1 | // $Id: SVD.h 420 2005-12-02 00:15:50Z jari $ |
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2 | |
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3 | #ifndef _theplu_utility_svd_ |
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4 | #define _theplu_utility_svd_ |
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5 | |
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6 | #include <c++_tools/gslapi/matrix.h> |
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7 | #include <c++_tools/gslapi/vector.h> |
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8 | |
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9 | #include <gsl/gsl_linalg.h> |
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10 | |
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11 | namespace theplu { |
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12 | namespace utility { |
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13 | |
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14 | // Jari check that interface is complete |
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15 | |
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16 | /** |
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17 | Class encapsulating GSL methods for singular value decomposition, |
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18 | SVD. |
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19 | |
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20 | A = U S V' = (MxN)(NxN)(NxN) = (MxN)\n |
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21 | |
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22 | A = Matrix to be decomposed, size MxN\n |
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23 | U = Orthogonal matrix, size MxN\n |
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24 | S = Diagonal matrix of singular values, size NxN\n |
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25 | V = Orthogonal matrix, size NxN\n |
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26 | */ |
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27 | |
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28 | class SVD |
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29 | { |
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30 | public: |
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31 | |
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32 | /// |
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33 | /// A number of SVD algorithms are implemented in GSL. They have |
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34 | /// their strengths and weaknesses, check the GSL documentation. |
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35 | /// |
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36 | /// There are restrictions on the matrix dimensions depending on |
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37 | /// which SVD algorithm is used. From the GSL's SVD source code |
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38 | /// one finds that the Golub-Reinsch algorithm implementation will |
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39 | /// not work on matrices with fewer rows than columns, the same is |
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40 | /// also true for the modified Golub-Reinsch algorithm. |
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41 | /// |
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42 | /// @see GSL's SVD documentation. |
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43 | /// |
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44 | enum SVDalgorithm { |
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45 | GolubReinsch, |
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46 | ModifiedGolubReinsch, |
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47 | Jacobi |
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48 | }; |
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49 | |
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50 | /// |
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51 | /// Constructs an SVD object using the matrix A as only input. The |
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52 | /// input matrix is copied for further use in the object. |
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53 | /// |
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54 | inline SVD(const gslapi::matrix& Ain) |
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55 | : U_(Ain), V_(Ain.columns(),Ain.columns()), s_(Ain.columns()) {} |
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56 | |
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57 | inline ~SVD(void) {} |
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58 | |
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59 | /// |
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60 | /// This function will perform SVD with the method specified by \a |
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61 | /// algo. |
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62 | /// |
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63 | /// @return Whatever GSL returns. |
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64 | /// |
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65 | int decompose(SVDalgorithm algo=GolubReinsch); |
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66 | |
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67 | /// |
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68 | /// Access to the s vector. |
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69 | /// |
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70 | /// @return A copy of the s vector. |
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71 | /// |
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72 | /// @note If decompose() has not been run the outcome of the call |
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73 | /// is undefined. |
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74 | /// |
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75 | inline const gslapi::vector& s(void) const { return s_; } |
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76 | |
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77 | /// |
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78 | /// Solve the system \f$Ax=b\f$ using the decomposition of A. |
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79 | /// |
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80 | /// @note If decompose() has not been run the outcome of the call |
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81 | /// is undefined. |
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82 | /// |
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83 | /// @return Whatever GSL returns. |
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84 | /// |
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85 | inline int solve(gslapi::vector b, gslapi::vector x) |
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86 | { return gsl_linalg_SV_solve(U_.gsl_matrix_p(), V_.gsl_matrix_p(), |
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87 | s_.gsl_vector_p(), b.gsl_vector_p(), |
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88 | x.gsl_vector_p()); } |
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89 | |
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90 | /// |
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91 | /// Access to the U matrix. |
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92 | /// |
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93 | /// @return A copy of the U matrix. |
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94 | /// |
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95 | /// @note If decompose() has not been run the outcome of the call |
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96 | /// is undefined. |
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97 | /// |
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98 | inline const gslapi::matrix& U(void) const { return U_; } |
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99 | |
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100 | /// |
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101 | /// Access to the V matrix. |
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102 | /// |
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103 | /// @return A copy of the V matrix. |
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104 | /// |
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105 | /// @note If decompose() has not been run the outcome of the call |
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106 | /// is undefined. |
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107 | /// |
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108 | inline const gslapi::matrix& V(void) const { return V_; } |
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109 | |
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110 | private: |
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111 | inline int jacobi(void) |
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112 | { return gsl_linalg_SV_decomp_jacobi(U_.gsl_matrix_p(), V_.gsl_matrix_p(), |
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113 | s_.gsl_vector_p()); } |
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114 | int golub_reinsch(void); |
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115 | int modified_golub_reinsch(void); |
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116 | |
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117 | gslapi::matrix U_, V_; |
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118 | gslapi::vector s_; |
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119 | }; |
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120 | |
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121 | }} // of namespace utility and namespace theplu |
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122 | |
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123 | #endif |
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