Commit Inicial

data/tanveer/thyroid/ann-thyroid.names

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+NOTE: all files associated to this .names file have the ann- prefix.
+
+1. Title: Thyroid Domain
+
+2. Sources: 
+
+   (a) Donors: Randolf Werner
+               evol@uniko.uni-koblenz.de
+   (b) Obtained from Daimler-Benz.
+   (c) Date: October 1992
+
+3. Past Usage:
+
+   (a) "Optimization of the Backpropagation Algorithm for Training Multilayer
+       Perceptrons":
+
+       ftp archive.cis.ohio-state.edu  or  ftp 128.146.8.52
+       cd pub/neuroprose
+       binary
+       get schiff.bp_speedup.ps.Z
+       quit
+
+       The report is an overview of many different backprop speedup techniques.
+       15 different algorithms are described in detail and compared by using
+       a big, very hard to solve, practical data set. Learning speed and network
+       classification performance with respect to the training data set and also
+       with respect to a testing data set are discussed.
+       These are the tested algorithms:
+
+       backprop
+       backprop (batch mode)
+       backprop + Learning rate calculated by Eaton and Oliver's formula
+       backprop + decreasing learning rate (Darken and Moody)
+       backprop + Learning rate adaptation for each training pattern (J. Schmidhuber)
+       backprop + evolutionarily learning rate adaptation (R. Salomon)
+       backprop + angle driven learning rate adaptation(Chan and Fallside)
+       Polak-R
ibiere + line search (Kramer and Vincentelli)
+       Conj. gradient + line search (Leonard and Kramer)
+       backprop + learning rate adaptation by sign changes (Silva and Almeida)
+       SuperSAB (T. Tollenaere)
+       Delta-Bar-Delta (Jacobs)
+       RPROP (Riedmiller and Braun)
+       Quickprop (Fahlman)
+       Cascade correlation (Fahlman)
+
+   (b) "Synthesis and Performance Analysis of Multilayer eural Network Architectures":
+
+       ftp archive.cis.ohio-state.edu  or  ftp 128.146.8.52
+       cd pub/neuroprose
+       binary
+       get schiff.gann.ps.Z
+       quit
+
+       In this paper we present various approaches for automatic topology-optimization
+       of backpropagation networks. First of all, we review the basics of genetic
+       algorithms which are our essential tool for a topology search. Then we give a
+       survey of backprop and the topological properties of feedforward networks. We
+       report on pioneer work in the filed of topology--optimization. Our first
+       approach was based on evolutions strategies which used only mutation to change
+       the parent's topologies. Now, we found a way to extend this approach by an
+       crossover operator which is essential to all genetic search methods.
+       In contrast to competing approaches it allows that two parent networks with
+       different number of units can mate and produce a (valid) child network, which
+       inherits genes from both of the parents. We applied our genetic algorithm to a
+       medical classification problem which is extremly difficult to solve. The
+       performance with respect to the training set and a test set of pattern samples
+       was compared to fixed network topologies. Our results confirm that the topology
+       optimization makes sense, because the generated networks outperform the fixed
+       topologies and reach classification performances near optimum.
+
+4. Relevant Information:
+
+   The problem is to determine whether a patient referred to the clinic is
+   hypothyroid. Therefore three classes are built: normal (not hypothyroid),
+   hyperfunction and subnormal functioning. Because 92 percent of the patients
+   are not hyperthyroid a good classifier must be significant better than 92%.
+
+   Note
+
+   These are the attributes Quinlans used in the case study of his article
+   "Simplifying Decision Trees" (International Journal of Man-Machine Studies 
+   (1987) 221-234).  Unfortunately this data differ from the version already
+   present (donated by Ross Quinlan)  I (Randolf Werner) don't know any more 
+   details about the dataset. But it's hard to train Backpropagation ANNs with 
+   it. The dataset is used in two technical reports (see above).
+
+5. Number of Instances: ann-train.data: 3772, ann-test.data: 3428
+
+6. Number of Classes: 3
+
+7. Number of Attributes: 21 (15 attributes are binary,
+			     6 attributes are continuous)
+
+

data/tanveer/thyroid/le_datos.m

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+printf('lendo problema %s ...\n', problema);
+
+n_entradas= 21; n_clases= 3; 
+n_fich= 2; fich{1}= 'ann-train.data'; n_patrons(1)= 3772; fich{2}= 'ann-test.data'; n_patrons(2)= 3428;
+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
+	cl(i_fich,i) = fscanf(f,'%i',1) - 1;  	% lectura da clase
+  end
+  fclose(f);
+end

data/tanveer/thyroid/thyroid.cost

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+% Rows	Columns
+3	3
+% Matrix elements
+ 0.0	 1.0	 1.0	
+ 1.0	 0.0	 1.0	
+ 1.0	 1.0	 0.0	

data/tanveer/thyroid/thyroid.txt

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+n_entradas= 21
+n_clases= 3
+n_arquivos= 2
+fich1= thyroid_train_R.dat
+n_patrons1= 3772
+fich2= thyroid_test_R.dat
+n_patrons2= 3428
+n_patrons_entrena= 1886
+n_patrons_valida= 1886
+n_conxuntos= 1