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Fixed more errors in docs 2

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James McMurray 2013-09-20 17:46:23 +01:00
parent 7f2472fa22
commit aa5ebcbd8f
15 changed files with 99 additions and 84 deletions
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20
GPy/core/gp.py
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@ -15,7 +15,7 @@ class GP(GPBase):
:param X: input observations
:param kernel: a GPy kernel, defaults to rbf+white
:parm likelihood: a GPy likelihood
:param likelihood: a GPy likelihood
:param normalize_X: whether to normalize the input data before computing (predictions will be in original scales)
:type normalize_X: False|True
:rtype: model object
@ -132,17 +132,16 @@ class GP(GPBase):
def predict(self, Xnew, which_parts='all', full_cov=False, likelihood_args=dict()):
"""
Predict the function(s) at the new point(s) Xnew.
Arguments
---------
:param Xnew: The points at which to make a prediction
:type Xnew: np.ndarray, Nnew x self.input_dim
:param which_parts: specifies which outputs kernel(s) to use in prediction
:type which_parts: ('all', list of bools)
:param full_cov: whether to return the full covariance matrix, or just the diagonal
:type full_cov: bool
:rtype: posterior mean, a Numpy array, Nnew x self.input_dim
:rtype: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
:rtype: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.input_dim
:returns: mean: posterior mean, a Numpy array, Nnew x self.input_dim
:returns: var: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
:returns: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.input_dim
If full_cov and self.input_dim > 1, the return shape of var is Nnew x Nnew x self.input_dim. If self.input_dim == 1, the return shape is Nnew x Nnew.
@ -160,8 +159,7 @@ class GP(GPBase):
def predict_single_output(self, Xnew, output=0, which_parts='all', full_cov=False):
"""
For a specific output, predict the function at the new point(s) Xnew.
Arguments
---------
:param Xnew: The points at which to make a prediction
:type Xnew: np.ndarray, Nnew x self.input_dim
:param output: output to predict
@ -170,9 +168,9 @@ class GP(GPBase):
:type which_parts: ('all', list of bools)
:param full_cov: whether to return the full covariance matrix, or just the diagonal
:type full_cov: bool
:rtype: posterior mean, a Numpy array, Nnew x self.input_dim
:rtype: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
:rtype: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.input_dim
:returns: posterior mean, a Numpy array, Nnew x self.input_dim
:returns: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
:returns: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.input_dim
.. Note:: For multiple output models only
"""

6
GPy/core/model.py
View file

@ -47,6 +47,7 @@ class Model(Parameterized):
:param state: the state of the model.
:type state: list as returned from getstate.
"""
self.preferred_optimizer = state.pop()
self.sampling_runs = state.pop()
@ -543,10 +544,11 @@ class Model(Parameterized):
"""
EM - like algorithm for Expectation Propagation and Laplace approximation
:stop_crit: convergence criterion
:param stop_crit: convergence criterion
:type stop_crit: float
..Note: kwargs are passed to update_likelihood and optimize functions. """
.. Note: kwargs are passed to update_likelihood and optimize functions.
"""
assert isinstance(self.likelihood, likelihoods.EP) or isinstance(self.likelihood, likelihoods.EP_Mixed_Noise), "pseudo_EM is only available for EP likelihoods"
ll_change = stop_crit + 1.
iteration = 0

3
GPy/core/sparse_gp.py
View file

@ -393,8 +393,7 @@ class SparseGP(GPBase):
def predict_single_output(self, Xnew, output=0, which_parts='all', full_cov=False):
"""
For a specific output, predict the function at the new point(s) Xnew.
Arguments
---------
:param Xnew: The points at which to make a prediction
:type Xnew: np.ndarray, Nnew x self.input_dim
:param output: output to predict