/**   * @throws IllegalArgumentException if {@link DataModel} does not have preference values   */  public PearsonCorrelationSimilarity(DataModel dataModel, Weighting weighting) throws TasteException {    //这里CenterData传的时true    /* pearson其实做的事情就是先把两个向量都减去他们的平均值，然后再计算cosine值。     * 在 AbstractSimilarity里的实现代码如下：     *  double result;        if (centerData) {          double meanX = sumX / count;          double meanY = sumY / count;          // double centeredSumXY = sumXY - meanY * sumX - meanX * sumY + n * meanX * meanY;          double centeredSumXY = sumXY - meanY * sumX;          // double centeredSumX2 = sumX2 - 2.0 * meanX * sumX + n * meanX * meanX;          double centeredSumX2 = sumX2 - meanX * sumX;          // double centeredSumY2 = sumY2 - 2.0 * meanY * sumY + n * meanY * meanY;          double centeredSumY2 = sumY2 - meanY * sumY;          result = computeResult(count, centeredSumXY, centeredSumX2, centeredSumY2, sumXYdiff2);        } else {          result = computeResult(count, sumXY, sumX2, sumY2, sumXYdiff2);        }     */    super(dataModel, weighting, true);    Preconditions.checkArgument(dataModel.hasPreferenceValues(), "DataModel doesn‘t have preference values");  }    @Override  double computeResult(int n, double sumXY, double sumX2, double sumY2, double sumXYdiff2) {    if (n == 0) {      return Double.NaN;    }    // Note that sum of X and sum of Y don‘t appear here since they are assumed to be 0;    // the data is assumed to be centered.    double denominator = Math.sqrt(sumX2) * Math.sqrt(sumY2);    if (denominator == 0.0) {      // One or both parties has -all- the same ratings;      // can‘t really say much similarity under this measure      return Double.NaN;    }    return sumXY / denominator;  }

@Override  public double userSimilarity(long userID1, long userID2) throws TasteException {    DataModel dataModel = getDataModel();
　　//获取用户偏好    PreferenceArray xPrefs = dataModel.getPreferencesFromUser(userID1);    PreferenceArray yPrefs = dataModel.getPreferencesFromUser(userID2);    int xLength = xPrefs.length();    int yLength = yPrefs.length();        if (xLength == 0 || yLength == 0) {      return Double.NaN;    }        long xIndex = xPrefs.getItemID(0);    long yIndex = yPrefs.getItemID(0);    int xPrefIndex = 0;    int yPrefIndex = 0;        double sumX = 0.0;    double sumX2 = 0.0;    double sumY = 0.0;    double sumY2 = 0.0;    double sumXY = 0.0;    double sumXYdiff2 = 0.0;    int count = 0;        boolean hasInferrer = inferrer != null;        while (true) {      int compare = xIndex < yIndex ? -1 : xIndex > yIndex ? 1 : 0;      if (hasInferrer || compare == 0) {        double x;        double y;        if (xIndex == yIndex) {          // Both users expressed a preference for the item          x = xPrefs.getValue(xPrefIndex);          y = yPrefs.getValue(yPrefIndex);        } else {            //如果不存在对应的分数,则进行推断...          // Only one user expressed a preference, but infer the other one‘s preference and tally          // as if the other user expressed that preference          if (compare < 0) {            // X has a value; infer Y‘s            x = xPrefs.getValue(xPrefIndex);            y = inferrer.inferPreference(userID2, xIndex);          } else {            // compare > 0            // Y has a value; infer X‘s            x = inferrer.inferPreference(userID1, yIndex);            y = yPrefs.getValue(yPrefIndex);          }        }        sumXY += x * y;        sumX += x;        sumX2 += x * x;        sumY += y;        sumY2 += y * y;        double diff = x - y;        sumXYdiff2 += diff * diff;        count++;      }      if (compare <= 0) {        if (++xPrefIndex >= xLength) {          if (hasInferrer) {            // Must count other Ys; pretend next X is far away            if (yIndex == Long.MAX_VALUE) {              // ... but stop if both are done!              break;            }            xIndex = Long.MAX_VALUE;          } else {            break;          }        } else {          xIndex = xPrefs.getItemID(xPrefIndex);        }      }      if (compare >= 0) {        if (++yPrefIndex >= yLength) {          if (hasInferrer) {            // Must count other Xs; pretend next Y is far away            if (xIndex == Long.MAX_VALUE) {              // ... but stop if both are done!              break;            }            yIndex = Long.MAX_VALUE;          } else {            break;          }        } else {          yIndex = yPrefs.getItemID(yPrefIndex);        }      }    }        // "Center" the data. If my math is correct, this‘ll do it.    double result;    if (centerData) {      double meanX = sumX / count;      double meanY = sumY / count;      // double centeredSumXY = sumXY - meanY * sumX - meanX * sumY + n * meanX * meanY;      double centeredSumXY = sumXY - meanY * sumX;      // double centeredSumX2 = sumX2 - 2.0 * meanX * sumX + n * meanX * meanX;      double centeredSumX2 = sumX2 - meanX * sumX;      // double centeredSumY2 = sumY2 - 2.0 * meanY * sumY + n * meanY * meanY;      double centeredSumY2 = sumY2 - meanY * sumY;      result = computeResult(count, centeredSumXY, centeredSumX2, centeredSumY2, sumXYdiff2);    } else {      result = computeResult(count, sumXY, sumX2, sumY2, sumXYdiff2);    }        if (!Double.isNaN(result)) {      result = normalizeWeightResult(result, count, cachedNumItems);    }    return result;  }