static void checkDataset() {
Attribute[] outputs = Attributes.getOutputAttributes();
if (outputs.length != 1) {
LogManager.printErr("Only datasets with one output are supported");
System.exit(1);
}
if (outputs[0].getType() != Attribute.NOMINAL) {
LogManager.printErr("Output attribute should be nominal");
System.exit(1);
}
Parameters.numClasses = outputs[0].getNumNominalValues();
Parameters.numAttributes = Attributes.getInputAttributes().length;
}
/**
* It does return a new header (not necessary the same header as the input file one). It only
* includes the valid attributes, those ones defined in @inputs and @outputs (or taken as that
* role following the keel format specification).
*
* @return a String with the new header
*/
public String getNewHeader() {
String line = "";
Attribute[] attrs = null;
// Getting the relation name and the attributes
if (storeAttributesAsNonStatic && attributes != null) {
line = "@relation " + attributes.getRelationName() + "\n";
attrs = attributes.getInputAttributes();
} else {
line = "@relation " + Attributes.getRelationName() + "\n";
attrs = Attributes.getInputAttributes();
}
for (int i = 0; i < attrs.length; i++) {
line += attrs[i].toString() + "\n";
}
// Gettin all the outputs attributes
if (storeAttributesAsNonStatic && attributes != null) {
attrs = attributes.getOutputAttributes();
line += attrs[0].toString() + "\n";
// Getting @inputs and @outputs
line += attributes.getInputHeader() + "\n";
line += attributes.getOutputHeader() + "\n";
} else {
attrs = Attributes.getOutputAttributes();
line += attrs[0].toString() + "\n";
// Getting @inputs and @outputs
line += Attributes.getInputHeader() + "\n";
line += Attributes.getOutputHeader() + "\n";
}
return line;
} // end getNewHeader
/**
* This function builds the data matrix for reference data and normalizes inputs values
*
* @throws keel.Algorithms.Preprocess.Basic.CheckException Can not be normalized.
*/
protected void normalizar() throws CheckException {
int i, j, k;
Instance temp;
double caja[];
StringTokenizer tokens;
boolean nulls[];
/*Check if dataset corresponding with a classification problem*/
if (Attributes.getOutputNumAttributes() < 1) {
throw new CheckException(
"This dataset haven?t outputs, so it not corresponding to a classification problem.");
} else if (Attributes.getOutputNumAttributes() > 1) {
throw new CheckException("This dataset have more of one output.");
}
if (Attributes.getOutputAttribute(0).getType() == Attribute.REAL) {
throw new CheckException(
"This dataset have an input attribute with floating values, so it not corresponding to a classification problem.");
}
entradas = Attributes.getInputAttributes();
salida = Attributes.getOutputAttribute(0);
nEntradas = Attributes.getInputNumAttributes();
tokens = new StringTokenizer(training.getHeader(), " \n\r");
tokens.nextToken();
relation = tokens.nextToken();
datosTrain = new double[training.getNumInstances()][Attributes.getInputNumAttributes()];
clasesTrain = new int[training.getNumInstances()];
caja = new double[1];
nulosTrain = new boolean[training.getNumInstances()][Attributes.getInputNumAttributes()];
nominalTrain = new int[training.getNumInstances()][Attributes.getInputNumAttributes()];
realTrain = new double[training.getNumInstances()][Attributes.getInputNumAttributes()];
for (i = 0; i < training.getNumInstances(); i++) {
temp = training.getInstance(i);
nulls = temp.getInputMissingValues();
datosTrain[i] = training.getInstance(i).getAllInputValues();
for (j = 0; j < nulls.length; j++)
if (nulls[j]) {
datosTrain[i][j] = 0.0;
nulosTrain[i][j] = true;
}
caja = training.getInstance(i).getAllOutputValues();
clasesTrain[i] = (int) caja[0];
for (k = 0; k < datosTrain[i].length; k++) {
if (Attributes.getInputAttribute(k).getType() == Attribute.NOMINAL) {
nominalTrain[i][k] = (int) datosTrain[i][k];
datosTrain[i][k] /= Attributes.getInputAttribute(k).getNominalValuesList().size() - 1;
} else {
realTrain[i][k] = datosTrain[i][k];
datosTrain[i][k] -= Attributes.getInputAttribute(k).getMinAttribute();
datosTrain[i][k] /=
Attributes.getInputAttribute(k).getMaxAttribute()
- Attributes.getInputAttribute(k).getMinAttribute();
if (Double.isNaN(datosTrain[i][k])) {
datosTrain[i][k] = realTrain[i][k];
}
}
}
}
datosTest = new double[test.getNumInstances()][Attributes.getInputNumAttributes()];
clasesTest = new int[test.getNumInstances()];
caja = new double[1];
for (i = 0; i < test.getNumInstances(); i++) {
temp = test.getInstance(i);
nulls = temp.getInputMissingValues();
datosTest[i] = test.getInstance(i).getAllInputValues();
for (j = 0; j < nulls.length; j++)
if (nulls[j]) {
datosTest[i][j] = 0.0;
}
caja = test.getInstance(i).getAllOutputValues();
clasesTest[i] = (int) caja[0];
}
} // end-method
