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[readtorst] rst was not returned before

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mzwiessele 2015-10-12 09:52:27 +01:00
parent 92d3df51a5
commit bc84d89b7d
3 changed files with 20 additions and 23 deletions
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30
doc/source/tuto_creating_new_kernels.rst
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@ -9,8 +9,8 @@ Structure of a kernel in GPy
In GPy a kernel object is made of a list of kernpart objects, which correspond to symetric positive definite functions. More precisely, the kernel should be understood as the sum of the kernparts. In order to implement a new covariance, the following steps must be followed
1. implement the new covariance as a :py:class:`GPy.kern._src.kern.Kern` object
2. update the :py:mod:`GPy.kern._src` file
1. implement the new covariance as a :py:class:`GPy.kern.src.kern.Kern` object
2. update the :py:mod:`GPy.kern.src` file
Theses three steps are detailed below.
@ -32,8 +32,8 @@ The header is similar to all kernels: ::
class RationalQuadratic(Kern):
:py:func:`GPy.kern._src.kern.Kern.__init__` ``(self, input_dim, param1, param2, *args)``
~~~~~~~~~~~~~~~~~~~
:py:func:`GPy.kern.src.kern.Kern.__init__` ``(self, input_dim, param1, param2, *args)``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The implementation of this function in mandatory.
@ -67,7 +67,7 @@ code. Updates from the optimization routine will be done
automatically.
:py:func:`~GPy.core.parameterization.parameter_core.Parameterizable.parameters_changed` ``(self)``
~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The implementation of this function is optional.
@ -84,8 +84,8 @@ the kernel::
pass
:py:func:`~GPy.kern._src.kern.Kern.K` ``(self,X,X2)``
~~~~~~~~~~~~~~~~~~~
:py:func:`~GPy.kern.src.kern.Kern.K` ``(self,X,X2)``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The implementation of this function in mandatory.
@ -98,8 +98,8 @@ the inputs X, X2 (np.arrays with arbitrary number of line (say
dist2 = np.square((X-X2.T)/self.lengthscale)
return self.variance*(1 + dist2/2.)**(-self.power)
:py:func:`~GPy.kern._src.kern.Kern.Kdiag` ``(self,X)``
~~~~~~~~~~~~~~~~~~~
:py:func:`~GPy.kern.src.kern.Kern.Kdiag` ``(self,X)``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The implementation of this function is mandatory.
@ -110,7 +110,7 @@ np.array of length :math:`n \times 1`. ::
def Kdiag(self,X):
return self.variance*np.ones(X.shape[0])
:py:func:`~GPy.kern._src.kern.Kern.update_gradients_full` ``(self, dL_dK, X, X2=None)``
:py:func:`~GPy.kern.src.kern.Kern.update_gradients_full` ``(self, dL_dK, X, X2=None)``
~~~~~~~~~~~~~~~~~~~
This function is required for the optimization of the parameters.
@ -147,7 +147,7 @@ to :math:`\theta`. ::
self.power.gradient = np.sum(dp*dL_dK)
:py:func:`~GPy.kern._src.kern.Kern.update_gradients_diag` ``(self,dL_dKdiag,X,target)``
:py:func:`~GPy.kern.src.kern.Kern.update_gradients_diag` ``(self,dL_dKdiag,X,target)``
~~~~~~~~~~~~~~~~~~~
This function is required for BGPLVM, sparse models and uncertain inputs.
@ -165,7 +165,7 @@ is set to each ``param``. ::
self.variance.gradient = np.sum(dL_dKdiag)
# here self.lengthscale and self.power have no influence on Kdiag so target[1:] are unchanged
:py:func:`~GPy.kern._src.kern.Kern.gradients_X` ``(self,dL_dK, X, X2)``
:py:func:`~GPy.kern.src.kern.Kern.gradients_X` ``(self,dL_dK, X, X2)``
~~~~~~~~~~~~~~~~~~~
This function is required for GPLVM, BGPLVM, sparse models and uncertain inputs.
@ -182,7 +182,7 @@ function which is a :math:`n \times q` np.array. ::
dX = -self.variance*self.power * (X-X2.T)/self.lengthscale**2 * (1 + dist2/2./self.lengthscale)**(-self.power-1)
return np.sum(dL_dK*dX,1)[:,None]
:py:func:`~GPy.kern._src.kern.Kern.gradients_X_diag` ``(self,dL_dKdiag,X)``
:py:func:`~GPy.kern.src.kern.Kern.gradients_X_diag` ``(self,dL_dKdiag,X)``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This function is required for BGPLVM, sparse models and uncertain
@ -206,8 +206,8 @@ as the second order derivatives:
.. math:: \frac{\partial^2 K}{\partial X\partial X2}
- :py:func:`GPy.kern._src.kern.gradients_XX` ``(self,dL_dK, X, X2)``
- :py:func:`GPy.kern._src.kern.gradients_XX_diag` ``(self,dL_dKdiag, X)``
- :py:func:`GPy.kern.src.kern.gradients_XX` ``(self,dL_dK, X, X2)``
- :py:func:`GPy.kern.src.kern.gradients_XX_diag` ``(self,dL_dKdiag, X)``
**Psi statistics**
~~~~~~~~~~~~~