Package org.apache.mahout.math

Examples of org.apache.mahout.math.Vector.times()

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          throw (IOException) te.getCause();
        } else {
          throw new IOException(te.getCause());
        }
      }
      columnVector.times(value).addTo(recommendationVector);
    }
   
    Queue<RecommendedItem> topItems = new PriorityQueue<RecommendedItem>(recommendationsPerUser + 1,
        Collections.reverseOrder());
   
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      s1 = v.clone();
    } else {
      s1 = s1.plus(v);
    }
    if (s2 == null) {
      s2 = v.times(v);
    } else {
      s2 = s2.plus(v.times(v));
    }
  }
 
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      s1 = s1.plus(v);
    }
    if (s2 == null) {
      s2 = v.times(v);
    } else {
      s2 = s2.plus(v.times(v));
    }
  }
 
  @Override
  public void computeParameters() {
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      s1 = x.clone();
    } else {
      s1 = s1.plus(x);
    }
    if (s2 == null) {
      s2 = x.times(x);
    } else {
      s2 = s2.plus(x.times(x));
    }
  }
 
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      s1 = s1.plus(x);
    }
    if (s2 == null) {
      s2 = x.times(x);
    } else {
      s2 = s2.plus(x.times(x));
    }
  }
 
  @Override
  public void computeParameters() {
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      s1 = point.clone();
    } else {
      s1 = s1.plus(wtPt);
    }
    if (s2 == null) {
      s2 = wtPt.times(wtPt);
    } else {
      s2 = s2.plus(wtPt.times(wtPt));
    }
  }
 
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      s1 = s1.plus(wtPt);
    }
    if (s2 == null) {
      s2 = wtPt.times(wtPt);
    } else {
      s2 = s2.plus(wtPt.times(wtPt));
    }
  }
 
  /** Compute a "standard deviation" value to use as the "radius" of the cluster for display purposes */
  public double std() {
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  /** Compute a "standard deviation" value to use as the "radius" of the cluster for display purposes */
  public double std() {
    if (s0 > 0) {
      Vector radical = s2.times(s0).minus(s1.times(s1));
      radical = radical.times(radical).assign(new SquareRootFunction());
      Vector stds = radical.assign(new SquareRootFunction()).divide(s0);
      return stds.zSum() / stds.size();
    } else {
      return 0;
    }
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      mean = x.like();
      delta = x.clone();
    }
    mean = mean.plus(delta.divide(n));
    if (m2 != null) {
      m2 = m2.plus(delta.times(x.minus(mean)));
    } else {
      m2 = delta.times(x.minus(mean));
    }
    variance = m2.divide(n - 1);
  }
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    }
    mean = mean.plus(delta.divide(n));
    if (m2 != null) {
      m2 = m2.plus(delta.times(x.minus(mean)));
    } else {
      m2 = delta.times(x.minus(mean));
    }
    variance = m2.divide(n - 1);
  }

  @Override
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