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Initial -> Initial Accuracy | |
Final -> final accuracy after boostmetric learning | |
Timings -> boostmetric timings | |
Final_LMNN -> final accuracy after lmnn learning | |
Timings_LMNN -> lmnn timings | |
Iris: | |
k Initial Final Timings Final_LMNN Timings_LMNN | |
3 96.000 96.000 0.063613 96.000 0.347348 | |
5 96.667 96.667 0.116031 96.667 0.501501 |
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iris 0.14874 97.3333 | |
satellite 2.802369 94.0793 | |
ecoli 0.016140 93.75 | |
vehicle 0.600228 78.8416 | |
balance 0.074875 93.44 | |
letter 31.89887 97.0 |
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$ bin/mlpack_boostmetric -i iris.csv -l iris_labels.txt -k 5 | |
[DEBUG] Compiled with debugging symbols. | |
[WARN ] Should pass '--output_file (-o)'; no output will be saved! | |
Iteration : 0, Out of 3750 instances, u is less than 1e-05 for 0 instances. | |
Iteration : 1, Out of 3750 instances, u is less than 1e-05 for 2019 instances. | |
Iteration : 2, Out of 3750 instances, u is less than 1e-05 for 2339 instances. | |
Iteration : 3, Out of 3750 instances, u is less than 1e-05 for 2723 instances. | |
Iteration : 4, Out of 3750 instances, u is less than 1e-05 for 2927 instances. | |
Iteration : 5, Out of 3750 instances, u is less than 1e-05 for 2921 instances. | |
Iteration : 6, Out of 3750 instances, u is less than 1e-05 for 2947 instances. |
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bin/mlpack_lmnn -i covertype.txt -p 50 -b 65536 -P -k 3 -O amsgrad -v | grep -v 'node combinations\|base cases\|DEBUG' | |
[WARN ] Should pass '--output_file (-o)'; no output will be saved! | |
[INFO ] Loading 'covertype.txt' as CSV data. Size is 55 x 581012. | |
[INFO ] Using last column of input dataset as labels. | |
[INFO ] Initial learning point have invalid dimensionality. Identity matrix will be used as initial learning point for optimization. | |
Iteration 0 : Out of 65536, Impostors will be recalculated for 65536 data points. | |
Iteration 1 : Out of 65536, Impostors will be recalculated for 65536 data points. | |
Iteration 2 : Out of 65536, Impostors will be recalculated for 65536 data points. | |
Iteration 3 : Out of 65536, Impostors will be recalculated for 65536 data points. | |
Iteration 4 : Out of 65536, Impostors will be recalculated for 65536 data points. |
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bin/mlpack_lmnn -i covertype.txt -n 20 -k 1 -O lbfgs -v | grep -v 'node combinations\|base cases\|DEBUG' | |
[WARN ] Should pass '--output_file (-o)'; no output will be saved! | |
[INFO ] Loading 'covertype.txt' as CSV data. Size is 55 x 581012. | |
[INFO ] Using last column of input dataset as labels. | |
[INFO ] Initial learning point have invalid dimensionality. Identity matrix will be used as initial learning point for optimization. | |
Iteraion 2 : Out of 581012, Impostors will be recalculated for 581012 data points. transformationDiff : 0.582531 | |
Iteraion 3 : Out of 581012, Impostors will be recalculated for 581012 data points.transformationDiff : 0.519489 | |
Iteraion 4 : Out of 581012, Impostors will be recalculated for 581012 data points.transformationDiff : 1.28358 | |
Iteraion 5 : Out of 581012, Impostors will be recalculated for 581012 data points.transformationDiff : 0.641792 | |
Iteraion 6 : Out of 581012, Impostors will be recalculated for 581012 data points.transformationDiff : 0.320896 |
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Iris: | |
k Accuracy Timings Accuracy_Master Timings_Master | |
5 96.6667 0.875329 96.6667 0.899712 | |
10 97.3333 1.681192 97.3333 1.547372 | |
vc2: | |
k Accuracy Timings Accuracy_Master Timings_Master | |
5 77.7778 3.390961 77.7778 3.158439 | |
10 81.6425 6.613346 81.6425 6.171987 |
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Iris: | |
k Accuracy Timings Accuracy_Master Timings_Master | |
5 96.6667 0.483215 96.6667 0.899712 | |
10 97.3333 1.091467 97.3333 1.547372 | |
vc2: | |
k Accuracy Timings Accuracy_Master Timings_Master | |
5 77.7778 1.971792 77.7778 3.158439 | |
10 81.6425 4.913914 81.6425 6.171987 |
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max Iterations : 500 | |
Iris: | |
k Initial Final Timings Final_LMNN Timings_LMNN Final_LMNN_NoImp Timings_LMNN_NoImp | |
1 95.333 96.667 0.246141 96.000 0.305383 96.6667 0.053881 | |
2 95.333 96.667 0.055891 96.000 0.460756 96.00 0.093379 | |
5 98.000 95.333 1.662990 96.667 0.808036 96.6667 0.261077 | |
10 95.3333 96.667 0.520501 97.3333 1.487357 97.3333 0.740489 | |
vc2: |
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double innerProduct(arma::mat& Ar, arma::mat& Z) | |
{ | |
double sum = 0.0; | |
for (size_t i = 0; i < Z.n_elem; i++) | |
sum += Ar(i) * Z(i); | |
return sum; | |
} |
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% File Type: Matlab | |
% Author: Junae Kim {junae.kim@gmail.com}, | |
% Chunhua Shen {chhshen@gmail.com} | |
% Creation Tuesday 26/02/2009 19:56. | |
% Last Revision: Friday 06/03/2009 10:40. | |
% | |
% Input : trn, training data | |
% [ dim, num ] = size(trn.X), | |
% trn.y is the labels | |
% varargin, parameters |
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