/*
* RapidMiner
*
* Copyright (C) 2001-2008 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.com
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.generator;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.table.AttributeFactory;
import com.rapidminer.example.table.DoubleArrayDataRow;
import com.rapidminer.example.table.MemoryExampleTable;
import com.rapidminer.operator.IOObject;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.RandomGenerator;
/**
* Generates a random example set for testing purposes. All attributes have only
* (random) nominal values and a classification label.
*
* @author Ingo Mierswa
* @version $Id: NominalExampleSetGenerator.java,v 1.9 2006/04/05 08:57:24
* ingomierswa Exp $
*/
public class NominalExampleSetGenerator extends Operator {
/** The parameter name for "The number of generated examples." */
public static final String PARAMETER_NUMBER_EXAMPLES = "number_examples";
/** The parameter name for "The number of attributes." */
public static final String PARAMETER_NUMBER_OF_ATTRIBUTES = "number_of_attributes";
/** The parameter name for "The number of nominal values for each attribute." */
public static final String PARAMETER_NUMBER_OF_VALUES = "number_of_values";
/** The parameter name for "Use the given random seed instead of global random numbers (-1: use global)." */
public static final String PARAMETER_LOCAL_RANDOM_SEED = "local_random_seed";
private static final Class[] INPUT_CLASSES = new Class[0];
private static final Class[] OUTPUT_CLASSES = { ExampleSet.class };
public NominalExampleSetGenerator(OperatorDescription description) {
super(description);
}
public IOObject[] apply() throws OperatorException {
// init
int numberOfExamples = getParameterAsInt(PARAMETER_NUMBER_EXAMPLES);
int numberOfAttributes = getParameterAsInt(PARAMETER_NUMBER_OF_ATTRIBUTES);
int numberOfValues = getParameterAsInt(PARAMETER_NUMBER_OF_VALUES);
if (numberOfValues < 2) {
logWarning("Less than 2 different values used, change to '2'.");
numberOfValues = 2;
}
// create table
List<Attribute> attributes = new LinkedList<Attribute>();
for (int m = 0; m < numberOfAttributes; m++) {
Attribute current = AttributeFactory.createAttribute("att" + (m + 1), Ontology.NOMINAL);
for (int v = 0; v < numberOfValues; v++)
current.getMapping().mapString("value" + v);
attributes.add(current);
}
Attribute label = AttributeFactory.createAttribute("label", Ontology.NOMINAL);
label.getMapping().mapString("negative");
label.getMapping().mapString("positive");
attributes.add(label);
MemoryExampleTable table = new MemoryExampleTable(attributes);
// create data
RandomGenerator random = RandomGenerator.getRandomGenerator(getParameterAsInt(PARAMETER_LOCAL_RANDOM_SEED));
for (int n = 0; n < numberOfExamples; n++) {
double[] features = new double[numberOfAttributes];
for (int a = 0; a < features.length; a++)
features[a] = random.nextIntInRange(0, numberOfValues - 1);
double[] example = features;
if (label != null) {
example = new double[numberOfAttributes + 1];
System.arraycopy(features, 0, example, 0, features.length);
if (features.length >= 2) {
example[example.length - 1] = ((features[0] == 0) || features[1] == 0) ? label.getMapping().mapString("positive") : label.getMapping().mapString("negative");
} else if (features.length == 1) {
example[example.length - 1] = (features[0] == 0) ? label.getMapping().mapString("positive") : label.getMapping().mapString("negative");
} else {
example[example.length - 1] = label.getMapping().mapString("positive");
}
}
table.addDataRow(new DoubleArrayDataRow(example));
}
// create example set and return it
return new IOObject[] { table.createExampleSet(label) };
}
public Class<?>[] getInputClasses() {
return INPUT_CLASSES;
}
public Class<?>[] getOutputClasses() {
return OUTPUT_CLASSES;
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeInt(PARAMETER_NUMBER_EXAMPLES, "The number of generated examples.", 1, Integer.MAX_VALUE, 100);
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_NUMBER_OF_ATTRIBUTES, "The number of attributes.", 0, Integer.MAX_VALUE, 5);
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_NUMBER_OF_VALUES, "The number of nominal values for each attribute.", 0, Integer.MAX_VALUE, 5);
type.setExpert(false);
types.add(type);
types.add(new ParameterTypeInt(PARAMETER_LOCAL_RANDOM_SEED, "Use the given random seed instead of global random numbers (-1: use global).", -1, Integer.MAX_VALUE, -1));
return types;
}
}