Ejemplo n.º 1
0
 public static TableAssignment fromDelimitedLines(VariableNumMap vars, Iterable<String> lines) {
   Preconditions.checkArgument(vars.getDiscreteVariables().size() == vars.size());
   CsvParser parser = CsvParser.defaultParser();
   SparseTensorBuilder builder =
       new SparseTensorBuilder(vars.getVariableNumsArray(), vars.getVariableSizes());
   for (String line : lines) {
     String[] parts = parser.parseLine(line);
     Assignment assignment = vars.outcomeToAssignment(parts);
     builder.put(vars.assignmentToIntArray(assignment), 1.0);
   }
   return new TableAssignment(vars, builder.build());
 }
  public void setUp() {
    DiscreteVariable var1 = new DiscreteVariable("alphabet", Arrays.asList("A", "B", "C"));
    DiscreteVariable var2 = new DiscreteVariable("truth", Arrays.asList("T", "F"));

    alphabetVar = VariableNumMap.singleton(0, "alphabet", var1);
    truthVar = VariableNumMap.singleton(1, "truth", var2);
    vars = alphabetVar.union(truthVar);

    TableFactorBuilder builder = new TableFactorBuilder(vars, SparseTensorBuilder.getFactory());
    builder.setWeight(1.0, "A", "T");
    builder.setWeight(1.0, "B", "F");
    builder.setWeight(1.0, "C", "F");
    builder.setWeight(2.0, "B", "T");
    featureIndicators = builder.build();
  }
  private void runTestAllFeatures(DenseIndicatorLogLinearFactor parametricFactor) {
    SufficientStatistics parameters = parametricFactor.getNewSufficientStatistics();
    Factor initial = parametricFactor.getModelFromParameters(parameters);
    assertEquals(0.0, initial.getUnnormalizedLogProbability("A", "T"), TOLERANCE);
    assertEquals(0.0, initial.getUnnormalizedLogProbability("B", "T"), TOLERANCE);
    assertEquals(0.0, initial.getUnnormalizedLogProbability("C", "F"), TOLERANCE);

    parametricFactor.incrementSufficientStatisticsFromAssignment(
        parameters, parameters, vars.outcomeArrayToAssignment("B", "F"), 2.0);
    parametricFactor.incrementSufficientStatisticsFromAssignment(
        parameters, parameters, vars.outcomeArrayToAssignment("B", "F"), -3.0);
    parametricFactor.incrementSufficientStatisticsFromAssignment(
        parameters, parameters, vars.outcomeArrayToAssignment("A", "T"), 1.0);
    parametricFactor.incrementSufficientStatisticsFromAssignment(
        parameters, parameters, vars.outcomeArrayToAssignment("C", "T"), 2.0);

    Factor factor = parametricFactor.getModelFromParameters(parameters);
    assertEquals(-1.0, factor.getUnnormalizedLogProbability("B", "F"), TOLERANCE);
    assertEquals(1.0, factor.getUnnormalizedLogProbability("A", "T"), TOLERANCE);
    assertEquals(2.0, factor.getUnnormalizedLogProbability("C", "T"), TOLERANCE);

    TableFactorBuilder incrementBuilder =
        new TableFactorBuilder(vars, SparseTensorBuilder.getFactory());
    incrementBuilder.setWeight(4.0, "A", "F");
    incrementBuilder.setWeight(6.0, "C", "F");
    Factor increment = incrementBuilder.build();
    parametricFactor.incrementSufficientStatisticsFromMarginal(
        parameters, parameters, increment, Assignment.EMPTY, 3.0, 2.0);

    factor = parametricFactor.getModelFromParameters(parameters);
    assertEquals(-1.0, factor.getUnnormalizedLogProbability("B", "F"), TOLERANCE);
    assertEquals(1.0, factor.getUnnormalizedLogProbability("A", "T"), TOLERANCE);
    assertEquals(2.0, factor.getUnnormalizedLogProbability("C", "T"), TOLERANCE);
    assertEquals(6.0, factor.getUnnormalizedLogProbability("A", "F"), TOLERANCE);
    assertEquals(9.0, factor.getUnnormalizedLogProbability("C", "F"), TOLERANCE);

    TableFactor pointDist =
        TableFactor.logPointDistribution(truthVar, truthVar.outcomeArrayToAssignment("T"));
    parametricFactor.incrementSufficientStatisticsFromMarginal(
        parameters, parameters, pointDist, alphabetVar.outcomeArrayToAssignment("B"), 3, 2.0);
    factor = parametricFactor.getModelFromParameters(parameters);
    assertEquals(1.5, factor.getUnnormalizedLogProbability("B", "T"), TOLERANCE);
  }