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);
}