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lots of bugfixes after refactoring

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This commit is contained in:
James Hensman 2013-06-05 16:14:43 +01:00
parent 3fbd7e4943
commit 527586a012
12 changed files with 53 additions and 53 deletions
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8
GPy/core/fitc.py
View file

@ -20,7 +20,7 @@ class FITC(SparseGP):
sparse FITC approximation
:param X: inputs
:type X: np.ndarray (N x Q)
:type X: np.ndarray (num_data x Q)
:param likelihood: a likelihood instance, containing the observed data
:type likelihood: GPy.likelihood.(Gaussian | EP)
:param kernel : the kernel (covariance function). See link kernels
@ -112,7 +112,7 @@ class FITC(SparseGP):
else:
if self.likelihood.is_heteroscedastic:
assert self.likelihood.output_dim == 1
tmp = self.psi1 * (np.sqrt(self.beta_star.flatten().reshape(1, self.N)))
tmp = self.psi1 * (np.sqrt(self.beta_star.flatten().reshape(1, self.num_data)))
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
self.A = tdot(tmp)
@ -168,7 +168,7 @@ class FITC(SparseGP):
self._dpsi1_dX_jkj = 0
self._dpsi1_dtheta_jkj = 0
for i,V_n,alpha_n,gamma_n,gamma_k in zip(range(self.N),self.V_star,alpha,gamma_2,gamma_3):
for i,V_n,alpha_n,gamma_n,gamma_k in zip(range(self.num_data),self.V_star,alpha,gamma_2,gamma_3):
K_pp_K = np.dot(Kmmipsi1[:,i:(i+1)],Kmmipsi1[:,i:(i+1)].T)
#Diag_dpsi1 = Diag_dA_dpsi1: yT*beta_star*y + Diag_dC_dpsi1 +Diag_dD_dpsi1
@ -215,7 +215,7 @@ class FITC(SparseGP):
def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """
A = -0.5 * self.N * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)
A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)
C = -self.output_dim * (np.sum(np.log(np.diag(self.LB))))
D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
return A + C + D

4
GPy/core/gp.py
View file

@ -46,12 +46,12 @@ class GP(GPBase):
#alpha = np.dot(self.Ki, self.likelihood.Y)
alpha,_ = linalg.lapack.flapack.dpotrs(self.L, self.likelihood.Y,lower=1)
self.dL_dK = 0.5 * (tdot(alpha) - self.input_dim * self.Ki)
self.dL_dK = 0.5 * (tdot(alpha) - self.output_dim * self.Ki)
else:
#tmp = mdot(self.Ki, self.likelihood.YYT, self.Ki)
tmp, _ = linalg.lapack.flapack.dpotrs(self.L, np.asfortranarray(self.likelihood.YYT), lower=1)
tmp, _ = linalg.lapack.flapack.dpotrs(self.L, np.asfortranarray(tmp.T), lower=1)
self.dL_dK = 0.5 * (tmp - self.input_dim * self.Ki)
self.dL_dK = 0.5 * (tmp - self.output_dim * self.Ki)
def _get_params(self):
return np.hstack((self.kern._get_params_transformed(), self.likelihood._get_params()))

4
GPy/core/gp_base.py
View file

@ -13,12 +13,12 @@ class GPBase(model.model):
def __init__(self, X, likelihood, kernel, normalize_X=False):
self.X = X
assert len(self.X.shape) == 2
self.N, self.input_dim = self.X.shape
self.num_data, self.input_dim = self.X.shape
assert isinstance(kernel, kern.kern)
self.kern = kernel
self.likelihood = likelihood
assert self.X.shape[0] == self.likelihood.data.shape[0]
self.N, self.output_dim = self.likelihood.data.shape
self.num_data, self.output_dim = self.likelihood.data.shape
if normalize_X:
self._Xmean = X.mean(0)[None, :]

32
GPy/core/sparse_gp.py
View file

@ -13,13 +13,13 @@ class SparseGP(GPBase):
Variational sparse GP model
:param X: inputs
:type X: np.ndarray (N x input_dim)
:type X: np.ndarray (num_data x input_dim)
:param likelihood: a likelihood instance, containing the observed data
:type likelihood: GPy.likelihood.(Gaussian | EP | Laplace)
:param kernel : the kernel (covariance function). See link kernels
:type kernel: a GPy.kern.kern instance
:param X_variance: The uncertainty in the measurements of X (Gaussian variance)
:type X_variance: np.ndarray (N x input_dim) | None
:type X_variance: np.ndarray (num_data x input_dim) | None
:param Z: inducing inputs (optional, see note)
:type Z: np.ndarray (num_inducing x input_dim) | None
:param num_inducing : Number of inducing points (optional, default 10. Ignored if Z is not None)
@ -69,7 +69,7 @@ class SparseGP(GPBase):
# The rather complex computations of self.A
if self.has_uncertain_inputs:
if self.likelihood.is_heteroscedastic:
psi2_beta = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1))).sum(0)
psi2_beta = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.num_data, 1, 1))).sum(0)
else:
psi2_beta = self.psi2.sum(0) * self.likelihood.precision
evals, evecs = linalg.eigh(psi2_beta)
@ -77,7 +77,7 @@ class SparseGP(GPBase):
tmp = evecs * np.sqrt(clipped_evals)
else:
if self.likelihood.is_heteroscedastic:
tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.N)))
tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.num_data)))
else:
tmp = self.psi1 * (np.sqrt(self.likelihood.precision))
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
@ -99,28 +99,28 @@ class SparseGP(GPBase):
# Compute dL_dKmm
tmp = tdot(self._LBi_Lmi_psi1V)
self.DBi_plus_BiPBi = backsub_both_sides(self.LB, self.input_dim * np.eye(self.num_inducing) + tmp)
self.DBi_plus_BiPBi = backsub_both_sides(self.LB, self.output_dim * np.eye(self.num_inducing) + tmp)
tmp = -0.5 * self.DBi_plus_BiPBi
tmp += -0.5 * self.B * self.input_dim
tmp += self.input_dim * np.eye(self.num_inducing)
tmp += -0.5 * self.B * self.output_dim
tmp += self.output_dim * np.eye(self.num_inducing)
self.dL_dKmm = backsub_both_sides(self.Lm, tmp)
# Compute dL_dpsi # FIXME: this is untested for the heterscedastic + uncertain inputs case
self.dL_dpsi0 = -0.5 * self.input_dim * (self.likelihood.precision * np.ones([self.N, 1])).flatten()
self.dL_dpsi0 = -0.5 * self.output_dim * (self.likelihood.precision * np.ones([self.num_data, 1])).flatten()
self.dL_dpsi1 = np.dot(self.Cpsi1V, self.likelihood.V.T)
dL_dpsi2_beta = 0.5 * backsub_both_sides(self.Lm, self.input_dim * np.eye(self.num_inducing) - self.DBi_plus_BiPBi)
dL_dpsi2_beta = 0.5 * backsub_both_sides(self.Lm, self.output_dim * np.eye(self.num_inducing) - self.DBi_plus_BiPBi)
if self.likelihood.is_heteroscedastic:
if self.has_uncertain_inputs:
self.dL_dpsi2 = self.likelihood.precision.flatten()[:, None, None] * dL_dpsi2_beta[None, :, :]
else:
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2_beta, self.psi1 * self.likelihood.precision.reshape(1, self.N))
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2_beta, self.psi1 * self.likelihood.precision.reshape(1, self.num_data))
self.dL_dpsi2 = None
else:
dL_dpsi2 = self.likelihood.precision * dL_dpsi2_beta
if self.has_uncertain_inputs:
# repeat for each of the N psi_2 matrices
self.dL_dpsi2 = np.repeat(dL_dpsi2[None, :, :], self.N, axis=0)
self.dL_dpsi2 = np.repeat(dL_dpsi2[None, :, :], self.num_data, axis=0)
else:
# subsume back into psi1 (==Kmn)
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2, self.psi1)
@ -135,24 +135,24 @@ class SparseGP(GPBase):
raise NotImplementedError, "heteroscedatic derivates not implemented"
else:
# likelihood is not heterscedatic
self.partial_for_likelihood = -0.5 * self.N * self.input_dim * self.likelihood.precision + 0.5 * self.likelihood.trYYT * self.likelihood.precision ** 2
self.partial_for_likelihood += 0.5 * self.input_dim * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision)
self.partial_for_likelihood = -0.5 * self.num_data * self.output_dim * self.likelihood.precision + 0.5 * self.likelihood.trYYT * self.likelihood.precision ** 2
self.partial_for_likelihood += 0.5 * self.output_dim * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision)
self.partial_for_likelihood += self.likelihood.precision * (0.5 * np.sum(self.A * self.DBi_plus_BiPBi) - np.sum(np.square(self._LBi_Lmi_psi1V)))
def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """
if self.likelihood.is_heteroscedastic:
A = -0.5 * self.N * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)
A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)
B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
else:
A = -0.5 * self.N * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A))
C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))
D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
return A + B + C + D + self.likelihood.Z
def _set_params(self, p):
self.Z = p[:self.num_inducing * self.output_dim].reshape(self.num_inducing, self.input_dim)
self.Z = p[:self.num_inducing * self.input_dim].reshape(self.num_inducing, self.input_dim)
self.kern._set_params(p[self.Z.size:self.Z.size + self.kern.Nparam])
self.likelihood._set_params(p[self.Z.size + self.kern.Nparam:])
self._compute_kernel_matrices()

16
GPy/models/bayesian_gplvm.py
View file

@ -73,8 +73,8 @@ class BayesianGPLVM(SparseGP, GPLVM):
self._oldps.insert(0, p.copy())
def _get_param_names(self):
X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.N)], [])
S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.N)], [])
X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
return (X_names + S_names + SparseGP._get_param_names(self))
def _get_params(self):
@ -96,7 +96,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
def _set_params(self, x, save_old=True, save_count=0):
# try:
x = self._clipped(x)
N, input_dim = self.N, self.input_dim
N, input_dim = self.num_data, self.input_dim
self.X = x[:self.X.size].reshape(N, input_dim).copy()
self.X_variance = x[(N * input_dim):(2 * N * input_dim)].reshape(N, input_dim).copy()
SparseGP._set_params(self, x[(2 * N * input_dim):])
@ -126,7 +126,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
def KL_divergence(self):
var_mean = np.square(self.X).sum()
var_S = np.sum(self.X_variance - np.log(self.X_variance))
return 0.5 * (var_mean + var_S) - 0.5 * self.input_dim * self.N
return 0.5 * (var_mean + var_S) - 0.5 * self.input_dim * self.num_data
def log_likelihood(self):
ll = SparseGP.log_likelihood(self)
@ -146,11 +146,11 @@ class BayesianGPLVM(SparseGP, GPLVM):
self._savedpsiKmm.append([self.f_call, [self.Kmm, self.dL_dKmm]])
# sf2 = self.scale_factor ** 2
if self.likelihood.is_heteroscedastic:
A = -0.5 * self.N * self.input_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.V * self.likelihood.Y)
A = -0.5 * self.num_data * self.input_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.V * self.likelihood.Y)
# B = -0.5 * self.input_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A) * sf2)
B = -0.5 * self.input_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
else:
A = -0.5 * self.N * self.input_dim * (np.log(2.*np.pi) + np.log(self.likelihood._variance)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
A = -0.5 * self.num_data * self.input_dim * (np.log(2.*np.pi) + np.log(self.likelihood._variance)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
# B = -0.5 * self.input_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A) * sf2)
B = -0.5 * self.input_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A))
C = -self.input_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))
@ -266,9 +266,9 @@ class BayesianGPLVM(SparseGP, GPLVM):
def _debug_filter_params(self, x):
start, end = 0, self.X.size,
X = x[start:end].reshape(self.N, self.input_dim)
X = x[start:end].reshape(self.num_data, self.input_dim)
start, end = end, end + self.X_variance.size
X_v = x[start:end].reshape(self.N, self.input_dim)
X_v = x[start:end].reshape(self.num_data, self.input_dim)
start, end = end, end + (self.num_inducing * self.input_dim)
Z = x[start:end].reshape(self.num_inducing, self.input_dim)
start, end = end, end + self.input_dim

6
GPy/models/fitc.py
View file

@ -52,7 +52,7 @@ class FITC(SparseGP):
else:
if self.likelihood.is_heteroscedastic:
assert self.likelihood.input_dim == 1
tmp = self.psi1 * (np.sqrt(self.beta_star.flatten().reshape(1, self.N)))
tmp = self.psi1 * (np.sqrt(self.beta_star.flatten().reshape(1, self.num_data)))
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
self.A = tdot(tmp)
@ -108,7 +108,7 @@ class FITC(SparseGP):
self._dpsi1_dX_jkj = 0
self._dpsi1_dtheta_jkj = 0
for i, V_n, alpha_n, gamma_n, gamma_k in zip(range(self.N), self.V_star, alpha, gamma_2, gamma_3):
for i, V_n, alpha_n, gamma_n, gamma_k in zip(range(self.num_data), self.V_star, alpha, gamma_2, gamma_3):
K_pp_K = np.dot(Kmmipsi1[:, i:(i + 1)], Kmmipsi1[:, i:(i + 1)].T)
# Diag_dpsi1 = Diag_dA_dpsi1: yT*beta_star*y + Diag_dC_dpsi1 +Diag_dD_dpsi1
@ -155,7 +155,7 @@ class FITC(SparseGP):
def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """
A = -0.5 * self.N * self.input_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)
A = -0.5 * self.num_data * self.input_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)
C = -self.input_dim * (np.sum(np.log(np.diag(self.LB))))
D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
return A + C + D

2
GPy/models/fitc_classification.py
View file

@ -16,7 +16,7 @@ class FITCClassification(FITC):
:param X: input observations
:param Y: observed values
:param likelihood: a GPy likelihood, defaults to binomial with probit link_function
:param likelihood: a GPy likelihood, defaults to Binomial with probit link_function
:param kernel: a GPy kernel, defaults to rbf+white
:param normalize_X: whether to normalize the input data before computing (predictions will be in original scales)
:type normalize_X: False|True

10
GPy/models/generalized_fitc.py
View file

@ -112,9 +112,9 @@ class GeneralizedFITC(SparseGP):
self.mu = self.w + np.dot(self.P,self.Gamma)
# Remove extra term from dL_dpsi1
self.dL_dpsi1 -= mdot(self.Lmi.T,Lmipsi1*self.likelihood.precision.flatten().reshape(1,self.N))
self.dL_dpsi1 -= mdot(self.Lmi.T,Lmipsi1*self.likelihood.precision.flatten().reshape(1,self.num_data))
#self.Kmmi, Lm, Lmi, Kmm_logdet = pdinv(self.Kmm)
#self.dL_dpsi1 -= mdot(self.Kmmi,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)) #dB
#self.dL_dpsi1 -= mdot(self.Kmmi,self.psi1*self.likelihood.precision.flatten().reshape(1,self.num_data)) #dB
#########333333
#self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B)
@ -142,7 +142,7 @@ class GeneralizedFITC(SparseGP):
else:
raise NotImplementedError, "homoscedastic derivatives not implemented"
#likelihood is not heterscedatic
#self.partial_for_likelihood = - 0.5 * self.N*self.input_dim*self.likelihood.precision + 0.5 * np.sum(np.square(self.likelihood.Y))*self.likelihood.precision**2
#self.partial_for_likelihood = - 0.5 * self.num_data*self.input_dim*self.likelihood.precision + 0.5 * np.sum(np.square(self.likelihood.Y))*self.likelihood.precision**2
#self.partial_for_likelihood += 0.5 * self.input_dim * trace_dot(self.Bi,self.A)*self.likelihood.precision
#self.partial_for_likelihood += self.likelihood.precision*(0.5*trace_dot(self.psi2_beta_scaled,self.E*sf2) - np.trace(self.Cpsi1VVpsi1))
#TODO partial derivative vector for the likelihood not implemented
@ -164,9 +164,9 @@ class GeneralizedFITC(SparseGP):
""" Compute the (lower bound on the) log marginal likelihood """
sf2 = self.scale_factor**2
if self.likelihood.is_heteroscedastic:
A = -0.5*self.N*self.input_dim*np.log(2.*np.pi) +0.5*np.sum(np.log(self.likelihood.precision)) -0.5*np.sum(self.V*self.likelihood.Y)
A = -0.5*self.num_data*self.input_dim*np.log(2.*np.pi) +0.5*np.sum(np.log(self.likelihood.precision)) -0.5*np.sum(self.V*self.likelihood.Y)
else:
A = -0.5*self.N*self.input_dim*(np.log(2.*np.pi) + np.log(self.likelihood._variance)) -0.5*self.likelihood.precision*self.likelihood.trYYT
A = -0.5*self.num_data*self.input_dim*(np.log(2.*np.pi) + np.log(self.likelihood._variance)) -0.5*self.likelihood.precision*self.likelihood.trYYT
C = -self.input_dim * (np.sum(np.log(np.diag(self.LB))) + 0.5*self.num_inducing*np.log(sf2))
#C = -0.5*self.input_dim * (self.B_logdet + self.num_inducing*np.log(sf2))
D = 0.5*np.sum(np.square(self._LBi_Lmi_psi1V))

4
GPy/models/gplvm.py
View file

@ -42,13 +42,13 @@ class GPLVM(GP):
return np.random.randn(Y.shape[0], input_dim)
def _get_param_names(self):
return sum([['X_%i_%i'%(n,q) for q in range(self.input_dim)] for n in range(self.N)],[]) + GP._get_param_names(self)
return sum([['X_%i_%i'%(n,q) for q in range(self.input_dim)] for n in range(self.num_data)],[]) + GP._get_param_names(self)
def _get_params(self):
return np.hstack((self.X.flatten(), GP._get_params(self)))
def _set_params(self,x):
self.X = x[:self.N*self.input_dim].reshape(self.N,self.input_dim).copy()
self.X = x[:self.num_data*self.input_dim].reshape(self.num_data,self.input_dim).copy()
GP._set_params(self, x[self.X.size:])
def _log_likelihood_gradients(self):

12
GPy/models/mrd.py
View file

@ -74,8 +74,8 @@ class MRD(model):
nparams = numpy.array([0] + [SparseGP._get_params(g).size - g.Z.size for g in self.bgplvms])
self.nparams = nparams.cumsum()
self.N = self.gref.N
self.NQ = self.N * self.input_dim
self.num_data = self.gref.num_data
self.NQ = self.num_data * self.input_dim
self.MQ = self.num_inducing * self.input_dim
model.__init__(self) # @UndefinedVariable
@ -142,8 +142,8 @@ class MRD(model):
self._init_Z(initz, self.X)
def _get_param_names(self):
# X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.N)], [])
# S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.N)], [])
# X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
# S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
n1 = self.gref._get_param_names()
n1var = n1[:self.NQ * 2 + self.MQ]
map_names = lambda ns, name: map(lambda x: "{1}_{0}".format(*x),
@ -169,8 +169,8 @@ class MRD(model):
return params
# def _set_var_params(self, g, X, X_var, Z):
# g.X = X.reshape(self.N, self.input_dim)
# g.X_variance = X_var.reshape(self.N, self.input_dim)
# g.X = X.reshape(self.num_data, self.input_dim)
# g.X_variance = X_var.reshape(self.num_data, self.input_dim)
# g.Z = Z.reshape(self.num_inducing, self.input_dim)
#
# def _set_kern_params(self, g, p):

4
GPy/models/sparse_gplvm.py
View file

@ -28,14 +28,14 @@ class SparseGPLVM(SparseGPRegression, GPLVM):
SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing)
def _get_param_names(self):
return (sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.N)], [])
return (sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
+ SparseGPRegression._get_param_names(self))
def _get_params(self):
return np.hstack((self.X.flatten(), SparseGPRegression._get_params(self)))
def _set_params(self, x):
self.X = x[:self.X.size].reshape(self.N, self.input_dim).copy()
self.X = x[:self.X.size].reshape(self.num_data, self.input_dim).copy()
SparseGPRegression._set_params(self, x[self.X.size:])
def log_likelihood(self):

4
GPy/testing/unit_tests.py
View file

@ -196,9 +196,9 @@ class GradientTests(unittest.TestCase):
k = GPy.kern.rbf(1) + GPy.kern.white(1)
Y = np.hstack([np.ones(N/2),-np.ones(N/2)])[:,None]
distribution = GPy.likelihoods.likelihood_functions.binomial()
distribution = GPy.likelihoods.likelihood_functions.Binomial()
likelihood = GPy.likelihoods.EP(Y, distribution)
#likelihood = GPy.inference.likelihoods.binomial(Y)
#likelihood = GPy.inference.likelihoods.Binomial(Y)
m = GPy.models.generalized_FITC(X,likelihood,k,inducing=4)
m.constrain_positive('(var|len)')
m.approximate_likelihood()