/usr/include/shogun/distributions/Distribution.h is in libshogun-dev 1.1.0-4ubuntu2.
This file is owned by root:root, with mode 0o644.
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* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 1999-2009 Soeren Sonnenburg
* Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
*/
#ifndef _DISTRIBUTION_H___
#define _DISTRIBUTION_H___
#include <shogun/features/Features.h>
#include <shogun/mathematics/Math.h>
#include <shogun/base/SGObject.h>
namespace shogun
{
class CFeatures;
class CMath;
/** @brief Base class Distribution from which all methods implementing a
* distribution are derived.
*
* Distributions are based on some general feature object and have to implement
* interfaces to
*
* train() - for learning a distribution
* get_num_model_parameters() - for the total number of model parameters
* get_log_model_parameter() - for the n-th model parameter (logarithmic)
* get_log_derivative() - for the partial derivative wrt. to the n-th
* model parameter
* get_log_likelihood_example() - for the likelihood for the
* n-th example
*
* This way methods building on CDistribution, might enumerate over all possible
* model parameters and obtain the parameter vector and the gradient. This is
* used to compute e.g. the TOP and Fisher Kernel (cf. CPluginEstimate, CHistogramKernel,
* CTOPFeatures and CFKFeatures ).
*/
class CDistribution : public CSGObject
{
public:
/** default constructor */
CDistribution();
virtual ~CDistribution();
/** learn distribution
*
* @param data training data (parameter can be avoided if distance or
* kernel-based classifiers are used and distance/kernels are
* initialized with train data)
*
* @return whether training was successful
*/
virtual bool train(CFeatures* data=NULL)=0;
/** get number of parameters in model
*
* abstract base method
*
* @return number of parameters in model
*/
virtual int32_t get_num_model_parameters()=0;
/** get number of parameters in model that are relevant,
* i.e. > ALMOST_NEG_INFTY
*
* @return number of relevant model parameters
*/
virtual int32_t get_num_relevant_model_parameters();
/** get model parameter (logarithmic)
*
* abstrac base method
*
* @return model parameter (logarithmic)
*/
virtual float64_t get_log_model_parameter(int32_t num_param)=0;
/** get partial derivative of likelihood function (logarithmic)
*
* abstract base method
*
* @param num_param derivative against which param
* @param num_example which example
* @return derivative of likelihood (logarithmic)
*/
virtual float64_t get_log_derivative(
int32_t num_param, int32_t num_example)=0;
/** compute log likelihood for example
*
* abstract base method
*
* @param num_example which example
* @return log likelihood for example
*/
virtual float64_t get_log_likelihood_example(int32_t num_example)=0;
/** compute log likelihood for whole sample
*
* @return log likelihood for whole sample
*/
virtual float64_t get_log_likelihood_sample();
/** compute log likelihood for each example
*
* @return log likelihood vector
*/
virtual SGVector<float64_t> get_log_likelihood();
/** get model parameter
*
* @param num_param which param
* @return model parameter
*/
virtual inline float64_t get_model_parameter(int32_t num_param)
{
return exp(get_log_model_parameter(num_param));
}
/** get partial derivative of likelihood function
*
* @param num_param partial derivative against which param
* @param num_example which example
* @return derivative of likelihood function
*/
virtual inline float64_t get_derivative(
int32_t num_param, int32_t num_example)
{
return exp(get_log_derivative(num_param, num_example));
}
/** compute likelihood for example
*
* @param num_example which example
* @return likelihood for example
*/
virtual inline float64_t get_likelihood_example(int32_t num_example)
{
return exp(get_log_likelihood_example(num_example));
}
/** set feature vectors
*
* @param f new feature vectors
*/
virtual inline void set_features(CFeatures* f)
{
SG_UNREF(features);
SG_REF(f);
features=f;
}
/** get feature vectors
*
* @return feature vectors
*/
virtual inline CFeatures* get_features()
{
SG_REF(features);
return features;
}
/** set pseudo count
*
* @param pseudo new pseudo count
*/
virtual inline void set_pseudo_count(float64_t pseudo) { pseudo_count=pseudo; }
/** get pseudo count
*
* @return pseudo count
*/
virtual inline float64_t get_pseudo_count() { return pseudo_count; }
protected:
/** feature vectors */
CFeatures* features;
/** pseudo count */
float64_t pseudo_count;
};
}
#endif
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