protected void displayNewTxf(String strLabel, String strValue) {
strLabel = (strLabel == null) ? "" : strLabel.trim();
strValue = (strValue == null) ? "" : strValue.trim();
JCheckBox chkBox1 = new JCheckBox(Util.getImageIcon("boxGray.gif"));
DataField txf1 = new DataField(strLabel);
DataField txf2 = new DataField(strValue);
JPanel pnlTxf = new JPanel(m_gbl);
m_nRow = m_nRow + 1;
txf1.setName("label");
txf2.setName("value");
/* 1st line of text field*/
m_gbc.weightx = 0;
showComp(m_gbl, m_gbc, 0, m_nRow, 1, chkBox1);
m_gbc.weightx = 1;
showComp(m_gbl, m_gbc, GridBagConstraints.RELATIVE, m_nRow, 1, txf1);
// showSpaces( gbl, gbc, 2, 6 );
showComp(m_gbl, m_gbc, GridBagConstraints.RELATIVE, m_nRow, 1, txf2);
m_gbc.weightx = 0;
txf1.addFocusListener(this);
txf2.addFocusListener(this);
m_objTxfValue.addToLabel(txf1);
m_objTxfValue.addToValue(txf2);
}
protected void toXml(Record record) throws SAXException {
if (!MarcFactory.newInstance().validateRecord(record)) {
throw new MarcException("Marc record didn't validate");
}
char temp[];
AttributesImpl atts = new AttributesImpl();
if (indent) handler.ignorableWhitespace("\n ".toCharArray(), 0, 3);
handler.startElement(Constants.MARCXML_NS_URI, RECORD, RECORD, atts);
if (indent) handler.ignorableWhitespace("\n ".toCharArray(), 0, 5);
handler.startElement(Constants.MARCXML_NS_URI, LEADER, LEADER, atts);
Leader leader = record.getLeader();
temp = leader.toString().toCharArray();
handler.characters(temp, 0, temp.length);
handler.endElement(Constants.MARCXML_NS_URI, LEADER, LEADER);
for (ControlField field : record.getControlFields()) {
atts = new AttributesImpl();
atts.addAttribute("", "tag", "tag", "CDATA", field.getTag());
if (indent) handler.ignorableWhitespace("\n ".toCharArray(), 0, 5);
handler.startElement(Constants.MARCXML_NS_URI, CONTROL_FIELD, CONTROL_FIELD, atts);
temp = getDataElement(field.getData());
handler.characters(temp, 0, temp.length);
handler.endElement(Constants.MARCXML_NS_URI, CONTROL_FIELD, CONTROL_FIELD);
}
for (DataField field : record.getDataFields()) {
atts = new AttributesImpl();
atts.addAttribute("", "tag", "tag", "CDATA", field.getTag());
atts.addAttribute("", "ind1", "ind1", "CDATA", String.valueOf(field.getIndicator1()));
atts.addAttribute("", "ind2", "ind2", "CDATA", String.valueOf(field.getIndicator2()));
if (indent) handler.ignorableWhitespace("\n ".toCharArray(), 0, 5);
handler.startElement(Constants.MARCXML_NS_URI, DATA_FIELD, DATA_FIELD, atts);
for (Subfield subfield : field.getSubfields()) {
atts = new AttributesImpl();
atts.addAttribute("", "code", "code", "CDATA", String.valueOf(subfield.getCode()));
if (indent) handler.ignorableWhitespace("\n ".toCharArray(), 0, 7);
handler.startElement(Constants.MARCXML_NS_URI, SUBFIELD, SUBFIELD, atts);
temp = getDataElement(subfield.getData());
handler.characters(temp, 0, temp.length);
handler.endElement(Constants.MARCXML_NS_URI, SUBFIELD, SUBFIELD);
}
if (indent) handler.ignorableWhitespace("\n ".toCharArray(), 0, 5);
handler.endElement(Constants.MARCXML_NS_URI, DATA_FIELD, DATA_FIELD);
}
if (indent) handler.ignorableWhitespace("\n ".toCharArray(), 0, 3);
handler.endElement(Constants.MARCXML_NS_URI, RECORD, RECORD);
}
/*
* Decode a tar file header. The stream is assumed to be at the beginning of the header.
*/
private TarFile decodeNextFile() throws IOException {
byte[] buffer = new byte[TAR_FILE_GRANULARITY];
// A header must be present for an OVA component file to be available..
this.inputStream.readFully(buffer);
// If last two empty sections of the TAR file were reached, exit.
String signature = SIGNATURE_FIELD.getFirstFieldValue(buffer);
if (!TAR_SIGNATURE.equals(signature)) {
logger.error("Invalid file signature: '{}'", signature);
logger.error("Input stream buffer content: [{}]", buffer);
return null;
}
// Get embedded file name.
TarFile fileInfo = new TarFile();
fileInfo.name = NAME_FIELD.getFirstFieldValue(buffer);
// Get embedded file length.
String octalByteLength = OCTAL_BYTE_LENGTH_FIELD.getFirstFieldValue(buffer);
long fileByteLength = Long.parseLong(octalByteLength.trim(), 8 /* octal base */);
// Get embedded file content.
fileInfo.content = getFileContentStream(fileByteLength);
return fileInfo;
}
public static StreamElement createElementFromREST(
DataField[] outputFormat, String[] fieldNames, Object[] fieldValues) {
ArrayList<Serializable> values = new ArrayList<Serializable>();
// ArrayList<String> fields = new ArrayList<String>();
long timestamp = -1;
for (int i = 0; i < fieldNames.length; i++) {
if (fieldNames[i].equalsIgnoreCase("TIMED")) {
timestamp = Long.parseLong((String) fieldValues[i]);
continue;
}
boolean found = false;
for (DataField f : outputFormat) {
if (f.getName().equalsIgnoreCase(fieldNames[i])) {
// fields.add(fieldNames[i]);
found = true;
break;
}
}
if (found == false) continue;
switch (findIndexInDataField(outputFormat, fieldNames[i])) {
case DataTypes.DOUBLE:
values.add(Double.parseDouble((String) fieldValues[i]));
break;
case DataTypes.BIGINT:
values.add(Long.parseLong((String) fieldValues[i]));
break;
case DataTypes.TINYINT:
values.add(Byte.parseByte((String) fieldValues[i]));
break;
case DataTypes.SMALLINT:
values.add(Short.parseShort((String) fieldValues[i]));
break;
case DataTypes.INTEGER:
values.add(Integer.parseInt((String) fieldValues[i]));
break;
case DataTypes.CHAR:
case DataTypes.VARCHAR:
values.add(new String((byte[]) fieldValues[i]));
break;
case DataTypes.BINARY:
try {
// StreamElementTest.md5Digest(fieldValues[ i ]);
} catch (Exception e) {
e.printStackTrace();
}
values.add((byte[]) fieldValues[i]);
break;
case -1:
default:
logger.error(
"The field name doesn't exit in the output structure : FieldName : "
+ (String) fieldNames[i]);
}
}
if (timestamp == -1) timestamp = System.currentTimeMillis();
return new StreamElement(outputFormat, values.toArray(new Serializable[] {}), timestamp);
}
public Object clone() {
DataField field = new DataField();
field.setName(getName());
field.setValue(value);
field.setDataType(dataType);
field.setAppend(this.getAppend());
return field;
}
@Override
protected void set(int index, int value) {
if (index == 0) {
return;
}
super.set(index, value);
}
protected void displayNewTxf(String strLabel, String strValue) {
strLabel = (strLabel != null) ? strLabel.trim() : "";
strValue = (strValue != null) ? strValue.trim() : "";
JCheckBox chk1 = new JCheckBox(Util.getImageIcon("boxGray.gif"));
final DataField txf1 = new DataField(strLabel);
final DataField txf2 = new DataField(strValue);
m_nRow = m_nRow + 1;
txf1.setName("label");
txf2.setName("value");
// new field
if (strLabel.equals("") && strValue.equals("")) {
txf2.setText(INFOSTR);
txf2.addMouseListener(m_mlTxf);
if (timer != null) timer.cancel();
timer = new java.util.Timer();
timer.schedule(
new TimerTask() {
public void run() {
WUtil.blink(txf2, WUtil.FOREGROUND);
}
},
delay,
delay);
}
/* 1st line of text field*/
m_gbc.weightx = 0;
showComp(m_gbl, m_gbc, 0, m_nRow, 1, chk1);
m_gbc.weightx = 1;
showComp(m_gbl, m_gbc, GridBagConstraints.RELATIVE, m_nRow, 1, txf1);
// showSpaces( gbl, gbc, 2, 6 );
showComp(m_gbl, m_gbc, GridBagConstraints.RELATIVE, m_nRow, 1, txf2);
m_gbc.weightx = 0;
txf1.addFocusListener(this);
txf2.addFocusListener(this);
// Add the textfields to the respective arrays, so that they
// can be retreived later for writing to the file.
m_objTxfValue.addToLabel(txf1);
m_objTxfValue.addToValue(txf2);
}
public void print() {
System.out.println(
"PK "
+ getUri()
+ " "
+ fieldStr.getName()
+ " "
+ getStringField(fieldStr)
+ " "
+ getLongField(fieldInt)
+ " "
+ getNumericField(fieldDbl)
+ " "
+ getDateField(fieldDate)
+ " "
+ getBooleanField(fieldBool));
}
public static PMML generateSimpleNeuralNetwork(
String modelName,
String[] inputfieldNames,
String[] outputfieldNames,
double[] inputMeans,
double[] inputStds,
double[] outputMeans,
double[] outputStds,
int hiddenSize,
double[] weights) {
int counter = 0;
int wtsIndex = 0;
PMML pmml = new PMML();
pmml.setVersion("4.0");
Header header = new Header();
Application app = new Application();
app.setName("Drools PMML Generator");
app.setVersion("0.01 Alpha");
header.setApplication(app);
header.setCopyright("BSD");
header.setDescription(" Smart Vent Model ");
Timestamp ts = new Timestamp();
ts.getContent().add(new java.util.Date().toString());
header.setTimestamp(ts);
pmml.setHeader(header);
DataDictionary dic = new DataDictionary();
dic.setNumberOfFields(BigInteger.valueOf(inputfieldNames.length + outputfieldNames.length));
for (String ifld : inputfieldNames) {
DataField dataField = new DataField();
dataField.setName(ifld);
dataField.setDataType(DATATYPE.DOUBLE);
dataField.setDisplayName(ifld);
dataField.setOptype(OPTYPE.CONTINUOUS);
dic.getDataFields().add(dataField);
}
for (String ofld : outputfieldNames) {
DataField dataField = new DataField();
dataField.setName(ofld);
dataField.setDataType(DATATYPE.DOUBLE);
dataField.setDisplayName(ofld);
dataField.setOptype(OPTYPE.CONTINUOUS);
dic.getDataFields().add(dataField);
}
pmml.setDataDictionary(dic);
NeuralNetwork nnet = new NeuralNetwork();
nnet.setActivationFunction(ACTIVATIONFUNCTION.LOGISTIC);
nnet.setFunctionName(MININGFUNCTION.REGRESSION);
nnet.setNormalizationMethod(NNNORMALIZATIONMETHOD.NONE);
nnet.setModelName(modelName);
MiningSchema miningSchema = new MiningSchema();
for (String ifld : inputfieldNames) {
MiningField mfld = new MiningField();
mfld.setName(ifld);
mfld.setOptype(OPTYPE.CONTINUOUS);
mfld.setUsageType(FIELDUSAGETYPE.ACTIVE);
miningSchema.getMiningFields().add(mfld);
}
for (String ofld : outputfieldNames) {
MiningField mfld = new MiningField();
mfld.setName(ofld);
mfld.setOptype(OPTYPE.CONTINUOUS);
mfld.setUsageType(FIELDUSAGETYPE.PREDICTED);
miningSchema.getMiningFields().add(mfld);
}
nnet.getExtensionsAndNeuralLayersAndNeuralInputs().add(miningSchema);
Output outputs = new Output();
for (String ofld : outputfieldNames) {
OutputField outFld = new OutputField();
outFld.setName("Out_" + ofld);
outFld.setTargetField(ofld);
outputs.getOutputFields().add(outFld);
}
nnet.getExtensionsAndNeuralLayersAndNeuralInputs().add(outputs);
NeuralInputs nins = new NeuralInputs();
nins.setNumberOfInputs(BigInteger.valueOf(inputfieldNames.length));
for (int j = 0; j < inputfieldNames.length; j++) {
String ifld = inputfieldNames[j];
NeuralInput nin = new NeuralInput();
nin.setId("" + counter++);
DerivedField der = new DerivedField();
der.setDataType(DATATYPE.DOUBLE);
der.setOptype(OPTYPE.CONTINUOUS);
NormContinuous nc = new NormContinuous();
nc.setField(ifld);
nc.setOutliers(OUTLIERTREATMENTMETHOD.AS_IS);
LinearNorm lin1 = new LinearNorm();
lin1.setOrig(0);
lin1.setNorm(-inputMeans[j] / inputStds[j]);
nc.getLinearNorms().add(lin1);
LinearNorm lin2 = new LinearNorm();
lin2.setOrig(inputMeans[j]);
lin2.setNorm(0);
nc.getLinearNorms().add(lin2);
der.setNormContinuous(nc);
nin.setDerivedField(der);
nins.getNeuralInputs().add(nin);
}
nnet.getExtensionsAndNeuralLayersAndNeuralInputs().add(nins);
NeuralLayer hidden = new NeuralLayer();
hidden.setNumberOfNeurons(BigInteger.valueOf(hiddenSize));
for (int j = 0; j < hiddenSize; j++) {
Neuron n = new Neuron();
n.setId("" + counter++);
n.setBias(weights[wtsIndex++]);
for (int k = 0; k < inputfieldNames.length; k++) {
Synapse con = new Synapse();
con.setFrom("" + k);
con.setWeight(weights[wtsIndex++]);
n.getCons().add(con);
}
hidden.getNeurons().add(n);
}
nnet.getExtensionsAndNeuralLayersAndNeuralInputs().add(hidden);
NeuralLayer outer = new NeuralLayer();
outer.setActivationFunction(ACTIVATIONFUNCTION.IDENTITY);
outer.setNumberOfNeurons(BigInteger.valueOf(outputfieldNames.length));
for (int j = 0; j < outputfieldNames.length; j++) {
Neuron n = new Neuron();
n.setId("" + counter++);
n.setBias(weights[wtsIndex++]);
for (int k = 0; k < hiddenSize; k++) {
Synapse con = new Synapse();
con.setFrom("" + (k + inputfieldNames.length));
con.setWeight(weights[wtsIndex++]);
n.getCons().add(con);
}
outer.getNeurons().add(n);
}
nnet.getExtensionsAndNeuralLayersAndNeuralInputs().add(outer);
NeuralOutputs finalOuts = new NeuralOutputs();
finalOuts.setNumberOfOutputs(BigInteger.valueOf(outputfieldNames.length));
for (int j = 0; j < outputfieldNames.length; j++) {
NeuralOutput output = new NeuralOutput();
output.setOutputNeuron("" + (j + inputfieldNames.length + hiddenSize));
DerivedField der = new DerivedField();
der.setDataType(DATATYPE.DOUBLE);
der.setOptype(OPTYPE.CONTINUOUS);
NormContinuous nc = new NormContinuous();
nc.setField(outputfieldNames[j]);
nc.setOutliers(OUTLIERTREATMENTMETHOD.AS_IS);
LinearNorm lin1 = new LinearNorm();
lin1.setOrig(0);
lin1.setNorm(-outputMeans[j] / outputStds[j]);
nc.getLinearNorms().add(lin1);
LinearNorm lin2 = new LinearNorm();
lin2.setOrig(outputMeans[j]);
lin2.setNorm(0);
nc.getLinearNorms().add(lin2);
der.setNormContinuous(nc);
output.setDerivedField(der);
finalOuts.getNeuralOutputs().add(output);
}
nnet.getExtensionsAndNeuralLayersAndNeuralInputs().add(finalOuts);
pmml.getAssociationModelsAndBaselineModelsAndClusteringModels().add(nnet);
return pmml;
}
/**
* Save the data fields.
*
* @param out The output file.
*/
private void saveData(final EncogWriteHelper out) {
saveSubSection(out, "DATA", "CONFIG");
out.addSubSection("STATS");
out.addColumn("name");
out.addColumn("isclass");
out.addColumn("iscomplete");
out.addColumn("isint");
out.addColumn("isreal");
out.addColumn("amax");
out.addColumn("amin");
out.addColumn("mean");
out.addColumn("sdev");
out.writeLine();
for (final DataField field : this.script.getFields()) {
out.addColumn(field.getName());
out.addColumn(field.isClass());
out.addColumn(field.isComplete());
out.addColumn(field.isInteger());
out.addColumn(field.isReal());
out.addColumn(field.getMax());
out.addColumn(field.getMin());
out.addColumn(field.getMean());
out.addColumn(field.getStandardDeviation());
out.writeLine();
}
out.flush();
out.addSubSection("CLASSES");
out.addColumn("field");
out.addColumn("code");
out.addColumn("name");
out.writeLine();
for (final DataField field : this.script.getFields()) {
if (field.isClass()) {
for (final AnalystClassItem col : field.getClassMembers()) {
out.addColumn(field.getName());
out.addColumn(col.getCode());
out.addColumn(col.getName());
out.addColumn(col.getCount());
out.writeLine();
}
}
}
}
