public static List getNcMLElements(String path, Document doc) {
// XPath doesn't support default namespaces, so we add nc as a prefix for the tags within the
// namespace!!!
if (!path.startsWith(NS_PREFIX_ON_TAG) && !path.startsWith("/")) path = NS_PREFIX_ON_TAG + path;
Pattern pattern = Pattern.compile("/\\w");
Matcher matcher = pattern.matcher(path);
StringBuilder sb = new StringBuilder();
int currentChar = 0;
while (matcher.find()) {
sb.append(path.substring(currentChar, matcher.start() - currentChar + 1));
if (!sb.toString().endsWith("/")) sb.append("/");
sb.append(NS_PREFIX_ON_TAG);
currentChar = matcher.start() + 1;
}
sb.append(path.substring(currentChar, path.length()));
XPath xpath;
try {
xpath = XPath.newInstance(sb.toString());
xpath.addNamespace(NS_PREFIX, doc.getRootElement().getNamespaceURI());
return xpath.selectNodes(doc);
} catch (JDOMException e) {
e.printStackTrace();
}
return null;
}
public void trainNNet(Document nnet) throws JDOMException {
// 1.search net map for all output layer neurodes, add to list, search for all hidden neurodes
// append to list, search for all input neurodes append to list
// 2.cycle through list, get EG. Cycle through list, MT adjust weights.
java.util.concurrent.ExecutorService executor =
java.util.concurrent.Executors.newFixedThreadPool(Erudite_gui.maxThreads);
java.util.List nodeList =
XPath.newInstance("//LAYER[@LAYER_NAME = 'OUTPUT']/NEURODE")
.selectNodes(Erudite_gui.NNetMap);
java.util.List Hnodes =
XPath.newInstance("//LAYER[@LAYER_NAME = 'HIDDEN']/NEURODE")
.selectNodes(Erudite_gui.NNetMap);
nodeList.addAll(Hnodes);
Iterator nodeEGlist = nodeList.iterator(); // iterate through list and calculate error gradients
do {
Element currentNode = (Element) nodeEGlist.next();
getErrorGradient(currentNode);
} while (nodeEGlist.hasNext());
Iterator nodeAWlist = nodeList.iterator(); // iterate through list and adjust weights and biases
do {
Element currentNode = (Element) nodeAWlist.next();
TrainNet train = new TrainNet();
if (Erudite_gui.jCheckBox2.isSelected()) { // auto-adapting learning rate
train.learningRate = -1.0;
}
train.neurode = currentNode;
executor.execute(train);
} while (nodeAWlist.hasNext());
try {
executor.shutdown();
executor.awaitTermination(2, TimeUnit.SECONDS);
} catch (Exception e) {
e.printStackTrace();
}
}
public void clearNNet(Document NNetMap) throws JDOMException {
java.util.concurrent.ExecutorService executor =
java.util.concurrent.Executors.newFixedThreadPool(Erudite_gui.maxThreads);
java.util.List nodes = XPath.newInstance("//NEURODE").selectNodes(NNetMap);
Iterator itNeurodes = nodes.iterator();
do {
Element CurrentNode = (Element) itNeurodes.next();
ClearNode clear = new ClearNode();
clear.neurode = CurrentNode;
executor.execute(clear);
} while (itNeurodes.hasNext());
try {
executor.shutdown();
executor.awaitTermination(1, TimeUnit.SECONDS);
} catch (Exception e) {
e.printStackTrace();
}
}
public void runNNet(Document NNetMap) throws JDOMException {
java.util.concurrent.ExecutorService executor =
java.util.concurrent.Executors.newFixedThreadPool(Erudite_gui.maxThreads);
java.util.List nodes =
XPath.newInstance("//NEURODE[SYNAPSE/@ORG_NEURODE != 'INPUT']").selectNodes(NNetMap);
Iterator itNeurodes = nodes.iterator();
do {
EvaluateNeurode evalN = new EvaluateNeurode();
Element CurrentNode = (Element) itNeurodes.next(); // get current NEURODE
evalN.NNetMap = Erudite_gui.NNetMap;
evalN.neurode = CurrentNode; // get summed input for current NEURODE
executor.execute(evalN);
} while (itNeurodes.hasNext());
try {
executor.shutdown();
executor.awaitTermination(2, TimeUnit.SECONDS);
} catch (Exception e) {
e.printStackTrace();
}
}
private void getErrorGradient(Element node) throws JDOMException {
// if-then-else for output/hidden/input layers, if-then for transfer functions, compute error
// gradient and update nnet map
String layerType = node.getParentElement().getAttributeValue("LAYER_NAME").toString();
int xfer = Integer.parseInt(node.getParentElement().getAttributeValue("TRANSFER_FUNCTION"));
String nid = node.getAttributeValue("N_ID").toString();
if (layerType.equals("OUTPUT")) { // if computing output neurode error gradient
if (xfer == 1) { // if hyperbolic tangent transfer function
for (int i = 0; i < Erudite_gui.outputNID.length; i++) {
if (nid.equals(Erudite_gui.outputNID[i])) {
double error = Erudite_gui.desiredOutput[i] - Erudite_gui.output[i];
double errorGradient =
error * ((1 - Erudite_gui.output[i]) * (1 + Erudite_gui.output[i]));
node.setAttribute("NNET_V4", String.valueOf(errorGradient));
}
}
} else if (xfer == 2) { // if sigmoid transfer function function
for (int i = 0; i < Erudite_gui.outputNID.length; i++) {
if (nid.equals(Erudite_gui.outputNID[i])) {
double error = Erudite_gui.desiredOutput[i] - Erudite_gui.output[i];
double errorGradient = error * (Erudite_gui.output[i] * (1 - Erudite_gui.output[i]));
node.setAttribute("NNET_V4", String.valueOf(errorGradient));
}
}
}
} else if (layerType.equals("HIDDEN")
|| layerType.equals("INPUT")) { // if computing hidden or input neurode error gradient
if (xfer == 1) {
double sumWouts = 0.0;
double nodeActivity = java.lang.Double.parseDouble(node.getAttributeValue("ACTIVITY"));
java.util.List outputs =
XPath.newInstance("//SYNAPSE[@ORG_NEURODE = '" + nid + "']")
.selectNodes(Erudite_gui.NNetMap);
Iterator synapseO = outputs.iterator();
do {
Element currentNode = (Element) synapseO.next();
sumWouts +=
java.lang.Double.parseDouble(currentNode.getAttributeValue("WEIGHT"))
* java.lang.Double.parseDouble(
currentNode.getParentElement().getAttributeValue("NNET_V4"));
} while (synapseO.hasNext());
double errorGradient = sumWouts * ((1 - nodeActivity) * (1 + nodeActivity));
node.setAttribute("NNET_V4", String.valueOf(errorGradient));
} else if (xfer == 2) {
double sumWouts = 0.0;
double nodeActivity = java.lang.Double.parseDouble(node.getAttributeValue("ACTIVITY"));
java.util.List outputs =
XPath.newInstance("//SYNAPSE[@ORG_NEURODE = '" + nid + "']")
.selectNodes(Erudite_gui.NNetMap);
Iterator synapseO = outputs.iterator();
do {
Element currentNode = (Element) synapseO.next();
sumWouts +=
java.lang.Double.parseDouble(currentNode.getAttributeValue("WEIGHT"))
* java.lang.Double.parseDouble(
currentNode.getParentElement().getAttributeValue("NNET_V4"));
} while (synapseO.hasNext());
double errorGradient = sumWouts * (nodeActivity * (1 - nodeActivity));
node.setAttribute("NNET_V4", String.valueOf(errorGradient));
}
}
}