Examples of gnu.trove.TIntIntHashMap.adjustOrPutValue()

gnu.trove.TIntIntHashMap.adjustOrPutValue()
Adjusts the primitive value mapped to the key if the key is present in the map. Otherwise, the initial_value is put in the map. @param key the key of the value to increment @param adjust_amount the amount to adjust the value by @param put_amount the value put into the map if the key is not initial present @return the value present in the map after the adjustment or put operation @since 2.0b1

      // Sample a topic assignment from this distribution
      newTopic = random.nextDiscrete (topicWeights, topicWeightsSum);


      // Put that new topic into the counts
      oneDocTopics[si] = newTopic;
      currentTypeTopicCounts.adjustOrPutValue(newTopic, 1, 1);
      localTopicCounts[newTopic] ++;
      tokensPerTopic[newTopic]++;
    }
  }

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      // Sample a topic assignment from this distribution
      newTopic = random.nextDiscrete (topicWeights, topicWeightsSum);


      // Put that new topic into the counts
      oneDocTopics[si] = newTopic;
      currentTypeTopicCounts.adjustOrPutValue(newTopic, 1, 1);
      localTopicCounts[newTopic] ++;
      tokensPerTopic[newTopic]++;
    }
  }

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      // Sample a topic assignment from this distribution
      newTopic = random.nextDiscrete (topicWeights, topicWeightsSum);


      // Put that new topic into the counts
      oneDocTopics[si] = newTopic;
      currentTypeTopicCounts.adjustOrPutValue(newTopic, 1, 1);
      tokensPerTopic[newTopic]++;
    }
  }


  //print human readable doc-topic matrix, for further IR use

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      smoothingOnlyMass -= alpha[newTopic] * beta / 
        (tokensPerTopic[newTopic] + betaSum);
      topicBetaMass -= beta * localTopicCounts.get(newTopic) /
        (tokensPerTopic[newTopic] + betaSum);


      localTopicCounts.adjustOrPutValue(newTopic, 1, 1);
      tokensPerTopic[newTopic]++;


      //      update the coefficients for the non-zero topics
      cachedCoefficients[newTopic] =
        (alpha[newTopic] + localTopicCounts.get(newTopic)) /

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    for (Instance instance: instances) {
      FeatureSequence features = (FeatureSequence) instance.getData();
            
      for (int i=0; i<features.getLength(); i++) {
        docCounts.adjustOrPutValue(features.getIndexAtPosition(i), 1, 1);
      }
            
      int[] keys = docCounts.keys();
      for (int i = 0; i < keys.length - 1; i++) {
        int feature = keys[i];

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      assert (currentTypeTopicCounts.size() != 0);


      if (readjustTopicsAndStats) {
        // Remove this token from all counts
        oneDocTopicCounts[oldTopic]--;
        adjustedValue = currentTypeTopicCounts.adjustOrPutValue(oldTopic, -1, -1);
        if (adjustedValue == 0) currentTypeTopicCounts.remove(oldTopic);
        else if (adjustedValue == -1) throw new IllegalStateException ("Token count in topic went negative.");
        tokensPerTopic[oldTopic]--;
      }

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    int docLength = tokenSequence.getLength();


    //    populate topic counts
    TIntIntHashMap localTopicCounts = new TIntIntHashMap();
    for (int position = 0; position < docLength; position++) {
      localTopicCounts.adjustOrPutValue(oneDocTopics[position], 1, 1);
    }


    //    Initialize the topic count/beta sampling bucket
    double topicBetaMass = 0.0;
    for (int topic: localTopicCounts.keys()) {

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      while(ip.next() != IterablePosting.EOL)
      {
        //System.err.println("Got id " + ip.getId());
        assertTrue("Got too big a docid ("+ip.getId()+") from inverted index input stream for term at index " + ithTerm, ip.getId() < numDocs);
        count++;
        calculatedDocLengths.adjustOrPutValue(ip.getId(), ip.getFrequency(), ip.getFrequency());
      }
      assertEquals(expected, count);
    }
    assertEquals("Number of documents is unexpected,", documentLengths.length - countZero(documentLengths), calculatedDocLengths.size());
    long tokens = 0;

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