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SHOGUN
4.1.0
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Class that models Gaussian likelihood.
\[ p(y|f)=\prod_{i=1}^n\frac{1} {\sqrt{2\pi\sigma^2}} exp\left(-\frac{(y_i-f_i)^2}{2\sigma^2}\right) \]
The hyperparameter of the Gaussian likelihood model is standard deviation: \(\sigma\).
在文件 GaussianLikelihood.h 第 55 行定义.
静态 Public 成员函数 | |
| static CGaussianLikelihood * | obtain_from_generic (CLikelihoodModel *lik) |
Public 属性 | |
| SGIO * | io |
| Parallel * | parallel |
| Version * | version |
| Parameter * | m_parameters |
| Parameter * | m_model_selection_parameters |
| Parameter * | m_gradient_parameters |
| uint32_t | m_hash |
Protected 成员函数 | |
| virtual void | load_serializable_pre () throw (ShogunException) |
| virtual void | load_serializable_post () throw (ShogunException) |
| virtual void | save_serializable_pre () throw (ShogunException) |
| virtual void | save_serializable_post () throw (ShogunException) |
default constructor
在文件 GaussianLikelihood.cpp 第 42 行定义.
| CGaussianLikelihood | ( | float64_t | sigma | ) |
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virtual |
在文件 GaussianLikelihood.cpp 第 59 行定义.
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inherited |
Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.
| dict | dictionary of parameters to be built. |
在文件 SGObject.cpp 第 597 行定义.
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virtualinherited |
Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.
在文件 SGObject.cpp 第 714 行定义.
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virtualinherited |
A deep copy. All the instance variables will also be copied.
在文件 SGObject.cpp 第 198 行定义.
Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!
May be overwritten but please do with care! Should not be necessary in most cases.
| other | object to compare with |
| accuracy | accuracy to use for comparison (optional) |
| tolerant | allows linient check on float equality (within accuracy) |
在文件 SGObject.cpp 第 618 行定义.
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virtual |
get derivative of log likelihood \(log(P(y|f))\) with respect to given parameter
| lab | labels used |
| func | function location |
| param | parameter |
重载 CLikelihoodModel .
在文件 GaussianLikelihood.cpp 第 151 行定义.
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virtual |
returns the first moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).
This method is useful for EP local likelihood approximation.
| mu | mean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
| s2 | variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
| lab | labels \(y_i\) |
| i | index i |
实现了 CLikelihoodModel.
在文件 GaussianLikelihood.cpp 第 276 行定义.
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virtualinherited |
returns the first moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\) for each \(f_i\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).
Wrapper method which calls get_first_moment multiple times.
| mu | mean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
| s2 | variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
| lab | labels \(y_i\) |
在文件 LikelihoodModel.cpp 第 72 行定义.
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inherited |
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inherited |
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inherited |
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virtual |
get derivative of log likelihood \(log(P(y|f))\) with respect to function location \(f\)
| lab | labels used |
| func | function location |
| i | index, choices are 1, 2, and 3 for first, second, and third derivatives respectively |
实现了 CLikelihoodModel.
在文件 GaussianLikelihood.cpp 第 118 行定义.
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virtual |
returns the logarithm of the point-wise likelihood \(log(p(y_i|f_i))\) for each label \(y_i\).
One can evaluate log-likelihood like: \(log(p(y|f)) = \sum_{i=1}^{n} log(p(y_i|f_i))\)
| lab | labels \(y_i\) |
| func | values of the function \(f_i\) |
实现了 CLikelihoodModel.
在文件 GaussianLikelihood.cpp 第 92 行定义.
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virtualinherited |
Returns the log-likelihood \(log(p(y|f)) = \sum_{i=1}^{n} log(p(y_i|f_i))\) for each of the provided functions \( f \) in the given matrix.
Wrapper method which calls get_log_probability_f multiple times.
| lab | labels \(y_i\) |
| F | values of the function \(f_i\) where each column of the matrix is one function \( f \). |
在文件 LikelihoodModel.cpp 第 51 行定义.
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virtual |
returns the zeroth moment of a given (unnormalized) probability distribution:
\[ log(Z_i) = log\left(\int p(y_i|f_i) \mathcal{N}(f_i|\mu,\sigma^2) df_i\right) \]
for each \(f_i\).
| mu | mean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
| s2 | variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
| lab | labels \(y_i\) |
实现了 CLikelihoodModel.
在文件 GaussianLikelihood.cpp 第 234 行定义.
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virtual |
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inherited |
在文件 SGObject.cpp 第 498 行定义.
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inherited |
Returns description of a given parameter string, if it exists. SG_ERROR otherwise
| param_name | name of the parameter |
在文件 SGObject.cpp 第 522 行定义.
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inherited |
Returns index of model selection parameter with provided index
| param_name | name of model selection parameter |
在文件 SGObject.cpp 第 535 行定义.
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virtual |
returns the name of the likelihood model
实现了 CSGObject.
在文件 GaussianLikelihood.h 第 73 行定义.
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virtualinherited |
returns the logarithm of the predictive density of \(y_*\):
\[ log(p(y_*|X,y,x_*)) = log\left(\int p(y_*|f_*) p(f_*|X,y,x_*) df_*\right) \]
which approximately equals to
\[ log\left(\int p(y_*|f_*) \mathcal{N}(f_*|\mu,\sigma^2) df_*\right) \]
where normal distribution \(\mathcal{N}(\mu,\sigma^2)\) is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\).
NOTE: if lab equals to NULL, then each \(y_*\) equals to one.
| mu | posterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
| s2 | posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
| lab | labels \(y_*\) |
被 CSoftMaxLikelihood 重载.
在文件 LikelihoodModel.cpp 第 45 行定义.
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virtual |
returns mean of the predictive marginal \(p(y_*|X,y,x_*)\).
NOTE: if lab equals to NULL, then each \(y_*\) equals to one.
| mu | posterior mean of a Gaussian distribution |
\(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\)
| s2 | posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
| lab | labels \(y_*\) |
实现了 CLikelihoodModel.
在文件 GaussianLikelihood.cpp 第 75 行定义.
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virtual |
returns variance of the predictive marginal \(p(y_*|X,y,x_*)\).
NOTE: if lab equals to NULL, then each \(y_*\) equals to one.
| mu | posterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
| s2 | posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
| lab | labels \(y_*\) |
实现了 CLikelihoodModel.
在文件 GaussianLikelihood.cpp 第 81 行定义.
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virtual |
get derivative of the first derivative of log likelihood with respect to function location, i.e. \(\frac{\partial log(P(y|f))}{\partial f}\) with respect to given parameter
| lab | labels used |
| func | function location |
| param | parameter |
重载 CLikelihoodModel .
在文件 GaussianLikelihood.cpp 第 181 行定义.
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virtual |
returns the second central moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).
This method is useful for EP local likelihood approximation.
| mu | mean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
| s2 | variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
| lab | labels \(y_i\) |
| i | index i |
实现了 CLikelihoodModel.
在文件 GaussianLikelihood.cpp 第 297 行定义.
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virtualinherited |
returns the second moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\) for each \(f_i\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).
Wrapper method which calls get_second_moment multiple times.
| mu | mean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
| s2 | variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
| lab | labels \(y_i\) |
在文件 LikelihoodModel.cpp 第 89 行定义.
| float64_t get_sigma | ( | ) |
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virtual |
get derivative of the second derivative of log likelihood with respect to function location, i.e. \(\frac{\partial^{2} log(P(y|f))}{\partial f^{2}}\) with respect to given parameter
| lab | labels used |
| func | function location |
| param | parameter |
重载 CLikelihoodModel .
在文件 GaussianLikelihood.cpp 第 209 行定义.
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virtualinherited |
If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
| generic | set to the type of the generic if returning TRUE |
在文件 SGObject.cpp 第 296 行定义.
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virtualinherited |
Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!
| file | where to load from |
| prefix | prefix for members |
在文件 SGObject.cpp 第 369 行定义.
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Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.
| ShogunException | will be thrown if an error occurs. |
被 CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel , 以及 CExponentialKernel 重载.
在文件 SGObject.cpp 第 426 行定义.
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Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.
| ShogunException | will be thrown if an error occurs. |
被 CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool > , 以及 CDynamicObjectArray 重载.
在文件 SGObject.cpp 第 421 行定义.
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static |
helper method used to specialize a base class instance
| lik | likelihood model |
在文件 GaussianLikelihood.cpp 第 63 行定义.
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virtualinherited |
在文件 SGObject.cpp 第 262 行定义.
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inherited |
prints all parameter registered for model selection and their type
在文件 SGObject.cpp 第 474 行定义.
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virtualinherited |
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virtualinherited |
Save this object to file.
| file | where to save the object; will be closed during returning if PREFIX is an empty string. |
| prefix | prefix for members |
在文件 SGObject.cpp 第 314 行定义.
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protectedvirtualinherited | |||||||||||||
Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.
| ShogunException | will be thrown if an error occurs. |
被 CKernel 重载.
在文件 SGObject.cpp 第 436 行定义.
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protectedvirtualinherited | |||||||||||||
Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.
| ShogunException | will be thrown if an error occurs. |
被 CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool > , 以及 CDynamicObjectArray 重载.
在文件 SGObject.cpp 第 431 行定义.
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inherited |
在文件 SGObject.cpp 第 41 行定义.
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inherited |
在文件 SGObject.cpp 第 46 行定义.
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inherited |
在文件 SGObject.cpp 第 51 行定义.
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inherited |
在文件 SGObject.cpp 第 56 行定义.
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inherited |
在文件 SGObject.cpp 第 61 行定义.
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inherited |
在文件 SGObject.cpp 第 66 行定义.
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inherited |
在文件 SGObject.cpp 第 71 行定义.
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inherited |
在文件 SGObject.cpp 第 76 行定义.
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inherited |
在文件 SGObject.cpp 第 81 行定义.
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inherited |
在文件 SGObject.cpp 第 86 行定义.
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inherited |
在文件 SGObject.cpp 第 91 行定义.
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inherited |
在文件 SGObject.cpp 第 96 行定义.
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inherited |
在文件 SGObject.cpp 第 101 行定义.
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inherited |
在文件 SGObject.cpp 第 106 行定义.
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inherited |
在文件 SGObject.cpp 第 111 行定义.
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inherited |
set generic type to T
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inherited |
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inherited |
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inherited |
| void set_sigma | ( | float64_t | sigma | ) |
sets the noise standard deviation
| sigma | noise standard deviation |
在文件 GaussianLikelihood.h 第 85 行定义.
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virtualinherited |
A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
被 CGaussianKernel 重载.
在文件 SGObject.cpp 第 192 行定义.
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virtualinherited |
return whether likelihood function supports binary classification
被 CVariationalLikelihood, CProbitLikelihood , 以及 CLogitLikelihood 重载.
在文件 LikelihoodModel.h 第 329 行定义.
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virtualinherited |
return whether likelihood function supports multiclass classification
被 CSoftMaxLikelihood , 以及 CVariationalLikelihood 重载.
在文件 LikelihoodModel.h 第 335 行定义.
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virtual |
return whether Gaussian likelihood function supports regression
重载 CLikelihoodModel .
在文件 GaussianLikelihood.h 第 262 行定义.
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inherited |
unset generic type
this has to be called in classes specializing a template class
在文件 SGObject.cpp 第 303 行定义.
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virtualinherited |
Updates the hash of current parameter combination
在文件 SGObject.cpp 第 248 行定义.
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inherited |
io
在文件 SGObject.h 第 369 行定义.
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inherited |
parameters wrt which we can compute gradients
在文件 SGObject.h 第 384 行定义.
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inherited |
Hash of parameter values
在文件 SGObject.h 第 387 行定义.
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inherited |
model selection parameters
在文件 SGObject.h 第 381 行定义.
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inherited |
parameters
在文件 SGObject.h 第 378 行定义.
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inherited |
parallel
在文件 SGObject.h 第 372 行定义.
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inherited |
version
在文件 SGObject.h 第 375 行定义.
