def __init__(self, Y, dim):
self.Xdim = dim
self.N, self.Ydim = Y.shape
"""Use PCA to initalise the problem. Uses EM version in this case..."""
myPCA_EM = PCA_EM(Y, dim)
myPCA_EM.learn(100)
X = np.array(myPCA_EM.m_Z)
self.GP = GP.GP(X, Y) #choose particular kernel here if so desired.
def __init__(self,Y,dim): self.Xdim = dim self.N,self.Ydim = Y.shape """Use PCA to initalise the problem. Uses EM version in this case...""" myPCA_EM = PCA_EM(Y,dim) myPCA_EM.learn(100) X = np.array(myPCA_EM.m_Z) self.GP = GP.GP(X,Y)#choose particular kernel here if so desired.
def __init__(self, Y, dim, nparticles=100):
self.Xdim = dim
self.T, self.Ydim = Y.shape
"""Use PCA to initalise the problem. Uses EM version in this case..."""
myPCA_EM = PCA_EM(Y, dim)
myPCA_EM.learn(300)
X = np.array(myPCA_EM.m_Z)
self.observation_GP = GP.GP(X, Y)
#create a linear kernel for the dynamics
k = kernels.linear(-1, -1)
self.dynamic_GP = GP.GP(X[:-1], X[1:], k)
#initialise the samples from the state /latent variables
self.particles = np.zeros((self.T, nparticles, self.Xdim))
#sample for x0 TODO: variable priors on X0
self.particles[0, :, :] = np.random.randn(nparticles, self.Xdim)
def __init__(self,Y,dim,nparticles=100): self.Xdim = dim self.T,self.Ydim = Y.shape """Use PCA to initalise the problem. Uses EM version in this case...""" myPCA_EM = PCA_EM(Y,dim) myPCA_EM.learn(300) X = np.array(myPCA_EM.m_Z) self.observation_GP = GP.GP(X,Y) #create a linear kernel for the dynamics k = kernels.linear(-1,-1) self.dynamic_GP = GP.GP(X[:-1],X[1:],k) #initialise the samples from the state /latent variables self.particles = np.zeros((self.T,nparticles,self.Xdim)) #sample for x0 TODO: variable priors on X0 self.particles[0,:,:] = np.random.randn(nparticles,self.Xdim)
