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[predict] documentation

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Max Zwiessele 2015-05-11 11:21:45 +01:00
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GPy/core/gp.py
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@ -229,13 +229,14 @@ class GP(Model):
:param Y_metadata: metadata about the predicting point to pass to the likelihood :param Y_metadata: metadata about the predicting point to pass to the likelihood
:param kern: The kernel to use for prediction (defaults to the model :param kern: The kernel to use for prediction (defaults to the model
kern). this is useful for examining e.g. subprocesses. kern). this is useful for examining e.g. subprocesses.
:returns: (mean, var, lower_upper): :returns: (mean, var):
mean: posterior mean, a Numpy array, Nnew x self.input_dim mean: posterior mean, a Numpy array, Nnew x self.input_dim
var: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise var: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
lower_upper: 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. 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. This is to allow for different normalizations of the output dimensions.
Note: If you want the predictive quantiles (e.g. 95% confidence interval) use :py:func:~GPy.core.gp.GP.predict_quantiles.
""" """
#predict the latent function values #predict the latent function values
mu, var = self._raw_predict(Xnew, full_cov=full_cov, kern=kern) mu, var = self._raw_predict(Xnew, full_cov=full_cov, kern=kern)
@ -255,7 +256,7 @@ class GP(Model):
:param quantiles: tuple of quantiles, default is (2.5, 97.5) which is the 95% interval :param quantiles: tuple of quantiles, default is (2.5, 97.5) which is the 95% interval
:type quantiles: tuple :type quantiles: tuple
:returns: list of quantiles for each X and predictive quantiles for interval combination :returns: list of quantiles for each X and predictive quantiles for interval combination
:rtype: [np.ndarray (Xnew x self.input_dim), np.ndarray (Xnew x self.input_dim)] :rtype: [np.ndarray (Xnew x self.output_dim), np.ndarray (Xnew x self.output_dim)]
""" """
m, v = self._raw_predict(X, full_cov=False) m, v = self._raw_predict(X, full_cov=False)
if self.normalizer is not None: if self.normalizer is not None: