bug1.lua

dataset=torch.Tensor{
   {742313794,6385273,1362403386,80,169085576,0,1698277100,451,0},
   {742313860,6385273,1362403386,80,169085576,0,1698300945,452,0},
   {742338872,6385273,167840643,137,169085576,0,1718221933,480,0},
   {742338872,6385273,167926801,53,169085576,0,1718222057,471,0},
   {742338872,6385273,167846975,8014,169085576,0,1718222380,487,0},
   {742338872,641958438357784123396879472047392017641614042483,167846975,8014,169085576,0,1718222453,517,0},
   {742338934,6385273,167926801,53,169085576,0,1718273961,472,0},
   {742338934,641958438357784123396879472047392017641614042483,167846975,8014,169085576,0,1718275304,519,0},
   {742338934,6385273,167840643,137,169085576,0,1718275541,483,0},
   {742339456,6385273,1515469714,80,169085576,0,1718710395,451,0},
   {742339482,6385273,167846952,389,169085576,0,1718735080,475,0},
   {742339482,6385273,167840642,53,169085576,0,1718735132,472,0},
   {742339482,6385273,167926801,53,169085576,0,1718735138,471,0},
   {742339482,6385273,167840642,389,169085576,0,1718735139,476,0},
   {742339482,6385273,167840642,53,169085576,0,1718735141,472,0},
   {742339482,6385273,167926801,53,169085576,0,1718735143,471,0},
   {742339482,6385273,167840642,389,169085576,0,1718735144,476,0},
   {742339482,6385273,167926801,53,169085576,0,1718735186,471,0},
   {742339482,6385273,167840642,53,169085576,0,1718735187,472,0},
   {742339482,6385273,167840642,389,169085576,0,1718735228,476,0},
   {742339482,6385273,167840641,389,169085576,0,1718735229,476,0},
   {742339482,6385273,167840643,137,169085576,0,1718735246,480,0},
   {742339482,6385273,167840642,53,169085576,0,1718735277,474,0},
   {742339482,6385273,167840646,80,169085576,0,1718736072,484,0},
   {742339484,6385273,1515469714,80,169085576,0,1718737011,476,0},
}

function distance (vect,taille)
       local som= 0
           for i=1,taille do
	      som = som + vect[i]^2
	          end
	   return math.sqrt(som)
	   end

function datadist (dataset)
   local distma = {};
   --print(dataset:size()[1])
   for i = 1,dataset:size()[1] do
      distma[i]=distance(dataset[i],dataset:size()[2]);
          end
       return distma
       end
d = {}
for i=1,dataset:size()[1] do
   local label = distance(dataset[i],dataset:size(2))
   d[i]={dataset[i],torch.Tensor({label})};
end

d.size = function() return dataset:size(1); end

require 'nn'
--exit()
mlp = nn.Sequential();

inputs = 9;output = 1 ;HUs = 2;

mlp:add(nn.Linear(inputs,HUs));
--exit()
mlp:add(nn.Tanh());
mlp:add(nn.Linear(HUs,output))

criterion = nn.MSECriterion()
trainer = nn.StochasticGradient(mlp, criterion)
trainer.learningRate = 0.001
trainer:train(d)

--d = datadist(dataset)
-- print(d)