com.clust4j.algo.preprocess.impute.MeanImputation.java Source code

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Here is the source code for com.clust4j.algo.preprocess.impute.MeanImputation.java

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/*******************************************************************************
 *    Copyright 2015, 2016 Taylor G Smith
 *
 *    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 com.clust4j.algo.preprocess.impute;

import java.util.Random;

import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;

import com.clust4j.log.LogTimer;
import com.clust4j.log.Log.Tag.Algo;
import com.clust4j.utils.MatUtils;

/**
 * Imputes the missing values in a matrix with the column means.
 * 
 * @author Taylor G Smith
 */
public class MeanImputation extends MatrixImputation {
    private static final long serialVersionUID = -1120617362212795699L;

    public MeanImputation() {
        this(new MeanImputationPlanner());
    }

    public MeanImputation(MeanImputationPlanner planner) {
        super(planner);
    }

    public static class MeanImputationPlanner extends ImputationPlanner {
        private boolean verbose = DEF_VERBOSE;
        private Random seed = new Random();

        public MeanImputationPlanner() {
        }

        @Override
        public Random getSeed() {
            return seed;
        }

        @Override
        public boolean getVerbose() {
            return verbose;
        }

        @Override
        public MeanImputationPlanner setSeed(final Random seed) {
            this.seed = seed;
            return this;
        }

        @Override
        public MeanImputationPlanner setVerbose(boolean b) {
            this.verbose = b;
            return this;
        }

    }

    @Override
    public MeanImputation copy() {
        return new MeanImputation(new MeanImputationPlanner().setSeed(getSeed()).setVerbose(verbose));
    }

    @Override
    public Algo getLoggerTag() {
        return Algo.IMPUTE;
    }

    @Override
    public String getName() {
        return "Mean imputation";
    }

    @Override
    public RealMatrix transform(final RealMatrix dat) {
        return new Array2DRowRealMatrix(transform(dat.getData()), false);
    }

    @Override
    public double[][] transform(final double[][] dat) {
        checkMat(dat);

        final LogTimer timer = new LogTimer();
        final double[][] copy = MatUtils.copy(dat);
        final int m = dat.length, n = dat[0].length;
        info("(" + getName() + ") performing mean imputation on " + m + " x " + n + " dataset");

        // Operates in 2M * N
        for (int col = 0; col < n; col++) {
            int count = 0;
            double sum = 0;
            for (int row = 0; row < m; row++) {
                if (!Double.isNaN(copy[row][col])) {
                    sum += copy[row][col];
                    count++;
                }
            }

            int nanCt = m - count;
            double mean = sum / (double) count;
            for (int row = 0; row < m; row++) {
                if (Double.isNaN(copy[row][col])) {
                    copy[row][col] = mean;
                }
            }

            info("(" + getName() + ") " + nanCt + " NaN" + (nanCt != 1 ? "s" : "") + " identified in column " + col
                    + " (column mean=" + mean + ")");
        }

        sayBye(timer);
        return copy;
    }

    @Override
    final public MeanImputation fit(RealMatrix x) {
        return this;
    }
}