List of classifyFull() Examples

Examples of classifyFull()

org.apache.mahout.classifier.AbstractVectorClassifier.classifyFull()
Returns n probabilities, one for each category. If you can use an n-1 coding, and are touchy about allocation performance, then the classify method is probably better to use. The 0-th element of the score vector returned by this method is the missing score as computed by the classify method. @see #classify(Vector) @see #classifyFull(Vector r,Vector instance) @param instance A vector of features to be classified. @return A vector of probabilities, one for each category.
org.apache.mahout.classifier.naivebayes.AbstractNaiveBayesClassifier.classifyFull()

Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classifyFull()


    AbstractVectorClassifier classifier = new StandardNaiveBayesClassifier(naiveBayesModel);


    assertEquals(2, classifier.numCategories());


    Vector prediction = classifier.classifyFull(trainingInstance(COLOR_RED, TYPE_SUV, ORIGIN_DOMESTIC).get());


    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
  }

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Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classifyFull()


    AbstractVectorClassifier classifier = new ComplementaryNaiveBayesClassifier(naiveBayesModel);


    assertEquals(2, classifier.numCategories());


    Vector prediction = classifier.classifyFull(trainingInstance(COLOR_RED, TYPE_SUV, ORIGIN_DOMESTIC).get());


    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
  }

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Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classifyFull()


    AbstractVectorClassifier classifier = new StandardNaiveBayesClassifier(naiveBayesModel);


    assertEquals(2, classifier.numCategories());


    Vector prediction = classifier.classifyFull(trainingInstance(COLOR_RED, TYPE_SUV, ORIGIN_DOMESTIC).get());


    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
  }

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Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classifyFull()


    AbstractVectorClassifier classifier = new ComplementaryNaiveBayesClassifier(naiveBayesModel);


    assertEquals(2, classifier.numCategories());


    Vector prediction = classifier.classifyFull(trainingInstance(COLOR_RED, TYPE_SUV, ORIGIN_DOMESTIC).get());


    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
  }

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Examples of org.apache.mahout.classifier.naivebayes.AbstractNaiveBayesClassifier.classifyFull()

      Reader reader = new Reader(fs, getInputPath(), getConf());
      Text key = new Text();
      VectorWritable vw = new VectorWritable();
      while (reader.next(key, vw)) {
        writer.append(new Text(SLASH.split(key.toString())[1]),
            new VectorWritable(classifier.classifyFull(vw.get())));
      }
      writer.close();
      reader.close();
    } else {
      boolean succeeded = runMapReduce(parsedArgs);

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Examples of org.apache.mahout.classifier.naivebayes.AbstractNaiveBayesClassifier.classifyFull()

      Reader reader = new Reader(fs, getInputPath(), getConf());
      Text key = new Text();
      VectorWritable vw = new VectorWritable();
      while (reader.next(key, vw)) {
        writer.append(new Text(SLASH.split(key.toString())[1]),
            new VectorWritable(classifier.classifyFull(vw.get())));
      }
      writer.close();
      reader.close();
    } else {
      boolean succeeded = runMapReduce(parsedArgs);

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Examples of org.apache.mahout.classifier.naivebayes.AbstractNaiveBayesClassifier.classifyFull()

      SequenceFile.Reader reader = new Reader(fs, getInputPath(), getConf());
      Text key = new Text();
      VectorWritable vw = new VectorWritable();
      while (reader.next(key, vw)) {
        writer.append(new Text(key.toString().split("/")[1]),
            new VectorWritable(classifier.classifyFull(vw.get())));
      }
      writer.close();
      reader.close();
    } else {
      boolean succeeded = runMapReduce(parsedArgs);

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Examples of org.apache.mahout.classifier.naivebayes.AbstractNaiveBayesClassifier.classifyFull()

      SequenceFileDirIterable<Text, VectorWritable> dirIterable =
          new SequenceFileDirIterable<Text, VectorWritable>(getInputPath(), PathType.LIST, PathFilters.partFilter(), getConf());
      // loop through the part-r-* files in getInputPath() and get classification scores for all entries
      for (Pair<Text, VectorWritable> pair : dirIterable) {
        writer.append(new Text(SLASH.split(pair.getFirst().toString())[1]),
            new VectorWritable(classifier.classifyFull(pair.getSecond().get())));
      }
    } finally {
      Closeables.close(writer, false);
    }
  }

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