def predict(self, Xnew: torch.tensor, Xtrain: torch.tensor,
ytrain: torch.tensor):
"""
Inputs:
:Xnew: n_newsamples * n_features
:Xtrain: n_samples * n_features
:ytrain: n_samples / n_samples * n_output
Returns:
A set of 1d normal distributions, their mean/var are scalar tensors
"""
device = Xnew.device
Xnew, Xtrain = self.transformer(Xnew), self.transformer(Xtrain)
K = self.to_matrix(self.kernel(Xtrain))
Kprime = K + self.alpha * torch.eye(K.shape[0], device=device)
L = torch.cholesky(Kprime)
kXnew = self.to_matrix(self.kernel(Xnew, Xtrain))
ytrain = ytrain.flatten()
ytrain_minus_mean = ytrain - self.mean(Xtrain)
# logging.debug(self.mean)
mean_ynew = self.mean(Xnew) + torch.squeeze(torch.matmul(
kXnew, torch.cholesky_solve(ytrain_minus_mean[:, None], L)),
dim=-1)
logging.debug("mean={}".format(mean_ynew))
# LL^T _x = kXnew^T
_x = torch.cholesky_solve(kXnew.T, L)
kXnewXnew = self.to_matrix(self.kernel(Xnew))
std_ynew = torch.sqrt(
torch.diag(kXnewXnew) - torch.einsum("ij,ji->i", kXnew, _x))
logging.debug("var={}".format(std_ynew))
return [Normal(mu, sigma) for mu, sigma in zip(mean_ynew, std_ynew)]
def _solve_linear_system(self, A, B):
"""Solves linear system AX = B.
If B is a tuple (B1, B2, ...), returns tuple (X1, X2, ...).
Otherwise returns X.
"""
B_sizes = None
# If B is a tuple, concatenate into single tensor:
if isinstance(B, (tuple, list)):
B_sizes = list(map(lambda x: x.size(-1), B))
B = torch.cat(B, dim=-1)
# Ensure B is 2D (bxmxn):
if len(B.size()) == 2:
B = B.unsqueeze(-1)
try: # Batchwise Cholesky solve
A_decomp = torch.cholesky(A, upper=False)
X = torch.cholesky_solve(B, A_decomp, upper=False) # bxmxn
except: # Revert to loop if batchwise solve fails
X = torch.zeros_like(B)
for i in range(A.size(0)):
try: # Cholesky solve
A_decomp = torch.cholesky(A[i, ...], upper=False)
X[i, ...] = torch.cholesky_solve(B[i, ...], A_decomp,
upper=False) # mxn
except: # Revert to LU solve
X[i, ...], _ = torch.solve(B[i, ...], A[i, ...]) # mxn
if B_sizes is not None:
X = X.split(B_sizes, dim=-1)
return X
def predict(self, Z, full=False, tensor=False):
with torch.no_grad():
Z = self._check_input(Z) # MxD
K = self.kernel(self.X) + self.noise() * self.eye # NxN
Ks = self.kernel(self.X, Z) # NxM
Kss = self.kernel(Z) + self.noise() * torch.eye(
Z.shape[0], device=config.device, dtype=config.dtype) # MxM
L = self._cholesky(K) # NxN
v = torch.triangular_solve(Ks, L, upper=False)[0] # NxM
if self.mean is not None:
y = self.y - self.mean(self.X).reshape(-1, 1) # Nx1
mu = Ks.T.mm(torch.cholesky_solve(y, L)) # Mx1
mu += self.mean(Z).reshape(-1, 1) # Mx1
else:
mu = Ks.T.mm(torch.cholesky_solve(self.y, L)) # Mx1
var = Kss - v.T.mm(v) # MxM
if not full:
var = var.diag().reshape(-1, 1) # Mx1
if tensor:
return mu, var
else:
return mu.cpu().numpy(), var.cpu().numpy()
def __computeMeansAndVarsGivenKernelMatrices(self, Kzz, KzzChol, Ktz,
KttDiag):
nTrials = KttDiag.shape[0]
nQuad = KttDiag.shape[1]
nLatent = KttDiag.shape[2]
qKMu = torch.empty((nTrials, nQuad, nLatent),
dtype=Kzz[0].dtype,
device=Kzz[0].device)
qKVar = torch.empty((nTrials, nQuad, nLatent),
dtype=Kzz[0].dtype,
device=Kzz[0].device)
qSigma = self._svPosteriorOnIndPoints.buildQSigma()
for k in range(len(self._svPosteriorOnIndPoints.getQMu())):
# Ak \in nTrials x nInd[k] x 1
Ak = torch.cholesky_solve(self._svPosteriorOnIndPoints.getQMu()[k],
KzzChol[k])
# qKMu \in nTrial x nQuad x nLatent
qKMu[:, :, k] = torch.squeeze(torch.matmul(Ktz[k], Ak))
# Bkf \in nTrials x nInd[k] x nQuad
Bkf = torch.cholesky_solve(Ktz[k].transpose(dim0=1, dim1=2),
KzzChol[k])
# mm1f \in nTrials x nInd[k] x nQuad
mm1f = torch.matmul(qSigma[k] - Kzz[k], Bkf)
# aux1 \in nTrials x nInd[k] x nQuad
aux1 = Bkf * mm1f
# aux2 \in nTrials x nQuad
aux2 = torch.sum(input=aux1, dim=1)
# aux3 \in nTrials x nQuad
aux3 = KttDiag[:, :, k] + aux2
# qKVar \in nTrials x nQuad x nLatent
qKVar[:, :, k] = aux3
return qKMu, qKVar
def _inner_no_grad(self, x, u, v=None, *, keepdim=False):
l = torch.cholesky(x)
x_inv_u = torch.cholesky_solve(u, l)
if v is None:
x_inv_v = x_inv_u
else:
x_inv_v = torch.cholesky_solve(v, l)
return multitrace(torch.matmul(x_inv_u, x_inv_v), keepdim=keepdim)
def compute_damped_gn_update(jacobian, output_error, damping):
"""
Compute the damped Gauss-Newton update, based on the given jacobian and
output error.
Args:
jacobian (torch.Tensor): 2D tensor containing the Jacobian of the
flattened output with respect to the flattened parameters for which
the GN update is computed.
output_error (torch.Tensor): tensor containing the gradient of the loss
with respect to the output layer of the network.
damping (float): positive damping hyperparameter
Returns: the damped Gauss-Newton update for the parameters for which the
jacobian was computed.
"""
if damping < 0:
raise ValueError('Positive value for damping expected, got '
'{}'.format(damping))
# The jacobian also flattens the output dimension, so we need to do
# the same.
output_error = output_error.view(-1, 1).detach()
if damping == 0:
# if the damping is 0, the curvature matrix C=J^TJ can be
# rank deficit. Therefore, it is numerically best to compute the
# pseudo inverse explicitly and after that multiply with it.
jacobian_pinv = torch.pinverse(jacobian)
gn_updates = jacobian_pinv.mm(output_error)
else:
# If damping is greater than 0, the curvature matrix C will be
# positive definite and symmetric. Numerically, it is the most
# efficient to use the cholesky decomposition to compute the
# resulting Gauss-newton updates
# As (J^T*J + l*I)^{-1}*J^T = J^T*(JJ^T + l*I)^{-1}, we select
# the one which is most computationally efficient, depending on
# the number of rows and columns of J (we want to take the inverse
# of the smallest possible matrix, as this is the most expensive
# operation. Note that we solve a linear system with cholesky
# instead of explicitly computing the inverse, as this is more
# efficient.
if jacobian.shape[0] >= jacobian.shape[1]:
G = jacobian.t().mm(jacobian)
C = G + damping * torch.eye(G.shape[0])
C_cholesky = torch.cholesky(C)
jacobian_error = jacobian.t().matmul(output_error)
gn_updates = torch.cholesky_solve(jacobian_error, C_cholesky)
else:
G = jacobian.mm(jacobian.t())
C = G + damping * torch.eye(G.shape[0])
C_cholesky = torch.cholesky(C)
inverse_error = torch.cholesky_solve(output_error, C_cholesky)
gn_updates = jacobian.t().matmul(inverse_error)
return gn_updates
def __call__(self, A, b):
l = self.cache.get(id(A))
if l is None:
l = robust_cholesky(A)
self.cache[id(A)] = l
if b.ndim == 1:
return torch.cholesky_solve(b.unsqueeze(-1), l).squeeze(-1)
else:
return torch.cholesky_solve(b, l)
def inner(self, x, u, v, keepdim=False):
# FIXME(ccruceru): This is not currently differentiable.
assert not x.requires_grad and \
not u.requires_grad and \
not v.requires_grad
l = self.chol(x)
x_inv_u = torch.cholesky_solve(u, l)
x_inv_v = torch.cholesky_solve(v, l)
return tb.trace(x_inv_u @ x_inv_v, keepdim=keepdim)
def test_cholesky_solve(batch_shape, size):
b = torch.randn(batch_shape + (size, 5))
x = torch.randn(batch_shape + (size, size))
x = x.transpose(-1, -2).matmul(x)
u = x.cholesky()
expected = torch.cholesky_solve(b, u)
assert not expected.requires_grad
actual = torch.cholesky_solve(b.requires_grad_(), u.requires_grad_())
assert actual.requires_grad
assert_close(expected, actual)
def forward(ctx, mu0, mu1, cho0, cho1):
"""
calculate of two gaussian. Here gaussian represent as (mu, chokesky),
here var be represent as cholesky^T cholesky, heew cholesky is upper triangular matrix
:param ctx: holder
:param mu0: mu for gaussian 1
:param mu1: mu for gaussian 2
:param cho0: cholesky matrix for gaussian 1
:param cho1: cholesky matrix for gaussian 2
:return: scale: the scale of two gaussian multiply
:return: mu_new: the mu for the new gaussian
:return: cho_new: the chokesky matrix of the new gaussian
"""
dim = mu0.size(-1)
# inverse
# TODO here inverse calculation can use numpy or pytorch, here we conside torch first.
# lambda0 = np.linalg.inv(var0)
# lambda1 = np.linalg.inv(var1)
eye = torch.eye(dim).unsqueeze_(0).double()
lambda0 = torch.cholesky_solve(cho0, eye, upper=True)
lambda1 = torch.cholesky_solve(cho1, eye, upper=True)
mu0 = mu0.unsqueeze(-1)
mu1 = mu1.unsqueeze(-1)
eta0 = torch.matmul(lambda0, mu0)
eta1 = torch.matmul(lambda1, mu1)
# calculate zeta
diag0 = torch.diagonal(cho0, dim1=1, dim2=2)
diag1 = torch.diagonal(cho1, dim1=1, dim2=2)
zeta0 = -0.5 * (dim * np.log(np.pi * 2) -
torch.sum(torch.log(diag0 * diag0), dim=-1) +
mu0.transpose(1, 2).matmul(eta0).reshape(-1))
zeta1 = -0.5 * (dim * np.log(np.pi * 2) -
torch.sum(torch.log(diag1 * diag1), dim=-1) +
mu1.transpose(1, 2).matmul(eta1).reshape(-1))
lambda_new = lambda0 + lambda1
eta_new = eta0 + eta1
var_new = torch.inverse(lambda_new)
cho_new = torch.cholesky(var_new, upper=True)
mu_new = torch.matmul(var_new, eta_new)
diag_new = torch.diagonal(cho_new, dim1=1, dim2=2)
zeta_new = -0.5 * (dim * np.log(np.pi * 2) -
torch.sum(torch.log(diag_new * diag_new), dim=-1) +
mu_new.transpose(1, 2).matmul(eta_new).reshape(-1))
scale = zeta0 + zeta1 - zeta_new
return scale, mu_new, cho_new
def _update_variational_moments(self, x, y):
C = self.current_C_matrix(x)
c = self.current_c_vec(x, y)
z_b = self.variational_strategy.inducing_points
Kbb = self.covar_module(z_b).evaluate()
L = psd_safe_cholesky(Kbb + C.evaluate(),
upper=False,
jitter=self._jitter)
m_b = Kbb @ torch.cholesky_solve(c, L, upper=False)
S_b = Kbb @ torch.cholesky_solve(Kbb, L, upper=False)
return m_b, S_b
def predict(self, x: Tensor, y_dim: int = 1) -> Tuple[Tensor, Tensor]:
"""Predicts mean and covariance.
Args:
x (torch.Tensor): Input data for test, size
`(batch_size, num_points, x_dim)`.
y_dim (int, optional): Output y dim size for prior.
Returns:
y_mean (torch.Tensor): Predicted output, size
`(batch_size, num_points, y_dim)`.
y_cov (torch.Tensor): Covariance of the joint predictive
distribution at the sample points, size
`(batch_size, num_points, num_points)`.
"""
if x.dim() != 3:
raise ValueError("Dim of x should be 3 (batch_size, num_points, "
f"x_dim), but given {x.size()}.")
# Predict y|x based on GP prior
if self._x_train is None or self._y_train is None:
batch_size, num_points, _ = x.size()
y_mean = torch.zeros(batch_size, num_points, y_dim)
y_cov = self.gaussian_kernel(x, x)
return y_mean, y_cov
# Predict y*|x*, x, y based on GP posterior
# Shift mean of y_train to 0
y_mean = self._y_train.mean(dim=[0, 1])
y_train = self._y_train - y_mean
# Kernel
K_nn = self.gaussian_kernel(self._x_train, self._x_train)
K_xx = self.gaussian_kernel(x, x)
K_xn = self.gaussian_kernel(x, self._x_train)
# Solve cholesky for each y_dim
L_ = torch.cholesky(K_nn.double()).float()
alpha_ = torch.cholesky_solve(y_train, L_)
# Mean prediction with undoing normalization
y_mean = K_xn.matmul(alpha_) + y_mean
# Cov
v = torch.cholesky_solve(K_xn.transpose(1, 2), L_)
y_cov = K_xx - K_xn.matmul(v)
return y_mean, y_cov
def sample(self, times, regFactor=1e-3):
Kzz = self._indPointsLocsKMS.getKzz()
KzzChol = self._indPointsLocsKMS.getKzzChol()
indPointsLocsAndAllTimesKMS = IndPointsLocsAndAllTimesKMS()
indPointsLocsAndAllTimesKMS.setKernels(
kernels=self._indPointsLocsKMS.getKernels())
indPointsLocsAndAllTimesKMS.setIndPointsLocs(
indPointsLocs=self._indPointsLocsKMS.getIndPointsLocs())
indPointsLocsAndAllTimesKMS.setTimes(times=times)
indPointsLocsAndAllTimesKMS.buildKernelsMatrices()
indPointsLocsAndAllTimesKMS.buildKttKernelsMatrices()
Ktz = indPointsLocsAndAllTimesKMS.getKtz()
Ktt = indPointsLocsAndAllTimesKMS.getKtt()
qMu = self._svPosteriorOnIndPoints.getQMu()
qSigma = self._svPosteriorOnIndPoints.buildQSigma()
nLatents = len(Kzz)
nTrials = Kzz[0].shape[0]
samples = [[] for r in range(nTrials)]
for r in range(nTrials):
samples[r] = torch.empty((nLatents, Ktt[0].shape[1]),
dtype=Kzz[0].dtype)
for k in range(nLatents):
print("Processing trial {:d} and latent {:d}".format(r, k))
Kzzrk = Kzz[k][r, :, :]
KzzCholrk = KzzChol[k][r, :, :]
Ktzrk = Ktz[k][r, :, :]
Kttrk = Ktt[k][r, :, :]
qMurk = qMu[k][r, :, :]
qSigmark = qSigma[k][r, :, :]
### being compute mean ###
b = torch.cholesky_solve(qMurk, KzzCholrk)
meanrk = torch.squeeze(Ktzrk.matmul(b))
### end compute mean ###
### being compute covar ###
B = torch.cholesky_solve(torch.t(Ktzrk), KzzCholrk)
covarrk = Kttrk + torch.t(B).matmul(qSigmark - Kzzrk).matmul(B)
### end compute covar ###
covarrk += torch.eye(covarrk.shape[0]) * regFactor
covarrk = covarrk.detach()
mn = scipy.stats.multivariate_normal(mean=meanrk, cov=covarrk)
samples[r][k, :] = torch.from_numpy(mn.rvs())
return samples
def log_marginal(self, Y, gauss_mean, gauss_cov, **kwargs):
""" Computes the log marginal likelihood w.r.t the prior
log p(y|x) = -1/2 (Y-mu)' @ (K+sigma²I)^{-1} @ (Y-mu) - 1/2 \log |K+sigma^2I| - N/2 log(2pi)
Args:
`Y` (torch.tensor) :->: Observations Y with shape (Dy,MB)
`gauss_mean` (torch.tensor) :->: mean from p(f). Shape (Dy,MB)
`gauss_cov` (torch.tensor) :->: full covariance from p(f). Shape (Dy,MB,MB)
"""
N = Y.size(1)
Dy = self.out_dim
# compute mean and covariance from the marginal distribution p(y|x).
# This basically add the observation noise to the covariance
mx,Kxx = self.marginal_moments(gauss_mean,gauss_cov, diagonal = False)
# reshapes
mx = mx.view(Dy,N,1)
Y = Y.view(Dy,N,1)
# solve using cholesky
Y_mx = Y-mx
Lxx = psd_safe_cholesky(Kxx, upper = False, jitter = cg.global_jitter)
# Compute (Y-mu)' @ (K+sigma²I)^{-1} @ (Y-mu)
rhs = torch.cholesky_solve(Y_mx, Lxx, upper = False)
data_fit_term = torch.matmul(Y_mx.transpose(1,2),rhs)
complexity_term = 2*torch.log(torch.diagonal(Lxx, dim1 = 1, dim2 = 2)).sum(1)
cte = -N/2. * torch.log(2*cg.pi)
return -0.5*(data_fit_term + complexity_term ) + cte
def _update_weights(self):
"""
Method internally used to update GP weights' posterior mean
"""
self.weights_train = torch.cholesky_solve(
self.training_vec, self.training_mat,
upper=True) # not to be confused with BLR weights sample
def step(self, actions, shadow=False):
actions = torch.clamp(actions, 0, 1)
x = self.data[:, :self.nstep, :-1]
y = self.data[:, :self.nstep, -1]
K = exponential_kernel(
x.unsqueeze(2), x.unsqueeze(1), self.length_scale) + torch.eye(
self.nstep, dtype=torch.float, device=self.device) * 1e-5
#print(K)
# K = torch.exp(-1 / 2 * ((x.view(self.batch_size, self.nstep, 1) - x.view(self.batch_size, 1, self.nstep)) / 0.1) ** 2) + torch.eye(self.nstep, dtype=torch.float, device=self.device) * 1e-5
u = torch.cholesky(K)
k = exponential_kernel(x, actions.view(self.batch_size, 1, self.dims),
self.length_scale)
#print(k)
# k = torch.exp(-1 / 2 * ((x.view(self.batch_size, self.nstep) - actions.view(self.batch_size,1)) / 0.1) ** 2)
sol = torch.cholesky_solve(
torch.cat((k.view(self.batch_size, self.nstep,
1), y.view(self.batch_size, self.nstep, 1)),
dim=2), u)
#print(sol)
mav = torch.matmul(k.view(self.batch_size, 1, self.nstep),
sol).view(self.batch_size, 2) #check shapes!
#print(mav)
newy = torch.normal(mav[:, 1], 1 - mav[:, 0])
newbest = torch.max(self.best, newy)
reward = newbest - self.best
if not shadow:
self.best = newbest
self.data[:, self.nstep, :-1] = actions.view(self.batch_size,
self.dims)
self.data[:, self.nstep, -1] = newy
self.nstep = self.nstep + 1
return self.data[:, self.nstep - 1], reward.unsqueeze(1), mav
def forward(ctx, A, b):
u = torch.cholesky(torch.matmul(A.transpose(-1, -2), A), upper=True)
ret = torch.cholesky_solve(torch.matmul(A.transpose(-1, -2), b),
u,
upper=True)
ctx.save_for_backward(u, ret, A, b)
return ret
def manual_predict(params, train_x, train_y, test_x):
with torch.no_grad():
print('computing K and Kt...')
K = mnl_cov_with_noise(params, train_x)
Kt = manual_cov(params, test_x, train_x)
print('computing L...')
L = torch.cholesky(K)
alpha = torch.cholesky_solve(torch.t(torch.stack([train_y])), L)
print('computing mean f...')
f = torch.matmul(Kt, alpha)[:, 0]
print('computing predictive variances...')
def var(i):
xi = test_x[[i], :]
ki = torch.t(Kt[[i], :])
v, _cc = torch.triangular_solve(ki, L, upper=False)
ret = (manual_cov(params, xi, xi) -
torch.matmul(torch.t(v), v)).item()
return ret
vars = torch.tensor([var(i) for i in range(len(test_x))])
return (f, vars)
def _e_step(self, data):
X, noise_covars = data
T = self.covars[None, :, :, :] + noise_covars[:, None, :, :]
try:
T_chol = torch.cholesky(T)
except RuntimeError:
return torch.tensor(float('-inf')), None
T_inv = torch.cholesky_solve(torch.eye(self.d, device=self.device),
T_chol)
diff = X[:, None, :] - self.means
T_inv_diff = torch.matmul(T_inv, diff[:, :, :, None])
log_resps = -0.5 * (torch.matmul(diff[:, :, None, :], T_inv_diff) +
self.d * math.log(2 * math.pi)).squeeze()
log_resps -= T_chol.diagonal(dim1=-2, dim2=-1).log().sum(-1)
log_resps += torch.log(self.weights[None, :, 0])
cond_means = self.means + torch.matmul( # n, j, d
self.covars[None, :, :, :], # 1, j, d, d
T_inv_diff)[:, :, :, 0]
cond_covars = self.covars - torch.matmul( # n, j, d, d
self.covars, # j, d, d
torch.matmul( # n, j, d, d
T_inv, # n, j, d, d
self.covars # j, d, d
))
log_prob = torch.logsumexp(log_resps, dim=1, keepdim=True)
log_resps -= log_prob
return torch.sum(log_prob), (log_resps, cond_means, cond_covars)
def forward(self):
K, B = self.GlobalKernel(self.X, self.X)
dK = K.diagonal()
dK += self.global_gp_noise_std**2 + self.jitter
# print(K)
L = torch.linalg.cholesky(K)
alpha = torch.cholesky_solve(self.y, L)
Apart1 = self.y.T @ alpha
Apart2 = torch.sum(torch.log(L.diagonal()))
# Apart2 = torch.det(K)
# print('Before Apart2', Apart2)
# Apart2 = Apart2.clamp(Apart2, min=10**-20)
# Apart2 = torch.log(Apart2)
# Apart3 = self.N * torch.log(2*self.pi)
A = 0.5 * (Apart1 + Apart2)[0, 0]
# Bpart1 = B
# Bpart2 = 0.5*(self.num_latent_points *
# self.input_dim*torch.log(2*self.pi))
# B = Bpart1# + Bpart2
# print("A1", Apart1, "A2", Apart2, "B", B, "Loss", A+B, 'local var', self.local_gp_std)
return (A + B) / self.X.nelement()
def train(self, X, y, method='cholesky', alpha=1e-2):
"""
Compute the output weights with a linear regression.
Parameters:
- X: input sequence of shape (seq_len, res_size)
- y: target output (seq_len, out_dim)
- method: "cholesky" or "sklearn ridge"
- alpha: L2-regularization parameter
Returns: a tensor of shape (res_size, out_dim)
"""
if method == 'cholesky':
# This technique uses the Cholesky decomposition
# It should be fast when res_size < seq_len
Xt_y = X.T @ y # size (res_size, out_dim)
K = X.T @ X # size (res_size, res_size)
K.view(-1)[::len(K) +
1] += alpha # add elements on the diagonal inplace
L = torch.cholesky(K, upper=False)
return torch.cholesky_solve(Xt_y, L, upper=False)
elif method == 'sklearn ridge':
from sklearn.linear_model import Ridge
clf = Ridge(fit_intercept=False, alpha=alpha)
clf.fit(X.cpu().numpy(), y.cpu().numpy())
return torch.from_numpy(clf.coef_.T).to(self.device)
def __call__(self, x, full_cov=False):
# Training mode
if self.training:
if self.train_inputs is None:
raise RuntimeError(
"train_inputs, train_targets cannot be None in training mode. "
"Call .eval() for prior predictions, or call .set_train_data() to add training data."
)
if settings.debug.on():
if not torch.equal(self.X, x):
raise RuntimeError("You must train on the training inputs!")
return self.forward(x)
# Prior mode
elif settings.prior_mode.on() or self.train_inputs is None or self.train_targets is None:
full_output = self.forward(x)
if settings.debug().on():
if not isinstance(full_output, gpytorch.distributions.MultivariateNormal):
raise RuntimeError("ExactGP.forward must return a MultivariateNormal")
return full_output
# Posterior mode
else:
cov_data_query = self.covar_module(self.X, x).evaluate()
prior_pred = self.forward(x)
pred_mean = prior_pred.mean.view(-1, 1) + cov_data_query.t() @ self.y_weights
cov_weights = torch.cholesky_solve(cov_data_query, self.chol_cov_data)
if full_cov:
pred_cov = prior_pred.covariance_matrix - cov_data_query.t() @ cov_weights
else: # Evaluates only diagonal (variances) as a diagonal lazy matrix
diag_k = gpytorch.lazy.DiagLazyTensor(prior_pred.lazy_covariance_matrix.diag())
pred_cov = diag_k.add_diag(-cov_data_query.t().matmul(cov_weights).diag())
return gpytorch.distributions.MultivariateNormal(pred_mean.view_as(prior_pred.mean), pred_cov)
def test_cg_with_tridiag(self):
size = 10
matrix = torch.randn(size, size, dtype=torch.float64)
matrix = matrix.matmul(matrix.transpose(-1, -2))
matrix.div_(matrix.norm())
matrix.add_(torch.eye(matrix.size(-1), dtype=torch.float64).mul_(1e-1))
rhs = torch.randn(size, 50, dtype=torch.float64)
solves, t_mats = linear_cg(matrix.matmul,
rhs=rhs,
n_tridiag=5,
max_tridiag_iter=10,
max_iter=size,
tolerance=0,
eps=1e-15)
# Check cg
matrix_chol = torch.linalg.cholesky(matrix)
actual = torch.cholesky_solve(rhs, matrix_chol)
self.assertTrue(torch.allclose(solves, actual, atol=1e-3, rtol=1e-4))
# Check tridiag
eigs = torch.linalg.eigvalsh(matrix)
for i in range(5):
approx_eigs = torch.linalg.eigvalsh(t_mats[i])
self.assertTrue(
torch.allclose(eigs, approx_eigs, atol=1e-3, rtol=1e-4))
def step(self, actions, shadow=False):
x = self.data[:, 0, :self.nstep]
y = self.data[:, 1, :self.nstep]
K = torch.exp(-1 / 2 * (
(x.view(self.batch_size, self.nstep, 1) -
x.view(self.batch_size, 1, self.nstep)) / 0.1)**2) + torch.eye(
self.nstep, dtype=torch.float, device=self.device) * 1e-5
u = torch.cholesky(K)
k = torch.exp(-1 / 2 * ((x.view(self.batch_size, self.nstep) -
actions.view(self.batch_size, 1)) / 0.1)**2)
sol = torch.cholesky_solve(
torch.cat((k.view(self.batch_size, self.nstep,
1), y.view(self.batch_size, self.nstep, 1)),
dim=2), u)
#print(sol)
mav = torch.matmul(k.view(self.batch_size, 1, self.nstep),
sol).view(self.batch_size, 2) #check shapes!
#print(mav)
newy = torch.normal(mav[:, 1], 1 - mav[:, 0])
newbest = torch.max(self.best, newy)
reward = newbest - self.best
if not shadow:
self.best = newbest
self.data[:, 0, self.nstep] = actions.view(self.batch_size)
self.data[:, 1, self.nstep] = newy
self.nstep = self.nstep + 1
return self.data[:, :, self.nstep - 1], reward
def _update_projections(self, Y, R, component_batches):
print('Updating projections...')
for bstart, bend in component_batches:
self.t_matrix[bstart:bend, :, :] = torch.cholesky_solve(
Y[bstart:bend, :, :].transpose(1, 2),
torch.cholesky(R[bstart:bend, :, :], upper=True),
upper=True)
def test_batch_cg_with_tridiag(self):
batch = 5
size = 10
matrix = torch.randn(batch, size, size, dtype=torch.float64)
matrix = matrix.matmul(matrix.transpose(-1, -2))
matrix.div_(matrix.norm())
matrix.add_(torch.eye(matrix.size(-1), dtype=torch.float64).mul_(1e-1))
rhs = torch.randn(batch, size, 10, dtype=torch.float64)
solves, t_mats = linear_cg(matrix.matmul,
rhs=rhs,
n_tridiag=8,
max_iter=size,
max_tridiag_iter=10,
tolerance=0,
eps=1e-30)
# Check cg
matrix_chol = torch.linalg.cholesky(matrix)
actual = torch.cholesky_solve(rhs, matrix_chol)
self.assertTrue(torch.allclose(solves, actual, atol=1e-3, rtol=1e-4))
# Check tridiag
for i in range(5):
eigs = matrix[i].symeig()[0]
for j in range(8):
approx_eigs = t_mats[j, i].symeig()[0]
self.assertTrue(
torch.allclose(eigs, approx_eigs, atol=1e-3, rtol=1e-4))
def fit(self, X, labels):
"""
Fit method
Args:
- X: a tensor, appropriately flattened, having sizes: (Batch Size, Features, 1)
- labels: a tensor of labels, having sizes: (Batch Size, 1)
"""
#for p in self.W_comp:
# p.data.clamp_(0) #projection to ensure positive semi-definiteness
#W_soft = F.softmax(self.W_comp)
self.kern = torch.sum(torch.stack([
self.W_comp[i] * self.kernel[i](X) for i in range(self.nb_kernels)
]),
dim=0)
K = self.kern + torch.eye(
self.kern.size()[0]).to(device) * self.lambda_reg
L = torch.cholesky(K, upper=False)
one_hot_y = F.one_hot(labels, num_classes=10).type(
torch.FloatTensor).to(device)
#A, _ = torch.solve(kern, L)
#V, _ = torch.solve(one_hot_y, L)
#alpha = A.T @ V
self.alpha = torch.cholesky_solve(one_hot_y, L, upper=False)
def KL(self):
"""
The KL divergence from the variational distribution to the prior
:return: KL divergence from N(q_mu, q_sqrt) to N(0, I), independently for each GP
"""
# if self.white:
# return gauss_kl(self.q_mu, self.q_sqrt)
# else:
# return gauss_kl(self.q_mu, self.q_sqrt, self.Ku)
self.build_cholesky_if_needed()
KL = -0.5 * self.num_outputs * self.num_inducing
KL -= 0.5 * torch.cumsum(torch.log(torch.stack(tuple(t.diag() for t in torch.unbind(self.q_sqrt,0))) ** 2),dim=0)[:,a.size(1)-1] #error check
if not self.white:
KL += torch.cumsum(torch.log(torch.stack(tuple(t.diag() for t in torch.unbind(self.q_sqrt,0)))),dim=0)[:,a.size(1)-1] * self.num_outputs
KL += 0.5 * torch.cumsum(torch.square(torch.triangular_solve(self.Lu_tiled, self.q_sqrt, upper=False)),dim=0)[:,a.size(1)-1]
Kinv_m = torch.cholesky_solve(self.q_mu , self.Lu)
KL += 0.5 * torch.cumsum(self.q_mu * Kinv_m, dim=0)[:,a.size(1)-1]
else:
KL += 0.5 * torch.cumsum(torch.square(self.q_sqrt),dim=0)[:,a.size(1)-1]
KL += 0.5 * torch.cumsum(self.q_mu**2,dim=0)[:,a.size(1)-1]
return KL
def _sample_posterior(self, x, num_samples, context=None):
log_weights = torch.log(self.module.soft_max(self.module.soft_weights))
T = self.module.covars[None, :, :, :] + x[1][:, None, :, :]
p_weights = log_weights + dist.MultivariateNormal(
loc=self.module.means, covariance_matrix=T
).log_prob(x[0][:, None, :])
p_weights -= torch.logsumexp(p_weights, axis=1)[:, None]
L_t = torch.cholesky(T)
T_inv = torch.cholesky_solve(
torch.eye(self.d, device=self.device), L_t)
diff = x[0][:, None, :] - self.module.means
T_prod = torch.matmul(T_inv, diff[:, :, :, None])
p_means = self.module.means + torch.matmul(
self.module.covars,
T_prod
).squeeze()
p_covars = self.module.covars - torch.matmul(
self.module.covars,
torch.matmul(T_inv, self.module.covars)
)
idx = dist.Categorical(logits=p_weights).sample([num_samples])
samples = dist.MultivariateNormal(
loc=p_means, covariance_matrix=p_covars).sample([num_samples])
return samples.transpose(0, 1)[
torch.arange(len(x), device=self.device)[:, None, None, None],
torch.arange(num_samples, device=self.device)[None, :, None, None],
idx.T[:, :, None, None],
torch.arange(self.d, device=self.device)[None, None, None, :]
].squeeze()
def backward(ctx, g):
l, = ctx.saved_tensors
n = l.shape[-1]
# TODO: Use cholesky_inverse once pytorch/pytorch/issues/7500 is solved.
grad_x = g.view(*l.shape[:-2], 1, 1) * torch.cholesky_solve(
torch.eye(n, out=l.new(n, n)), l)
return grad_x, None, None