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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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% 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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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;
}