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/*******************************************************************************
 * Copyright (c) 2010 Haifeng Li
 *   
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *  
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *******************************************************************************/

package smile.imputation;

/**
 * Impute missing values with the average of other attributes in the instance.
 * Assume the attributes of the dataset are of same kind, e.g. microarray gene
 * expression data, the missing values can be estimated as the average of
 * non-missing attributes in the same instance. Note that this is not the
 * average of same attribute across different instances.
 * 
 * @author Haifeng Li
 */
public class AverageImputation implements MissingValueImputation {
    /**
     * Constructor.
     */
    public AverageImputation() {

    }

    @Override
    public void impute(double[][] data) throws MissingValueImputationException {
        for (int i = 0; i < data.length; i++) {
            int n = 0;
            double sum = 0.0;

            for (double x : data[i]) {
                if (!Double.isNaN(x)) {
                    n++;
                    sum += x;
                }
            }

            if (n == 0) {
                throw new MissingValueImputationException("The whole row " + i + " is missing");
            }

            if (n < data[i].length) {
                double avg = sum / n;
                for (int j = 0; j < data[i].length; j++) {
                    if (Double.isNaN(data[i][j])) {
                        data[i][j] = avg;
                    }
                }
            }
        }
    }
}