def likelihood_UB(hyp):
X = ModelInfo["X_batch"]
y = ModelInfo["y_batch"]
Z = ModelInfo["Z"]
m = ModelInfo["m"]
S = ModelInfo["S"]
jitter = ModelInfo["jitter"]
jitter_cov = ModelInfo["jitter_cov"]
N = X.shape[0]
M = Z.shape[0]
logsigma_n = hyp[-1]
sigma_n = np.exp(logsigma_n)
# Compute K_u_inv
K_u = kernel(Z, Z, hyp[:-1])
K_u_inv = np.linalg.solve(K_u + np.eye(M) * jitter_cov, np.eye(M))
# L = np.linalg.cholesky(K_u + np.eye(M)*jitter_cov)
# K_u_inv = np.linalg.solve(np.transpose(L), np.linalg.solve(L,np.eye(M)))
ModelInfo.update({"K_u_inv": K_u_inv})
# Compute mu
psi = kernel(Z, X, hyp[:-1])
K_u_inv_m = np.matmul(K_u_inv, m)
MU = np.matmul(psi.T, K_u_inv_m)
# Compute cov
Alpha = np.matmul(K_u_inv, psi)
COV = kernel(X, X, hyp[:-1]) - np.matmul(psi.T, np.matmul(K_u_inv,psi)) + \
np.matmul(Alpha.T, np.matmul(S,Alpha))
COV_inv = np.linalg.solve(COV + np.eye(N) * sigma_n + np.eye(N) * jitter,
np.eye(N))
# L = np.linalg.cholesky(COV + np.eye(N)*sigma_n + np.eye(N)*jitter)
# COV_inv = np.linalg.solve(np.transpose(L), np.linalg.solve(L,np.eye(N)))
# Compute cov(Z, X)
cov_ZX = np.matmul(S, Alpha)
# Update m and S
alpha = np.matmul(COV_inv, cov_ZX.T)
m = m + np.matmul(cov_ZX, np.matmul(COV_inv, y - MU))
S = S - np.matmul(cov_ZX, alpha)
ModelInfo.update({"m": m})
ModelInfo.update({"S": S})
# Compute NLML
Beta = y - MU
NLML_1 = np.matmul(Beta.T, Beta) / (2.0 * sigma_n * N)
NLML_2 = np.trace(COV) / (2.0 * sigma_n)
NLML_3 = N * logsigma_n / 2.0 + N * np.log(2.0 * np.pi) / 2.0
NLML = NLML_1 + NLML_2 + NLML_3
return NLML[0, 0]
def predict(X_star):
Z = ModelInfo["Z"]
m = ModelInfo["m"].value
S = ModelInfo["S"].value
hyp = ModelInfo["hyp"]
K_u_inv = ModelInfo["K_u_inv"]
N_star = X_star.shape[0]
partitions_size = 10000
(number_of_partitions,
remainder_partition) = divmod(N_star, partitions_size)
mean_star = np.zeros((N_star, 1))
var_star = np.zeros((N_star, 1))
for partition in range(0, number_of_partitions):
print("Predicting partition: %d" % (partition))
idx_1 = partition * partitions_size
idx_2 = (partition + 1) * partitions_size
# Compute mu
psi = kernel(Z, X_star[idx_1:idx_2, :], hyp[:-1])
K_u_inv_m = np.matmul(K_u_inv, m)
mu = np.matmul(psi.T, K_u_inv_m)
mean_star[idx_1:idx_2, 0:1] = mu
# Compute cov
Alpha = np.matmul(K_u_inv, psi)
cov = kernel(X_star[idx_1:idx_2,:], X_star[idx_1:idx_2,:], hyp[:-1]) - \
np.matmul(psi.T, np.matmul(K_u_inv,psi)) + np.matmul(Alpha.T, np.matmul(S,Alpha))
var = np.abs(np.diag(cov)) + np.exp(hyp[-1])
var_star[idx_1:idx_2, 0] = var
print("Predicting the last partition")
idx_1 = number_of_partitions * partitions_size
idx_2 = number_of_partitions * partitions_size + remainder_partition
# Compute mu
psi = kernel(Z, X_star[idx_1:idx_2, :], hyp[:-1])
K_u_inv_m = np.matmul(K_u_inv, m)
mu = np.matmul(psi.T, K_u_inv_m)
mean_star[idx_1:idx_2, 0:1] = mu
# Compute cov
Alpha = np.matmul(K_u_inv, psi)
cov = kernel(X_star[idx_1:idx_2,:], X_star[idx_1:idx_2,:], hyp[:-1]) - \
np.matmul(psi.T, np.matmul(K_u_inv,psi)) + np.matmul(Alpha.T, np.matmul(S,Alpha))
var = np.abs(np.diag(cov)) + np.exp(hyp[-1])
var_star[idx_1:idx_2, 0] = var
return mean_star, var_star
def predict(self, X_star):
Z = self.sess.run(self.Z)
m = self.sess.run(self.m)
S = self.sess.run(self.S)
hyp = self.hyp
K_u_inv = self.sess.run(self.K_u_inv)
N_star = X_star.shape[0]
partitions_size = 10000
(number_of_partitions,
remainder_partition) = divmod(N_star, partitions_size)
mean_star = np.zeros((N_star, 1))
var_star = np.zeros((N_star, 1))
for partition in range(0, number_of_partitions):
print("Predicting partition: %d" % (partition))
idx_1 = partition * partitions_size
idx_2 = (partition + 1) * partitions_size
# Compute mu
psi = kernel(Z, X_star[idx_1:idx_2, :], hyp[:-1])
K_u_inv_m = np.matmul(K_u_inv, m)
mu = np.matmul(psi.T, K_u_inv_m)
mean_star[idx_1:idx_2, 0:1] = mu
# Compute cov
Alpha = np.matmul(K_u_inv, psi)
cov = kernel(X_star[idx_1:idx_2,:], X_star[idx_1:idx_2,:], hyp[:-1]) - \
np.matmul(psi.T, np.matmul(K_u_inv,psi)) + np.matmul(Alpha.T, np.matmul(S,Alpha))
var = np.abs(np.diag(cov)) # + np.exp(hyp[-1])
var_star[idx_1:idx_2, 0] = var
print("Predicting the last partition")
idx_1 = number_of_partitions * partitions_size
idx_2 = number_of_partitions * partitions_size + remainder_partition
# Compute mu
psi = kernel(Z, X_star[idx_1:idx_2, :], hyp[:-1])
K_u_inv_m = np.matmul(K_u_inv, m)
mu = np.matmul(psi.T, K_u_inv_m)
mean_star[idx_1:idx_2, 0:1] = mu
# Compute cov
Alpha = np.matmul(K_u_inv, psi)
cov = kernel(X_star[idx_1:idx_2,:], X_star[idx_1:idx_2,:], hyp[:-1]) - \
np.matmul(psi.T, np.matmul(K_u_inv,psi)) + np.matmul(Alpha.T, np.matmul(S,Alpha))
var = np.abs(np.diag(cov)) # + np.exp(hyp[-1])
var_star[idx_1:idx_2, 0] = var
return mean_star, var_star
def likelihood(self, hyp):
M = self.M
Z = self.Z
m = self.m
S = self.S
X_batch = self.X_batch
y_batch = self.y_batch
jitter = self.jitter
jitter_cov = self.jitter_cov
N = X_batch.shape[0]
logsigma_n = hyp[-1]
sigma_n = np.exp(logsigma_n)
# Compute K_u_inv
K_u = kernel(Z, Z, hyp[:-1])
# K_u_inv = np.linalg.solve(K_u + np.eye(M)*jitter_cov, np.eye(M))
L = np.linalg.cholesky(K_u + np.eye(M) * jitter_cov)
K_u_inv = np.linalg.solve(L.T, np.linalg.solve(L, np.eye(M)))
self.K_u_inv = K_u_inv
# Compute mu
psi = kernel(Z, X_batch, hyp[:-1])
K_u_inv_m = np.matmul(K_u_inv, m)
MU = np.matmul(psi.T, K_u_inv_m)
# Compute cov
Alpha = np.matmul(K_u_inv, psi)
COV = kernel(X_batch, X_batch, hyp[:-1]) - np.matmul(psi.T, np.matmul(K_u_inv,psi)) + \
np.matmul(Alpha.T, np.matmul(S,Alpha))
COV_inv = np.linalg.solve(
COV + np.eye(N) * sigma_n + np.eye(N) * jitter, np.eye(N))
# L = np.linalg.cholesky(COV + np.eye(N)*sigma_n + np.eye(N)*jitter)
# COV_inv = np.linalg.solve(np.transpose(L), np.linalg.solve(L,np.eye(N)))
# Compute cov(Z, X)
cov_ZX = np.matmul(S, Alpha)
# Update m and S
alpha = np.matmul(COV_inv, cov_ZX.T)
m = m + np.matmul(cov_ZX, np.matmul(COV_inv, y_batch - MU))
S = S - np.matmul(cov_ZX, alpha)
self.m = m
self.S = S
# Compute NLML
K_u_inv_m = np.matmul(K_u_inv, m)
NLML = 0.5 * np.matmul(m.T, K_u_inv_m) + np.sum(np.log(
np.diag(L))) + 0.5 * np.log(2. * np.pi) * M
return NLML[0, 0]
def __init__(self,
X,
y,
M=10,
max_iter=2000,
N_batch=1,
monitor_likelihood=10,
lrate=1e-3):
(N, D) = X.shape
# kmeans on a subset of data
N_subset = min(N, 10000)
idx = np.random.choice(N, N_subset, replace=False)
kmeans = KMeans(n_clusters=M, random_state=0).fit(X[idx, :])
Z = kmeans.cluster_centers_
hyp = np.log(np.ones(D + 1))
logsigma_n = np.array([-4.0])
hyp = np.concatenate([hyp, logsigma_n])
m = np.zeros((M, 1))
S = kernel(Z, Z, hyp[:-1])
self.X = X
self.y = y
self.M = M
self.Z = tf.Variable(Z, dtype=tf.float64, trainable=False)
self.K_u_inv = tf.Variable(np.eye(M),
dtype=tf.float64,
trainable=False)
self.m = tf.Variable(m, dtype=tf.float64, trainable=False)
self.S = tf.Variable(S, dtype=tf.float64, trainable=False)
self.nlml = tf.Variable(0.0, dtype=tf.float64, trainable=False)
self.hyp = hyp
self.max_iter = max_iter
self.N_batch = N_batch
self.monitor_likelihood = monitor_likelihood
self.jitter = 1e-8
self.jitter_cov = 1e-8
self.lrate = lrate
self.optimizer = tf.train.AdamOptimizer(self.lrate)
# Tensor Flow Session
# self.sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
self.sess = tf.Session()
def init_params():
X = ModelInfo["X"]
M = ModelInfo["M"]
(N, D) = X.shape
idx = np.random.permutation(N)
N_subset = min(N, 10000)
kmeans = KMeans(n_clusters=M, random_state=0).fit(X[idx[0:N_subset], :])
Z = kmeans.cluster_centers_
hyp = np.log(np.ones(D + 1))
logsigma_n = np.array([-4.0])
hyp = np.concatenate([hyp, logsigma_n])
m = np.zeros((M, 1))
S = kernel(Z, Z, hyp[:-1])
ModelInfo.update({"hyp": hyp})
ModelInfo.update({"Z": Z})
ModelInfo.update({"m": m})
ModelInfo.update({"S": S})
def __init__(self,
X,
y,
M=10,
max_iter=2000,
N_batch=1,
monitor_likelihood=10,
lrate=1e-3):
(N, D) = X.shape
N_subset = min(N, 10000)
idx = np.random.choice(N, N_subset, replace=False)
kmeans = KMeans(n_clusters=M, random_state=0).fit(X[idx, :])
Z = kmeans.cluster_centers_
hyp = np.log(np.ones(D + 1))
logsigma_n = np.array([-4.0])
hyp = np.concatenate([hyp, logsigma_n])
m = np.zeros((M, 1))
S = kernel(Z, Z, hyp[:-1])
self.X = X
self.y = y
self.M = M
self.Z = Z
self.m = m
self.S = S
self.hyp = hyp
self.max_iter = max_iter
self.N_batch = N_batch
self.monitor_likelihood = monitor_likelihood
self.jitter = 1e-8
self.jitter_cov = 1e-8
# Adam optimizer parameters
self.mt_hyp = np.zeros(hyp.shape)
self.vt_hyp = np.zeros(hyp.shape)
self.lrate = lrate
