@Override
public void close() {
if (attributeEditor.hasDataChanged()) {
int selectedOption =
JOptionPane.showConfirmDialog(
this,
"It seems that you have changed the data without saving it afterwards."
+ Tools.getLineSeparator()
+ "Do you still want to proceed and close the editor (changes will be lost)?",
"Save data file?",
JOptionPane.YES_NO_OPTION,
JOptionPane.QUESTION_MESSAGE);
if (selectedOption == JOptionPane.YES_OPTION) dispose();
} else if (attributeEditor.hasMetaDataChanged()) {
int selectedOption =
JOptionPane.showConfirmDialog(
this,
"It seems that you have changed the attribute descriptions without saving an attribute description file (.aml) afterwards."
+ Tools.getLineSeparator()
+ "Do you still want to proceed and close the editor (changes will be lost)?",
"Save attribute description file?",
JOptionPane.YES_NO_OPTION,
JOptionPane.QUESTION_MESSAGE);
if (selectedOption == JOptionPane.YES_OPTION) dispose();
} else {
dispose();
}
}
@Override
public String toResultString() {
StringBuilder result =
new StringBuilder(
Tools.getLineSeparator() + "Principal Components:" + Tools.getLineSeparator());
if (manualNumber) {
result.append("Number of Components: " + numberOfComponents + Tools.getLineSeparator());
} else {
result.append("Proportion Threshold: " + proportionThreshold + Tools.getLineSeparator());
}
for (int i = 0; i < vMatrix.getColumnDimension(); i++) {
result.append("PC " + (i + 1) + ": ");
for (int j = 0; j < attributeNames.length; j++) {
double value = vMatrix.get(i, j);
if (value > 0) {
result.append(" + ");
} else {
result.append(" - ");
}
result.append(Tools.formatNumber(Math.abs(value)) + " * " + attributeNames[j]);
}
result.append(Tools.getLineSeparator());
}
return result.toString();
}
@Override
public String toString() {
StringBuilder result =
new StringBuilder(
Tools.getLineSeparator() + "Principal Components:" + Tools.getLineSeparator());
if (manualNumber) {
result.append("Number of Components: " + numberOfComponents + Tools.getLineSeparator());
} else {
result.append("Variance Threshold: " + varianceThreshold + Tools.getLineSeparator());
}
return result.toString();
}
@Override
public String toString() {
StringBuffer result = new StringBuffer(Tools.getLineSeparator() + "PerformanceVector [");
for (int i = 0; i < size(); i++) {
Averagable avg = getAveragable(i);
if ((mainCriterion != null) && (avg.getName().equals(mainCriterion))) {
result.append(Tools.getLineSeparator() + "*****");
} else {
result.append(Tools.getLineSeparator() + "-----");
}
result.append(avg);
}
result.append(Tools.getLineSeparator() + "]");
return result.toString();
}
public String toString(int maxNumber) {
Collections.sort(associationRules);
StringBuffer buffer = new StringBuffer("Association Rules" + Tools.getLineSeparator());
int counter = 0;
for (AssociationRule rule : associationRules) {
if ((maxNumber >= 0) && (counter > maxNumber)) {
buffer.append("... " + (associationRules.size() - maxNumber) + " other rules ...");
break;
}
buffer.append(rule.toString());
buffer.append(Tools.getLineSeparator());
counter++;
}
return buffer.toString();
}
@Override
public String toString() {
StringBuilder builder = new StringBuilder();
builder.append("Threshold: ");
builder.append(threshold);
builder.append(Tools.getLineSeparator());
builder.append("first class: ");
builder.append(zeroClass);
builder.append(Tools.getLineSeparator());
builder.append("second class: ");
builder.append(oneClass);
builder.append(Tools.getLineSeparator());
builder.append("if confidence(" + oneClass + ") > " + threshold + " then " + oneClass);
builder.append(Tools.getLineSeparator());
builder.append("else " + zeroClass);
return builder.toString();
}
/** Sends the output to the LogService. */
private void logOutput(String message, InputStream in) throws IOException {
BufferedReader bin = new BufferedReader(new InputStreamReader(in));
String line = null;
StringBuffer buffer = new StringBuffer(message);
while ((line = bin.readLine()) != null) {
buffer.append(Tools.getLineSeparator());
buffer.append(line);
}
logNote(buffer.toString());
}
@Override
public String toResultString() {
StringBuilder builder = new StringBuilder();
builder.append(
"Denormalization Model of the following Normalization:" + Tools.getLineSeparator());
builder.append(invertedModel.toResultString());
return builder.toString();
}
@Override
public String toString() {
StringBuffer result =
new StringBuffer(
"IOContainer (" + ioObjects.size() + " objects):" + Tools.getLineSeparator());
Iterator i = ioObjects.iterator();
while (i.hasNext()) {
IOObject current = (IOObject) i.next();
if (current != null) {
result.append(
current.toString()
+ Tools.getLineSeparator()
+ (current.getSource() != null
? "(created by " + current.getSource() + ")" + Tools.getLineSeparator()
: ""));
}
}
return result.toString();
}
/** @return a <code>String</code> representation of this scaling model. */
@Override
public String toString() {
String result =
super.toString()
+ " ("
+ this.parameters.toString()
+ ") "
+ Tools.getLineSeparator()
+ "Model: "
+ model.toResultString();
return result;
}
@Override
public String toString() {
StringBuffer buffer = new StringBuffer();
if ((classPositive != null) && (classNegative != null))
buffer.append(
"Hyperplane seperating "
+ classPositive
+ " and "
+ classNegative
+ "."
+ Tools.getLineSeparator());
else buffer.append("Hyperplane for linear regression." + Tools.getLineSeparator());
buffer.append("Intercept: ");
buffer.append(Double.toString(intercept));
buffer.append(Tools.getLineSeparator());
buffer.append("Coefficients: " + Tools.getLineSeparator());
int counter = 0;
for (double value : coefficients) {
buffer.append(
"w("
+ coefficientNames[counter]
+ ") = "
+ Tools.formatIntegerIfPossible(value, 3)
+ Tools.getLineSeparator());
counter++;
}
buffer.append(Tools.getLineSeparator());
return buffer.toString();
}
@Override
public String toResultString() {
StringBuilder result =
new StringBuilder(
Tools.getLineSeparator() + "Principal Components:" + Tools.getLineSeparator());
if (manualNumber) {
result.append("Number of Components: " + numberOfComponents + Tools.getLineSeparator());
} else {
result.append("Variance Threshold: " + varianceThreshold + Tools.getLineSeparator());
}
for (int i = 0; i < eigenVectors.size(); i++) {
result.append("PC " + (i + 1) + ": ");
for (int j = 0; j < attributeNames.length; j++) {
double value = eigenVectors.get(i).getEigenvector()[j];
if (value > 0) result.append(" + ");
else result.append(" - ");
result.append(Tools.formatNumber(Math.abs(value)) + " * " + attributeNames[j]);
}
result.append(Tools.getLineSeparator());
}
return result.toString();
}
/** @return a <code>String</code> representation of this boosting model. */
@Override
public String toString() {
StringBuffer result =
new StringBuffer(
super.toString()
+ Tools.getLineSeparator()
+ "Number of inner models: "
+ this.getNumberOfModels()
+ Tools.getLineSeparators(2));
for (int i = 0; i < this.getNumberOfModels(); i++) {
Model model = this.getModel(i);
result.append(
(i > 0 ? Tools.getLineSeparator() : "")
+ "Embedded model #"
+ i
+ " (weight: "
+ Tools.formatNumber(this.getWeightForModel(i))
+ "): "
+ Tools.getLineSeparator()
+ model.toResultString());
}
return result.toString();
}
public String toString(int numberOfExamples, boolean onlySV) {
StringBuffer result =
new StringBuffer(
"SVM Example Set ("
+ (onlySV
? (getNumberOfSupportVectors() + " support vectors")
: (train_size + " examples"))
+ "):"
+ Tools.getLineSeparator()
+ "b: "
+ b
+ Tools.getLineSeparator());
for (int e = 0; e < numberOfExamples; e++) {
if (!onlySV || (alphas[e] != 0.0d)) {
for (int a = 0; a < atts[e].length; a++) {
result.append(index[e][a] + ":");
result.append(atts[e][a] + " ");
}
result.append(", alpha: " + alphas[e]);
result.append(", y: " + ys[e] + Tools.getLineSeparator());
}
}
return result.toString();
}
/** @return a <code>String</code> representation of this rule model. */
public String toString() {
StringBuffer result =
new StringBuffer(
super.toString()
+ Tools.getLineSeparator()
+ " ("
+ this.getLabel().getName()
+ "="
+ (this.getLabel().getMapping().mapIndex(this.predictedLabel))
+ ") <-- ");
for (int i = 0; i < this.getRuleLength(); i++) {
Attribute att = this.getAttributeOfLiteral(i);
String val = att.getMapping().mapIndex((int) this.getTestedValueAtLiteral(i));
result.append((i > 0 ? ", " : "") + ("(" + att.getName() + "=" + val + ")"));
}
return result.toString();
}
protected String createProcessTree(
int indent, String selfPrefix, String childPrefix, Operator markOperator, String mark) {
String tree = Tools.indent(indent) + " subprocess '" + getName() + "'";
Iterator<Operator> i = operators.iterator();
while (i.hasNext()) {
tree +=
Tools.getLineSeparator()
+ i.next()
.createProcessTree(
indent,
childPrefix + "+- ",
childPrefix + (i.hasNext() ? "| " : " "),
markOperator,
mark);
}
return tree;
}
@Override
public String toString() {
StringBuffer distributionDescription = new StringBuffer();
boolean first = true;
for (int i = 0; i < valueNames.length; i++) {
if (!first) {
distributionDescription.append("\t");
}
distributionDescription.append(valueNames[i]);
first = false;
}
first = true;
distributionDescription.append(Tools.getLineSeparator());
for (int i = 0; i < valueNames.length; i++) {
if (!first) {
distributionDescription.append("\t");
}
distributionDescription.append(Tools.formatNumber(probabilities[i]));
first = false;
}
return distributionDescription.toString();
}
public String toString() {
StringBuffer s = new StringBuffer("A cluster model with the following properties:");
s.append(Tools.getLineSeparator());
if (properties.getKeys().hasNext()) s.append(Tools.getLineSeparator() + properties.toString());
return s.toString();
}
@Override
public String toString() {
return indexToSymbolMap.toString() + Tools.getLineSeparator() + symbolToIndexMap.toString();
}
