mirror of
https://github.com/SheffieldML/GPy.git
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Scale Factor removed and moved V=Y*beta into likelihoods
This commit is contained in:
Max Zwiessele
parent
65ead17ff5
commit
f39afb9ea5
3 changed files with 140 additions and 122 deletions
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@ -32,6 +32,7 @@ class EP(likelihood):
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self.precision = np.ones(self.N)[:,None]
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self.Z = 0
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self.YYT = None
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self.V = self.precision * self.Y
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def restart(self):
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self.tau_tilde = np.zeros(self.N)
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@ -41,6 +42,7 @@ class EP(likelihood):
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self.precision = np.ones(self.N)[:,None]
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self.Z = 0
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self.YYT = None
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self.V = self.precision * self.Y
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def predictive_values(self,mu,var,full_cov):
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if full_cov:
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@ -67,6 +69,7 @@ class EP(likelihood):
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self.YYT = np.dot(self.Y,self.Y.T)
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self.covariance_matrix = np.diag(1./self.tau_tilde)
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self.precision = self.tau_tilde[:,None]
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self.V = self.precision * self.Y
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def fit_full(self,K):
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"""
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@ -11,33 +11,34 @@ class Gaussian(likelihood):
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:param normalize: whether to normalize the data before computing (predictions will be in original scales)
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:type normalize: False|True
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"""
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def __init__(self,data,variance=1.,normalize=False):
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def __init__(self, data, variance=1., normalize=False):
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self.is_heteroscedastic = False
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self.Nparams = 1
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self.Z = 0. # a correction factor which accounts for the approximation made
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self.Z = 0. # a correction factor which accounts for the approximation made
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N, self.D = data.shape
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#normalization
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# normalization
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if normalize:
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self._bias = data.mean(0)[None,:]
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self._scale = data.std(0)[None,:]
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# Don't scale outputs which have zero variance to zero.
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self._scale[np.nonzero(self._scale==0.)] = 1.0e-3
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self._bias = data.mean(0)[None, :]
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self._scale = data.std(0)[None, :]
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# Don't scale outputs which have zero variance to zero.
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self._scale[np.nonzero(self._scale == 0.)] = 1.0e-3
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else:
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self._bias = np.zeros((1,self.D))
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self._scale = np.ones((1,self.D))
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self._bias = np.zeros((1, self.D))
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self._scale = np.ones((1, self.D))
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self.set_data(data)
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self._variance = np.asarray(variance) + 1.
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self._set_params(np.asarray(variance))
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def set_data(self,data):
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def set_data(self, data):
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self.data = data
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self.N,D = data.shape
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self.N, D = data.shape
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assert D == self.D
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self.Y = (self.data - self._bias)/self._scale
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self.Y = (self.data - self._bias) / self._scale
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if D > self.N:
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self.YYT = np.dot(self.Y,self.Y.T)
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self.YYT = np.dot(self.Y, self.Y.T)
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self.trYYT = np.trace(self.YYT)
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else:
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self.YYT = None
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@ -49,27 +50,30 @@ class Gaussian(likelihood):
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def _get_param_names(self):
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return ["noise_variance"]
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def _set_params(self,x):
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self._variance = float(x)
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self.covariance_matrix = np.eye(self.N)*self._variance
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self.precision = 1./self._variance
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def _set_params(self, x):
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x = float(x)
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if self._variance != x:
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self._variance = x
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self.covariance_matrix = np.eye(self.N) * self._variance
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self.precision = 1. / self._variance
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self.V = (self.precision) * self.Y
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def predictive_values(self,mu,var, full_cov):
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def predictive_values(self, mu, var, full_cov):
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"""
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Un-normalize the prediction and add the likelihood variance, then return the 5%, 95% interval
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"""
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mean = mu*self._scale + self._bias
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mean = mu * self._scale + self._bias
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if full_cov:
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if self.D >1:
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if self.D > 1:
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raise NotImplementedError, "TODO"
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#Note. for D>1, we need to re-normalise all the outputs independently.
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# Note. for D>1, we need to re-normalise all the outputs independently.
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# This will mess up computations of diag(true_var), below.
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#note that the upper, lower quantiles should be the same shape as mean
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true_var = (var + np.eye(var.shape[0])*self._variance)*self._scale**2
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# note that the upper, lower quantiles should be the same shape as mean
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true_var = (var + np.eye(var.shape[0]) * self._variance) * self._scale ** 2
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_5pc = mean - 2.*np.sqrt(np.diag(true_var))
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_95pc = mean + 2.*np.sqrt(np.diag(true_var))
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else:
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true_var = (var + self._variance)*self._scale**2
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true_var = (var + self._variance) * self._scale ** 2
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_5pc = mean - 2.*np.sqrt(true_var)
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_95pc = mean + 2.*np.sqrt(true_var)
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return mean, true_var, _5pc, _95pc
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@ -80,5 +84,5 @@ class Gaussian(likelihood):
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"""
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pass
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def _gradients(self,partial):
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def _gradients(self, partial):
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return np.sum(partial)
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@ -9,10 +9,10 @@ from .. import kern
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from GP import GP
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from scipy import linalg
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def backsub_both_sides(L,X):
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def backsub_both_sides(L, X):
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""" Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
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tmp,_ = linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(X),lower=1,trans=1)
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return linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(tmp.T),lower=1,trans=1)[0].T
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tmp, _ = linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(X), lower=1, trans=1)
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return linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(tmp.T), lower=1, trans=1)[0].T
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class sparse_GP(GP):
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"""
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@ -35,22 +35,22 @@ class sparse_GP(GP):
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"""
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def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
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self.scale_factor = 100.0# a scaling factor to help keep the algorithm stable
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self.auto_scale_factor = False
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# self.scale_factor = 100.0 # a scaling factor to help keep the algorithm stable
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# self.auto_scale_factor = False
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self.Z = Z
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self.M = Z.shape[0]
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self.likelihood = likelihood
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if X_variance is None:
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self.has_uncertain_inputs=False
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self.has_uncertain_inputs = False
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else:
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assert X_variance.shape==X.shape
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self.has_uncertain_inputs=True
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assert X_variance.shape == X.shape
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self.has_uncertain_inputs = True
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self.X_variance = X_variance
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GP.__init__(self, X, likelihood, kernel=kernel, normalize_X=normalize_X)
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#normalize X uncertainty also
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# normalize X uncertainty also
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if self.has_uncertain_inputs:
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self.X_variance /= np.square(self._Xstd)
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@ -59,141 +59,152 @@ class sparse_GP(GP):
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# kernel computations, using BGPLVM notation
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self.Kmm = self.kern.K(self.Z)
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if self.has_uncertain_inputs:
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self.psi0 = self.kern.psi0(self.Z,self.X, self.X_variance)
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self.psi1 = self.kern.psi1(self.Z,self.X, self.X_variance).T
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self.psi2 = self.kern.psi2(self.Z,self.X, self.X_variance)
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self.psi0 = self.kern.psi0(self.Z, self.X, self.X_variance)
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self.psi1 = self.kern.psi1(self.Z, self.X, self.X_variance).T
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self.psi2 = self.kern.psi2(self.Z, self.X, self.X_variance)
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else:
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self.psi0 = self.kern.Kdiag(self.X)
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self.psi1 = self.kern.K(self.Z,self.X)
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self.psi1 = self.kern.K(self.Z, self.X)
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self.psi2 = None
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def _computations(self):
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sf = self.scale_factor
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sf2 = sf**2
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# sf = self.scale_factor
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# sf2 = sf ** 2
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#factor Kmm
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# factor Kmm
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self.Lm = jitchol(self.Kmm)
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#The rather complex computations of self.A
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# The rather complex computations of self.A
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if self.likelihood.is_heteroscedastic:
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assert self.likelihood.D == 1 #TODO: what if the likelihood is heterscedatic and there are multiple independent outputs?
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assert self.likelihood.D == 1 # TODO: what if the likelihood is heterscedatic and there are multiple independent outputs?
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if self.has_uncertain_inputs:
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psi2_beta_scaled = (self.psi2*(self.likelihood.precision.flatten().reshape(self.N,1,1)/sf2)).sum(0)
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# psi2_beta_scaled = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1) / sf2)).sum(0)
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psi2_beta_scaled = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1))).sum(0)
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evals, evecs = linalg.eigh(psi2_beta_scaled)
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clipped_evals = np.clip(evals,0.,1e6) # TODO: make clipping configurable
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clipped_evals = np.clip(evals, 0., 1e6) # TODO: make clipping configurable
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if not np.allclose(evals, clipped_evals):
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print "Warning: clipping posterior eigenvalues"
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tmp = evecs*np.sqrt(clipped_evals)
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp),lower=1)
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tmp = evecs * np.sqrt(clipped_evals)
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
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self.A = tdot(tmp)
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else:
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tmp = self.psi1*(np.sqrt(self.likelihood.precision.flatten().reshape(1,self.N))/sf)
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp),lower=1)
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# tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.N)) / sf)
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tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.N)))
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
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self.A = tdot(tmp)
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else:
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if self.has_uncertain_inputs:
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psi2_beta_scaled = (self.psi2*(self.likelihood.precision/sf2)).sum(0)
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# psi2_beta_scaled = (self.psi2 * (self.likelihood.precision / sf2)).sum(0)
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psi2_beta_scaled = (self.psi2 * (self.likelihood.precision)).sum(0)
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evals, evecs = linalg.eigh(psi2_beta_scaled)
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clipped_evals = np.clip(evals,0.,1e6) # TODO: make clipping configurable
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clipped_evals = np.clip(evals, 0., 1e6) # TODO: make clipping configurable
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if not np.allclose(evals, clipped_evals):
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print "Warning: clipping posterior eigenvalues"
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tmp = evecs*np.sqrt(clipped_evals)
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp),lower=1)
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tmp = evecs * np.sqrt(clipped_evals)
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
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self.A = tdot(tmp)
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else:
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tmp = self.psi1*(np.sqrt(self.likelihood.precision)/sf)
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp),lower=1)
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# tmp = self.psi1 * (np.sqrt(self.likelihood.precision) / sf)
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tmp = self.psi1 * (np.sqrt(self.likelihood.precision))
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
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self.A = tdot(tmp)
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#factor B
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self.B = np.eye(self.M)/sf2 + self.A
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# factor B
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# self.B = np.eye(self.M) / sf2 + self.A
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self.B = np.eye(self.M) + self.A
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self.LB = jitchol(self.B)
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self.V = (self.likelihood.precision/self.scale_factor)*self.likelihood.Y
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self.psi1V = np.dot(self.psi1, self.V)
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# TODO: make a switch for either first compute psi1V, or VV.T
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self.psi1V = np.dot(self.psi1, self.likelihood.V)
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#back substutue C into psi1V
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tmp,info1 = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(self.psi1V),lower=1,trans=0)
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self._LBi_Lmi_psi1V,_ = linalg.lapack.flapack.dtrtrs(self.LB,np.asfortranarray(tmp),lower=1,trans=0)
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tmp,info2 = linalg.lapack.flapack.dpotrs(self.LB,tmp,lower=1)
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self.Cpsi1V,info3 = linalg.lapack.flapack.dtrtrs(self.Lm,tmp,lower=1,trans=1)
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# back substutue C into psi1V
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tmp, info1 = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(self.psi1V), lower=1, trans=0)
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self._LBi_Lmi_psi1V, _ = linalg.lapack.flapack.dtrtrs(self.LB, np.asfortranarray(tmp), lower=1, trans=0)
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tmp, info2 = linalg.lapack.flapack.dpotrs(self.LB, tmp, lower=1)
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self.Cpsi1V, info3 = linalg.lapack.flapack.dtrtrs(self.Lm, tmp, lower=1, trans=1)
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# Compute dL_dKmm
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tmp = tdot(self._LBi_Lmi_psi1V)
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self.DBi_plus_BiPBi = backsub_both_sides(self.LB, self.D*np.eye(self.M) + tmp)
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tmp = -0.5*self.DBi_plus_BiPBi/sf2
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tmp += -0.5*self.B*sf2*self.D
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tmp += self.D*np.eye(self.M)
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self.dL_dKmm = backsub_both_sides(self.Lm,tmp)
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self.DBi_plus_BiPBi = backsub_both_sides(self.LB, self.D * np.eye(self.M) + tmp)
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# tmp = -0.5 * self.DBi_plus_BiPBi / sf2
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# tmp += -0.5 * self.B * sf2 * self.D
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tmp = -0.5 * self.DBi_plus_BiPBi
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tmp += -0.5 * self.B * self.D
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tmp += self.D * np.eye(self.M)
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self.dL_dKmm = backsub_both_sides(self.Lm, tmp)
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# Compute dL_dpsi # FIXME: this is untested for the heterscedastic + uncertain inputs case
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self.dL_dpsi0 = - 0.5 * self.D * (self.likelihood.precision * np.ones([self.N,1])).flatten()
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self.dL_dpsi1 = np.dot(self.Cpsi1V,self.V.T)
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dL_dpsi2_beta = 0.5*backsub_both_sides(self.Lm,self.D*np.eye(self.M) - self.DBi_plus_BiPBi)
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self.dL_dpsi0 = -0.5 * self.D * (self.likelihood.precision * np.ones([self.N, 1])).flatten()
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self.dL_dpsi1 = np.dot(self.Cpsi1V, self.likelihood.V.T)
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dL_dpsi2_beta = 0.5 * backsub_both_sides(self.Lm, self.D * np.eye(self.M) - self.DBi_plus_BiPBi)
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if self.likelihood.is_heteroscedastic:
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if self.has_uncertain_inputs:
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self.dL_dpsi2 = self.likelihood.precision[:,None,None]*dL_dpsi2_beta[None,:,:]
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self.dL_dpsi2 = self.likelihood.precision[:, None, None] * dL_dpsi2_beta[None, :, :]
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else:
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self.dL_dpsi1 += 2.*np.dot(dL_dpsi2_beta,self.psi1*self.likelihood.precision.reshape(1,self.N))
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self.dL_dpsi1 += 2.*np.dot(dL_dpsi2_beta, self.psi1 * self.likelihood.precision.reshape(1, self.N))
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self.dL_dpsi2 = None
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else:
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dL_dpsi2 = self.likelihood.precision*dL_dpsi2_beta
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dL_dpsi2 = self.likelihood.precision * dL_dpsi2_beta
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if self.has_uncertain_inputs:
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#repeat for each of the N psi_2 matrices
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self.dL_dpsi2 = np.repeat(dL_dpsi2[None,:,:],self.N,axis=0)
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# repeat for each of the N psi_2 matrices
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self.dL_dpsi2 = np.repeat(dL_dpsi2[None, :, :], self.N, axis=0)
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else:
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#subsume back into psi1 (==Kmn)
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self.dL_dpsi1 += 2.*np.dot(dL_dpsi2,self.psi1)
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# subsume back into psi1 (==Kmn)
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self.dL_dpsi1 += 2.*np.dot(dL_dpsi2, self.psi1)
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self.dL_dpsi2 = None
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#the partial derivative vector for the likelihood
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if self.likelihood.Nparams ==0:
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#save computation here.
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# the partial derivative vector for the likelihood
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if self.likelihood.Nparams == 0:
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# save computation here.
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self.partial_for_likelihood = None
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elif self.likelihood.is_heteroscedastic:
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raise NotImplementedError, "heteroscedatic derivates not implemented"
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else:
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#likelihood is not heterscedatic
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self.partial_for_likelihood = - 0.5 * self.N*self.D*self.likelihood.precision + 0.5 * self.likelihood.trYYT*self.likelihood.precision**2
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self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum()*self.likelihood.precision**2 - np.trace(self.A)*self.likelihood.precision*sf2)
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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)))
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# likelihood is not heterscedatic
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self.partial_for_likelihood = -0.5 * self.N * self.D * self.likelihood.precision + 0.5 * self.likelihood.trYYT * self.likelihood.precision ** 2
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# self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision * sf2)
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self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision)
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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)))
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def log_likelihood(self):
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""" Compute the (lower bound on the) log marginal likelihood """
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sf2 = self.scale_factor**2
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# sf2 = self.scale_factor ** 2
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if self.likelihood.is_heteroscedastic:
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A = -0.5*self.N*self.D*np.log(2.*np.pi) +0.5*np.sum(np.log(self.likelihood.precision)) -0.5*np.sum(self.V*self.likelihood.Y)
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B = -0.5*self.D*(np.sum(self.likelihood.precision.flatten()*self.psi0) - np.trace(self.A)*sf2)
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A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)
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# B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A) * sf2)
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B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
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else:
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A = -0.5*self.N*self.D*(np.log(2.*np.pi) + np.log(self.likelihood._variance)) -0.5*self.likelihood.precision*self.likelihood.trYYT
|
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B = -0.5*self.D*(np.sum(self.likelihood.precision*self.psi0) - np.trace(self.A)*sf2)
|
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C = -self.D * (np.sum(np.log(np.diag(self.LB))) + 0.5*self.M*np.log(sf2))
|
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D = 0.5*np.sum(np.square(self._LBi_Lmi_psi1V))
|
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return A+B+C+D
|
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A = -0.5 * self.N * self.D * (np.log(2.*np.pi) + np.log(self.likelihood._variance)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
|
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# B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A) * sf2)
|
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B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A))
|
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# C = -self.D * (np.sum(np.log(np.diag(self.LB))) + 0.5 * self.M * np.log(sf2))
|
||||
C = -self.D * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.M * np.log(sf2))
|
||||
D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
|
||||
return A + B + C + D
|
||||
|
||||
def _set_params(self, p):
|
||||
self.Z = p[:self.M*self.Q].reshape(self.M, self.Q)
|
||||
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.Z = p[:self.M * self.Q].reshape(self.M, self.Q)
|
||||
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()
|
||||
#if self.auto_scale_factor:
|
||||
# if self.auto_scale_factor:
|
||||
# self.scale_factor = np.sqrt(self.psi2.sum(0).mean()*self.likelihood.precision)
|
||||
#if self.auto_scale_factor:
|
||||
#if self.likelihood.is_heteroscedastic:
|
||||
#self.scale_factor = max(100,np.sqrt(self.psi2_beta_scaled.sum(0).mean()))
|
||||
#else:
|
||||
#self.scale_factor = np.sqrt(self.psi2.sum(0).mean()*self.likelihood.precision)
|
||||
self.scale_factor = 1.
|
||||
# if self.auto_scale_factor:
|
||||
# if self.likelihood.is_heteroscedastic:
|
||||
# self.scale_factor = max(100,np.sqrt(self.psi2_beta_scaled.sum(0).mean()))
|
||||
# else:
|
||||
# self.scale_factor = np.sqrt(self.psi2.sum(0).mean()*self.likelihood.precision)
|
||||
# self.scale_factor = 100.
|
||||
self._computations()
|
||||
|
||||
def _get_params(self):
|
||||
return np.hstack([self.Z.flatten(),GP._get_params(self)])
|
||||
return np.hstack([self.Z.flatten(), GP._get_params(self)])
|
||||
|
||||
def _get_param_names(self):
|
||||
return sum([['iip_%i_%i'%(i,j) for j in range(self.Z.shape[1])] for i in range(self.Z.shape[0])],[]) + GP._get_param_names(self)
|
||||
return sum([['iip_%i_%i' % (i, j) for j in range(self.Z.shape[1])] for i in range(self.Z.shape[0])], []) + GP._get_param_names(self)
|
||||
|
||||
def update_likelihood_approximation(self):
|
||||
"""
|
||||
|
|
@ -205,9 +216,9 @@ class sparse_GP(GP):
|
|||
if self.has_uncertain_inputs:
|
||||
raise NotImplementedError, "EP approximation not implemented for uncertain inputs"
|
||||
else:
|
||||
self.likelihood.fit_DTC(self.Kmm,self.psi1)
|
||||
#self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
|
||||
self._set_params(self._get_params()) # update the GP
|
||||
self.likelihood.fit_DTC(self.Kmm, self.psi1)
|
||||
# self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
|
||||
self._set_params(self._get_params()) # update the GP
|
||||
|
||||
|
||||
def _log_likelihood_gradients(self):
|
||||
|
|
@ -217,13 +228,13 @@ class sparse_GP(GP):
|
|||
"""
|
||||
Compute and return the derivative of the log marginal likelihood wrt the parameters of the kernel
|
||||
"""
|
||||
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm,self.Z)
|
||||
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm, self.Z)
|
||||
if self.has_uncertain_inputs:
|
||||
dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z,self.X,self.X_variance)
|
||||
dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1.T,self.Z,self.X, self.X_variance)
|
||||
dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2, self.Z,self.X, self.X_variance)
|
||||
dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z, self.X, self.X_variance)
|
||||
dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1.T, self.Z, self.X, self.X_variance)
|
||||
dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2, self.Z, self.X, self.X_variance)
|
||||
else:
|
||||
dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1,self.Z,self.X)
|
||||
dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1, self.Z, self.X)
|
||||
dL_dtheta += self.kern.dKdiag_dtheta(self.dL_dpsi0, self.X)
|
||||
|
||||
return dL_dtheta
|
||||
|
|
@ -243,17 +254,17 @@ class sparse_GP(GP):
|
|||
def _raw_predict(self, Xnew, which_parts='all', full_cov=False):
|
||||
"""Internal helper function for making predictions, does not account for normalization"""
|
||||
|
||||
Bi,_ = linalg.lapack.flapack.dpotri(self.LB,lower=0) # WTH? this lower switch should be 1, but that doesn't work!
|
||||
Bi, _ = linalg.lapack.flapack.dpotri(self.LB, lower=0) # WTH? this lower switch should be 1, but that doesn't work!
|
||||
symmetrify(Bi)
|
||||
Kmmi_LmiBLmi = backsub_both_sides(self.Lm,np.eye(self.M) - Bi)
|
||||
Kmmi_LmiBLmi = backsub_both_sides(self.Lm, np.eye(self.M) - Bi)
|
||||
|
||||
Kx = self.kern.K(self.Z, Xnew, which_parts=which_parts)
|
||||
mu = np.dot(Kx.T, self.Cpsi1V/self.scale_factor)
|
||||
mu = np.dot(Kx.T, self.Cpsi1V / self.scale_factor)
|
||||
if full_cov:
|
||||
Kxx = self.kern.K(Xnew,which_parts=which_parts)
|
||||
var = Kxx - mdot(Kx.T, Kmmi_LmiBLmi, Kx) #NOTE this won't work for plotting
|
||||
Kxx = self.kern.K(Xnew, which_parts=which_parts)
|
||||
var = Kxx - mdot(Kx.T, Kmmi_LmiBLmi, Kx) # NOTE this won't work for plotting
|
||||
else:
|
||||
Kxx = self.kern.Kdiag(Xnew,which_parts=which_parts)
|
||||
var = Kxx - np.sum(Kx*np.dot(Kmmi_LmiBLmi, Kx),0)
|
||||
Kxx = self.kern.Kdiag(Xnew, which_parts=which_parts)
|
||||
var = Kxx - np.sum(Kx * np.dot(Kmmi_LmiBLmi, Kx), 0)
|
||||
|
||||
return mu,var[:,None]
|
||||
return mu, var[:, None]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue