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);
}
public Object clone() {
DataField field = new DataField();
field.setName(getName());
field.setValue(value);
field.setDataType(dataType);
field.setAppend(this.getAppend());
return field;
}
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 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;
}
