Commit Inicial

data/tanveer/ionosphere/conxuntos.dat

@@ -0,0 +1,2 @@
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data/tanveer/ionosphere/conxuntos_kfold.dat

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data/tanveer/ionosphere/ionosphere.arff

@@ -0,0 +1,387 @@
+@relation ionosphere
+@attribute 'f1' real
+@attribute 'f2' real
+@attribute 'f3' real
+@attribute 'f4' real
+@attribute 'f5' real
+@attribute 'f6' real
+@attribute 'f7' real
+@attribute 'f8' real
+@attribute 'f9' real
+@attribute 'f10' real
+@attribute 'f11' real
+@attribute 'f12' real
+@attribute 'f13' real
+@attribute 'f14' real
+@attribute 'f15' real
+@attribute 'f16' real
+@attribute 'f17' real
+@attribute 'f18' real
+@attribute 'f19' real
+@attribute 'f20' real
+@attribute 'f21' real
+@attribute 'f22' real
+@attribute 'f23' real
+@attribute 'f24' real
+@attribute 'f25' real
+@attribute 'f26' real
+@attribute 'f27' real
+@attribute 'f28' real
+@attribute 'f29' real
+@attribute 'f30' real
+@attribute 'f31' real
+@attribute 'f32' real
+@attribute 'f33' real
+@attribute 'clase' {0,1}
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data/tanveer/ionosphere/ionosphere.cost

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data/tanveer/ionosphere/ionosphere.names

@@ -0,0 +1,65 @@
+1. Title: Johns Hopkins University Ionosphere database
+
+2. Source Information:
+   -- Donor: Vince Sigillito (vgs@aplcen.apl.jhu.edu)
+   -- Date: 1989
+   -- Source: Space Physics Group
+              Applied Physics Laboratory
+              Johns Hopkins University
+              Johns Hopkins Road
+              Laurel, MD 20723 
+
+3. Past Usage:
+   -- Sigillito, V. G., Wing, S. P., Hutton, L. V., \& Baker, K. B. (1989).
+      Classification of radar returns from the ionosphere using neural 
+      networks. Johns Hopkins APL Technical Digest, 10, 262-266.
+
+      They investigated using backprop and the perceptron training algorithm
+      on this database.  Using the first 200 instances for training, which
+      were carefully split almost 50% positive and 50% negative, they found
+      that a "linear" perceptron attained 90.7%, a "non-linear" perceptron
+      attained 92%, and backprop an average of over 96% accuracy on the 
+      remaining 150 test instances, consisting of 123 "good" and only 24 "bad"
+      instances.  (There was a counting error or some mistake somewhere; there
+      are a total of 351 rather than 350 instances in this domain.) Accuracy
+      on "good" instances was much higher than for "bad" instances.  Backprop
+      was tested with several different numbers of hidden units (in [0,15])
+      and incremental results were also reported (corresponding to how well
+      the different variants of backprop did after a periodic number of 
+      epochs).
+
+      David Aha (aha@ics.uci.edu) briefly investigated this database.
+      He found that nearest neighbor attains an accuracy of 92.1%, that
+      Ross Quinlan's C4 algorithm attains 94.0% (no windowing), and that
+      IB3 (Aha \& Kibler, IJCAI-1989) attained 96.7% (parameter settings:
+      70% and 80% for acceptance and dropping respectively).
+
+4. Relevant Information:
+   This radar data was collected by a system in Goose Bay, Labrador.  This
+   system consists of a phased array of 16 high-frequency antennas with a
+   total transmitted power on the order of 6.4 kilowatts.  See the paper
+   for more details.  The targets were free electrons in the ionosphere.
+   "Good" radar returns are those showing evidence of some type of structure 
+   in the ionosphere.  "Bad" returns are those that do not; their signals pass
+   through the ionosphere.  
+
+   Received signals were processed using an autocorrelation function whose
+   arguments are the time of a pulse and the pulse number.  There were 17
+   pulse numbers for the Goose Bay system.  Instances in this databse are
+   described by 2 attributes per pulse number, corresponding to the complex
+   values returned by the function resulting from the complex electromagnetic
+   signal.
+
+5. Number of Instances: 351
+
+6. Number of Attributes: 34 plus the class attribute
+   -- All 34 predictor attributes are continuous
+
+7. Attribute Information:     
+   -- All 34 are continuous, as described above
+   -- The 35th attribute is either "good" or "bad" according to the definition
+      summarized above.  This is a binary classification task.
+
+8. Missing Values: None
+
+

data/tanveer/ionosphere/ionosphere.txt

@@ -0,0 +1,8 @@
+n_entradas= 33
+n_clases= 2
+n_arquivos= 1
+fich1= ionosphere_R.dat
+n_patrons1= 351
+n_patrons_entrena= 176
+n_patrons_valida= 175
+n_conxuntos= 1

data/tanveer/ionosphere/ionosphere_R.dat

@@ -0,0 +1,352 @@
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+346	0.347937	0.0508896	-0.131462	0.717445	-0.103251	-0.110006	0.733576	0.483137	-0.380438	0.311373	-0.597696	0.00134816	-1.17704	0.073405	1.12638	0.624269	0.64805	0.680429	0.264729	0.227465	-0.883396	1.05591	0.121784	0.363774	0.277008	0.28074	0.437103	0.863426	0.208233	0.351307	0.486188	-0.401833	0.995842	1
+347	0.347937	0.389261	0.0874605	0.262228	-0.570623	0.59554	-0.336112	0.774184	-0.470321	0.741313	-0.0479782	0.660845	-0.0529099	0.68764	-0.349984	1.00005	-0.0350131	0.969236	0.173779	0.884437	0.00628004	0.683598	0.234305	0.964032	0.0573593	0.567893	0.128662	1.0793	0.307215	0.899565	-0.201229	1.06397	-0.122882	1
+348	0.347937	0.622429	-0.0910256	0.674722	-0.310581	0.78003	-0.266079	0.856008	-0.341604	0.86765	-0.247021	0.869368	-0.0406441	0.922281	-0.184114	0.954307	0.010099	0.940415	0.162606	0.967493	0.146332	0.942026	0.300074	0.9492	0.166761	0.762448	0.215968	0.960512	0.215734	1.02916	0.00670548	1.08189	0.0696931	1
+349	0.347937	0.614151	-0.101288	0.636712	-0.321519	0.815329	-0.295094	0.875611	-0.324243	0.825307	-0.277639	0.844038	-0.0940316	0.912003	-0.187059	0.916509	0.047622	0.923648	0.0731197	0.946766	0.132147	0.881506	0.225395	0.940003	0.202789	0.742437	0.172642	0.94247	0.0990741	1.00104	0.0159933	1.10512	-0.0432383	1
+350	0.347937	0.531914	-0.138054	0.731256	-0.294653	0.825762	-0.299223	0.681592	-0.372523	0.747223	-0.380323	0.787238	-0.217781	0.860749	-0.254013	0.860588	-0.0105145	0.715894	-0.202774	0.902868	0.00743797	0.925976	0.0648504	0.889212	0.0987166	0.677695	-0.0146582	0.78381	-0.28441	1.06338	-0.065767	1.00383	-0.377741	1
+351	0.347937	0.413411	0.206051	0.260285	-0.384972	0.667071	-0.0705912	0.744345	-0.563677	0.552208	-0.17837	0.652343	-0.195856	0.686951	-0.17818	0.769701	-0.0846949	0.818039	-0.0214329	0.787967	-0.199156	0.575626	0.192141	0.809983	-0.157235	0.522717	0.0387575	0.700877	0.0410987	0.708606	-0.122643	0.972474	-0.162255	1

data/tanveer/ionosphere/le_datos.m

@@ -0,0 +1,30 @@
+printf('lendo problema %s ...\n', problema);
+
+n_entradas= 34; n_clases= 2; n_fich= 1; fich{1}= 'ionosphere.data'; n_patrons(1)= 351;
+
+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=='b'
+	  cl(i_fich,i) = 0;
+	elseif t=='g'
+	  cl(i_fich,i) = 1;
+	else
+	  error('clase %c descoñecida', t)
+	end
+  end
+  fclose(f);
+end