code: /*
   * Copyright (C) 2016 University of Pittsburgh.
   *
   * This library is free software; you can redistribute it and/or
   * modify it under the terms of the GNU Lesser General Public
   * License as published by the Free Software Foundation; either
   * version 2.1 of the License, or (at your option) any later version.
   *
   * This library 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
   * Lesser General Public License for more details.
   *
   * You should have received a copy of the GNU Lesser General Public
   * License along with this library; if not, write to the Free Software
   * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
   * MA 02110-1301  USA
   */
  package edu.cmu.tetrad.stat;
  
  /**
   *
   * Feb 9, 2016 3:14:08 PM
   *
   * @author Kevin V. Bui (kvb2@pitt.edu)
   */
  public class RealVarianceVector implements RealVariance {
  
      private final double[][] data;
  
      private final int numOfRows;
  
      private final int numOfCols;
  
      public RealVarianceVector(double[][] data) {
          this.data = data;
          this.numOfRows = data.length;
          this.numOfCols = data[0].length;
      }
  
      private double[] computeMeans() {
          double[] mean = new double[numOfCols];
          for (int col = 0; col < numOfCols; col++) {
              double sum = 0;
              for (int row = 0; row < numOfRows; row++) {
                  sum += data[row][col];
              }
              mean[col] = sum / numOfRows;
          }
  
          return mean;
      }
  
      @Override
      public double[] compute(boolean biasCorrected) {
          double[] meanVariance = computeMeans();
  
          for (int col = 0; col < numOfCols; col++) {
              double mean = meanVariance[col];
              double value = 0;
              double squareValue = 0;
              for (int row = 0; row < numOfRows; row++) {
                  double val = data[row][col] - mean;
                  squareValue += val * val;
                  value += val;
              }
              meanVariance[col] = (biasCorrected)
                      ? (squareValue - (value * value / numOfRows)) / (numOfRows - 1.0f)
                      : (squareValue - (value * value / numOfRows)) / numOfRows;
          }
  
          return meanVariance;
      }
  
  }
  