kusanagi.ghost.regression.cov Namespace Reference

Functions
def	SEard
	Squared exponential kernel with diagonal scaling matrix (one lengthscale per dimension) More...
def	Noise
	Noise kernel. More...
def	Sum
	Returns the sum of multiple covariance functions. More...

Function Documentation

def kusanagi.ghost.regression.cov.Noise	(		loghyp,
			X1,
			X2 = None,
			all_pairs = True
	)

Noise kernel.

Takes as an input a distance matrix D and creates a new matrix as Kij = sn2 if Dij == 0 else 0

def kusanagi.ghost.regression.cov.SEard	(		loghyp,
			X1,
			X2 = None,
			all_pairs = True
	)

Squared exponential kernel with diagonal scaling matrix (one lengthscale per dimension)

def kusanagi.ghost.regression.cov.Sum	(		loghyp_l,
			cov_l,
			X1,
			X2 = None,
			all_pairs = True
	)

Returns the sum of multiple covariance functions.