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Fixed docstring warnings - could still be mistakes

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This commit is contained in:
James McMurray 2013-09-20 13:38:20 +01:00
parent c36a6b341c
commit be3880c0bd
20 changed files with 261 additions and 144 deletions
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12
GPy/core/parameterized.py
View file

@ -231,17 +231,19 @@ class Parameterized(object):
def constrain_fixed(self, regexp, value=None):
"""
Arguments
---------
:param regexp: which parameters need to be fixed.
:type regexp: ndarray(dtype=int) or regular expression object or string
:param value: the vlaue to fix the parameters to. If the value is not specified,
the parameter is fixed to the current value
:type value: float
Notes
-----
**Notes**
Fixing a parameter which is tied to another, or constrained in some way will result in an error.
To fix multiple parameters to the same value, simply pass a regular expression which matches both parameter names, or pass both of the indexes
To fix multiple parameters to the same value, simply pass a regular expression which matches both parameter names, or pass both of the indexes.
"""
matches = self.grep_param_names(regexp)
overlap = set(matches).intersection(set(self.all_constrained_indices()))

12
GPy/core/sparse_gp.py
View file

@ -16,16 +16,17 @@ class SparseGP(GPBase):
:type X: np.ndarray (num_data x input_dim)
:param likelihood: a likelihood instance, containing the observed data
:type likelihood: GPy.likelihood.(Gaussian | EP | Laplace)
:param kernel : the kernel (covariance function). See link kernels
:param kernel: the kernel (covariance function). See link kernels
:type kernel: a GPy.kern.kern instance
:param X_variance: The uncertainty in the measurements of X (Gaussian variance)
:type X_variance: np.ndarray (num_data x input_dim) | None
:param Z: inducing inputs (optional, see note)
:type Z: np.ndarray (num_inducing x input_dim) | None
:param num_inducing : Number of inducing points (optional, default 10. Ignored if Z is not None)
:param num_inducing: Number of inducing points (optional, default 10. Ignored if Z is not None)
:type num_inducing: int
:param normalize_(X|Y) : whether to normalize the data before computing (predictions will be in original scales)
:param normalize_(X|Y): whether to normalize the data before computing (predictions will be in original scales)
:type normalize_(X|Y): bool
"""
def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
@ -306,10 +307,11 @@ class SparseGP(GPBase):
def predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
"""
Predict the function(s) at the new point(s) Xnew.
Arguments
---------
**Arguments**
:param Xnew: The points at which to make a prediction
:type Xnew: np.ndarray, Nnew x self.input_dim
:param X_variance_new: The uncertainty in the prediction points

14
GPy/core/svigp.py
View file

@ -14,6 +14,7 @@ import sys
class SVIGP(GPBase):
"""
Stochastic Variational inference in a Gaussian Process
:param X: inputs
@ -22,25 +23,26 @@ class SVIGP(GPBase):
:type Y: np.ndarray of observations (N x D)
:param batchsize: the size of a h
Additional kwargs are used as for a sparse GP. They include
Additional kwargs are used as for a sparse GP. They include:
:param q_u: canonical parameters of the distribution squasehd into a 1D array
:type q_u: np.ndarray
:param M : Number of inducing points (optional, default 10. Ignored if Z is not None)
:param M: Number of inducing points (optional, default 10. Ignored if Z is not None)
:type M: int
:param kernel : the kernel/covariance function. See link kernels
:param kernel: the kernel/covariance function. See link kernels
:type kernel: a GPy kernel
:param Z: inducing inputs (optional, see note)
:type Z: np.ndarray (M x Q) | None
:param X_uncertainty: The uncertainty in the measurements of X (Gaussian variance)
:type X_uncertainty: np.ndarray (N x Q) | None
:param Zslices: slices for the inducing inputs (see slicing TODO: link)
:param M : Number of inducing points (optional, default 10. Ignored if Z is not None)
:param M: Number of inducing points (optional, default 10. Ignored if Z is not None)
:type M: int
:param beta: noise precision. TODO> ignore beta if doing EP
:param beta: noise precision. TODO: ignore beta if doing EP
:type beta: float
:param normalize_(X|Y) : whether to normalize the data before computing (predictions will be in original scales)
:param normalize_(X|Y): whether to normalize the data before computing (predictions will be in original scales)
:type normalize_(X|Y): bool
"""