def next() :
global xx
global delta, gamma
global model, evolve, alpha
delta, gamma = steps.next()
xx = step( xx, delta, gamma )
model = WrapParamModel( SYS, xx.params_est )
evolve, alpha = bootstrap.bootstrap_evolve( xx.samples, xx.y, model, k=3 )
def particle_sysid_filter( n, y, samples, pHMM, params, delta, gamma, k=None ) :
a, b = params.perturbation_twosided( delta )
HMM1 = WrapParamModel( pHMM, a )
HMM2 = WrapParamModel( pHMM, b )
_, alpha1 = bootstrap.bootstrap_evolve( samples, y, HMM1, k )
_, alpha2 = bootstrap.bootstrap_evolve( samples, y, HMM2, k )
#print alpha1, alpha2
J1 = np.log( np.average( alpha1 ) )
J2 = np.log( np.average( alpha2 ) )
params_next = params.copy().update( a, b, J2 - J1, gamma )
# then actually filter ya know...
HMM_next = WrapParamModel( pHMM, params_next )
samples_next = bootstrap.bootstrap_filter( n, y, samples, HMM_next, k )
return samples_next, params_next
