| /external/eigen/Eigen/src/Geometry/ |
| ParametrizedLine.h | 62 explicit ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane);
99 Scalar intersectionParameter(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const;
102 Scalar intersection(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const;
105 VectorType intersectionPoint(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const;
140 /** Constructs a parametrized line from a 2D hyperplane
142 * \warning the ambient space must have dimension 2 such that the hyperplane actually describes a lin
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| Hyperplane.h | 18 * \class Hyperplane
20 * \brief A hyperplane
22 * A hyperplane is an affine subspace of dimension n-1 in a space of dimension n.
23 * For example, a hyperplane in a plane is a line; a hyperplane in 3-space is a plane.
27 * Notice that the dimension of the hyperplane is _AmbientDim-1.
29 * This class represents an hyperplane as the zero set of the implicit equation
34 class Hyperplane
53 inline Hyperplane() {}
56 Hyperplane(const Hyperplane<Scalar,AmbientDimAtCompileTime,OtherOptions>& other
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| /external/eigen/Eigen/src/Eigen2Support/Geometry/ |
| Hyperplane.h | 17 * \class Hyperplane
19 * \brief A hyperplane
21 * A hyperplane is an affine subspace of dimension n-1 in a space of dimension n.
22 * For example, a hyperplane in a plane is a line; a hyperplane in 3-space is a plane.
26 * Notice that the dimension of the hyperplane is _AmbientDim-1.
28 * This class represents an hyperplane as the zero set of the implicit equation
33 class Hyperplane
47 inline Hyperplane() {}
49 /** Constructs a dynamic-size hyperplane with \a _dim the dimensio
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| ParametrizedLine.h | 51 explicit ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim>& hyperplane);
85 Scalar intersection(const Hyperplane<_Scalar, _AmbientDim>& hyperplane);
120 /** Constructs a parametrized line from a 2D hyperplane
122 * \warning the ambient space must have dimension 2 such that the hyperplane actually describes a line
125 inline ParametrizedLine<_Scalar, _AmbientDim>::ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim>& hyperplane)
128 direction() = hyperplane.normal().unitOrthogonal();
129 origin() = -hyperplane.normal()*hyperplane.offset()
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| All.h | 19 #include "Hyperplane.h"
57 #define Hyperplane eigen2_Hyperplane
72 #include "Hyperplane.h"
112 #undef Hyperplane
|
| /external/eigen/test/eigen2/ |
| eigen2_regression.cpp | 17 HyperplaneType *hyperplane,
22 // pick a random hyperplane, store the coefficients of its equation
23 hyperplane->coeffs().resize(size + 1);
27 hyperplane->coeffs().coeffRef(j) = ei_random<Scalar>();
28 } while(ei_abs(hyperplane->coeffs().coeff(j)) < 0.5);
31 // now pick numPoints random points on this hyperplane
38 // project cur_point onto the hyperplane
39 Scalar x = - (hyperplane->coeffs().start(size).cwise()*cur_point).sum();
40 cur_point *= hyperplane->coeffs().coeff(size) / x;
90 Hyperplane<float,2> coeffs3f
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| eigen2_hyperplane.cpp | 16 template<typename HyperplaneType> void hyperplane(const HyperplaneType& _plane) function
19 Hyperplane.h
73 Hyperplane<OtherScalar,Dim> hp1f = pl1.template cast<OtherScalar>();
75 Hyperplane<Scalar,Dim> hp1d = pl1.template cast<Scalar>();
81 typedef Hyperplane<Scalar, 2> HLine;
119 CALL_SUBTEST_1( hyperplane(Hyperplane<float,2>()) );
120 CALL_SUBTEST_2( hyperplane(Hyperplane<float,3>()) );
121 CALL_SUBTEST_3( hyperplane(Hyperplane<double,4>()) )
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| /external/eigen/test/ |
| geo_hyperplane.cpp | 16 template<typename HyperplaneType> void hyperplane(const HyperplaneType& _plane) function
19 Hyperplane.h
73 Hyperplane<OtherScalar,Dim,Options> hp1f = pl1.template cast<OtherScalar>();
75 Hyperplane<Scalar,Dim,Options> hp1d = pl1.template cast<Scalar>();
82 typedef Hyperplane<Scalar, 2> HLine;
120 typedef Hyperplane<Scalar, 3> Plane;
145 typedef Hyperplane<Scalar,3,AutoAlign> Plane3a;
146 typedef Hyperplane<Scalar,3,DontAlign> Plane3u;
174 CALL_SUBTEST_1( hyperplane(Hyperplane<float,2>()) )
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| geo_parametrizedline.cpp | 27 typedef Hyperplane<Scalar,LineType::AmbientDimAtCompileTime> HyperplaneType;
|
| /external/opencv3/doc/tutorials/ml/introduction_to_svm/ |
| introduction_to_svm.markdown | 16 hyperplane. In other words, given labeled training data (*supervised learning*), the algorithm
17 outputs an optimal hyperplane which categorizes new examples.
19 In which sense is the hyperplane obtained optimal? Let's consider the following simple problem:
38 Then, the operation of the SVM algorithm is based on finding the hyperplane that gives the largest
40 **margin** within SVM's theory. Therefore, the optimal separating hyperplane *maximizes* the margin
43 
45 How is the optimal hyperplane computed?
48 Let's introduce the notation used to define formally a hyperplane:
58 The optimal hyperplane can be represented in an infinite number of different ways by
60 representations of the hyperplane, the one chosen i
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| /external/eigen/Eigen/ |
| Geometry | 45 #include "src/Geometry/Hyperplane.h"
|
| /external/opencv3/doc/tutorials/ml/non_linear_svms/ |
| non_linear_svms.markdown | 30 Remember that using SVMs we obtain a separating hyperplane. Therefore, since the training data is
31 now non-linearly separable, we must admit that the hyperplane found will misclassify some of the
33 account. The new model has to include both the old requirement of finding the hyperplane that gives
37 We start here from the formulation of the optimization problem of finding the hyperplane which
58 separating hyperplane and the distances to their correct regions of the samples that are
84 finding a hyperplane with big margin.
175 separating hyperplane. Since the training data is non-linearly separable, it can be seen that
|
| /external/eigen/Eigen/src/Core/util/ |
| ForwardDeclarations.h | 255 template <typename _Scalar, int _AmbientDim> class Hyperplane;
263 template <typename _Scalar, int _AmbientDim, int Options=AutoAlign> class Hyperplane;
|
| Constants.h | 388 * \sa Transform, Hyperplane::transform(). */
|
| /external/opencv3/doc/py_tutorials/py_ml/py_svm/py_svm_basics/ |
| py_svm_basics.markdown | 32 find a straight line (or hyperplane) with largest minimum distance to the training samples. See the
126 finding a hyperplane with big margin.
|
| /external/eigen/Eigen/src/Eigen2Support/ |
| LeastSquares.h | 91 typedef Hyperplane<Scalar, VectorType::SizeAtCompileTime> HyperplaneType;
|
