private Organism getHumanOrganism(DataSet data) throws DataStoreException {
String human = "H. Sapiens";
human = human.toLowerCase();
List<Organism> organisms = data.getMediatorProvider().getOrganismMediator().getAllOrganisms();
for (Organism organism : organisms) {
String organismName = organism.getName();
String organismAlias = organism.getAlias();
if (organismName.toLowerCase().equals(human) || organismAlias.toLowerCase().equals(human)) {
return organism;
}
}
return null;
}
public static boolean xor_epoch(Population pop, int generation, String filename) {
boolean esito = false;
// Evaluate each organism if exist the winner.........
boolean win = false;
Iterator itr_organism;
itr_organism = pop.organisms.iterator();
while (itr_organism.hasNext()) {
// point to organism
Organism _organism = ((Organism) itr_organism.next());
// evaluate
esito = xor_evaluate(_organism);
// if is a winner , store a flag
if (esito) win = true;
}
// compute average and max fitness for each species
Iterator itr_specie;
itr_specie = pop.species.iterator();
while (itr_specie.hasNext()) {
Species _specie = ((Species) itr_specie.next());
_specie.compute_average_fitness();
_specie.compute_max_fitness();
}
// Only print to file every print_every generations
if (win || (generation % Neat.p_print_every) == 0)
pop.print_to_file_by_species("c:\\jneat\\dati\\" + filename);
// if exist a winner write to file
if (win) {
int cnt = 0;
itr_organism = pop.getOrganisms().iterator();
while (itr_organism.hasNext()) {
Organism _organism = ((Organism) itr_organism.next());
if (_organism.winner) {
System.out.print("\n -WINNER IS #" + _organism.genome.genome_id);
_organism.getGenome().print_to_filename("c:\\jneat\\dati\\xor_win" + cnt);
cnt++;
}
}
}
// wait an epoch and make a reproductionof the best species
pop.epoch(generation);
if (win) {
System.out.print("\t\t** I HAVE FOUND A CHAMPION **");
return true;
} else return false;
}
public Organism pickPrey() {
Organism preferred = null; // declare a preferred prey
int maxpreference = 0;
for (Organism potentialPrey :
getParent()
.getInhabitants()) // cycle through all potential prey (organisms in the same ecosystem)
{
if (foods.getPrefFor(potentialPrey.getSpecies())
> maxpreference) // if the organism is the most preferred of all encountered so far
{
preferred = potentialPrey; // set this organism to tne new preferred prey
maxpreference =
foods.getPrefFor(
preferred
.getSpecies()); // set its preference to the new maxpreference, for comparison
// purposes
}
}
return preferred; // return the preferred organism
}
private EnrichmentEngineRequestDto createEnrichmentRequest(
RelatedGenesEngineResponseDto response) {
if (human.getOntology() == null) {
return null;
}
EnrichmentEngineRequestDto request = new EnrichmentEngineRequestDto();
request.setProgressReporter(NullProgressReporter.instance());
request.setMinCategories(MIN_CATEGORIES);
request.setqValueThreshold(Q_VALUE_THRESHOLD);
request.setOrganismId(human.getId());
request.setOntologyId(human.getOntology().getId());
Set<Long> nodes = new HashSet<Long>();
for (NetworkDto network : response.getNetworks()) {
for (InteractionDto interaction : network.getInteractions()) {
nodes.add(interaction.getNodeVO1().getId());
nodes.add(interaction.getNodeVO2().getId());
}
}
request.setNodes(nodes);
return request;
}
private RelatedGenesEngineRequestDto createRequest() throws ApplicationException {
RelatedGenesEngineRequestDto request = new RelatedGenesEngineRequestDto();
request.setNamespace(GeneMania.DEFAULT_NAMESPACE);
request.setOrganismId(human.getId());
request.setInteractionNetworks(collapseNetworks(networks));
Set<Long> nodes = new HashSet<Long>();
for (String geneName : genes) {
nodes.add(data.getCompletionProvider(human).getNodeId(geneName));
}
request.setPositiveNodes(nodes);
request.setLimitResults(geneLimit);
request.setCombiningMethod(combiningMethod);
request.setScoringMethod(org.genemania.type.ScoringMethod.DISCRIMINANT);
return request;
}
public static void main(String[] args) {
try {
Genome g = Organism.findGenome(args[0]);
File blasttab = new File(args[1]);
ProbesFromPrimers pfp = new ProbesFromPrimers(g, blasttab);
Collection<NamedRegion> probes = pfp.parseProbes();
for (NamedRegion probe : probes) {
String probeName = probe.getName();
System.out.println(
String.format(
"%s\t%s\t%d\t%d", probeName, probe.getChrom(), probe.getStart(), probe.getEnd()));
}
} catch (NotFoundException e) {
e.printStackTrace();
} catch (IOException e) {
e.printStackTrace();
}
}
public void setNetworks(Set<String> n) {
Map<InteractionNetworkGroup, Collection<InteractionNetwork>> groupMembers =
new HashMap<InteractionNetworkGroup, Collection<InteractionNetwork>>();
Collection<InteractionNetworkGroup> groups = human.getInteractionNetworkGroups();
Set<String> notHandled = n;
for (InteractionNetworkGroup group : groups) {
if (n.contains(group.getName())) {
notHandled.remove(group.getName());
List<InteractionNetwork> networkMembers = new ArrayList<InteractionNetwork>();
Collection<InteractionNetwork> networks = group.getInteractionNetworks();
for (InteractionNetwork network : networks) {
networkMembers.add(network);
}
if (networkMembers.size() > 0) {
groupMembers.put(group, networkMembers);
}
}
}
networks = groupMembers;
}
public void eat(Organism other) {
addEnergy(other.getFoodValue()); // increase own energy by amount gained from eating
other.die(); // make the other organism die
}
/** Insert the method's description here. Creation date: (16/01/2002 9.53.37) */
public static boolean xor_evaluate(Organism organism) {
Network _net = null;
boolean success = false;
double errorsum = 0.0;
double[] out = new double[4]; // The four outputs
// int numnodes = 0;
int net_depth = 0; // The max depth of the network to be activated
int count = 0;
// The four possible input combinations to xor
// The first number is for biasing
double in[][] = {{1.0, 0.0, 0.0}, {1.0, 0.0, 1.0}, {1.0, 1.0, 0.0}, {1.0, 1.0, 1.0}};
_net = organism.net;
// numnodes = organism.genome.nodes.size();
net_depth = _net.max_depth();
// for each example , 'count', propagate signal .... and compute results
for (count = 0; count <= 3; count++) {
// first activation from sensor to first next levelof neurons
_net.load_sensors(in[count]);
success = _net.activate();
// next activation while last level is reached !
// use depth to ensure relaxation
for (int relax = 0; relax <= net_depth; relax++) success = _net.activate();
// ok : the propagation is completed : repeat until all examples are presented
out[count] = ((NNode) _net.getOutputs().firstElement()).getActivation();
_net.flush();
}
// control the result
if (success) {
errorsum =
(double)
(Math.abs(out[0])
+ Math.abs(1.0 - out[1])
+ Math.abs(1.0 - out[2])
+ Math.abs(out[3]));
organism.setFitness(Math.pow((4.0 - errorsum), 2));
organism.setError(errorsum);
} else {
errorsum = 999.0;
organism.setFitness(0.001);
organism.setError(errorsum);
}
String mask03 = "0.000";
DecimalFormat fmt03 = new DecimalFormat(mask03);
if ((out[0] < 0.5) && (out[1] >= 0.5) && (out[2] >= 0.5) && (out[3] < 0.5)) {
organism.setWinner(true);
return true;
} else {
organism.setWinner(false);
return false;
}
}