org.eclipse.stem.tests.diseasemodels/src/org/eclipse/stem/diseasemodels/standard/tests/SEIRLabelValueTestUtil.java - stem/org.eclipse.stem - Git at Google

 package org.eclipse.stem.diseasemodels.standard.tests;

 /*******************************************************************************
  * Copyright (c) 2009 IBM Corporation and others.
  * All rights reserved. This program and the accompanying materials
  * are made available under the terms of the Eclipse Public License v1.0
  * which accompanies this distribution, and is available at
  * http://www.eclipse.org/legal/epl-v10.html
  *
  * Contributors:
  *     IBM Corporation - initial API and implementation
  *******************************************************************************/

 import java.util.LinkedList;
 import java.util.List;
 import java.util.Random;

 import junit.framework.TestCase;

 import org.eclipse.stem.core.graph.Node;
 import org.eclipse.stem.core.graph.impl.NodeImpl;
 import org.eclipse.stem.diseasemodels.standard.DiseaseModelState;
 import org.eclipse.stem.diseasemodels.standard.impl.SEIRLabelImpl;
 import org.eclipse.stem.diseasemodels.standard.impl.SEIRLabelValueImpl;
 import org.eclipse.stem.diseasemodels.standard.impl.SIDiseaseModelStateImpl;

 public abstract class SEIRLabelValueTestUtil extends TestCase{

 	/**
 	 * Number of random values to test with
 	 */
 	private static final int NUMBER_OF_RANDOMS = 0;

 	/**
 	 * max double value to be used in the test
 	 */
 	private static final double TEST_MAX_DOUBLE_VALUE = 1e12d;

 	/**
 	 * max long value to be used in the test
 	 */
 	private static final long TEST_MAX_LONG_VALUE = Long.MAX_VALUE;

 	/**
 	 * if there is a system property with this value then the test will be comperhensive. If not
 	 * then the test will check only few cases.
 	 */
 	private static final String COMPERHANSIVE_TEST = "comperhensive.test";

 	private static Boolean comperhensive = null;

 	/**
 	 * This method returns a list of {@link SEIRLabelValueImpl} objects.
 	 * Some objects in the list are random and some not random at all.
 	 * The ones that are not random represents extreme cases.
 	 * The numeric values for the extreme cases are 0.0, MAX_DOUBLE, and I added the number 1.0.
 	 * The number of random numbers to test with is NUMBER_OF_RANDOMS.
 	 * Note that with NUMBER_OF_RANDOMS = 0 you will have 4374 permutations of all values of {@SEIRLabelValueImpl}.
 	 * If you wish to make NUMBER_OF_RANDOMS higher then 0 then be ready for a long test :-)
 	 */
 	public static List<SEIRLabelValueImpl> createRandomSEIRLabelValueImpl(){
 		Random random = new Random();
 		double[] values = new double[3 + NUMBER_OF_RANDOMS];
 		values[0] = 0.0d;
 		values[1] = TEST_MAX_DOUBLE_VALUE;
 	    values[2] = 1.0d;
 		for(int i = (values.length + 1); i < values.length; i++){
 			values[i] = random.nextDouble() * TEST_MAX_DOUBLE_VALUE;
 		}
 		List<SEIRLabelValueImpl> modelLevelValues = new LinkedList<SEIRLabelValueImpl>();
 		int iteration = 0;
 		for(double s : values){
 			for(double e : values){
 				for(double i : values){
 					for(double r : values){
 						for(double births : values){
 							for(double deaths : values){
 								for(double diseaseDeaths : values){
 									if(diseaseDeaths > deaths){
 										continue;
 									}
 									if(skipValueSet(iteration++, diseaseDeaths, deaths, s, e, i, r)){
 										continue;
 									}
 									SEIRLabelValueImpl modelLableValue = new SEIRLabelValueImpl(s, e, i, r, diseaseDeaths);
 									assertTrue("The SEIRLabelValueImpl: " + modelLableValue + " is insane!", modelLableValue.sane());
 									modelLevelValues.add(modelLableValue);
 									System.out.println(modelLevelValues.size());
 								}
 							}
 						}
 					}
 				}
 			}
 		}
 	return modelLevelValues;
 	}

 	private static boolean skipValueSet(double iteration, double diseaseDeaths, double deaths, double s, double e, double i, double r) {
 		if(!isComperhensive()){
 			if(iteration % 500 > 1){
 				//do only one of any 500 permutations
 				System.out.print(".");
 				return true;
 			}
 		}
 		if(diseaseDeaths > deaths){
 			return true;
 		}
 		double population = s + i + r;
 		if(deaths > population){
 			return true;
 		}
 		if(diseaseDeaths > population){
 			return true;
 		}
 		return false;
 	}

 	private static boolean isComperhensive() {
 		if(null != comperhensive ){
 			return comperhensive.booleanValue();
 		}
 		String comperhensiveProperty = System.getProperty(COMPERHANSIVE_TEST);
 		if(null != comperhensiveProperty){
 			comperhensiveProperty = comperhensiveProperty.trim();
 			if(comperhensiveProperty.equalsIgnoreCase("yes") || comperhensiveProperty.equalsIgnoreCase("true")){
 				comperhensive = new Boolean(true);
 			}
 		}
 		if(null == comperhensive ){
 			comperhensive = new Boolean(false);
 		}
 		return comperhensive.booleanValue();
 	}
 	/**
 	 * Clone the SEIRLabelValueImpl
 	 * @param original will not be modefiles
 	 * @return a clone of 'original'
 	 */
 	public static SEIRLabelValueImpl cloneSEIRLabelValueImpl(SEIRLabelValueImpl original){
 		SEIRLabelValueImpl value =  new SEIRLabelValueImpl(original.getS(), original.getE(), original.getI(),
 				original.getR(),  original.getDiseaseDeaths());
 		return value;
 	}

 	/**
 	 * This method returns a list of {@link SEIRLabelImpl} objects.
 	 * Some objects in the list are random and some not random at all.
 	 * The ones that are not random represents extreme cases.
 	 * The numeric values for the extreme cases are 0.0, MAX_DOUBLE, and I added the number 1.0.
 	 * The number of random numbers to test with is NUMBER_OF_RANDOMS.
 	 * Note that with NUMBER_OF_RANDOMS = 0 you will have 4374 permutations of all values of {SEIRLabelImpl}.
 	 * If you wish to make NUMBER_OF_RANDOMS higher then 0 then be ready for a long test :-)
 	 *
 	 */
 	public List<SEIRLabelImpl> createRandomSEIRLabelImpl(){
 		Random random = new Random();
 		double[] values = new double[3 + NUMBER_OF_RANDOMS];
 		values[0] = 0.0d;
 		values[1] = TEST_MAX_DOUBLE_VALUE;
 		values[2] = 1.0d;
 		for(int i = 3; i < values.length; i++){
 			values[i] = random.nextDouble() * TEST_MAX_DOUBLE_VALUE;
 		}
 		List<SEIRLabelImpl> modelLevelValues = new LinkedList<SEIRLabelImpl>();
 		int iteration = 0;
 		for(double s : values){
 			for(double e : values){
 				for(double i : values){
 					for(double r : values){
 						for(double births : values){
 							for(double deaths : values){
 								for(double diseaseDeaths : values){
 									if(skipValueSet(iteration++, diseaseDeaths, deaths, s, e, i, r)){
 										continue;
 									}
 									SEIRLabelImpl modelLable = new SEIRLabelTesterImpl(s, e, i, r, births, deaths,	diseaseDeaths,
 											new SIDiseaseModelStateTesterImpl(), new NodeTesterImpl());
 									assertTrue("The SEIRLabelImpl: " + modelLable + " is insane!", modelLable.sane());
 									modelLevelValues.add(modelLable);
 								}
 							}
 						}
 					}
 				}
 			}
 		}
 	return modelLevelValues;
 	}


 	private class SIDiseaseModelStateTesterImpl extends SIDiseaseModelStateImpl{
 		public SIDiseaseModelStateTesterImpl(){
 			super();
 		}
 	}


 	private class SEIRLabelTesterImpl extends SEIRLabelImpl{
 		/**
 		 * <!-- begin-user-doc -->
 		 *
 		 * This is used to create instances for testing purposes.
 		 *
 		 * @param s
 		 *            the number of susceptible population members
 		 * @param e
 		 *            the number of exposed population members
 		 * @param iR
 		 *            the number of recovering infectious population members
 		 * @param iF
 		 *            the number of fatally infectious population members
 		 * @param r
 		 *            the number of recovered population members
 		 * @param births
 		 *            the number of births that have occured in the population
 		 * @param deaths
 		 *            the total number of deaths that have occured in the population
 		 * @param diseaseDeaths
 		 *            the number of deaths due to the disease that have occured in
 		 *            the population
 		 *
 		 * <!-- end-user-doc -->
 		 * @param newDiseaseModelState
 		 * @param newNode
 		 */
 		public SEIRLabelTesterImpl(final double s, final double e, final double i,
 				final double r, final double births, final double deaths,
 				final double diseaseDeaths, final DiseaseModelState newDiseaseModelState, Node newNode) {
 			super();
 			setS(s);
 			setE(e);
 			setI(i);
 			setR(r);
 			setDiseaseDeaths(diseaseDeaths);
 			setDiseaseModelState(newDiseaseModelState);
 			setNode(newNode);
 		} // SEIRLabelValueImpl
 	}

 	private class NodeTesterImpl extends NodeImpl{
 		public NodeTesterImpl(){
 			super();
 		}
 	}


 	public long[] createrandomDeltas() {
 		Random random = new Random();
 		long[] values = new long[2 + NUMBER_OF_RANDOMS];
 		values[0] = TEST_MAX_LONG_VALUE;
 		values[1] = 1l;
 		for(int i = 3; i < values.length; i++){
 			values[i] = random.nextLong() * TEST_MAX_LONG_VALUE;
 		}
 		return values;
 	}

 }
	package org.eclipse.stem.diseasemodels.standard.tests;

	/*******************************************************************************
	* Copyright (c) 2009 IBM Corporation and others.
	* All rights reserved. This program and the accompanying materials
	* are made available under the terms of the Eclipse Public License v1.0
	* which accompanies this distribution, and is available at
	* http://www.eclipse.org/legal/epl-v10.html
	*
	* Contributors:
	* IBM Corporation - initial API and implementation
	*******************************************************************************/

	import java.util.LinkedList;
	import java.util.List;
	import java.util.Random;

	import junit.framework.TestCase;

	import org.eclipse.stem.core.graph.Node;
	import org.eclipse.stem.core.graph.impl.NodeImpl;
	import org.eclipse.stem.diseasemodels.standard.DiseaseModelState;
	import org.eclipse.stem.diseasemodels.standard.impl.SEIRLabelImpl;
	import org.eclipse.stem.diseasemodels.standard.impl.SEIRLabelValueImpl;
	import org.eclipse.stem.diseasemodels.standard.impl.SIDiseaseModelStateImpl;

	public abstract class SEIRLabelValueTestUtil extends TestCase{

	/**
	* Number of random values to test with
	*/
	private static final int NUMBER_OF_RANDOMS = 0;

	/**
	* max double value to be used in the test
	*/
	private static final double TEST_MAX_DOUBLE_VALUE = 1e12d;

	/**
	* max long value to be used in the test
	*/
	private static final long TEST_MAX_LONG_VALUE = Long.MAX_VALUE;

	/**
	* if there is a system property with this value then the test will be comperhensive. If not
	* then the test will check only few cases.
	*/
	private static final String COMPERHANSIVE_TEST = "comperhensive.test";

	private static Boolean comperhensive = null;

	/**
	* This method returns a list of {@link SEIRLabelValueImpl} objects.
	* Some objects in the list are random and some not random at all.
	* The ones that are not random represents extreme cases.
	* The numeric values for the extreme cases are 0.0, MAX_DOUBLE, and I added the number 1.0.
	* The number of random numbers to test with is NUMBER_OF_RANDOMS.
	* Note that with NUMBER_OF_RANDOMS = 0 you will have 4374 permutations of all values of {@SEIRLabelValueImpl}.
	* If you wish to make NUMBER_OF_RANDOMS higher then 0 then be ready for a long test :-)
	*/
	public static List<SEIRLabelValueImpl> createRandomSEIRLabelValueImpl(){
	Random random = new Random();
	double[] values = new double[3 + NUMBER_OF_RANDOMS];
	values[0] = 0.0d;
	values[1] = TEST_MAX_DOUBLE_VALUE;
	values[2] = 1.0d;
	for(int i = (values.length + 1); i < values.length; i++){
	values[i] = random.nextDouble() * TEST_MAX_DOUBLE_VALUE;
	}
	List<SEIRLabelValueImpl> modelLevelValues = new LinkedList<SEIRLabelValueImpl>();
	int iteration = 0;
	for(double s : values){
	for(double e : values){
	for(double i : values){
	for(double r : values){
	for(double births : values){
	for(double deaths : values){
	for(double diseaseDeaths : values){
	if(diseaseDeaths > deaths){
	continue;
	}
	if(skipValueSet(iteration++, diseaseDeaths, deaths, s, e, i, r)){
	continue;
	}
	SEIRLabelValueImpl modelLableValue = new SEIRLabelValueImpl(s, e, i, r, diseaseDeaths);
	assertTrue("The SEIRLabelValueImpl: " + modelLableValue + " is insane!", modelLableValue.sane());
	modelLevelValues.add(modelLableValue);
	System.out.println(modelLevelValues.size());
	}
	}
	}
	}
	}
	}
	}
	return modelLevelValues;
	}

	private static boolean skipValueSet(double iteration, double diseaseDeaths, double deaths, double s, double e, double i, double r) {
	if(!isComperhensive()){
	if(iteration % 500 > 1){
	//do only one of any 500 permutations
	System.out.print(".");
	return true;
	}
	}
	if(diseaseDeaths > deaths){
	return true;
	}
	double population = s + i + r;
	if(deaths > population){
	return true;
	}
	if(diseaseDeaths > population){
	return true;
	}
	return false;
	}

	private static boolean isComperhensive() {
	if(null != comperhensive ){
	return comperhensive.booleanValue();
	}
	String comperhensiveProperty = System.getProperty(COMPERHANSIVE_TEST);
	if(null != comperhensiveProperty){
	comperhensiveProperty = comperhensiveProperty.trim();
	if(comperhensiveProperty.equalsIgnoreCase("yes") \|\| comperhensiveProperty.equalsIgnoreCase("true")){
	comperhensive = new Boolean(true);
	}
	}
	if(null == comperhensive ){
	comperhensive = new Boolean(false);
	}
	return comperhensive.booleanValue();
	}
	/**
	* Clone the SEIRLabelValueImpl
	* @param original will not be modefiles
	* @return a clone of 'original'
	*/
	public static SEIRLabelValueImpl cloneSEIRLabelValueImpl(SEIRLabelValueImpl original){
	SEIRLabelValueImpl value = new SEIRLabelValueImpl(original.getS(), original.getE(), original.getI(),
	original.getR(), original.getDiseaseDeaths());
	return value;
	}

	/**
	* This method returns a list of {@link SEIRLabelImpl} objects.
	* Some objects in the list are random and some not random at all.
	* The ones that are not random represents extreme cases.
	* The numeric values for the extreme cases are 0.0, MAX_DOUBLE, and I added the number 1.0.
	* The number of random numbers to test with is NUMBER_OF_RANDOMS.
	* Note that with NUMBER_OF_RANDOMS = 0 you will have 4374 permutations of all values of {SEIRLabelImpl}.
	* If you wish to make NUMBER_OF_RANDOMS higher then 0 then be ready for a long test :-)
	*
	*/
	public List<SEIRLabelImpl> createRandomSEIRLabelImpl(){
	Random random = new Random();
	double[] values = new double[3 + NUMBER_OF_RANDOMS];
	values[0] = 0.0d;
	values[1] = TEST_MAX_DOUBLE_VALUE;
	values[2] = 1.0d;
	for(int i = 3; i < values.length; i++){
	values[i] = random.nextDouble() * TEST_MAX_DOUBLE_VALUE;
	}
	List<SEIRLabelImpl> modelLevelValues = new LinkedList<SEIRLabelImpl>();
	int iteration = 0;
	for(double s : values){
	for(double e : values){
	for(double i : values){
	for(double r : values){
	for(double births : values){
	for(double deaths : values){
	for(double diseaseDeaths : values){
	if(skipValueSet(iteration++, diseaseDeaths, deaths, s, e, i, r)){
	continue;
	}
	SEIRLabelImpl modelLable = new SEIRLabelTesterImpl(s, e, i, r, births, deaths, diseaseDeaths,
	new SIDiseaseModelStateTesterImpl(), new NodeTesterImpl());
	assertTrue("The SEIRLabelImpl: " + modelLable + " is insane!", modelLable.sane());
	modelLevelValues.add(modelLable);
	}
	}
	}
	}
	}
	}
	}
	return modelLevelValues;
	}



	private class SIDiseaseModelStateTesterImpl extends SIDiseaseModelStateImpl{
	public SIDiseaseModelStateTesterImpl(){
	super();
	}
	}


	private class SEIRLabelTesterImpl extends SEIRLabelImpl{
	/**
	* <!-- begin-user-doc -->
	*
	* This is used to create instances for testing purposes.
	*
	* @param s
	* the number of susceptible population members
	* @param e
	* the number of exposed population members
	* @param iR
	* the number of recovering infectious population members
	* @param iF
	* the number of fatally infectious population members
	* @param r
	* the number of recovered population members
	* @param births
	* the number of births that have occured in the population
	* @param deaths
	* the total number of deaths that have occured in the population
	* @param diseaseDeaths
	* the number of deaths due to the disease that have occured in
	* the population
	*
	* <!-- end-user-doc -->
	* @param newDiseaseModelState
	* @param newNode
	*/
	public SEIRLabelTesterImpl(final double s, final double e, final double i,
	final double r, final double births, final double deaths,
	final double diseaseDeaths, final DiseaseModelState newDiseaseModelState, Node newNode) {
	super();
	setS(s);
	setE(e);
	setI(i);
	setR(r);
	setDiseaseDeaths(diseaseDeaths);
	setDiseaseModelState(newDiseaseModelState);
	setNode(newNode);
	} // SEIRLabelValueImpl
	}

	private class NodeTesterImpl extends NodeImpl{
	public NodeTesterImpl(){
	super();
	}
	}


	public long[] createrandomDeltas() {
	Random random = new Random();
	long[] values = new long[2 + NUMBER_OF_RANDOMS];
	values[0] = TEST_MAX_LONG_VALUE;
	values[1] = 1l;
	for(int i = 3; i < values.length; i++){
	values[i] = random.nextLong() * TEST_MAX_LONG_VALUE;
	}
	return values;
	}

	}