more docstring fixes
@@ -266,7 +266,7 @@ class LazyVariable(object):
def cpu(self):
"""
Returns:
- :obj:`gpytorch.lazy.LazyVariable`: a new LazyVariable identical to ``self``, but on the CPU.
+ :obj:`~gpytorch.lazy.LazyVariable`: a new LazyVariable identical to ``self``, but on the CPU.
"""
new_args = []
new_kwargs = {}
@@ -289,7 +289,7 @@ class LazyVariable(object):
Args:
device_id (:obj:`str`, optional): Device ID of GPU to use.
Returns:
- :obj:`gpytorch.lazy.LazyVariable`: a new LazyVariable identical to ``self``, but on the GPU.
+ :obj:`~gpytorch.lazy.LazyVariable`: a new LazyVariable identical to ``self``, but on the GPU.
"""
new_args = []
new_kwargs = {}
@@ -448,15 +448,15 @@ class LazyVariable(object):
def _exact_predictive_covar_inv_quad_form_root(self, precomputed_cache, test_train_covar):
"""
- Computes K_X*X S given a precomputed cache
- Where S is a tensor such that S S^T = (K_XX + sigma^2 I)^-1
+ Computes :math:`K_{X^{*}X} S` given a precomputed cache
+ Where :math:`S` is a tensor such that :math:`SS^{\\top} = (K_{XX} + \sigma^2 I)^{-1}`
Args:
precomputed_cache (:obj:`torch.tensor`): What was computed in _exact_predictive_covar_inv_quad_form_cache
test_train_covar (:obj:`torch.tensor`): The observed noise (from the likelihood)
Returns
- :obj:`gpytorch.lazy.LazyVariable`: K_X^*X S
+ :obj:`~gpytorch.lazy.LazyVariable`: :math:`K_{X^{*}X} S`
"""
# Here the precomputed cache represents S,
# where S S^T = (K_XX + sigma^2 I)^-1
@@ -512,13 +512,13 @@ class LazyVariable(object):
def inv_matmul(self, tensor):
"""
- Computes a linear solve (w.r.t self) with several right hand sides.
+ Computes a linear solve (w.r.t self = :math:`K`) with several right hand sides :math:`M`.
Args:
- - tensor (tensor nxk) - Matrix or tensor
+ - :obj:`torch.tensor` (n x k) - Matrix :math:`M` right hand sides
Returns:
- - tensor - (self)^{-1} tensor
+ - :obj:`torch.tensor` - :math:`K^{-1}M`
"""
# Work out batch dimension, if necessary
lazy_var = self
@@ -739,7 +739,7 @@ class LazyVariable(object):
def representation_tree(self):
"""
- Returns a `:obj:gpytorch.lazy.LazyVariableRepresentationTree` tree object that recursively encodes the
+ Returns a :obj:`gpytorch.lazy.LazyVariableRepresentationTree` tree object that recursively encodes the
representation of this lazy variable. In particular, if the definition of this lazy variable depends on other
lazy variables, the tree is an object that can be used to reconstruct the full structure of this lazy variable,
including all subobjects. This is used internally.
