<commit_before>/*
 * 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) 2011 <NAME>
 * Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
 */

#ifndef GAUSSIANNAIVEBAYES_H_
#define GAUSSIANNAIVEBAYES_H_

#include "Classifier.h"
#include "features/DotFeatures.h"

namespace shogun {

/** @brief Class GaussianNaiveBayes, a Gaussian Naive Bayes classifier
 *
 *	Formally, chooses class c with maximum \f$ P(c)P(x|c) \f$
 *	probability. Naive bayes assumes \f$ P(x|c) \f$ as product
 *  \f$ \prod_i P(x_i|c) \f$ and gaussian naive bayes assume
 *  \f$P(x_i|c) \sim \mathcal{N} (\mu_{i,c}, {\sigma}^2_{i,c})\f$, where \f$ \mathcal{N} \f$ is
 *  normal distribution and \f$ \mu_{i,c}, {\sigma}^2_{i,c} \f$ are estimates of i-th
 *  feature mean and variance among class \f$ c \f$.
 *
 *  Note that classifier requires ~ (dimensionality)*(number of classes)
 *  memory.
 *
 */
<commit_msg>fix include path to be relative to libshogun/<commit_after>/*
 * 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) 2011 <NAME>
 * Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
 */

#ifndef GAUSSIANNAIVEBAYES_H_
#define GAUSSIANNAIVEBAYES_H_

#include "classifier/Classifier.h"
#include "features/DotFeatures.h"

namespace shogun {

/** @brief Class GaussianNaiveBayes, a Gaussian Naive Bayes classifier
 *
 *	Formally, chooses class c with maximum \f$ P(c)P(x|c) \f$
 *	probability. Naive bayes assumes \f$ P(x|c) \f$ as product
 *  \f$ \prod_i P(x_i|c) \f$ and gaussian naive bayes assume
 *  \f$P(x_i|c) \sim \mathcal{N} (\mu_{i,c}, {\sigma}^2_{i,c})\f$, where \f$ \mathcal{N} \f$ is
 *  normal distribution and \f$ \mu_{i,c}, {\sigma}^2_{i,c} \f$ are estimates of i-th
 *  feature mean and variance among class \f$ c \f$.
 *
 *  Note that classifier requires ~ (dimensionality)*(number of classes)
 *  memory.
 *
 */

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