/**
* Copyright (C) 2001-2017 by RapidMiner and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapidminer.com
*
* This program is free software: you can redistribute it and/or modify it under the terms of the
* GNU Affero General Public License as published by the Free Software Foundation, either version 3
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without
* even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License along with this program.
* If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.features.weighting;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.AttributeWeights;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.Statistics;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeCategory;
/**
* <p>
* Creates weights from the standard deviations of all attributes. The values can be normalized by
* the average, the minimum, or the maximum of the attribute.
* </p>
*
* @author Ingo Mierswa
*/
public class StandardDeviationWeighting extends AbstractWeighting {
/**
* The parameter name for "Indicates if the standard deviation should be divided by the
* minimum, maximum, or average of the attribute."
*/
public static final String PARAMETER_NORMALIZE = "normalize";
private static final String[] NORMALIZATIONS = { "none", "average", "minimum", "maximum" };
private static final int NONE = 0;
private static final int AVERAGE = 1;
private static final int MINIMUM = 2;
private static final int MAXIMUM = 3;
public StandardDeviationWeighting(OperatorDescription description) {
super(description, false);
}
@Override
protected AttributeWeights calculateWeights(ExampleSet exampleSet) throws OperatorException {
exampleSet.recalculateAllAttributeStatistics();
int normalization = getParameterAsInt(PARAMETER_NORMALIZE);
AttributeWeights weights = new AttributeWeights();
for (Attribute attribute : exampleSet.getAttributes()) {
double data = Math.sqrt(exampleSet.getStatistics(attribute, Statistics.VARIANCE));
switch (normalization) {
case NONE:
break;
case AVERAGE:
data /= exampleSet.getStatistics(attribute, Statistics.AVERAGE);
break;
case MINIMUM:
data /= exampleSet.getStatistics(attribute, Statistics.MINIMUM);
break;
case MAXIMUM:
data /= exampleSet.getStatistics(attribute, Statistics.MAXIMUM);
break;
default:
break;
}
data = Math.abs(data);
weights.setWeight(attribute.getName(), data);
}
return weights;
}
@Override
public boolean supportsCapability(OperatorCapability capability) {
switch (capability) {
case NUMERICAL_ATTRIBUTES:
case NO_LABEL:
return true;
default:
return false;
}
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeCategory(
PARAMETER_NORMALIZE,
"Indicates if the standard deviation should be divided by the minimum, maximum, or average of the attribute.",
NORMALIZATIONS, 0);
type.setExpert(false);
types.add(type);
return types;
}
}