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Few bugs fixed in the documentation

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Nicolas 2013-03-11 18:45:04 +00:00
parent 129bb3924e
commit 3dd62c8251
2 changed files with 13 additions and 14 deletions
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24
doc/tuto_creating_new_kernels.rst
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@ -22,7 +22,7 @@ We advise the reader to start with copy-pasting an existing kernel and to modify
**Header**
The header is similar to all kernels::
The header is similar to all kernels: ::
from kernpart import kernpart
import numpy as np
@ -35,7 +35,7 @@ The implementation of this function in mandatory.
For all kernparts the first parameter ``D`` corresponds to the dimension of the input space, and the following parameters stand for the parameterization of the kernel.
The following attributes are compulsory: ``self.D`` (the dimension, integer), ``self.name`` (name of the kernel, string), ``self.Nparam`` (number of parameters, integer).::
The following attributes are compulsory: ``self.D`` (the dimension, integer), ``self.name`` (name of the kernel, string), ``self.Nparam`` (number of parameters, integer). ::
def __init__(self,D,variance=1.,lengthscale=1.,power=1.):
assert D == 1, "For this kernel we assume D=1"
@ -50,7 +50,7 @@ The following attributes are compulsory: ``self.D`` (the dimension, integer), ``
The implementation of this function in mandatory.
This function returns a one dimensional array of length ``self.Nparam`` containing the value of the parameters.::
This function returns a one dimensional array of length ``self.Nparam`` containing the value of the parameters. ::
def _get_params(self):
return np.hstack((self.variance,self.lengthscale,self.power))
@ -59,7 +59,7 @@ This function returns a one dimensional array of length ``self.Nparam`` containi
The implementation of this function in mandatory.
The input is a one dimensional array of length ``self.Nparam`` containing the value of the parameters. The function has no output but it updates the values of the attribute associated to the parameters (such as ``self.variance``, ``self.lengthscale``, ...).::
The input is a one dimensional array of length ``self.Nparam`` containing the value of the parameters. The function has no output but it updates the values of the attribute associated to the parameters (such as ``self.variance``, ``self.lengthscale``, ...). ::
def _set_params(self,x):
self.variance = x[0]
@ -70,7 +70,7 @@ The input is a one dimensional array of length ``self.Nparam`` containing the va
The implementation of this function in mandatory.
It returns a list of strings of length ``self.Nparam`` corresponding to the parameter names.::
It returns a list of strings of length ``self.Nparam`` corresponding to the parameter names. ::
def _get_param_names(self):
return ['variance','lengthscale','power']
@ -79,7 +79,7 @@ It returns a list of strings of length ``self.Nparam`` corresponding to the para
The implementation of this function in mandatory.
This function is used to compute the covariance matrix associated with the inputs X, X2 (np.arrays with arbitrary number of line (say :math:`n_1`, :math:`n_2`) and ``self.D`` columns). This function does not returns anything but it adds the :math:`n_1 \times n_2` covariance matrix to the kernpart to the object ``target`` (a :math:`n_1 \times n_2` np.array). This trick allows to compute the covariance matrix of a kernel containing many kernparts with a limited memory use.::
This function is used to compute the covariance matrix associated with the inputs X, X2 (np.arrays with arbitrary number of line (say :math:`n_1`, :math:`n_2`) and ``self.D`` columns). This function does not returns anything but it adds the :math:`n_1 \times n_2` covariance matrix to the kernpart to the object ``target`` (a :math:`n_1 \times n_2` np.array). This trick allows to compute the covariance matrix of a kernel containing many kernparts with a limited memory use. ::
def K(self,X,X2,target):
if X2 is None: X2 = X
@ -90,7 +90,7 @@ This function is used to compute the covariance matrix associated with the input
The implementation of this function in mandatory.
This function is similar to ``K`` but it computes only the values of the kernel on the diagonal. Thus, ``target`` is a 1-dimensional np.array of length :math:`n_1`.::
This function is similar to ``K`` but it computes only the values of the kernel on the diagonal. Thus, ``target`` is a 1-dimensional np.array of length :math:`n_1`. ::
def Kdiag(self,X,target):
target += self.variance
@ -100,7 +100,7 @@ This function is similar to ``K`` but it computes only the values of the kernel
This function is required for the optimization of the parameters.
Computes the derivative of the likelihood. As previously, the values are added to the object target which is a 1-dimensional np.array of length ``self.Nparam``. For example, if the kernel is parameterized by :math:`\sigma^2,\ \theta`, then :math:`\frac{dL}{d\sigma^2} = \frac{dL}{d K} \frac{dK}{d\sigma^2}` is added to the first element of target and :math:`\frac{dL}{d\theta} = \frac{dL}{d K} \frac{dK}{d\theta}` to the second.::
Computes the derivative of the likelihood. As previously, the values are added to the object target which is a 1-dimensional np.array of length ``self.Nparam``. For example, if the kernel is parameterized by :math:`\sigma^2,\ \theta`, then :math:`\frac{dL}{d\sigma^2} = \frac{dL}{d K} \frac{dK}{d\sigma^2}` is added to the first element of target and :math:`\frac{dL}{d\theta} = \frac{dL}{d K} \frac{dK}{d\theta}` to the second. ::
def dK_dtheta(self,dL_dK,X,X2,target):
if X2 is None: X2 = X
@ -119,7 +119,7 @@ Computes the derivative of the likelihood. As previously, the values are added t
This function is required for BGPLVM, sparse models and uncertain inputs.
As previously, target is an ``self.Nparam`` array and :math:`\frac{dL}{d Kdiag} \frac{dKdiag}{dparam}` is added to each element.::
As previously, target is an ``self.Nparam`` array and :math:`\frac{dL}{d Kdiag} \frac{dKdiag}{dparam}` is added to each element. ::
def dKdiag_dtheta(self,dL_dKdiag,X,target):
target[0] += np.sum(dL_dKdiag)
@ -129,7 +129,7 @@ As previously, target is an ``self.Nparam`` array and :math:`\frac{dL}{d Kdiag}
This function is required for GPLVM, BGPLVM, sparse models and uncertain inputs.
Computes the derivative of the likelihood with respect to the inputs ``X`` (a :math:`n \times D` np.array). The result is added to target which is a :math:`n \times D` np.array.::
Computes the derivative of the likelihood with respect to the inputs ``X`` (a :math:`n \times D` np.array). The result is added to target which is a :math:`n \times D` np.array. ::
def dK_dX(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
@ -141,7 +141,7 @@ Computes the derivative of the likelihood with respect to the inputs ``X`` (a :m
**dKdiag_dX(self,dL_dKdiag,X,target)**
This function is required for BGPLVM, sparse models and uncertain inputs. As for ``dKdiag_dtheta``, :math:`\frac{dL}{d Kdiag} \frac{dKdiag}{dX}` is added to each element of target.::
This function is required for BGPLVM, sparse models and uncertain inputs. As for ``dKdiag_dtheta``, :math:`\frac{dL}{d Kdiag} \frac{dKdiag}{dX}` is added to each element of target. ::
def dKdiag_dX(self,dL_dKdiag,X,target):
pass
@ -167,7 +167,7 @@ The following line should be added in the preamble of the file::
from rational_quadratic import rational_quadratic as rational_quadratic_part
as well as the following block::
as well as the following block ::
def rational_quadratic(D,variance=1., lengthscale=1., power=1.):
part = rational_quadraticpart(D,variance, lengthscale, power)