opennlp.tools.doccat

## Class DocumentCategorizerME

- java.lang.Object
- - opennlp.tools.doccat.DocumentCategorizerME

- All Implemented Interfaces:
    DocumentCategorizer

      public class DocumentCategorizerME
      extends Object
      implements DocumentCategorizer

  Maxent implementation of `DocumentCategorizer`.

- - ### Constructor Summary

    Constructors
    Constructor and Description
    `DocumentCategorizerME(DoccatModel model)` Initializes the current instance with a doccat model.

  - ### Method Summary

    All Methods Static Methods Instance Methods Concrete Methods
    Modifier and Type | Method and Description
    --- | ---
    `double[]` | `categorize(String[] text)` Categorizes the given text.
    `double[]` | `categorize(String[] text, Map<String,Object> extraInformation)` Categorize the given text provided as tokens along with the provided extra information
    `String` | `getAllResults(double[] results)` get the name of the category associated with the given probabilties
    `String` | `getBestCategory(double[] outcome)` get the best category from previously generated outcome probabilities
    `String` | `getCategory(int index)` get the category at a given index
    `int` | `getIndex(String category)` get the index of a certain category
    `int` | `getNumberOfCategories()` get the number of categories
    `Map<String,Double>` | `scoreMap(String[] text)` Returns a map in which the key is the category name and the value is the score
    `SortedMap<Double,Set<String>>` | `sortedScoreMap(String[] text)` Returns a map with the score as a key in ascending order.
    `static DoccatModel` | `train(String languageCode, ObjectStream<DocumentSample> samples, TrainingParameters mlParams, DoccatFactory factory)`

    - ### Methods inherited from class java.lang.Object

      `equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

- - ### Constructor Detail

    - #### DocumentCategorizerME

          public DocumentCategorizerME(DoccatModel model)

      Initializes the current instance with a doccat model. Default feature generation is used.
      Parameters:
        `model` - the doccat model

  - ### Method Detail

    - #### categorize

          public double[] categorize(String[] text,
                                     Map<String,Object> extraInformation)

      Categorize the given text provided as tokens along with the provided extra information
      Specified by:
        `categorize` in interface `DocumentCategorizer`
      Parameters:
        `text` - text tokens to categorize
        `extraInformation` - additional information
      Returns:
        per category probabilities

    - #### categorize

          public double[] categorize(String[] text)

      Categorizes the given text.
      Specified by:
        `categorize` in interface `DocumentCategorizer`
      Parameters:
        `text` - the text to categorize
      Returns:
        per category probabilities

    - #### scoreMap

          public Map<String,Double> scoreMap(String[] text)

      Returns a map in which the key is the category name and the value is the score
      Specified by:
        `scoreMap` in interface `DocumentCategorizer`
      Parameters:
        `text` - the input text to classify
      Returns:
        the score map

    - #### sortedScoreMap

          public SortedMap<Double,Set<String>> sortedScoreMap(String[] text)

      Returns a map with the score as a key in ascending order. The value is a Set of categories with the score. Many categories can have the same score, hence the Set as value
      Specified by:
        `sortedScoreMap` in interface `DocumentCategorizer`
      Parameters:
        `text` - the input text to classify
      Returns:
        the sorted score map

    - #### getBestCategory

          public String getBestCategory(double[] outcome)

      Description copied from interface: `DocumentCategorizer`
      get the best category from previously generated outcome probabilities
      Specified by:
        `getBestCategory` in interface `DocumentCategorizer`
      Parameters:
        `outcome` - a vector of outcome probabilities
      Returns:
        the best category String

    - #### getIndex

          public int getIndex(String category)

      Description copied from interface: `DocumentCategorizer`
      get the index of a certain category
      Specified by:
        `getIndex` in interface `DocumentCategorizer`
      Parameters:
        `category` - the category
      Returns:
        an index

    - #### getCategory

          public String getCategory(int index)

      Description copied from interface: `DocumentCategorizer`
      get the category at a given index
      Specified by:
        `getCategory` in interface `DocumentCategorizer`
      Parameters:
        `index` - the index
      Returns:
        a category

    - #### getNumberOfCategories

          public int getNumberOfCategories()

      Description copied from interface: `DocumentCategorizer`
      get the number of categories
      Specified by:
        `getNumberOfCategories` in interface `DocumentCategorizer`
      Returns:
        the no. of categories

    - #### getAllResults

          public String getAllResults(double[] results)

      Description copied from interface: `DocumentCategorizer`
      get the name of the category associated with the given probabilties
      Specified by:
        `getAllResults` in interface `DocumentCategorizer`
      Parameters:
        `results` - the probabilities of each category
      Returns:
        the name of the outcome

    - #### train

          public static DoccatModel train(String languageCode,
                                          ObjectStream<DocumentSample> samples,
                                          TrainingParameters mlParams,
                                          DoccatFactory factory)
                                   throws IOException

      Throws:
        `IOException`

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