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1 % makro_get_significance_cupaper
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2 %%
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3 % ------------------------------ Algorithms Compared id-sampling
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4 % ---
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5 'mlr vs euclidean, mlr all average feat, ID-sampling'
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6 file1 = 'runlog_0b506247a68167addf97fcb0296650eb_1a58077f1232c33b787b661039df107d_finalresults';
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7
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8 % euclidean average feat - TODO: which sampling is this ?
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9 file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults';
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10 % ---
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11 % NOTE: all 4 test-set runs have exactly the same result
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12 % ---
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13 run1 = -1;
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14 run2 = -1;
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15 mode = 'join_datasets'; % '', 'avgbase', 'join_datasets'
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16 [p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run1,0,mode);
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17 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
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18
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19
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20 %%
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21 % ---
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22 'SVM vs euclidean, SVM all average feat, ID-sampling'
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23 file1 = 'runlog_0b506247a68167addf97fcb0296650eb_1841892e9df07039bbe4c3a55d11026a_finalresults';
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24
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25 % euclidean average feat - TODO: which sampling is this ?
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26 file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults';
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27
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28 [p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2,0,mode);
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29 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
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30
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31 %%
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32 % ------------------------------ Algorithms Compared TD-sampling
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33 % ---
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34 'mlr unclustered vs euclidean unclustered, mlr all average feat TD-sampling'
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35 file1 = 'runlog_0b506247a68167addf97fcb0296650eb_c364cb0803822d55f2940656c44b184d_finalresults';
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36
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37 % euclidean average feat TD-sampling
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38 file2 = 'runlog_0b506247a68167addf97fcb0296650eb_72303bfa642aad872665dee7a3b1e28c_finalresults';
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39
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40 [p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2,0,mode);
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41 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
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42
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43 %%
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44 % ---
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45 'SVM unclustered vs euclidean unclustered, SVM all average feat TD-sampling'
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46 file1 = 'runlog_0b506247a68167addf97fcb0296650eb_3434f0534fa910b26bbf927c65a7fb74_finalresults';
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47
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48 % euclidean average feat TD-sampling
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49 file2 = 'runlog_0b506247a68167addf97fcb0296650eb_72303bfa642aad872665dee7a3b1e28c_finalresults';
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50 % ---
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51
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52 [p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2,0,mode);
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53 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
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54
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55
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56 %%
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57 % ------------------------------ Features Compared
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58 'All 12-dim PCA Features compared'
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59 % ---
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60 namePCA12 = {'AcousticPCA12', ...
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61 'Slaney08PCA12', ...
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62 'TimbrePCA12', ...
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63 'ChromaPCA12', ...
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64 'AllPCA12', ...
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65 'GenrePCA12'};
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66 % this is the index how the feature types appear in the paper
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67 paperidx = [4 3 2 6 1 5];
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68
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69 filePCA12 = {'runlog_a18bd2111694ac59c9ba0a6810121796_1841892e9df07039bbe4c3a55d11026a_finalresults', ...
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70 'runlog_37e47c187886f73ec9a7d8dc24a84a52_1841892e9df07039bbe4c3a55d11026a_finalresults', ...
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71 'runlog_4c6787b403a07f5faf1ec26e891da4fa_1841892e9df07039bbe4c3a55d11026a_finalresults', ...
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72 'runlog_c5566f74e6a0d00b50f5eea05fdacfee_1841892e9df07039bbe4c3a55d11026a_finalresults', ...
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73 'runlog_e2c22696e7af9e7eea1fa1fd10a1f785_1841892e9df07039bbe4c3a55d11026a_finalresults', ...
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74 'runlog_efc6e5e9c56291cd1744092a1c59a293_1841892e9df07039bbe4c3a55d11026a_finalresults'};
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75
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76
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77 namePCA12 = namePCA12(paperidx);
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78 filePCA12 = filePCA12(paperidx);
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79
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80 p = zeros(numel(namePCA12),numel(namePCA12));
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81 med = zeros(numel(namePCA12),numel(namePCA12));
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82 avg = zeros(numel(namePCA12),numel(namePCA12));
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83 for i=1:numel(name)
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84 for j = 1:i-1
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85 [p(i,j), med(i,j), avg(i,j)] = test_generic_significance_signrank(filePCA12{i},-1,filePCA12{j},-1,0,mode);
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86 end
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87 % p(i,i) = 0.5;
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88 end
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89 % p = p + p';
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90 imagesc(p);
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91 colormap(hot)
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92 axis xy
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93 set(gca,'XTick',1:numel(namePCA12), 'XTickLabel', namePCA12);
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94 set(gca,'YTick',1:numel(namePCA12), 'YTickLabel', namePCA12);
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95 matrix2latex(p,'%1.3f')
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96
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97 %%
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98 'All 52-dim PCA Features compared'
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99 % ---
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100 namePCA52 = {'GenrePCA52', ...
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101 'AllPCA52', ...
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102 'TimbrePCA52', ...
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103 'AcousticPCA52', ...
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104 'ChromaPCA52' ...
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105 };
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106 % this is the index how the feature types appear in the paper
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107 paperidx = [5 3 1 4 2];
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108
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109 filePCA52 = {'runlog_3cbf4759cf58af0728aaab0b5f2660e3_1841892e9df07039bbe4c3a55d11026a_finalresults', ...
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110 'runlog_7d5fafec0dc504215acc8cb7a9202a56_1841892e9df07039bbe4c3a55d11026a_finalresults', ...
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111 'runlog_a3c2c0a5742a42fd54497e69b8f44e8d_1841892e9df07039bbe4c3a55d11026a_finalresults', ...
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112 'runlog_c7164074206998aa184538bedcfdcf2f_1841892e9df07039bbe4c3a55d11026a_finalresults', ...
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113 'runlog_efbf7c8e75ae154c2f192acd08fbdcbc_1841892e9df07039bbe4c3a55d11026a_finalresults' ...
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114 };
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115
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116
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117 namePCA52 = namePCA52(paperidx);
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118 filePCA52 = filePCA52(paperidx);
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119
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120 p = zeros(numel(namePCA52),numel(namePCA52));
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121 med = zeros(numel(namePCA52),numel(namePCA52));
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122 avg = zeros(numel(namePCA52),numel(namePCA52));
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123 for i=1:numel(name)
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124 for j = 1:i-1
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125 [p(i,j), med(i,j), avg(i,j)] = test_generic_significance_signrank(...
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126 filePCA52{i},run1,filePCA52{j},run2,0,mode);
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127 end
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128 % p(i,i) = 0.5;
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129 end
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130 % p = p + p';
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131 imagesc(p);
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132 colormap(hot)
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133 axis xy
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134 set(gca,'XTick',1:numel(namePCA52), 'XTickLabel', namePCA52);
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135 set(gca,'YTick',1:numel(namePCA52), 'YTickLabel', namePCA52);
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136 matrix2latex(p,'%1.3f')
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137
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138 %%
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139 'All full-dim Features compared (SVM)'
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140 % ---
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141 name = {'4Chroma', ...
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142 '1Chroma', ...
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143 '4Timbre', ...
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144 '1Timbre', ...
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145 '1Acoustic', ...
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146 'Genre', ...
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147 'Slaney08', ...
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148 '1All', ...
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149 '4All', ...
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150 '4Acoustic' ...
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151 };
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152
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153 % this is the index how the feature types appear in the paper
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154 % Features & Chroma(1/4) & Timbre(1/4) & Slaney08 & Genre & Comb. Audio (1/4) & Comb. All(1/4) \\
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155 paperidx = [2 1 4 3 7 6 5 10 8 9];
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156
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157 file = {'runlog_20a2f6a0f20f488e9386ebb8c5026fcf_85c439d9ef3d135936e7645ebc0efe36_finalresults', ...
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158 'runlog_20a2f6a0f20f488e9386ebb8c5026fcf_85c439d9ef3d135936e7645ebc0efe36_finalresults', ...
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159 'runlog_3154f36c34c18f60218c5d3f0c0b5931_85c439d9ef3d135936e7645ebc0efe36_finalresults', ...
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160 'runlog_3154f36c34c18f60218c5d3f0c0b5931_85c439d9ef3d135936e7645ebc0efe36_finalresults', ...
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161 'runlog_31981d48dd0d25564ef3c2b3ca650b3b_1841892e9df07039bbe4c3a55d11026a_finalresults', ...
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162 'runlog_37867d3b5bd4c74b7b548732b80fb947_85c439d9ef3d135936e7645ebc0efe36_finalresults', ...
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163 'runlog_f52d37439805ac4edc70b0432281abc3_85c439d9ef3d135936e7645ebc0efe36_finalresults', ...
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164 'runlog_0b506247a68167addf97fcb0296650eb_1841892e9df07039bbe4c3a55d11026a_finalresults', ...
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165 'runlog_800d97be9ef6274dc3bbe6b9be2406a6_1a58077f1232c33b787b661039df107d_finalresults', ...
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166 'runlog_cf5a61cca09e2a3182b794b70ee1ab91_1841892e9df07039bbe4c3a55d11026a_finalresults' ...
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167 };
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168
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169 sets2join = {[1:4],[5:8],[1:4],[5:8],[],[],[],[],[],[]};
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170
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171 name = name(paperidx);
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172 file = file(paperidx);
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173
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174 p = zeros(numel(name),numel(name));
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175 med = zeros(numel(name),numel(name));
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176 avg = zeros(numel(name),numel(name));
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177 for i=1:numel(name)
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178 for j = 1:i-1
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179 [p(i,j), med(i,j), avg(i,j)] = test_generic_significance_signrank(...
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180 file{i},sets2join{i},file{j},sets2join{j},0,mode);
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181 end
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182 % p(i,i) = 0.5;
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183 end
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184 p = flipud(p);
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185 % p = p + p';
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186 imagesc(p);
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187 colormap(hot)
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188 axis ij
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189 set(gca,'XTick',1:numel(name), 'XTickLabel', name);
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190 set(gca,'YTick',1:numel(name), 'YTickLabel', fliplr(name));
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191 matrix2latex(p,'%1.3f')
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192
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193 %%
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194 'Comparing Feature dimensions and PCA effect on combined all features'
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195 % ---
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196 % 'SVM all average feat, ID-sampling'
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197 allavg = 'runlog_0b506247a68167addf97fcb0296650eb_1841892e9df07039bbe4c3a55d11026a_finalresults';
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198
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199 % 'SVM 4 cluster feat, ID-sampling'
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200 allfourcluster = 'runlog_800d97be9ef6274dc3bbe6b9be2406a6_1a58077f1232c33b787b661039df107d_finalresults';
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201
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202 % 'SVM 12dim feat, ID-sampling'
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203 pca12 = 'runlog_e2c22696e7af9e7eea1fa1fd10a1f785_1841892e9df07039bbe4c3a55d11026a_finalresults';
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204
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205 % 'SVM 52dim feat, ID-sampling'
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206 pca52 = 'runlog_7d5fafec0dc504215acc8cb7a9202a56_1841892e9df07039bbe4c3a55d11026a_finalresults';
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207 % ---
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208 % NOTE: all 4 test-set runs have exactly the same result
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209 % ---
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210 run1 = -1;
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211 run2 = -1;
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212 mode = 'join_datasets'; % '', 'avgbase', 'join_datasets'
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213
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214 'AllAvg vs Pca12'
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215 [p, med, avg] = test_generic_significance_signrank(allavg,run1,pca12,run2,0,mode);
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216 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
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217
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218 'AllAvg vs pca52'
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219 [p, med, avg] = test_generic_significance_signrank(allavg,run1,pca52,run2,0,mode);
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220 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
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221
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222 'Pca12 vs pca52'
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223 [p, med, avg] = test_generic_significance_signrank(pca12,run1,pca52,run2,0,mode);
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224 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
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225
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226
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227 %%
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228 % ------------------------------ Algorithms Weighted Training Weighted Performance Compared
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229 'MLR t:w,e:w vs euclidean t:w,e:w'
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230 % ---
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231 file1 = 'runlog_0b506247a68167addf97fcb0296650eb_3cdcca7596fed97f87b0ec051cb8bba0_finalresults';
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232
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233 % euclidean baseline, file same as the unweighted above
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234 file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults';
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235 % ---
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wolffd@0
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236 % NOTE: using the "weighted" parameter here
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237 % ---
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238 [p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 1 ,mode);
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239 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
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240
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241 %%
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242 'DMLR t:w,e:w vs euclidean t:w,e:w'
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243 % ---
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244 file1 = 'runlog_0b506247a68167addf97fcb0296650eb_1cc76d534804229cbdec8b20f8b75dba_finalresults';
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245
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246 % euclidean baseline, file same as the unweighted above
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247 file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults';
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248 % ---
|
wolffd@0
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249 % NOTE: using the "weighted" parameter here
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wolffd@0
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250 % ---
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wolffd@0
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251 [p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 1 ,mode);
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wolffd@0
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252 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
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wolffd@0
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253
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wolffd@0
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254 %%
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wolffd@0
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255 'SVM t:w,e:w vs euclidean t:w,e:w'
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wolffd@0
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256 % ---
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wolffd@0
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257 file1 = 'runlog_0b506247a68167addf97fcb0296650eb_9bd9ccddb2e4e622e2ba9826466442ba_finalresults';
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wolffd@0
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258
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wolffd@0
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259 % euclidean baseline, file same as the unweighted above
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wolffd@0
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260 file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults';
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wolffd@0
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261 % ---
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wolffd@0
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262 % NOTE: using the "weighted" parameter here
|
wolffd@0
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263 % ---
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wolffd@0
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264 [p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 1 ,mode);
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wolffd@0
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265 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
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wolffd@0
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266
|
wolffd@0
|
267 %%
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wolffd@0
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268 % ------------------------------ Algorithms Weighted Training UNWEIGHTED Performance Compared
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wolffd@0
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269 'MLR t:w,e:uw vs euclidean t:w,e:uw'
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wolffd@0
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270 % ---
|
wolffd@0
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271 file1 = 'runlog_0b506247a68167addf97fcb0296650eb_3cdcca7596fed97f87b0ec051cb8bba0_finalresults';
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wolffd@0
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272
|
wolffd@0
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273 % euclidean baseline, file same as the unweighted above
|
wolffd@0
|
274 file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults';
|
wolffd@0
|
275 % ---
|
wolffd@0
|
276 % NOTE: using the "weighted" parameter here
|
wolffd@0
|
277 % ---
|
wolffd@0
|
278 [p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 0 ,mode);
|
wolffd@0
|
279 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
|
wolffd@0
|
280
|
wolffd@0
|
281 %%
|
wolffd@0
|
282 'DMLR t:w,e:uw vs euclidean t:w,e:uw'
|
wolffd@0
|
283 % ---
|
wolffd@0
|
284 file1 = 'runlog_0b506247a68167addf97fcb0296650eb_1cc76d534804229cbdec8b20f8b75dba_finalresults';
|
wolffd@0
|
285
|
wolffd@0
|
286 % euclidean baseline, file same as the unweighted above
|
wolffd@0
|
287 file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults';
|
wolffd@0
|
288 % ---
|
wolffd@0
|
289 % NOTE: using the "weighted" parameter here
|
wolffd@0
|
290 % ---
|
wolffd@0
|
291 [p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 0 ,mode);
|
wolffd@0
|
292 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
|
wolffd@0
|
293
|
wolffd@0
|
294 %%
|
wolffd@0
|
295 'SVM t:w,e:uw vs euclidean t:w,e:uw'
|
wolffd@0
|
296 % ---
|
wolffd@0
|
297 file1 = 'runlog_0b506247a68167addf97fcb0296650eb_9bd9ccddb2e4e622e2ba9826466442ba_finalresults';
|
wolffd@0
|
298
|
wolffd@0
|
299 % euclidean baseline, file same as the unweighted above
|
wolffd@0
|
300 file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults';
|
wolffd@0
|
301 % ---
|
wolffd@0
|
302 % NOTE: using the "weighted" parameter here
|
wolffd@0
|
303 % ---
|
wolffd@0
|
304 [p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 0 ,mode);
|
wolffd@0
|
305 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
|
wolffd@0
|
306
|
wolffd@0
|
307
|
wolffd@0
|
308 %%
|
wolffd@0
|
309 % ------------------------------ Algorithms w / uw Training WEIGHTED Performance Compared
|
wolffd@0
|
310 'MLR t:w,e:w vs MLR t:uw,e:w'
|
wolffd@0
|
311 % ---
|
wolffd@0
|
312 % MLR t:w,e:w
|
wolffd@0
|
313 file1 = 'runlog_0b506247a68167addf97fcb0296650eb_3cdcca7596fed97f87b0ec051cb8bba0_finalresults';
|
wolffd@0
|
314
|
wolffd@0
|
315 % MLR t:uw,e:w
|
wolffd@0
|
316 file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a58077f1232c33b787b661039df107d_finalresults';
|
wolffd@0
|
317 % ---
|
wolffd@0
|
318 % NOTE: using the "weighted" parameter here
|
wolffd@0
|
319 % ---
|
wolffd@0
|
320 [p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 1 ,mode);
|
wolffd@0
|
321 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
|
wolffd@0
|
322
|
wolffd@0
|
323 %%
|
wolffd@0
|
324 % ---
|
wolffd@0
|
325 'SVM t:w,e:w vs SVM t:uw,e:w'
|
wolffd@0
|
326 % ---
|
wolffd@0
|
327 % SVM t:w,e:w
|
wolffd@0
|
328 file1 = 'runlog_0b506247a68167addf97fcb0296650eb_9bd9ccddb2e4e622e2ba9826466442ba_finalresults';
|
wolffd@0
|
329
|
wolffd@0
|
330 % SVM t:uw,e:w
|
wolffd@0
|
331 file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1841892e9df07039bbe4c3a55d11026a_finalresults';
|
wolffd@0
|
332 % ---
|
wolffd@0
|
333 % NOTE: using the "weighted" parameter here
|
wolffd@0
|
334 % ---
|
wolffd@0
|
335 [p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 1 ,mode);
|
wolffd@0
|
336 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
|
wolffd@0
|
337
|
wolffd@0
|
338 %%
|
wolffd@0
|
339 % ------------------------------ Algorithms w / uw Training WEIGHTED Performance Compared
|
wolffd@0
|
340 'DMLR t:w,e:w vs DMLR t:uw,e:w'
|
wolffd@0
|
341 % ---
|
wolffd@0
|
342 % DMLR t:w,e:w
|
wolffd@0
|
343 file1 = 'runlog_0b506247a68167addf97fcb0296650eb_1cc76d534804229cbdec8b20f8b75dba_finalresults';
|
wolffd@0
|
344
|
wolffd@0
|
345 % DMLR t:uw,e:w
|
wolffd@0
|
346 file2 = 'runlog_0b506247a68167addf97fcb0296650eb_bf08b16f453683d96ddffc31c6439730_finalresults';
|
wolffd@0
|
347 % ---
|
wolffd@0
|
348 % NOTE: using the "weighted" parameter here
|
wolffd@0
|
349 % ---
|
wolffd@0
|
350 [p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 1 ,mode);
|
wolffd@0
|
351 fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
|