def __init__(self, vars, nObs, T, N, observed_jumps, model=None):
# DES Temp:
self.logp = []
self.nObs = nObs
self.T = T
self.N = N
self.zeroIndices = np.roll(self.T.cumsum(), 1)
self.zeroIndices[0] = 0
# self.max_obs = max_obs
model = modelcontext(model)
vars = inputvars(vars)
shared = make_shared_replacements(vars, model)
super(ForwardS, self).__init__(vars, shared)
self.observed_jumps = observed_jumps
step_sizes = np.sort(np.unique(observed_jumps))
self.step_sizes = step_sizes = step_sizes[step_sizes > 0]
pi = stick_breaking.backward(self.shared["pi_stickbreaking"])
lower = model.free_RVs[1].distribution.dist.lower
upper = model.free_RVs[1].distribution.dist.upper
Q = rate_matrix_one_way(lower, upper).backward(self.shared["Q_ratematrixoneway"])
B0 = logodds.backward(self.shared["B0_logodds"])
B = logodds.backward(self.shared["B_logodds"])
X = self.shared["X"]
# at this point parameters are still symbolic so we
# must create get_params function to actually evaluate them
self.get_params = evaluate_symbolic_shared(pi, Q, B0, B, X)
def __init__(self, vars, N, T, K, D, Dd, O, nObs, model=None):
#DES Temp:
self.logp = []
self.N = N
self.T = T
self.K = K
self.D = D
self.Dd = Dd
self.O = O
self.nObs = nObs
#self.max_obs = max_obs
self.zeroIndices = np.roll(self.T.cumsum(),1)
self.zeroIndices[0] = 0
#self.pos_O_idx = np.zeros((D,max_obs,N), dtype=np.bool_)
#for n in xrange(N):
# for t in xrange(self.T[n]):
# self.pos_O_idx[:,t,n] = np.in1d(np.arange(self.D), self.O[:,t,n])
#self.OO = np.zeros((self.nObs,self.Dd),dtype=np.int)
#self.OO = np.zeros((self.Dd,self.N,self.max_obs),dtype=np.int)
self.negMask = np.zeros((self.nObs,D),dtype=np.int)
#self.negMask = np.zeros((self.N,self.max_obs,D),dtype=np.int)
for n in range(self.N):
n0 = self.zeroIndices[n]
for t in range(self.T[n]):
#for t in range(self.max_obs):
#self.OO[n0+t,:] = self.O[n0+t,:]
self.negMask[n0+t,:] = 1-np.in1d(np.arange(self.D), self.O[n0+t,:]).astype(np.int)
self.posMask = (self.O != -1).astype(np.int)
#self.betaMask = np.zeros((max_obs,N,2))
#for n in range(self.N):
# self.betaMask[:(T[n]-1),n,:] = 1
model = modelcontext(model)
vars = inputvars(vars)
shared = make_shared_replacements(vars, model)
super(ForwardX, self).__init__(vars, shared)
S = self.shared['S']
B0 = logodds.backward(self.shared['B0_logodds'])
B = logodds.backward(self.shared['B_logodds'])
Z = model.vars[6].distribution.transform_used.backward(self.shared['Z_anchoredbeta'])
#Z = anchoredbeta.backward(self.shared['Z_anchoredbeta'])
#Z = logodds.backward(self.shared['Z_logodds'])
L = logodds.backward(self.shared['L_logodds'])
#at this point parameters are still symbolic so we
#must create get_params function to actually evaluate them
self.get_params = evaluate_symbolic_shared(S, B0, B, Z, L)
