apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-18 13:55:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

some tidying in the EP likelihood

Browse source 
Changes self.N to self.num_data for consistency with everywhere else
added the factor of 2pi to Z.
This commit is contained in:
James Hensman 2013-09-27 17:39:38 +01:00
parent 0dc987de15
commit 00cbb5f742
1 changed files with 47 additions and 46 deletions
Download patch file Download diff file Expand all files Collapse all files

93
GPy/likelihoods/ep.py
View file

@ -16,20 +16,20 @@ class EP(likelihood):
""" """
self.noise_model = noise_model self.noise_model = noise_model
self.data = data self.data = data
self.N, self.output_dim = self.data.shape self.num_data, self.output_dim = self.data.shape
self.is_heteroscedastic = True self.is_heteroscedastic = True
self.Nparams = 0 self.Nparams = 0
self._transf_data = self.noise_model._preprocess_values(data) self._transf_data = self.noise_model._preprocess_values(data)
#Initial values - Likelihood approximation parameters: #Initial values - Likelihood approximation parameters:
#p(y|f) = t(f|tau_tilde,v_tilde) #p(y|f) = t(f|tau_tilde,v_tilde)
self.tau_tilde = np.zeros(self.N) self.tau_tilde = np.zeros(self.num_data)
self.v_tilde = np.zeros(self.N) self.v_tilde = np.zeros(self.num_data)
#initial values for the GP variables #initial values for the GP variables
self.Y = np.zeros((self.N,1)) self.Y = np.zeros((self.num_data,1))
self.covariance_matrix = np.eye(self.N) self.covariance_matrix = np.eye(self.num_data)
self.precision = np.ones(self.N)[:,None] self.precision = np.ones(self.num_data)[:,None]
self.Z = 0 self.Z = 0
self.YYT = None self.YYT = None
self.V = self.precision * self.Y self.V = self.precision * self.Y
@ -39,11 +39,11 @@ class EP(likelihood):
super(EP, self).__init__() super(EP, self).__init__()
def restart(self): def restart(self):
self.tau_tilde = np.zeros(self.N) self.tau_tilde = np.zeros(self.num_data)
self.v_tilde = np.zeros(self.N) self.v_tilde = np.zeros(self.num_data)
self.Y = np.zeros((self.N,1)) self.Y = np.zeros((self.num_data,1))
self.covariance_matrix = np.eye(self.N) self.covariance_matrix = np.eye(self.num_data)
self.precision = np.ones(self.N)[:,None] self.precision = np.ones(self.num_data)[:,None]
self.Z = 0 self.Z = 0
self.YYT = None self.YYT = None
self.V = self.precision * self.Y self.V = self.precision * self.Y
@ -77,6 +77,7 @@ class EP(likelihood):
sigma_sum = 1./self.tau_ + 1./self.tau_tilde sigma_sum = 1./self.tau_ + 1./self.tau_tilde
mu_diff_2 = (self.v_/self.tau_ - mu_tilde)**2 mu_diff_2 = (self.v_/self.tau_ - mu_tilde)**2
self.Z = np.sum(np.log(self.Z_hat)) + 0.5*np.sum(np.log(sigma_sum)) + 0.5*np.sum(mu_diff_2/sigma_sum) #Normalization constant, aka Z_ep self.Z = np.sum(np.log(self.Z_hat)) + 0.5*np.sum(np.log(sigma_sum)) + 0.5*np.sum(mu_diff_2/sigma_sum) #Normalization constant, aka Z_ep
self.Z += 0.5*self.num_data*np.log(2*np.pi)
self.Y = mu_tilde[:,None] self.Y = mu_tilde[:,None]
self.YYT = np.dot(self.Y,self.Y.T) self.YYT = np.dot(self.Y,self.Y.T)
@ -101,7 +102,7 @@ class EP(likelihood):
self.eta, self.delta = power_ep self.eta, self.delta = power_ep
#Initial values - Posterior distribution parameters: q(f|X,Y) = N(f|mu,Sigma) #Initial values - Posterior distribution parameters: q(f|X,Y) = N(f|mu,Sigma)
mu = np.zeros(self.N) mu = np.zeros(self.num_data)
Sigma = K.copy() Sigma = K.copy()
""" """
@ -110,15 +111,15 @@ class EP(likelihood):
sigma_ = 1./tau_ sigma_ = 1./tau_
mu_ = v_/tau_ mu_ = v_/tau_
""" """
self.tau_ = np.empty(self.N,dtype=float) self.tau_ = np.empty(self.num_data,dtype=float)
self.v_ = np.empty(self.N,dtype=float) self.v_ = np.empty(self.num_data,dtype=float)
#Initial values - Marginal moments #Initial values - Marginal moments
z = np.empty(self.N,dtype=float) z = np.empty(self.num_data,dtype=float)
self.Z_hat = np.empty(self.N,dtype=float) self.Z_hat = np.empty(self.num_data,dtype=float)
phi = np.empty(self.N,dtype=float) phi = np.empty(self.num_data,dtype=float)
mu_hat = np.empty(self.N,dtype=float) mu_hat = np.empty(self.num_data,dtype=float)
sigma2_hat = np.empty(self.N,dtype=float) sigma2_hat = np.empty(self.num_data,dtype=float)
#Approximation #Approximation
epsilon_np1 = self.epsilon + 1. epsilon_np1 = self.epsilon + 1.
@ -127,7 +128,7 @@ class EP(likelihood):
self.np1 = [self.tau_tilde.copy()] self.np1 = [self.tau_tilde.copy()]
self.np2 = [self.v_tilde.copy()] self.np2 = [self.v_tilde.copy()]
while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon: while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:
update_order = np.random.permutation(self.N) update_order = np.random.permutation(self.num_data)
for i in update_order: for i in update_order:
#Cavity distribution parameters #Cavity distribution parameters
self.tau_[i] = 1./Sigma[i,i] - self.eta*self.tau_tilde[i] self.tau_[i] = 1./Sigma[i,i] - self.eta*self.tau_tilde[i]
@ -145,13 +146,13 @@ class EP(likelihood):
self.iterations += 1 self.iterations += 1
#Sigma recomptutation with Cholesky decompositon #Sigma recomptutation with Cholesky decompositon
Sroot_tilde_K = np.sqrt(self.tau_tilde)[:,None]*K Sroot_tilde_K = np.sqrt(self.tau_tilde)[:,None]*K
B = np.eye(self.N) + np.sqrt(self.tau_tilde)[None,:]*Sroot_tilde_K B = np.eye(self.num_data) + np.sqrt(self.tau_tilde)[None,:]*Sroot_tilde_K
L = jitchol(B) L = jitchol(B)
V,info = dtrtrs(L,Sroot_tilde_K,lower=1) V,info = dtrtrs(L,Sroot_tilde_K,lower=1)
Sigma = K - np.dot(V.T,V) Sigma = K - np.dot(V.T,V)
mu = np.dot(Sigma,self.v_tilde) mu = np.dot(Sigma,self.v_tilde)
epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.N epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.num_data
epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.N epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.num_data
self.np1.append(self.tau_tilde.copy()) self.np1.append(self.tau_tilde.copy())
self.np2.append(self.v_tilde.copy()) self.np2.append(self.v_tilde.copy())
@ -199,7 +200,7 @@ class EP(likelihood):
Sigma = Diag + P*R.T*R*P.T + K Sigma = Diag + P*R.T*R*P.T + K
mu = w + P*Gamma mu = w + P*Gamma
""" """
mu = np.zeros(self.N) mu = np.zeros(self.num_data)
LLT = Kmm.copy() LLT = Kmm.copy()
Sigma_diag = Qnn_diag.copy() Sigma_diag = Qnn_diag.copy()
@ -209,15 +210,15 @@ class EP(likelihood):
sigma_ = 1./tau_ sigma_ = 1./tau_
mu_ = v_/tau_ mu_ = v_/tau_
""" """
self.tau_ = np.empty(self.N,dtype=float) self.tau_ = np.empty(self.num_data,dtype=float)
self.v_ = np.empty(self.N,dtype=float) self.v_ = np.empty(self.num_data,dtype=float)
#Initial values - Marginal moments #Initial values - Marginal moments
z = np.empty(self.N,dtype=float) z = np.empty(self.num_data,dtype=float)
self.Z_hat = np.empty(self.N,dtype=float) self.Z_hat = np.empty(self.num_data,dtype=float)
phi = np.empty(self.N,dtype=float) phi = np.empty(self.num_data,dtype=float)
mu_hat = np.empty(self.N,dtype=float) mu_hat = np.empty(self.num_data,dtype=float)
sigma2_hat = np.empty(self.N,dtype=float) sigma2_hat = np.empty(self.num_data,dtype=float)
#Approximation #Approximation
epsilon_np1 = 1 epsilon_np1 = 1
@ -226,7 +227,7 @@ class EP(likelihood):
np1 = [self.tau_tilde.copy()] np1 = [self.tau_tilde.copy()]
np2 = [self.v_tilde.copy()] np2 = [self.v_tilde.copy()]
while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon: while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:
update_order = np.random.permutation(self.N) update_order = np.random.permutation(self.num_data)
for i in update_order: for i in update_order:
#Cavity distribution parameters #Cavity distribution parameters
self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i] self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]
@ -255,8 +256,8 @@ class EP(likelihood):
Sigma_diag = np.sum(V*V,-2) Sigma_diag = np.sum(V*V,-2)
Knmv_tilde = np.dot(Kmn,self.v_tilde) Knmv_tilde = np.dot(Kmn,self.v_tilde)
mu = np.dot(V2.T,Knmv_tilde) mu = np.dot(V2.T,Knmv_tilde)
epsilon_np1 = sum((self.tau_tilde-np1[-1])**2)/self.N epsilon_np1 = sum((self.tau_tilde-np1[-1])**2)/self.num_data
epsilon_np2 = sum((self.v_tilde-np2[-1])**2)/self.N epsilon_np2 = sum((self.v_tilde-np2[-1])**2)/self.num_data
np1.append(self.tau_tilde.copy()) np1.append(self.tau_tilde.copy())
np2.append(self.v_tilde.copy()) np2.append(self.v_tilde.copy())
@ -297,9 +298,9 @@ class EP(likelihood):
Sigma = Diag + P*R.T*R*P.T + K Sigma = Diag + P*R.T*R*P.T + K
mu = w + P*Gamma mu = w + P*Gamma
""" """
self.w = np.zeros(self.N) self.w = np.zeros(self.num_data)
self.Gamma = np.zeros(num_inducing) self.Gamma = np.zeros(num_inducing)
mu = np.zeros(self.N) mu = np.zeros(self.num_data)
P = P0.copy() P = P0.copy()
R = R0.copy() R = R0.copy()
Diag = Diag0.copy() Diag = Diag0.copy()
@ -312,15 +313,15 @@ class EP(likelihood):
sigma_ = 1./tau_ sigma_ = 1./tau_
mu_ = v_/tau_ mu_ = v_/tau_
""" """
self.tau_ = np.empty(self.N,dtype=float) self.tau_ = np.empty(self.num_data,dtype=float)
self.v_ = np.empty(self.N,dtype=float) self.v_ = np.empty(self.num_data,dtype=float)
#Initial values - Marginal moments #Initial values - Marginal moments
z = np.empty(self.N,dtype=float) z = np.empty(self.num_data,dtype=float)
self.Z_hat = np.empty(self.N,dtype=float) self.Z_hat = np.empty(self.num_data,dtype=float)
phi = np.empty(self.N,dtype=float) phi = np.empty(self.num_data,dtype=float)
mu_hat = np.empty(self.N,dtype=float) mu_hat = np.empty(self.num_data,dtype=float)
sigma2_hat = np.empty(self.N,dtype=float) sigma2_hat = np.empty(self.num_data,dtype=float)
#Approximation #Approximation
epsilon_np1 = 1 epsilon_np1 = 1
@ -329,7 +330,7 @@ class EP(likelihood):
self.np1 = [self.tau_tilde.copy()] self.np1 = [self.tau_tilde.copy()]
self.np2 = [self.v_tilde.copy()] self.np2 = [self.v_tilde.copy()]
while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon: while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:
update_order = np.random.permutation(self.N) update_order = np.random.permutation(self.num_data)
for i in update_order: for i in update_order:
#Cavity distribution parameters #Cavity distribution parameters
self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i] self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]
@ -368,8 +369,8 @@ class EP(likelihood):
self.w = Diag * self.v_tilde self.w = Diag * self.v_tilde
self.Gamma = np.dot(R.T, np.dot(RPT,self.v_tilde)) self.Gamma = np.dot(R.T, np.dot(RPT,self.v_tilde))
mu = self.w + np.dot(P,self.Gamma) mu = self.w + np.dot(P,self.Gamma)
epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.N epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.num_data
epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.N epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.num_data
self.np1.append(self.tau_tilde.copy()) self.np1.append(self.tau_tilde.copy())
self.np2.append(self.v_tilde.copy()) self.np2.append(self.v_tilde.copy())