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Fixed docstring warnings - could still be mistakes
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20 changed files with 261 additions and 144 deletions
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@ -16,16 +16,17 @@ class SparseGP(GPBase):
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:type X: np.ndarray (num_data x input_dim)
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:param likelihood: a likelihood instance, containing the observed data
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:type likelihood: GPy.likelihood.(Gaussian | EP | Laplace)
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:param kernel : the kernel (covariance function). See link kernels
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:param kernel: the kernel (covariance function). See link kernels
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:type kernel: a GPy.kern.kern instance
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:param X_variance: The uncertainty in the measurements of X (Gaussian variance)
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:type X_variance: np.ndarray (num_data x input_dim) | None
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:param Z: inducing inputs (optional, see note)
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:type Z: np.ndarray (num_inducing x input_dim) | None
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:param num_inducing : Number of inducing points (optional, default 10. Ignored if Z is not None)
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:param num_inducing: Number of inducing points (optional, default 10. Ignored if Z is not None)
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:type num_inducing: int
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:param normalize_(X|Y) : whether to normalize the data before computing (predictions will be in original scales)
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:param normalize_(X|Y): whether to normalize the data before computing (predictions will be in original scales)
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:type normalize_(X|Y): bool
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"""
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def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
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@ -306,10 +307,11 @@ class SparseGP(GPBase):
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def predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
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"""
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Predict the function(s) at the new point(s) Xnew.
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Arguments
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---------
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**Arguments**
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:param Xnew: The points at which to make a prediction
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:type Xnew: np.ndarray, Nnew x self.input_dim
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:param X_variance_new: The uncertainty in the prediction points
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