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Now several trajectories can be simulated in one go.

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Arno Solin 2013-12-03 20:50:22 +02:00
parent 59afe14434
commit 9729a9a3a5
1 changed files with 20 additions and 7 deletions
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27
GPy/models/state_space.py
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@ -189,8 +189,12 @@ class StateSpace(Model):
Y = np.empty((size,X.shape[0]))
# Simulate random draws
for j in range(0,size):
Y[j,:] = H.dot(self.simulate(F,L,Qc,Pinf,X.T))
#for j in range(0,size):
# Y[j,:] = H.dot(self.simulate(F,L,Qc,Pinf,X.T))
Y = self.simulate(F,L,Qc,Pinf,X.T,size)
# Only observations
Y = np.tensordot(H[0],Y,(0,0))
# Reorder simulated values
Y = Y[:,return_inverse]
@ -360,23 +364,32 @@ class StateSpace(Model):
# Return likelihood
return lik[0,0]
def simulate(self,F,L,Qc,Pinf,X):
def simulate(self,F,L,Qc,Pinf,X,size=1):
# Simulate a trajectory using the state space model
# Allocate space for results
f = np.zeros((F.shape[0],X.shape[1]))
f = np.zeros((F.shape[0],size,X.shape[1]))
# Initial state
f[:,0:1] = np.linalg.cholesky(Pinf).dot(np.random.randn(F.shape[0],1))
f[:,:,1] = np.linalg.cholesky(Pinf).dot(np.random.randn(F.shape[0],size))
# Time step lengths
dt = np.empty(X.shape)
dt[:,0] = X[:,1]-X[:,0]
dt[:,1:] = np.diff(X)
# Solve the LTI SDE for these time steps
As, Qs, index = self.lti_disc(F,L,Qc,dt)
# Sweep through remaining time points
for k in range(1,X.shape[1]):
# Form discrete-time model
(A,Q) = self.lti_disc(F,L,Qc,X[:,k]-X[:,k-1])
A = As[:,:,index[1-k]]
Q = Qs[:,:,index[1-k]]
# Draw the state
f[:,k] = A.dot(f[:,k-1]).T + np.dot(np.linalg.cholesky(Q),np.random.randn(A.shape[0],1)).T
f[:,:,k] = A.dot(f[:,:,k-1]) + np.dot(np.linalg.cholesky(Q),np.random.randn(A.shape[0],size))
# Return values
return f