).sum()
D0 = Estimate.Estimate.getEstimate(sim.X0, n=200, method="tajimaD", normalizeTajimaD=False)
Dt = pd.Series(b * np.log(1 - nu) - a * nu ** 2, index=t)
return map(lambda x: x, ctd), map(lambda x: x, ctdn), Dt
