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docstrings in new functions

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Ricardo 2013-09-13 12:35:12 +01:00
parent 86b5b3aa05
commit f0569d6d88
1 changed files with 22 additions and 17 deletions
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39
GPy/core/gp.py
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@ -20,9 +20,6 @@ class GP(GPBase):
: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
:param epsilon_ep: convergence criterion for the Expectation Propagation algorithm, defaults to 0.1
:param powerep: power-EP parameters [$\eta$,$\delta$], defaults to [1.,1.]
:type powerep: list
.. Note:: Multiple independent outputs are allowed using columns of Y
@ -134,7 +131,7 @@ class GP(GPBase):
: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 folll covariance matrix, or just the diagonal
: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
@ -155,23 +152,22 @@ class GP(GPBase):
def predict_single_output(self, Xnew, output=0, which_parts='all', full_cov=False):
"""
Predict the function(s) at the new point(s) Xnew.
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
:type output: integer in {0,..., num_outputs-1}
: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 folll covariance matrix, or just the diagonal
: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
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.
This is to allow for different normalizations of the output dimensions.
.. Note:: For multiple output models only
"""
assert hasattr(self,'multioutput')
index = np.ones_like(Xnew)*output
@ -182,23 +178,32 @@ class GP(GPBase):
mu, var = self._raw_predict(Xnew, full_cov=full_cov, which_parts=which_parts)
# now push through likelihood
if isinstance(self.likelihood,EP_Mixed_Noise):
mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, noise_model = output)
else:
mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, noise_model = output)
mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, noise_model = output)
return mean, var, _025pm, _975pm
def _raw_predict_single_output(self, _Xnew, output=0, which_parts='all', full_cov=False,stop=False):
"""
Internal helper function for making predictions, does not account
for normalization or likelihood
Internal helper function for making predictions for a specific output,
does not account for normalization or likelihood
---------
:param Xnew: The points at which to make a prediction
:type Xnew: np.ndarray, Nnew x self.input_dim
:param output: output to predict
:type output: integer in {0,..., num_outputs-1}
: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
.. Note:: For multiple output models only
"""
assert hasattr(self,'multioutput')
# creates an index column and appends it to _Xnew
index = np.ones_like(_Xnew)*output
_Xnew = np.hstack((_Xnew,index))
Kx = self.kern.K(_Xnew,self.X,which_parts=which_parts).T
#KiKx = np.dot(self.Ki, Kx)
KiKx, _ = dpotrs(self.L, np.asfortranarray(Kx), lower=1)
mu = np.dot(KiKx.T, self.likelihood.Y)
if full_cov: