Examples of weka.estimators.DiscreteEstimator.addValue()

weka.estimators.DiscreteEstimator.addValue()
Add a new data value to the current estimator. @param data the new data value @param weight the weight assigned to the data value


      // if no DiscreteEstimator is present in the map, create one 
      if (df == null) {
  df = new DiscreteEstimator(instances.numClasses(), 0);
      }
      df.addValue(instance.classValue(), instance.weight()); // update
      m_estimatedDistributions.put(c, df); // put back in map
    }


    // Create the attributes for m_baseMin and m_baseMax. 
    // These are identical to those of m_train, except that the

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      // if no DiscreteEstimator is present in the map, create one 
      if (df == null) {
  df = new DiscreteEstimator(instances.numClasses(), 0);
      }
      df.addValue(instance.classValue(),instance.weight()); // update
      distributions.put(c,df); // put back in map
    }




    // build the map of cumulative distribution functions

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    DiscreteEstimator de = new DiscreteEstimator(numValues, false);
    Iterator it = new EnumerationIterator(instances.enumerateInstances());
    while (it.hasNext()) {
      Instance instance = (Instance) it.next();
      if (!instance.isMissing(attributeIndex)) {
  de.addValue(instance.value(attributeIndex), instance.weight());
      }
    }
    return de;
  }

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  current = Integer.parseInt(argv[i]);


  System.out.println(newEst);
  System.out.println("Prediction for " + current + " = " 
         + newEst.getProbability(current));
  newEst.addValue(current, 1);
      } 
    } catch (Exception e) {
      System.out.println(e.getMessage());
    } 
  }    // main

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  current = Integer.parseInt(argv[i]);


  System.out.println(newEst);
  System.out.println("Prediction for " + current + " = " 
         + newEst.getProbability(current));
  newEst.addValue(current, 1);
      } 
    } catch (Exception e) {
      System.out.println(e.getMessage());
    } 
  }    // main

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      // if no DiscreteEstimator is present in the map, create one 
      if (df == null) {
  df = new DiscreteEstimator(instances.numClasses(),0);
      }
      df.addValue(instance.classValue(),instance.weight()); // update
      m_estimatedDistributions.put(c,df); // put back in map
    }




    // build the map of cumulative distribution functions

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    // Remove instance temporarily from the Maps with the distributions
    DiscreteEstimator df = 
      (DiscreteEstimator) m_estimatedDistributions.get(c);


    // remove from df
    df.addValue(instance.classValue(),-instance.weight());


    if (Math.abs(df.getSumOfCounts() - 0) < Utils.SMALL) {


      /* There was apparently only one example with coordinates c
       * in the training set, and now we removed it.

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    // If no DiscreteEstimator is present in the map, create one 
    if (df == null) {
      df = new DiscreteEstimator(instance.dataset().numClasses(),0);
    }
    df.addValue(instance.classValue(),instance.weight()); // update df
    m_estimatedDistributions.put(c,df); // put back in map
    m_estimatedCumulativeDistributions.put
    (c, new CumulativeDiscreteDistribution(df));
  }

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