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replace scipy math functions by numpy

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Martin Bubel 2024-05-20 14:30:03 +02:00
parent 4e5bdae893
commit 4b552cb5f5
3 changed files with 288 additions and 201 deletions
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172
GPy/kern/src/sde_standard_periodic.py
View file

@ -13,6 +13,7 @@ import warnings
from scipy import special as special from scipy import special as special
class sde_StdPeriodic(StdPeriodic): class sde_StdPeriodic(StdPeriodic):
""" """
@ -27,6 +28,7 @@ class sde_StdPeriodic(StdPeriodic):
\left( \frac{\sin(\frac{\pi}{\lambda_i} (x_i - y_i) )}{l_i} \right)^2 \right] } \left( \frac{\sin(\frac{\pi}{\lambda_i} (x_i - y_i) )}{l_i} \right)^2 \right] }
""" """
# TODO: write comment to the constructor arguments # TODO: write comment to the constructor arguments
def __init__(self, *args, **kwargs): def __init__(self, *args, **kwargs):
""" """
@ -42,18 +44,17 @@ class sde_StdPeriodic(StdPeriodic):
:type balance: bool :type balance: bool
""" """
#import pdb; pdb.set_trace() # import pdb; pdb.set_trace()
if 'approx_order' in kwargs: if "approx_order" in kwargs:
self.approx_order = kwargs.get('approx_order') self.approx_order = kwargs.get("approx_order")
del kwargs['approx_order'] del kwargs["approx_order"]
else: else:
self.approx_order = 7 self.approx_order = 7
if "balance" in kwargs:
if 'balance' in kwargs: self.balance = bool(kwargs.get("balance"))
self.balance = bool( kwargs.get('balance') ) del kwargs["balance"]
del kwargs['balance']
else: else:
self.balance = False self.balance = False
@ -84,40 +85,57 @@ class sde_StdPeriodic(StdPeriodic):
300 data points the low limit is 0.15. 300 data points the low limit is 0.15.
""" """
#import pdb; pdb.set_trace() # import pdb; pdb.set_trace()
# Params to use: (in that order) # Params to use: (in that order)
#self.variance # self.variance
#self.period # self.period
#self.lengthscale # self.lengthscale
if self.approx_order is not None: if self.approx_order is not None:
N = int(self.approx_order) N = int(self.approx_order)
else: else:
N = 7 # approximation order N = 7 # approximation order
p_period = float(self.period) p_period = float(self.period)
p_lengthscale = 2*float(self.lengthscale) p_lengthscale = 2 * float(self.lengthscale)
p_variance = float(self.variance) p_variance = float(self.variance)
w0 = 2*np.pi/p_period # frequency w0 = 2 * np.pi / p_period # frequency
# lengthscale is multiplied by 2 because of different definition of lengthscale # lengthscale is multiplied by 2 because of different definition of lengthscale
[q2,dq2l] = seriescoeff(N, p_lengthscale, p_variance) [q2, dq2l] = seriescoeff(N, p_lengthscale, p_variance)
dq2l = 2*dq2l # This is because the lengthscale if multiplied by 2. dq2l = 2 * dq2l # This is because the lengthscale if multiplied by 2.
eps = 1e-12 eps = 1e-12
if np.any( np.isfinite(q2) == False) or np.any( np.abs(q2) > 1.0/eps) or np.any( np.abs(q2) < eps): if (
warnings.warn("sde_Periodic: Infinite, too small, or too large (eps={0:e}) values in q2 :".format(eps) + q2.__format__("") ) np.any(np.isfinite(q2) == False)
or np.any(np.abs(q2) > 1.0 / eps)
or np.any(np.abs(q2) < eps)
):
warnings.warn(
"sde_Periodic: Infinite, too small, or too large (eps={0:e}) values in q2 :".format(
eps
)
+ q2.__format__("")
)
if np.any( np.isfinite(dq2l) == False) or np.any( np.abs(dq2l) > 1.0/eps) or np.any( np.abs(dq2l) < eps): if (
warnings.warn("sde_Periodic: Infinite, too small, or too large (eps={0:e}) values in dq2l :".format(eps) + q2.__format__("") ) np.any(np.isfinite(dq2l) == False)
or np.any(np.abs(dq2l) > 1.0 / eps)
or np.any(np.abs(dq2l) < eps)
):
warnings.warn(
"sde_Periodic: Infinite, too small, or too large (eps={0:e}) values in dq2l :".format(
eps
)
+ q2.__format__("")
)
F = np.kron(np.diag(range(0, N + 1)), np.array(((0, -w0), (w0, 0))))
F = np.kron(np.diag(range(0,N+1)),np.array( ((0, -w0), (w0, 0)) ) ) L = np.eye(2 * (N + 1))
L = np.eye(2*(N+1)) Qc = np.zeros((2 * (N + 1), 2 * (N + 1)))
Qc = np.zeros((2*(N+1), 2*(N+1))) P_inf = np.kron(np.diag(q2), np.eye(2))
P_inf = np.kron(np.diag(q2),np.eye(2)) H = np.kron(np.ones((1, N + 1)), np.array((1, 0)))
H = np.kron(np.ones((1,N+1)),np.array((1,0)) )
P0 = P_inf.copy() P0 = P_inf.copy()
# Derivatives # Derivatives
@ -126,32 +144,35 @@ class sde_StdPeriodic(StdPeriodic):
dP_inf = np.empty((P_inf.shape[0], P_inf.shape[1], 3)) dP_inf = np.empty((P_inf.shape[0], P_inf.shape[1], 3))
# Derivatives wrt self.variance # Derivatives wrt self.variance
dF[:,:,0] = np.zeros(F.shape) dF[:, :, 0] = np.zeros(F.shape)
dQc[:,:,0] = np.zeros(Qc.shape) dQc[:, :, 0] = np.zeros(Qc.shape)
dP_inf[:,:,0] = P_inf / p_variance dP_inf[:, :, 0] = P_inf / p_variance
# Derivatives self.period # Derivatives self.period
dF[:,:,1] = np.kron(np.diag(range(0,N+1)),np.array( ((0, w0), (-w0, 0)) ) / p_period ); dF[:, :, 1] = np.kron(
dQc[:,:,1] = np.zeros(Qc.shape) np.diag(range(0, N + 1)), np.array(((0, w0), (-w0, 0))) / p_period
dP_inf[:,:,1] = np.zeros(P_inf.shape) )
dQc[:, :, 1] = np.zeros(Qc.shape)
dP_inf[:, :, 1] = np.zeros(P_inf.shape)
# Derivatives self.lengthscales # Derivatives self.lengthscales
dF[:,:,2] = np.zeros(F.shape) dF[:, :, 2] = np.zeros(F.shape)
dQc[:,:,2] = np.zeros(Qc.shape) dQc[:, :, 2] = np.zeros(Qc.shape)
dP_inf[:,:,2] = np.kron(np.diag(dq2l),np.eye(2)) dP_inf[:, :, 2] = np.kron(np.diag(dq2l), np.eye(2))
dP0 = dP_inf.copy() dP0 = dP_inf.copy()
if self.balance: if self.balance:
# Benefits of this are not very sound. # Benefits of this are not very sound.
import GPy.models.state_space_main as ssm import GPy.models.state_space_main as ssm
(F, L, Qc, H, P_inf, P0, dF, dQc, dP_inf,dP0) = ssm.balance_ss_model(F, L, Qc, H, P_inf, P0, dF, dQc, dP_inf, dP0 )
(F, L, Qc, H, P_inf, P0, dF, dQc, dP_inf, dP0) = ssm.balance_ss_model(
F, L, Qc, H, P_inf, P0, dF, dQc, dP_inf, dP0
)
return (F, L, Qc, H, P_inf, P0, dF, dQc, dP_inf, dP0) return (F, L, Qc, H, P_inf, P0, dF, dQc, dP_inf, dP0)
def seriescoeff(m=6, lengthScale=1.0, magnSigma2=1.0, true_covariance=False):
def seriescoeff(m=6,lengthScale=1.0,magnSigma2=1.0, true_covariance=False):
""" """
Calculate the coefficients q_j^2 for the covariance function Calculate the coefficients q_j^2 for the covariance function
approximation: approximation:
@ -192,34 +213,67 @@ def seriescoeff(m=6,lengthScale=1.0,magnSigma2=1.0, true_covariance=False):
if true_covariance: if true_covariance:
bb = lambda j,m: (1.0 + np.array((j != 0), dtype=np.float64) ) / (2**(j)) *\ bb = (
sp.special.binom(j, sp.floor( (j-m)/2.0 * np.array(m<=j, dtype=np.float64) ))*\ lambda j, m: (1.0 + np.array((j != 0), dtype=np.float64))
np.array(m<=j, dtype=np.float64) *np.array(sp.mod(j-m,2)==0, dtype=np.float64) / (2 ** (j))
* sp.special.binom(
j, sp.floor((j - m) / 2.0 * np.array(m <= j, dtype=np.float64))
)
* np.array(m <= j, dtype=np.float64)
* np.array(sp.mod(j - m, 2) == 0, dtype=np.float64)
)
M,J = np.meshgrid(range(0,m+1),range(0,m+1)) M, J = np.meshgrid(range(0, m + 1), range(0, m + 1))
coeffs = bb(J,M) / sp.misc.factorial(J) * sp.exp( -lengthScale**(-2) ) *\ coeffs = (
(lengthScale**(-2))**J *magnSigma2 bb(J, M)
/ sp.misc.factorial(J)
* np.exp(-(lengthScale ** (-2)))
* (lengthScale ** (-2)) ** J
* magnSigma2
)
coeffs_dl = np.sum( coeffs*lengthScale**(-3)*(2.0-2.0*J*lengthScale**2),0) coeffs_dl = np.sum(
coeffs * lengthScale ** (-3) * (2.0 - 2.0 * J * lengthScale**2), 0
)
coeffs = np.sum(coeffs,0) coeffs = np.sum(coeffs, 0)
else: else:
coeffs = 2*magnSigma2*sp.exp( -lengthScale**(-2) ) * special.iv(range(0,m+1),1.0/lengthScale**(2)) coeffs = (
if np.any( np.isfinite(coeffs) == False): 2
* magnSigma2
* np.exp(-(lengthScale ** (-2)))
* special.iv(range(0, m + 1), 1.0 / lengthScale ** (2))
)
if np.any(np.isfinite(coeffs) == False):
raise ValueError("sde_standard_periodic: Coefficients are not finite!") raise ValueError("sde_standard_periodic: Coefficients are not finite!")
#import pdb; pdb.set_trace() # import pdb; pdb.set_trace()
coeffs[0] = 0.5*coeffs[0] coeffs[0] = 0.5 * coeffs[0]
#print(coeffs) # print(coeffs)
# Derivatives wrt (lengthScale) # Derivatives wrt (lengthScale)
coeffs_dl = np.zeros(m+1) coeffs_dl = np.zeros(m + 1)
coeffs_dl[1:] = magnSigma2*lengthScale**(-3) * sp.exp(-lengthScale**(-2))*\ coeffs_dl[1:] = (
(-4*special.iv(range(0,m),lengthScale**(-2)) + 4*(1+np.arange(1,m+1)*lengthScale**(2))*special.iv(range(1,m+1),lengthScale**(-2)) ) magnSigma2
* lengthScale ** (-3)
* np.exp(-(lengthScale ** (-2)))
* (
-4 * special.iv(range(0, m), lengthScale ** (-2))
+ 4
* (1 + np.arange(1, m + 1) * lengthScale ** (2))
* special.iv(range(1, m + 1), lengthScale ** (-2))
)
)
# The first element # The first element
coeffs_dl[0] = magnSigma2*lengthScale**(-3) * np.exp(-lengthScale**(-2))*\ coeffs_dl[0] = (
(2*special.iv(0,lengthScale**(-2)) - 2*special.iv(1,lengthScale**(-2)) ) magnSigma2
* lengthScale ** (-3)
* np.exp(-(lengthScale ** (-2)))
* (
2 * special.iv(0, lengthScale ** (-2))
- 2 * special.iv(1, lengthScale ** (-2))
)
)
return coeffs.squeeze(), coeffs_dl.squeeze() return coeffs.squeeze(), coeffs_dl.squeeze()

158
GPy/kern/src/sde_stationary.py
View file

@ -11,12 +11,14 @@ from .stationary import RatQuad
import numpy as np import numpy as np
import scipy as sp import scipy as sp
try: try:
from scipy.linalg import solve_continuous_lyapunov as lyap from scipy.linalg import solve_continuous_lyapunov as lyap
except ImportError: except ImportError:
from scipy.linalg import solve_lyapunov as lyap from scipy.linalg import solve_lyapunov as lyap
import warnings import warnings
class sde_RBF(RBF): class sde_RBF(RBF):
""" """
@ -30,6 +32,7 @@ class sde_RBF(RBF):
k(r) = \sigma^2 \exp \\bigg(- \\frac{1}{2} r^2 \\bigg) \\ \\ \\ \\ \text{ where } r = \sqrt{\sum_{i=1}^{input dim} \frac{(x_i-y_i)^2}{\ell_i^2} } k(r) = \sigma^2 \exp \\bigg(- \\frac{1}{2} r^2 \\bigg) \\ \\ \\ \\ \text{ where } r = \sqrt{\sum_{i=1}^{input dim} \frac{(x_i-y_i)^2}{\ell_i^2} }
""" """
def __init__(self, *args, **kwargs): def __init__(self, *args, **kwargs):
""" """
Init constructior. Init constructior.
@ -44,21 +47,18 @@ class sde_RBF(RBF):
:type balance: bool :type balance: bool
""" """
if 'balance' in kwargs: if "balance" in kwargs:
self.balance = bool( kwargs.get('balance') ) self.balance = bool(kwargs.get("balance"))
del kwargs['balance'] del kwargs["balance"]
else: else:
self.balance = True self.balance = True
if "approx_order" in kwargs:
if 'approx_order' in kwargs: self.approx_order = kwargs.get("approx_order")
self.approx_order = kwargs.get('approx_order') del kwargs["approx_order"]
del kwargs['approx_order']
else: else:
self.approx_order = 6 self.approx_order = 6
super(sde_RBF, self).__init__(*args, **kwargs) super(sde_RBF, self).__init__(*args, **kwargs)
def sde_update_gradient_full(self, gradients): def sde_update_gradient_full(self, gradients):
@ -83,76 +83,101 @@ class sde_RBF(RBF):
The above facts do not take into accout regularization. The above facts do not take into accout regularization.
""" """
#import pdb; pdb.set_trace() # import pdb; pdb.set_trace()
if self.approx_order is not None: if self.approx_order is not None:
N = self.approx_order N = self.approx_order
else: else:
N = 10# approximation order ( number of terms in exponent series expansion) N = 10 # approximation order ( number of terms in exponent series expansion)
roots_rounding_decimals = 6 roots_rounding_decimals = 6
fn = np.math.factorial(N) fn = np.math.factorial(N)
p_lengthscale = float( self.lengthscale ) p_lengthscale = float(self.lengthscale)
p_variance = float(self.variance) p_variance = float(self.variance)
kappa = 1.0/2.0/p_lengthscale**2 kappa = 1.0 / 2.0 / p_lengthscale**2
Qc = np.array( ((p_variance*np.sqrt(np.pi/kappa)*fn*(4*kappa)**N,),) ) Qc = np.array(((p_variance * np.sqrt(np.pi / kappa) * fn * (4 * kappa) ** N,),))
eps = 1e-12 eps = 1e-12
if (float(Qc) > 1.0/eps) or (float(Qc) < eps): if (float(Qc) > 1.0 / eps) or (float(Qc) < eps):
warnings.warn("""sde_RBF kernel: the noise variance Qc is either very large or very small. warnings.warn(
It influece conditioning of P_inf: {0:e}""".format(float(Qc)) ) """sde_RBF kernel: the noise variance Qc is either very large or very small.
It influece conditioning of P_inf: {0:e}""".format(
float(Qc)
)
)
pp1 = np.zeros((2*N+1,)) # array of polynomial coefficients from higher power to lower pp1 = np.zeros(
(2 * N + 1,)
) # array of polynomial coefficients from higher power to lower
for n in range(0, N+1): # (2N+1) - number of polynomial coefficients for n in range(0, N + 1): # (2N+1) - number of polynomial coefficients
pp1[2*(N-n)] = fn*(4.0*kappa)**(N-n)/np.math.factorial(n)*(-1)**n pp1[2 * (N - n)] = (
fn * (4.0 * kappa) ** (N - n) / np.math.factorial(n) * (-1) ** n
)
pp = sp.poly1d(pp1) pp = np.poly1d(pp1)
roots = sp.roots(pp) roots = np.roots(pp)
neg_real_part_roots = roots[np.round(np.real(roots) ,roots_rounding_decimals) < 0] neg_real_part_roots = roots[
aa = sp.poly1d(neg_real_part_roots, r=True).coeffs np.round(np.real(roots), roots_rounding_decimals) < 0
]
aa = np.poly1d(neg_real_part_roots, r=True).coeffs
F = np.diag(np.ones((N-1,)),1) F = np.diag(np.ones((N - 1,)), 1)
F[-1,:] = -aa[-1:0:-1] F[-1, :] = -aa[-1:0:-1]
L= np.zeros((N,1)) L = np.zeros((N, 1))
L[N-1,0] = 1 L[N - 1, 0] = 1
H = np.zeros((1,N)) H = np.zeros((1, N))
H[0,0] = 1 H[0, 0] = 1
# Infinite covariance: # Infinite covariance:
Pinf = lyap(F, -np.dot(L,np.dot( Qc[0,0],L.T))) Pinf = lyap(F, -np.dot(L, np.dot(Qc[0, 0], L.T)))
Pinf = 0.5*(Pinf + Pinf.T) Pinf = 0.5 * (Pinf + Pinf.T)
# Allocating space for derivatives # Allocating space for derivatives
dF = np.empty([F.shape[0],F.shape[1],2]) dF = np.empty([F.shape[0], F.shape[1], 2])
dQc = np.empty([Qc.shape[0],Qc.shape[1],2]) dQc = np.empty([Qc.shape[0], Qc.shape[1], 2])
dPinf = np.empty([Pinf.shape[0],Pinf.shape[1],2]) dPinf = np.empty([Pinf.shape[0], Pinf.shape[1], 2])
# Derivatives: # Derivatives:
dFvariance = np.zeros(F.shape) dFvariance = np.zeros(F.shape)
dFlengthscale = np.zeros(F.shape) dFlengthscale = np.zeros(F.shape)
dFlengthscale[-1,:] = -aa[-1:0:-1]/p_lengthscale * np.arange(-N,0,1) dFlengthscale[-1, :] = -aa[-1:0:-1] / p_lengthscale * np.arange(-N, 0, 1)
dQcvariance = Qc/p_variance dQcvariance = Qc / p_variance
dQclengthscale = np.array(( (p_variance*np.sqrt(2*np.pi)*fn*2**N*p_lengthscale**(-2*N)*(1-2*N),),)) dQclengthscale = np.array(
(
(
p_variance
* np.sqrt(2 * np.pi)
* fn
* 2**N
* p_lengthscale ** (-2 * N)
* (1 - 2 * N),
),
)
)
dPinf_variance = Pinf/p_variance dPinf_variance = Pinf / p_variance
lp = Pinf.shape[0] lp = Pinf.shape[0]
coeff = np.arange(1,lp+1).reshape(lp,1) + np.arange(1,lp+1).reshape(1,lp) - 2 coeff = (
coeff[np.mod(coeff,2) != 0] = 0 np.arange(1, lp + 1).reshape(lp, 1)
dPinf_lengthscale = -1/p_lengthscale*Pinf*coeff + np.arange(1, lp + 1).reshape(1, lp)
- 2
)
coeff[np.mod(coeff, 2) != 0] = 0
dPinf_lengthscale = -1 / p_lengthscale * Pinf * coeff
dF[:,:,0] = dFvariance dF[:, :, 0] = dFvariance
dF[:,:,1] = dFlengthscale dF[:, :, 1] = dFlengthscale
dQc[:,:,0] = dQcvariance dQc[:, :, 0] = dQcvariance
dQc[:,:,1] = dQclengthscale dQc[:, :, 1] = dQclengthscale
dPinf[:,:,0] = dPinf_variance dPinf[:, :, 0] = dPinf_variance
dPinf[:,:,1] = dPinf_lengthscale dPinf[:, :, 1] = dPinf_lengthscale
P0 = Pinf.copy() P0 = Pinf.copy()
dP0 = dPinf.copy() dP0 = dPinf.copy()
@ -161,10 +186,14 @@ class sde_RBF(RBF):
# Benefits of this are not very sound. Helps only in one case: # Benefits of this are not very sound. Helps only in one case:
# SVD Kalman + RBF kernel # SVD Kalman + RBF kernel
import GPy.models.state_space_main as ssm import GPy.models.state_space_main as ssm
(F, L, Qc, H, Pinf, P0, dF, dQc, dPinf,dP0) = ssm.balance_ss_model(F, L, Qc, H, Pinf, P0, dF, dQc, dPinf, dP0 )
(F, L, Qc, H, Pinf, P0, dF, dQc, dPinf, dP0) = ssm.balance_ss_model(
F, L, Qc, H, Pinf, P0, dF, dQc, dPinf, dP0
)
return (F, L, Qc, H, Pinf, P0, dF, dQc, dPinf, dP0) return (F, L, Qc, H, Pinf, P0, dF, dQc, dPinf, dP0)
class sde_Exponential(Exponential): class sde_Exponential(Exponential):
""" """
@ -195,30 +224,31 @@ class sde_Exponential(Exponential):
variance = float(self.variance.values) variance = float(self.variance.values)
lengthscale = float(self.lengthscale) lengthscale = float(self.lengthscale)
F = np.array(((-1.0/lengthscale,),)) F = np.array(((-1.0 / lengthscale,),))
L = np.array(((1.0,),)) L = np.array(((1.0,),))
Qc = np.array( ((2.0*variance/lengthscale,),) ) Qc = np.array(((2.0 * variance / lengthscale,),))
H = np.array(((1.0,),)) H = np.array(((1.0,),))
Pinf = np.array(((variance,),)) Pinf = np.array(((variance,),))
P0 = Pinf.copy() P0 = Pinf.copy()
dF = np.zeros((1,1,2)); dF = np.zeros((1, 1, 2))
dQc = np.zeros((1,1,2)); dQc = np.zeros((1, 1, 2))
dPinf = np.zeros((1,1,2)); dPinf = np.zeros((1, 1, 2))
dF[:,:,0] = 0.0 dF[:, :, 0] = 0.0
dF[:,:,1] = 1.0/lengthscale**2 dF[:, :, 1] = 1.0 / lengthscale**2
dQc[:,:,0] = 2.0/lengthscale dQc[:, :, 0] = 2.0 / lengthscale
dQc[:,:,1] = -2.0*variance/lengthscale**2 dQc[:, :, 1] = -2.0 * variance / lengthscale**2
dPinf[:,:,0] = 1.0 dPinf[:, :, 0] = 1.0
dPinf[:,:,1] = 0.0 dPinf[:, :, 1] = 0.0
dP0 = dPinf.copy() dP0 = dPinf.copy()
return (F, L, Qc, H, Pinf, P0, dF, dQc, dPinf, dP0) return (F, L, Qc, H, Pinf, P0, dF, dQc, dPinf, dP0)
class sde_RatQuad(RatQuad): class sde_RatQuad(RatQuad):
""" """
@ -238,12 +268,12 @@ class sde_RatQuad(RatQuad):
Return the state space representation of the covariance. Return the state space representation of the covariance.
""" """
assert False, 'Not Implemented' assert False, "Not Implemented"
# Params to use: # Params to use:
# self.lengthscale # self.lengthscale
# self.variance # self.variance
#self.power # self.power
#return (F, L, Qc, H, Pinf, dF, dQc, dPinf) # return (F, L, Qc, H, Pinf, dF, dQc, dPinf)

3
GPy/testing/test_grid.py
View file

@ -29,6 +29,7 @@ class TestGridModel:
self.dim = self.X.shape[1] self.dim = self.X.shape[1]
def test_alpha_match(self): def test_alpha_match(self):
self.setup()
kernel = GPy.kern.RBF(input_dim=self.dim, variance=1, ARD=True) kernel = GPy.kern.RBF(input_dim=self.dim, variance=1, ARD=True)
m = GPy.models.GPRegressionGrid(self.X, self.Y, kernel) m = GPy.models.GPRegressionGrid(self.X, self.Y, kernel)
@ -38,6 +39,7 @@ class TestGridModel:
np.testing.assert_almost_equal(m.posterior.alpha, m2.posterior.woodbury_vector) np.testing.assert_almost_equal(m.posterior.alpha, m2.posterior.woodbury_vector)
def test_gradient_match(self): def test_gradient_match(self):
self.setup()
kernel = GPy.kern.RBF(input_dim=self.dim, variance=1, ARD=True) kernel = GPy.kern.RBF(input_dim=self.dim, variance=1, ARD=True)
m = GPy.models.GPRegressionGrid(self.X, self.Y, kernel) m = GPy.models.GPRegressionGrid(self.X, self.Y, kernel)
@ -55,6 +57,7 @@ class TestGridModel:
) )
def test_prediction_match(self): def test_prediction_match(self):
self.setup()
kernel = GPy.kern.RBF(input_dim=self.dim, variance=1, ARD=True) kernel = GPy.kern.RBF(input_dim=self.dim, variance=1, ARD=True)
m = GPy.models.GPRegressionGrid(self.X, self.Y, kernel) m = GPy.models.GPRegressionGrid(self.X, self.Y, kernel)