def __init__(self, observations, timepoints, xkernel=cov.sq_exp_kernel(), ykernel=cov.sq_exp_kernel()): zt = observations[:,0] zx = observations[:,1] zy = observations[:,2] self.xgp = GaussianProcess(zt, zx, timepoints, xkernel) self.ygp = GaussianProcess(zt, zy, timepoints, ykernel)
def __init__(self,
observations,
timepoints,
xkernel=cov.sq_exp_kernel(),
ykernel=cov.sq_exp_kernel()):
zt = observations[:, 0]
zx = observations[:, 1]
zy = observations[:, 2]
self.xgp = GaussianProcess(zt, zx, timepoints, xkernel)
self.ygp = GaussianProcess(zt, zy, timepoints, ykernel)
def __init__(self, zx, zy, testpoints, kernel=cov.sq_exp_kernel()):
''' Creates a new Gaussian process from the given observations. '''
self.timepoints = testpoints
self.kernel = kernel
# covariance of observations
self.K = kernel(zx, zx, 'train')
self.K += 1e-9 * np.eye(self.K.shape[0])
Ltrain = np.linalg.cholesky(self.K)
# compute the predictive mean at our test points
self.Kstar = kernel(zx, testpoints, 'cross')
v = np.linalg.solve(Ltrain, self.Kstar)
self.mu = np.dot(v.T, np.linalg.solve(Ltrain, zy))
# compute the predictive variance at our test points
self.Kss = kernel(testpoints, testpoints, 'test')
self.Kss += 1e-9 * np.eye(self.Kss.shape[0])
self.prior_L = np.linalg.cholesky(self.Kss)
self.Kss = self.kernel(testpoints, testpoints, 'train')
# self.Kss += 1e-3*np.eye(self.Kss.shape[0])
self.L = np.linalg.cholesky(self.Kss - np.dot(v.T, v))
def __init__(self, zx, zy, testpoints, kernel=cov.sq_exp_kernel()): ''' Creates a new Gaussian process from the given observations. ''' self.timepoints = testpoints self.kernel = kernel # covariance of observations self.K = kernel(zx, zx, 'train') self.K += 1e-9*np.eye(self.K.shape[0]) Ltrain = np.linalg.cholesky(self.K) # compute the predictive mean at our test points self.Kstar = kernel(zx, testpoints, 'cross') v = np.linalg.solve(Ltrain, self.Kstar) self.mu = np.dot(v.T, np.linalg.solve(Ltrain, zy)) # compute the predictive variance at our test points self.Kss = kernel(testpoints, testpoints, 'test') self.Kss += 1e-9*np.eye(self.Kss.shape[0]) self.prior_L = np.linalg.cholesky(self.Kss) self.Kss = self.kernel(testpoints, testpoints, 'train') # self.Kss += 1e-3*np.eye(self.Kss.shape[0]) self.L = np.linalg.cholesky(self.Kss - np.dot(v.T, v))
def __init__(self,
xkernel=cov.sq_exp_kernel(),
ykernel=cov.sq_exp_kernel()):
self.xkernel = xkernel
self.ykernel = ykernel
def __init__(self, xkernel=cov.sq_exp_kernel(), ykernel=cov.sq_exp_kernel()): self.xkernel = xkernel self.ykernel = ykernel
