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annotate test/test_diag_perm.m @ 8936:42e24f4ebc8c

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add tests for diag & perm matrices.
author Jason Riedy <jason@acm.org>
date Sun, 08 Mar 2009 16:40:10 -0400
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8936
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1 ## Copyright (C) 2009 E. Jason Riedy
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2 ##
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3 ## This file is part of Octave.
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4 ##
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5 ## Octave is free software; you can redistribute it and/or modify it
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6 ## under the terms of the GNU General Public License as published by
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7 ## the Free Software Foundation; either version 3 of the License, or (at
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8 ## your option) any later version.
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9 ##
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10 ## Octave is distributed in the hope that it will be useful, but
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11 ## WITHOUT ANY WARRANTY; without even the implied warranty of
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12 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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13 ## General Public License for more details.
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14 ##
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15 ## You should have received a copy of the GNU General Public License
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16 ## along with Octave; see the file COPYING. If not, see
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17 ## <http://www.gnu.org/licenses/>.
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18
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19 ########################################
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20 ## Permutation matrices
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21
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22 ## row permutation
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23 %!test
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24 %! n = 5;
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25 %! A = rand (n);
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26 %! perm = randperm (n);
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27 %! Prow = eye (n) (perm, :);
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28 %! assert (A(perm, :), Prow * A);
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29 %! invperm(perm) = 1:n;
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30 %! assert (Prow \ A, A(invperm, :));
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31 %! assert (Prow' * A, A(invperm, :));
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32
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33 ## column permutation
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34 %!test
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35 %! n = 7;
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36 %! A = rand (n);
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37 %! perm = randperm (n);
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38 %! Pcol = eye (n) (:, perm);
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39 %! assert (A(:, perm), A * Pcol);
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40 %! invperm(perm) = 1:n;
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41 %! assert (A / Pcol, A(:, invperm));
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42 %! assert (A * Pcol.', A(:, invperm));
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43
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44 ## fall back to a matrix in addition
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45 %!test
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46 %! n = 4;
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47 %! P1 = eye (n) (:, randperm (n));
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48 %! A = zeros (n) + P1;
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49 %! assert (sum (A), ones (1, n));
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50 %! assert (sum (A, 2), ones (n, 1));
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51
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52 ## preserve dense matrix structure
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53 %!test
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54 %! n = 7;
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55 %! Pc = eye (n) (:, randperm (n));
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56 %! Pr = eye (n) (randperm (n), :);
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57 %! assert (typeinfo (rand (n) * Pc), "matrix");
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58 %! assert (typeinfo (Pr * rand (n)), "matrix");
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59
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60 ## permuting a matrix with exceptional values does not introduce new ones.
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61 %!test
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62 %! n = 5;
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63 %! pc = randperm (n);
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64 %! Pc = eye (n) (:, pc);
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65 %! pr = randperm (n);
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66 %! Pr = eye (n) (pr, :);
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67 %! A = rand (n);
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68 %! A(n, n-2) = NaN;
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69 %! A(3, 1) = Inf;
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70 %! assert (Pr * A * Pc, A(pr, pc));
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71
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72 ########################################
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73 ## Diagonal matrices
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74
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75 ## square row scaling
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76 %!test
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77 %! m = 7;
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78 %! n = 11;
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79 %! A = rand (m, n);
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80 %! scalefact = rand (m, 1);
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81 %! Dr = diag (scalefact);
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82 %! assert (Dr * A, repmat (scalefact, 1, n) .* A);
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83 %! assert (Dr \ A, A ./ repmat (scalefact, 1, n));
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84 %! scalefact(m-1) = Inf;
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85 %! Dr(m-1, m-1) = 0;
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86 %! assert (Dr \ A, A ./ repmat (scalefact, 1, n));
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87
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88 ## square column scaling
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89 %!test
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90 %! m = 13;
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91 %! n = 11;
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92 %! A = rand (m, n);
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93 %! scalefact = rand (1, n);
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94 %! Dc = diag (scalefact);
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95 %! assert (A * Dc, repmat (scalefact, m, 1) .* A);
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96 %! assert (A / Dc, A ./ repmat (scalefact, m, 1));
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97 %! scalefact(n-1) = Inf;
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98 %! Dc(n-1, n-1) = 0;
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99 %! assert (A / Dc, A ./ repmat (scalefact, m, 1));
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100
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101 ## arithmetic
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102 %!test
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103 %! m = 9;
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104 %! n = 7;
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105 %! mn = min (m, n);
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106 %! d1 = rand (mn, 1) + I () * rand (mn, 1);
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107 %! D1 = diag (d1, m, n);
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108 %! d2 = rand (mn, 1);
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109 %! D2 = diag (d2, m, n);
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110 %! D1D2 = D1 + D2;
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111 %! assert (typeinfo (D1D2), "complex diagonal matrix");
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112 %! assert (diag (D1D2), d1 + d2);
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113 %! D1D2 = D2.' * D1;
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114 %! assert (typeinfo (D1D2), "complex diagonal matrix");
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115 %! assert (diag (D1D2), d1 .* d2);
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116
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117 ## slicing
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118 %!test
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119 %! m = 13;
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120 %! n = 6;
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121 %! mn = min (m, n);
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122 %! d = rand (mn, 1);
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123 %! D = diag (d, m, n);
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124 %! Dslice = D (1:(m-3), 1:(n-2));
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125 %! assert (typeinfo (Dslice), "diagonal matrix");
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126
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127 ## preserve dense matrix structure
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128 %!assert (typeinfo (rand (8) * (3 * eye (8))), "matrix");
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129 %!assert (typeinfo ((3 * eye (8)) * rand (8)), "matrix");
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130
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131 ## scaling a matrix with exceptional values does not introduce new ones.
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132 %!test
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133 %! n = 6;
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134 %! dr = rand (n, 1);
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135 %! Dr = diag (dr);
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136 %! dc = rand (1, n);
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137 %! Dc = diag (dc);
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138 %! A = rand (n);
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139 %! A(n, n-2) = NaN;
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140 %! A(4, 1) = Inf;
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141 %! assert (Dr * A * Dc, A .* kron (dr, dc), eps);
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142