Mercurial > hg > segmenter-vamp-plugin
comparison armadillo-2.4.4/include/armadillo_bits/op_cov_meat.hpp @ 0:8b6102e2a9b0
Armadillo Library
author | maxzanoni76 <max.zanoni@eecs.qmul.ac.uk> |
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date | Wed, 11 Apr 2012 09:27:06 +0100 |
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-1:000000000000 | 0:8b6102e2a9b0 |
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1 // Copyright (C) 2009-2011 NICTA (www.nicta.com.au) | |
2 // Copyright (C) 2009-2011 Conrad Sanderson | |
3 // Copyright (C) 2009-2010 Dimitrios Bouzas | |
4 // | |
5 // This file is part of the Armadillo C++ library. | |
6 // It is provided without any warranty of fitness | |
7 // for any purpose. You can redistribute this file | |
8 // and/or modify it under the terms of the GNU | |
9 // Lesser General Public License (LGPL) as published | |
10 // by the Free Software Foundation, either version 3 | |
11 // of the License or (at your option) any later version. | |
12 // (see http://www.opensource.org/licenses for more info) | |
13 | |
14 | |
15 | |
16 //! \addtogroup op_cov | |
17 //! @{ | |
18 | |
19 | |
20 | |
21 template<typename eT> | |
22 inline | |
23 void | |
24 op_cov::direct_cov(Mat<eT>& out, const Mat<eT>& A, const uword norm_type) | |
25 { | |
26 arma_extra_debug_sigprint(); | |
27 | |
28 if(A.is_vec()) | |
29 { | |
30 if(A.n_rows == 1) | |
31 { | |
32 out = var(trans(A), norm_type); | |
33 } | |
34 else | |
35 { | |
36 out = var(A, norm_type); | |
37 } | |
38 } | |
39 else | |
40 { | |
41 const uword N = A.n_rows; | |
42 const eT norm_val = (norm_type == 0) ? ( (N > 1) ? eT(N-1) : eT(1) ) : eT(N); | |
43 | |
44 const Row<eT> acc = sum(A); | |
45 | |
46 out = trans(A) * A; | |
47 out -= (trans(acc) * acc)/eT(N); | |
48 out /= norm_val; | |
49 } | |
50 } | |
51 | |
52 | |
53 | |
54 template<typename T> | |
55 inline | |
56 void | |
57 op_cov::direct_cov(Mat< std::complex<T> >& out, const Mat< std::complex<T> >& A, const uword norm_type) | |
58 { | |
59 arma_extra_debug_sigprint(); | |
60 | |
61 typedef typename std::complex<T> eT; | |
62 | |
63 if(A.is_vec()) | |
64 { | |
65 if(A.n_rows == 1) | |
66 { | |
67 const Mat<T> tmp_mat = var(trans(A), norm_type); | |
68 out.set_size(1,1); | |
69 out[0] = tmp_mat[0]; | |
70 } | |
71 else | |
72 { | |
73 const Mat<T> tmp_mat = var(A, norm_type); | |
74 out.set_size(1,1); | |
75 out[0] = tmp_mat[0]; | |
76 } | |
77 } | |
78 else | |
79 { | |
80 const uword N = A.n_rows; | |
81 const eT norm_val = (norm_type == 0) ? ( (N > 1) ? eT(N-1) : eT(1) ) : eT(N); | |
82 | |
83 const Row<eT> acc = sum(A); | |
84 | |
85 out = trans(A) * A; // out = strans(conj(A)) * A; | |
86 out -= (trans(acc) * acc)/eT(N); // out -= (strans(conj(acc)) * acc)/eT(N); | |
87 out /= norm_val; | |
88 } | |
89 } | |
90 | |
91 | |
92 | |
93 template<typename T1> | |
94 inline | |
95 void | |
96 op_cov::apply(Mat<typename T1::elem_type>& out, const Op<T1,op_cov>& in) | |
97 { | |
98 arma_extra_debug_sigprint(); | |
99 | |
100 typedef typename T1::elem_type eT; | |
101 | |
102 const unwrap_check<T1> tmp(in.m, out); | |
103 const Mat<eT>& A = tmp.M; | |
104 | |
105 const uword norm_type = in.aux_uword_a; | |
106 | |
107 op_cov::direct_cov(out, A, norm_type); | |
108 } | |
109 | |
110 | |
111 | |
112 //! @} |