Commit Inicial

data/tanveer/magic/le_datos.m

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+printf('lendo problema %s ...\n', problema);
+
+n_entradas= 10; n_clases= 2; n_fich= 1; fich{1}= 'magic04.data'; n_patrons(1)= 19020;
+
+n_max= max(n_patrons);
+x = zeros(n_fich, n_max, n_entradas); cl= zeros(n_fich, n_max);
+
+n_patrons_total = sum(n_patrons); n_iter=0;
+
+for i_fich=1:n_fich
+  f=fopen(fich{i_fich}, 'r');
+  if -1==f
+	error('erro en fopen abrindo %s\n', fich{i_fich});
+  end
+  for i=1:n_patrons(i_fich)
+  	fprintf(2,'%5.1f%%\r', 100*n_iter++/n_patrons_total);
+	for j = 1:n_entradas
+	  x(i_fich,i,j) = fscanf(f,'%g',1);
+	end	
+	t= fscanf(f,'%s',1);   	% lectura da clase
+	if t=='g'
+	  cl(i_fich,i) = 0;
+	elseif t=='h'
+	  cl(i_fich,i) = 1;
+	else
+	  error('clase %s descoñecida\n', t)
+	end
+  end
+  fclose(f);
+end

data/tanveer/magic/magic.cost

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+% Rows	Columns
+2	2
+% Matrix elements
+ 0.0	 1.0	
+ 1.0	 0.0	

data/tanveer/magic/magic.txt

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+n_entradas= 10
+n_clases= 2
+n_arquivos= 1
+fich1= magic_R.dat
+n_patrons1= 19020
+n_patrons_entrena= 9510
+n_patrons_valida= 9510
+n_conxuntos= 1

data/tanveer/magic/magic04.names

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+1. Title of Database: MAGIC gamma telescope data 2004
+
+2. Sources:
+
+   (a) Original owner of the database:
+
+       R. K. Bock
+       Major Atmospheric Gamma Imaging Cherenkov Telescope project (MAGIC)
+       http://wwwmagic.mppmu.mpg.de
+       rkb@mail.cern.ch
+
+   (b) Donor:
+
+       P. Savicky
+       Institute of Computer Science, AS of CR
+       Czech Republic
+       savicky@cs.cas.cz
+
+   (c) Date received: May 2007
+
+3. Past Usage:
+
+   (a) Bock, R.K., Chilingarian, A., Gaug, M., Hakl, F., Hengstebeck, T.,
+       Jirina, M., Klaschka, J., Kotrc, E., Savicky, P., Towers, S.,
+       Vaicilius, A., Wittek W. (2004).
+       Methods for multidimensional event classification: a case study
+       using images from a Cherenkov gamma-ray telescope.
+       Nucl.Instr.Meth. A, 516, pp. 511-528.
+
+   (b) P. Savicky, E. Kotrc.
+       Experimental Study of Leaf Confidences for Random Forest.
+       Proceedings of COMPSTAT 2004, In: Computational Statistics.
+       (Ed.: Antoch J.) - Heidelberg, Physica Verlag 2004, pp. 1767-1774.
+
+   (c) J. Dvorak, P. Savicky.
+       Softening Splits in Decision Trees Using Simulated Annealing.
+       Proceedings of ICANNGA 2007, Warsaw, (Ed.: Beliczynski et. al),
+       Part I, LNCS 4431, pp. 721-729.
+
+4. Relevant Information:
+
+   The data are MC generated (see below) to simulate registration of high energy
+   gamma particles in a ground-based atmospheric Cherenkov gamma telescope using the
+   imaging technique. Cherenkov gamma telescope observes high energy gamma rays,
+   taking advantage of the radiation emitted by charged particles produced
+   inside the electromagnetic showers initiated by the gammas, and developing in the
+   atmosphere. This Cherenkov radiation (of visible to UV wavelengths) leaks
+   through the atmosphere and gets recorded in the detector, allowing reconstruction
+   of the shower parameters. The available information consists of pulses left by
+   the incoming Cherenkov photons on the photomultiplier tubes, arranged in a
+   plane, the camera. Depending on the energy of the primary gamma, a total of
+   few hundreds to some 10000 Cherenkov photons get collected, in patterns
+   (called the shower image), allowing to discriminate statistically those
+   caused by primary gammas (signal) from the images of hadronic showers
+   initiated by cosmic rays in the upper atmosphere (background).
+
+   Typically, the image of a shower after some pre-processing is an elongated
+   cluster. Its long axis is oriented towards the camera center if the shower axis
+   is parallel to the telescope's optical axis, i.e. if the telescope axis is
+   directed towards a point source. A principal component analysis is performed
+   in the camera plane, which results in a correlation axis and defines an ellipse.
+   If the depositions were distributed as a bivariate Gaussian, this would be
+   an equidensity ellipse. The characteristic parameters of this ellipse
+   (often called Hillas parameters) are among the image parameters that can be
+   used for discrimination. The energy depositions are typically asymmetric
+   along the major axis, and this asymmetry can also be used in discrimination.
+   There are, in addition, further discriminating characteristics, like the
+   extent of the cluster in the image plane, or the total sum of depositions.
+
+   The data set was generated by a Monte Carlo program, Corsika, described in 
+      D. Heck et al., CORSIKA, A Monte Carlo code to simulate extensive air showers,
+      Forschungszentrum Karlsruhe FZKA 6019 (1998).
+   The program was run with parameters allowing to observe events with energies down
+   to below 50 GeV.
+
+5. Number of Instances: 19020
+
+6. Number of Attributes: 11 (including the class)
+
+7. Attribute information:
+
+    1.  fLength:  continuous  # major axis of ellipse [mm]
+    2.  fWidth:   continuous  # minor axis of ellipse [mm] 
+    3.  fSize:    continuous  # 10-log of sum of content of all pixels [in #phot]
+    4.  fConc:    continuous  # ratio of sum of two highest pixels over fSize  [ratio]
+    5.  fConc1:   continuous  # ratio of highest pixel over fSize  [ratio]
+    6.  fAsym:    continuous  # distance from highest pixel to center, projected onto major axis [mm]
+    7.  fM3Long:  continuous  # 3rd root of third moment along major axis  [mm] 
+    8.  fM3Trans: continuous  # 3rd root of third moment along minor axis  [mm]
+    9.  fAlpha:   continuous  # angle of major axis with vector to origin [deg]
+   10.  fDist:    continuous  # distance from origin to center of ellipse [mm]
+   11.  class:    g,h         # gamma (signal), hadron (background)
+
+8. Missing Attribute Values: None
+
+9. Class Distribution:
+
+   g = gamma (signal):     12332
+   h = hadron (background): 6688
+
+   For technical reasons, the number of h events is underestimated.
+   In the real data, the h class represents the majority of the events.
+
+   The simple classification accuracy is not meaningful for this data, since
+   classifying a background event as signal is worse than classifying a signal
+   event as background. For comparison of different classifiers an ROC curve
+   has to be used. The relevant points on this curve are those, where the
+   probability of accepting a background event as signal is below one of the
+   following thresholds: 0.01, 0.02, 0.05, 0.1, 0.2 depending on the required
+   quality of the sample of the accepted events for different experiments.
+