def sde(self): 
        
        variance = float(self.variances.values) # this is initial variancve in Bayesian linear regression
        t0 = float(self.t0)
        
        F = np.array( ((0,1.0),(0,0) ))
        L = np.array( ((0,),(1.0,)) )
        Qc = np.zeros((1,1))
        H = np.array( ((1.0,0),) )
        
        Pinf   = np.zeros((2,2))
        P0 = np.array( ( (t0**2, t0), (t0, 1) ) ) * variance        
        dF = np.zeros((2,2,1))
        dQc    = np.zeros( (1,1,1) )
        
        dPinf = np.zeros((2,2,1))
        dP0 = np.zeros((2,2,1))
        dP0[:,:,0]  = P0 / variance
  
        return (F, L, Qc, H, Pinf, P0, dF, dQc, dPinf, dP0)
Retrieve resource information.
dataset/ETHPy150Open SheffieldML/GPy/GPy/kern/_src/sde_linear.py/sde_Linear.sde--crossover with--dataset/ETHPy150Open BU-NU-CLOUD-SP16/Trusted-Platform-Module-nova/nova/virt/xenapi/driver.py/XenAPIDriver.get_available_resource