def runPF(params):
[alpha,phi] = params
env_rng = np.random.mtrand.RandomState()
#env = ge.GaussianEnv(gamma=gamma,eta=eta,zeta=zeta,x0=0.0,
# y0=.0,L=1.0,N=1,order=1,sigma=0.1,
# Lx=1.0,Ly=1.0,randomstate=env_rng)
env = ge.BistableEnv(gamma=gamma,eta=eta,x0=x0,order=1,randomstate=env_rng)
env.reset(np.array([0.0]))
#code is the population of neurons, plastic poisson neurons
code_rng = np.random.mtrand.RandomState()
code = pn.PoissonPlasticCode(A=alpha,phi=phi,tau=tau,
thetas=np.arange(-3.0,3.0,0.15),
dm=dm,randomstate=code_rng,alpha=alpha)
# s is the stimulus, sps holds the spikes, rates the rates of each
# neuron and particles give the position of the particles
# weights gives the weights associated with each particle
env_rng.seed(12345)
code_rng.seed(67890)
env.reset(np.array([0.0]))
code.reset()
[mmse,spikecount] = pf.mse_particle_filter(code,env,timewindow=timewindow,
dt=dt,nparticles=nparticles,
mode = 'Silent',testf = f)
print "ping "+str(alpha)+" "+str(phi)+" "+str(mmse)+" "+str(spikecount)
return [alpha,phi,mmse, spikecount]
def run_filters(args):
i,j,dtheta,alpha = args
print i,j
dt = 0.001
phi = 1.0
zeta = 1.0
eta = 1.0
gamma = 1.0
timewindow = 30000
dm = 0.0
tau = 1.0
nparticles = 1000
#env is the "environment", that is, the true process to which we don't have access
env_rng = np.random.mtrand.RandomState()
env = ge.GaussianEnv(gamma=gamma,eta=eta,zeta=zeta,x0=0.0,
y0=.0,L=1.0,N=1,order=1,sigma=0.1,
Lx=1.0,Ly=1.0,randomstate=env_rng)
env.reset(np.array([0.0]))
#code is the population of neurons, plastic poisson neurons
code_rng = np.random.mtrand.RandomState()
code = pn.PoissonPlasticCode(phi=phi,tau=tau,alpha=alpha,
thetas=np.arange(-dtheta,1.1*dtheta,2*dtheta),
dm=dm,randomstate=code_rng)
env_rng.seed(12345)
code_rng.seed(67890)
env.reset(np.array([0.0]))
code.reset()
[densem,densevar,spsg,sg,dense_mse] = pf.gaussian_filter(code,env,timewindow=timewindow,dt=dt,
dense=True)
env_rng.seed(12345)
code_rng.seed(67890)
env.reset(np.array([0.0]))
code.reset()
[sparsem,sparsevar,spsg,sg,sparse_mse] = pf.gaussian_filter(code,env,timewindow=timewindow,dt=dt,
dense=False)
env_rng.seed(12345)
code_rng.seed(67890)
env.reset(np.array([0.0]))
code.reset()
[particle_mse,ws] = pf.mse_particle_filter(code, env, timewindow=timewindow,
dt=dt, nparticles=nparticles,
testf=(lambda x:x))
return i,j,dense_mse,sparse_mse,particle_mse
def run_filters(arg):
i,dtheta = arg
print arg
dt = 0.001
phi = 1.0
zeta = 1.0
eta = 1.0
gamma = 1.0
alpha = 0.5
timewindow = 200000
dm = 0.0
tau = 1.0
nparticles = 500
env_rng = np.random.mtrand.RandomState()
env = ge.GaussianEnv(gamma=gamma,eta=eta,zeta=zeta,x0=0.0,
y0=.0,L=1.0,N=1,order=1,sigma=0.1,
Lx=1.0,Ly=1.0,randomstate=env_rng)
env.reset(np.array([0.0]))
#code is the population of neurons, plastic poisson neurons
code_rng = np.random.mtrand.RandomState()
code = pn.PoissonPlasticCode(A=alpha,phi=phi/2,tau=tau,
thetas=np.arange(-20.0,20.0,dtheta),
dm=dm,randomstate=code_rng)
env_rng.seed(12345)
code_rng.seed(67890)
env.reset(np.array([0.0]))
code.reset()
[densem,densevar,spsg,sg,dense_mse] = pf.gaussian_filter(code,env,timewindow=timewindow,dt=dt,
dense=True)
env_rng.seed(12345)
code_rng.seed(67890)
env.reset(np.array([0.0]))
code.reset()
[sparsem,sparsevar,spsg,sg,sparse_mse] = pf.gaussian_filter(code,env,timewindow=timewindow,dt=dt,
dense=False)
env_rng.seed(12345)
code_rng.seed(67890)
env.reset(np.array([0.0]))
code.reset()
[msep,ws] = pf.mse_particle_filter(code, env, timewindow=timewindow,
dt=dt, nparticles=nparticles,
testf=(lambda x:x))
return i,dense_mse,sparse_mse,msep
