/** Creates a new FeatureGenerator for a given function name. */
public static FeatureGenerator createGeneratorForFunction(String functionName) {
if (functionName == null) return null;
Class genClass = generatorMap.get(functionName);
if (genClass == null) {
if (functionName.startsWith(ConstantGenerator.FUNCTION_NAME)) {
FeatureGenerator gen = new ConstantGenerator();
gen.setFunction(functionName);
return gen;
} else {
return null;
}
} else {
try {
FeatureGenerator gen = (FeatureGenerator) genClass.newInstance();
gen.setFunction(functionName);
return gen;
} catch (Exception e) {
// LogService.getGlobal().log("Cannot instantiate '" + genClass.getName() + "'",
// LogService.ERROR);
LogService.getRoot()
.log(
Level.SEVERE,
"com.rapidminer.generator.FeatureGenerator.instantiating_error",
genClass.getName());
return null;
}
}
}
/**
* A FeatureGenerator equals another FeatureGenerator if its class is equal and its arguments are
* equal and its function names are equal.
*/
@Override
public boolean equals(Object o) {
if (o == null) // necessary here because otherwise the next line will throw a NPE
return false;
if (!this.getClass().equals(o.getClass())) return false;
FeatureGenerator fg = (FeatureGenerator) o;
if (!this.getFunction().equals(fg.getFunction())) return false;
if (this.arguments.length != fg.arguments.length) return false;
for (int i = 0; i < arguments.length; i++) {
if (!this.arguments[i].equals(fg.arguments[i])) return false;
}
return true;
}
/**
* Randomly selects a generator from the generator list. The probability of a generator to be
* selected is proportional to its number of attribute combinations as delivered by {@link
* #getInputCandidates(ExampleSet, String[])} method. Returns null if no generators are
* applicable.
*
* @param generators List of {@link FeatureGenerator}s
*/
public static FeatureGenerator selectGenerator(
ExampleSet exampleSet, List generators, String[] functions, RandomGenerator random) {
int combinationSum = 0;
Iterator i = generators.iterator();
double[] probs = new double[generators.size()];
int k = 0;
while ((i.hasNext())) {
FeatureGenerator generator = (FeatureGenerator) i.next();
// probs[k] =
// generator.getNumberOfApplicableGenerations(exampleSet);
probs[k] = generator.getInputCandidates(exampleSet, functions).size();
combinationSum += probs[k];
k++;
}
if (combinationSum == 0) return null;
else {
for (k = 0; k < probs.length; k++) probs[k] /= combinationSum;
int index = random.randomIndex(probs);
FeatureGenerator selected = (FeatureGenerator) generators.get(index);
return selected;
}
}