7:00 – 8:00 Research
- Wow, what a weekend. Had to decide whether or not it was better to go to the BWI protest, or finish the abstract, which is an attempt to model and hopefully influence situations like we find ourselves in. Decided to finish the abstract. Hopefully that’s the right choice.
- Working on trying to figure out why I can’t classify in WEKA any more.
- Installing the latest and greatest (3.8.1)
- Using this data:
- Yay! So you don’t have to do a lot of preprocessing to classify in WEKA.
- Read in the training data under the ‘Preprocess’ tab
- Switch to the ‘Classify’ tab
- (In this case) select NaiveBayes, and what to classify against – AgentBias
- Build the model using cross-validation
- Load the test model, selecting AgentBias to classify against
- Then right-click and select re-evaluate model on current test set
- Run the tests! Here’s a screenshot of classifier errors when using mean angle stats (BIG signal) That
- That is a beautiful thing. The chart shows the variance of each agent for the duration of the run with respect to the direction cosine. Polarized agents (red) have a low variance and ‘explorer’ agents (blue) have a high variance. Here’s the raw output
=== Run information === Scheme: weka.classifiers.misc.InputMappedClassifier -I -trim -W weka.classifiers.bayes.NaiveBayes Relation: ANGLE_FROM_MEAN_STATS Instances: 200 Attributes: 7 name_ AgentBias_ Mean Fifth Fiftieth NintyFifth Variance Test mode: user supplied test set: size unknown (reading incrementally) === Classifier model (full training set) === InputMappedClassifier: Naive Bayes Classifier Class Attribute EXPLORER EXPLOITER (0.5) (0.5) =================================== name_ shape_0 2.0 1.0 shape_1 2.0 1.0 shape_10 2.0 1.0 shape_100 1.0 2.0 shape_101 1.0 2.0 shape_102 1.0 2.0 shape_103 1.0 2.0 shape_104 1.0 2.0 shape_105 1.0 2.0 shape_106 1.0 2.0 shape_107 1.0 2.0 shape_108 1.0 2.0 shape_109 1.0 2.0 shape_11 2.0 1.0 shape_110 1.0 2.0 shape_111 1.0 2.0 shape_112 1.0 2.0 shape_113 1.0 2.0 shape_114 1.0 2.0 shape_115 1.0 2.0 shape_116 1.0 2.0 shape_117 1.0 2.0 shape_118 1.0 2.0 shape_119 1.0 2.0 shape_12 2.0 1.0 shape_120 1.0 2.0 shape_121 1.0 2.0 shape_122 1.0 2.0 shape_123 1.0 2.0 shape_124 1.0 2.0 shape_125 1.0 2.0 shape_126 1.0 2.0 shape_127 1.0 2.0 shape_128 1.0 2.0 shape_129 1.0 2.0 shape_13 2.0 1.0 shape_130 1.0 2.0 shape_131 1.0 2.0 shape_132 1.0 2.0 shape_133 1.0 2.0 shape_134 1.0 2.0 shape_135 1.0 2.0 shape_136 1.0 2.0 shape_137 1.0 2.0 shape_138 1.0 2.0 shape_139 1.0 2.0 shape_14 2.0 1.0 shape_140 1.0 2.0 shape_141 1.0 2.0 shape_142 1.0 2.0 shape_143 1.0 2.0 shape_144 1.0 2.0 shape_145 1.0 2.0 shape_146 1.0 2.0 shape_147 1.0 2.0 shape_148 1.0 2.0 shape_149 1.0 2.0 shape_15 2.0 1.0 shape_150 1.0 2.0 shape_151 1.0 2.0 shape_152 1.0 2.0 shape_153 1.0 2.0 shape_154 1.0 2.0 shape_155 1.0 2.0 shape_156 1.0 2.0 shape_157 1.0 2.0 shape_158 1.0 2.0 shape_159 1.0 2.0 shape_16 2.0 1.0 shape_160 1.0 2.0 shape_161 1.0 2.0 shape_162 1.0 2.0 shape_163 1.0 2.0 shape_164 1.0 2.0 shape_165 1.0 2.0 shape_166 1.0 2.0 shape_167 1.0 2.0 shape_168 1.0 2.0 shape_169 1.0 2.0 shape_17 2.0 1.0 shape_170 1.0 2.0 shape_171 1.0 2.0 shape_172 1.0 2.0 shape_173 1.0 2.0 shape_174 1.0 2.0 shape_175 1.0 2.0 shape_176 1.0 2.0 shape_177 1.0 2.0 shape_178 1.0 2.0 shape_179 1.0 2.0 shape_18 2.0 1.0 shape_180 1.0 2.0 shape_181 1.0 2.0 shape_182 1.0 2.0 shape_183 1.0 2.0 shape_184 1.0 2.0 shape_185 1.0 2.0 shape_186 1.0 2.0 shape_187 1.0 2.0 shape_188 1.0 2.0 shape_189 1.0 2.0 shape_19 2.0 1.0 shape_190 1.0 2.0 shape_191 1.0 2.0 shape_192 1.0 2.0 shape_193 1.0 2.0 shape_194 1.0 2.0 shape_195 1.0 2.0 shape_196 1.0 2.0 shape_197 1.0 2.0 shape_198 1.0 2.0 shape_199 1.0 2.0 shape_2 2.0 1.0 shape_20 2.0 1.0 shape_21 2.0 1.0 shape_22 2.0 1.0 shape_23 2.0 1.0 shape_24 2.0 1.0 shape_25 2.0 1.0 shape_26 2.0 1.0 shape_27 2.0 1.0 shape_28 2.0 1.0 shape_29 2.0 1.0 shape_3 2.0 1.0 shape_30 2.0 1.0 shape_31 2.0 1.0 shape_32 2.0 1.0 shape_33 2.0 1.0 shape_34 2.0 1.0 shape_35 2.0 1.0 shape_36 2.0 1.0 shape_37 2.0 1.0 shape_38 2.0 1.0 shape_39 2.0 1.0 shape_4 2.0 1.0 shape_40 2.0 1.0 shape_41 2.0 1.0 shape_42 2.0 1.0 shape_43 2.0 1.0 shape_44 2.0 1.0 shape_45 2.0 1.0 shape_46 2.0 1.0 shape_47 2.0 1.0 shape_48 2.0 1.0 shape_49 2.0 1.0 shape_5 2.0 1.0 shape_50 2.0 1.0 shape_51 2.0 1.0 shape_52 2.0 1.0 shape_53 2.0 1.0 shape_54 2.0 1.0 shape_55 2.0 1.0 shape_56 2.0 1.0 shape_57 2.0 1.0 shape_58 2.0 1.0 shape_59 2.0 1.0 shape_6 2.0 1.0 shape_60 2.0 1.0 shape_61 2.0 1.0 shape_62 2.0 1.0 shape_63 2.0 1.0 shape_64 2.0 1.0 shape_65 2.0 1.0 shape_66 2.0 1.0 shape_67 2.0 1.0 shape_68 2.0 1.0 shape_69 2.0 1.0 shape_7 2.0 1.0 shape_70 2.0 1.0 shape_71 2.0 1.0 shape_72 2.0 1.0 shape_73 2.0 1.0 shape_74 2.0 1.0 shape_75 2.0 1.0 shape_76 2.0 1.0 shape_77 2.0 1.0 shape_78 2.0 1.0 shape_79 2.0 1.0 shape_8 2.0 1.0 shape_80 2.0 1.0 shape_81 2.0 1.0 shape_82 2.0 1.0 shape_83 2.0 1.0 shape_84 2.0 1.0 shape_85 2.0 1.0 shape_86 2.0 1.0 shape_87 2.0 1.0 shape_88 2.0 1.0 shape_89 2.0 1.0 shape_9 2.0 1.0 shape_90 2.0 1.0 shape_91 2.0 1.0 shape_92 2.0 1.0 shape_93 2.0 1.0 shape_94 2.0 1.0 shape_95 2.0 1.0 shape_96 2.0 1.0 shape_97 2.0 1.0 shape_98 2.0 1.0 shape_99 2.0 1.0 [total] 300.0 300.0 Mean mean 84.763 5.237 std. dev. 27.2068 0.7459 weight sum 100 100 precision 0.6792 0.6792 Fifth mean 15.0052 0.3847 std. dev. 15.1486 0.1425 weight sum 100 100 precision 0.3966 0.3966 Fiftieth mean 0 0 std. dev. 0.0017 0.0017 weight sum 100 100 precision 0.01 0.01 NintyFifth mean 162.5673 23.0501 std. dev. 18.6542 2.4072 weight sum 100 100 precision 0.7954 0.7954 Variance mean 147.5186 22.7738 std. dev. 20.0571 2.3901 weight sum 100 100 precision 0.7627 0.7627 Attribute mappings: Model attributes Incoming attributes ---------------------- ---------------- (nominal) name_ --> 1 (nominal) name_ (nominal) AgentBias_ --> 2 (nominal) AgentBias_ (numeric) Mean --> 3 (numeric) Mean (numeric) Fifth --> 4 (numeric) Fifth (numeric) Fiftieth --> 5 (numeric) Fiftieth (numeric) NintyFifth --> 6 (numeric) NintyFifth (numeric) Variance --> 7 (numeric) Variance Time taken to build model: 0 seconds === Evaluation on test set === Time taken to test model on supplied test set: 0 seconds === Summary === Correctly Classified Instances 100 100 % Incorrectly Classified Instances 0 0 % Kappa statistic 1 Mean absolute error 0 Root mean squared error 0 Relative absolute error 0 % Root relative squared error 0 % Total Number of Instances 100 === Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 EXPLORER 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 EXPLOITER Weighted Avg. 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 === Confusion Matrix === a b -- classified as 50 0 | a = EXPLORER 0 50 | b = EXPLOITER
8:30 – 2:30 BRC
- Working on white paper
viztales 