/usr/include/shogun/distance/EuclideanDistance.h is in libshogun-dev 3.2.0-7.3build4.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 | /*
* 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) 2007-2009 Soeren Sonnenburg
* Copyright (C) 2007-2009 Fraunhofer Institute FIRST and Max-Planck-Society
*/
#ifndef _EUCLIDEANDISTANCE_H__
#define _EUCLIDEANDISTANCE_H__
#include <shogun/lib/common.h>
#include <shogun/distance/RealDistance.h>
#include <shogun/features/DenseFeatures.h>
namespace shogun
{
/** @brief class EuclideanDistance
*
* The familiar Euclidean distance for real valued features computes
* the square root of the sum of squared disparity between the
* corresponding feature dimensions of two data points.
*
* \f[\displaystyle
* d({\bf x},{\bf x'})= \sqrt{\sum_{i=0}^{n}|{\bf x_i}-{\bf x'_i}|^2}
* \f]
*
* This special case of Minkowski metric is invariant to an arbitrary
* translation or rotation in feature space.
*
* The Euclidean Squared distance does not take the square root:
*
* \f[\displaystyle
* d({\bf x},{\bf x'})= \sum_{i=0}^{n}|{\bf x_i}-{\bf x'_i}|^2
* \f]
*
* @see CMinkowskiMetric
* @see <a href="http://en.wikipedia.org/wiki/Distance#Distance_in_Euclidean_space">
* Wikipedia: Distance in Euclidean space</a>
*/
class CEuclideanDistance: public CRealDistance
{
public:
/** default constructor */
CEuclideanDistance();
/** constructor
*
* @param l features of left-hand side
* @param r features of right-hand side
*/
CEuclideanDistance(CDenseFeatures<float64_t>* l, CDenseFeatures<float64_t>* r);
virtual ~CEuclideanDistance();
/** init distance
*
* @param l features of left-hand side
* @param r features of right-hand side
* @return if init was successful
*/
virtual bool init(CFeatures* l, CFeatures* r);
/** cleanup distance */
virtual void cleanup();
/** get distance type we are
*
* @return distance type EUCLIDEAN
*/
virtual EDistanceType get_distance_type() { return D_EUCLIDEAN; }
/** get feature type the distance can deal with
*
* @return feature type DREAL
*/
virtual EFeatureType get_feature_type() { return F_DREAL; }
/** get name of the distance
*
* @return name Euclidean
*/
virtual const char* get_name() const { return "EuclideanDistance"; }
/** disable application of sqrt on matrix computation
* the matrix can then also be named norm squared
*
* @return if application of sqrt is disabled
*/
virtual bool get_disable_sqrt() { return disable_sqrt; };
/** disable application of sqrt on matrix computation
* the matrix can then also be named norm squared
*
* @param state new disable_sqrt
*/
virtual void set_disable_sqrt(bool state) { disable_sqrt=state; };
/** compute the distance between lhs feature vector a
* and rhs feature vector b. The computation of the
* distance stops if the intermediate result is
* larger than upper_bound. This is useful to use
* with John Langford's Cover Tree
*
* @param idx_a feature vector a at idx_a
* @param idx_b feature vector b at idx_b
* @param upper_bound value above which the computation
* halts
* @return distance value or upper_bound
*/
virtual float64_t distance_upper_bounded(int32_t idx_a, int32_t idx_b, float64_t upper_bound);
protected:
/// compute kernel function for features a and b
/// idx_{a,b} denote the index of the feature vectors
/// in the corresponding feature object
virtual float64_t compute(int32_t idx_a, int32_t idx_b);
private:
void init();
protected:
/** if application of sqrt on matrix computation is disabled */
bool disable_sqrt;
};
} // namespace shogun
#endif /* _EUCLIDEANDISTANCE_H__ */
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