====== Profile MMSE - pomiar 1 ======

  * Dane: statystyki opisowe (6 cech) + charakteryzacja kształtów roskładu profili (4 cechy: średnia, odchylenie, skośność i kurtoza z gęstości)


===== Płeć =====

  * Test: GM stand 10x5CV 
  * Nic powyżej baserate (lub chociaż 30% )

===== Klasyfikacja 2 klasowa =====

  * KLasy: przeskalowane wyniki testu < 11.3
  * Testy: GM + std + 10x5CV 
    * 58.1%/12.1 SVN gauss c=1.0, bias=0.5
    * 55.7%/17.1 kNN k=1 euclid
    * Reszta ponizej baserate

==== Wizualizacja QPC ====

  >> w=qpctrain(xn,c2,'display','short')
  procedure     = qpc
  dataname      = data
  vectors       = 43
  features      = 10
  OptMethod     = gd
  learningRate  = 0.100000
  prototypes    = 0
  proto lr      = 0.000000
  eps           = 0.001000
  start init.   = 10
  end init      = 3
  multistart    = yes
  maxIterations = 1000
  qpc function  = @(wx,p)qpcfunction(x,ci,wx,[],func)
  function      = @(x)f_gauss(x,parameters.beta)
  beta          = 0.100000
  Performing 10 initiations - Gradient based optimization (MultiStart)
  Sprawdzic dokladniej czy dobrze
  6 initiations was killed
  Finish N = 220   QPC = 0.088294 [3 ]
     -0.2625   -0.2240   -0.5884   -0.4564   -0.0720   -0.3458   -0.2978   -0.1616    0.2821   -0.0852
  
  Finish N = 280   QPC = 0.081809 [2 ]
     -0.2626   -0.2302    0.3327   -0.1507   -0.2403   -0.4847   -0.5708   -0.2200    0.2416   -0.1394
  
  Finish N = 295   QPC = 0.082032 [7 ]
     -0.3728   -0.2509    0.3371   -0.1551   -0.2610   -0.3071   -0.5174   -0.2834    0.2985   -0.2467
  
  Finish N = 315   QPC = 0.088345 [8 ]
     -0.2133   -0.2457   -0.5858   -0.4604   -0.1122   -0.3483   -0.3022   -0.1561    0.2857   -0.0886
  
  Best:   0.088345     -0.2133  -0.2457  -0.5858  -0.4604  -0.1122  -0.3483  -0.3022  -0.1561  0.2857  -0.0886
  ---------------Sumary--------------------------
  Initialization  8 was the best
  N     QPC      W
  0  0.0883      -0.2133  -0.2457  -0.5858  -0.4604  -0.1122  -0.3483  -0.3022  -0.1561  0.2857  -0.0886
  -----------------------------------------------
  procedure     = qpc
  dataname      = data
  vectors       = 43
  features      = 10
  OptMethod     = gd
  learningRate  = 0.100000
  prototypes    = 0
  proto lr      = 0.000000
  eps           = 0.001000
  start init.   = 10
  end init      = 3
  multistart    = yes
  maxIterations = 1000
  qpc function  = @(wx,p)qpcfunction(x,ci,wx,[],func)
  function      = @(x)f_gauss(x,parameters.beta)
  beta          = 0.100000
  Performing 10 initiations - Gradient based optimization (MultiStart)
  Sprawdzic dokladniej czy dobrze
  6 initiations was killed
  Finish N = 325   QPC = 0.060643 [8 ]
     -0.2338   -0.3198    0.3677   -0.4303   -0.0890   -0.3894    0.0253   -0.2643   -0.5001   -0.2063
  
  Finish N = 350   QPC = 0.060591 [7 ]
     -0.1145   -0.3625    0.3456   -0.4490   -0.1763   -0.4404    0.0713   -0.2678   -0.4229   -0.2319
  
  Finish N = 385   QPC = 0.060768 [3 ]
     -0.1647   -0.2409    0.3231   -0.4437   -0.0999   -0.5500   -0.0499   -0.2346   -0.4657   -0.1634
  
  Finish N = 430   QPC = 0.060598 [1 ]
     -0.1338   -0.2715    0.3599   -0.4280   -0.1425   -0.4570    0.0535   -0.2980   -0.4398   -0.2852
  
  Best:   0.060768     -0.1647  -0.2409  0.3231  -0.4437  -0.0999  -0.5500  -0.0499  -0.2346  -0.4657  -0.1634
  ---------------Sumary--------------------------
  Initialization  3 was the best
  N     QPC      W
  0  0.0608      -0.1647  -0.2409  0.3231  -0.4437  -0.0999  -0.5500  -0.0499  -0.2346  -0.4657  -0.1634
  -----------------------------------------------


{{projects:psycho:150123:mmse1_scale_raven_c2_qpc_scatterplot.png?700|}}