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Replaced Q by input_dim

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Alan Saul 2013-06-05 11:17:15 +01:00
parent 312cfebcb1
commit 97f3357b6d
22 changed files with 271 additions and 271 deletions
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2
GPy/core/GP.py
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@ -126,7 +126,7 @@ class GP(GPBase):
Arguments
---------
:param Xnew: The points at which to make a prediction
:type Xnew: np.ndarray, Nnew x self.Q
: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

6
GPy/core/gp_base.py
View file

@ -13,7 +13,7 @@ class GPBase(model.model):
def __init__(self, X, likelihood, kernel, normalize_X=False):
self.X = X
assert len(self.X.shape) == 2
self.N, self.Q = self.X.shape
self.N, self.input_dim = self.X.shape
assert isinstance(kernel, kern.kern)
self.kern = kernel
self.likelihood = likelihood
@ -25,8 +25,8 @@ class GPBase(model.model):
self._Xstd = X.std(0)[None, :]
self.X = (X.copy() - self._Xmean) / self._Xstd
else:
self._Xmean = np.zeros((1,self.Q))
self._Xstd = np.ones((1,self.Q))
self._Xmean = np.zeros((1,self.input_dim))
self._Xstd = np.ones((1,self.input_dim))
model.model.__init__(self)

12
GPy/core/sparse_GP.py
View file

@ -13,15 +13,15 @@ class sparse_GP(GPBase):
Variational sparse GP model
:param X: inputs
:type X: np.ndarray (N x Q)
:type X: np.ndarray (N 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
:type kernel: a GPy.kern.kern instance
:param X_variance: The uncertainty in the measurements of X (Gaussian variance)
:type X_variance: np.ndarray (N x Q) | None
:type X_variance: np.ndarray (N x input_dim) | None
:param Z: inducing inputs (optional, see note)
:type Z: np.ndarray (M x Q) | None
:type Z: np.ndarray (M x input_dim) | None
:param M : Number of inducing points (optional, default 10. Ignored if Z is not None)
:type M: int
:param normalize_(X|Y) : whether to normalize the data before computing (predictions will be in original scales)
@ -152,7 +152,7 @@ class sparse_GP(GPBase):
return A + B + C + D + self.likelihood.Z
def _set_params(self, p):
self.Z = p[:self.M * self.Q].reshape(self.M, self.Q)
self.Z = p[:self.M * self.input_dim].reshape(self.M, self.input_dim)
self.kern._set_params(p[self.Z.size:self.Z.size + self.kern.Nparam])
self.likelihood._set_params(p[self.Z.size + self.kern.Nparam:])
self._compute_kernel_matrices()
@ -252,9 +252,9 @@ class sparse_GP(GPBase):
Arguments
---------
:param Xnew: The points at which to make a prediction
:type Xnew: np.ndarray, Nnew x self.Q
:type Xnew: np.ndarray, Nnew x self.input_dim
:param X_variance_new: The uncertainty in the prediction points
:type X_variance_new: np.ndarray, Nnew x self.Q
:type X_variance_new: 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