コード例 #1
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 @Override
 public TensorSufficientStatistics getNewSufficientStatistics() {
   return new TensorSufficientStatistics(
       featureVars,
       new DenseTensorBuilder(
           new int[] {0}, new int[] {initialWeights.getWeights().getValues().length}));
 }
コード例 #2
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  @Override
  public void incrementSufficientStatisticsFromAssignment(
      SufficientStatistics gradient,
      SufficientStatistics currentParameters,
      Assignment assignment,
      double count) {
    Preconditions.checkArgument(assignment.containsAll(getVars().getVariableNumsArray()));
    Assignment subAssignment = assignment.intersection(getVars().getVariableNumsArray());

    long keyNum =
        initialWeights
            .getWeights()
            .dimKeyToKeyNum(initialWeights.getVars().assignmentToIntArray(subAssignment));
    int index = initialWeights.getWeights().keyNumToIndex(keyNum);

    ((TensorSufficientStatistics) gradient).incrementFeatureByIndex(count, index);
  }
コード例 #3
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 private Tensor getFeatureWeights(SufficientStatistics parameters) {
   TensorSufficientStatistics featureParameters = (TensorSufficientStatistics) parameters;
   // Check that the parameters are a vector of the appropriate size.
   Preconditions.checkArgument(featureParameters.get().getDimensionNumbers().length == 1);
   Preconditions.checkArgument(
       featureParameters.get().getDimensionSizes()[0]
           == initialWeights.getWeights().getValues().length);
   return featureParameters.get();
 }
コード例 #4
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  @Override
  public String getParameterDescription(SufficientStatistics parameters, int numFeatures) {
    Tensor featureWeights = getFeatureWeights(parameters);

    TableFactor featureValues =
        new TableFactor(
            initialWeights.getVars(),
            initialWeights.getWeights().replaceValues(featureWeights.getValues()));

    List<Assignment> biggestAssignments =
        featureValues.product(featureValues).getMostLikelyAssignments(numFeatures);
    return featureValues.describeAssignments(biggestAssignments);
  }
コード例 #5
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  @Override
  public TableFactor getModelFromParameters(SufficientStatistics parameters) {
    Tensor featureWeights = getFeatureWeights(parameters);

    double[] logProbs = featureWeights.getValues();
    double[] probs = new double[logProbs.length];
    for (int i = 0; i < logProbs.length; i++) {
      probs[i] = Math.exp(logProbs[i]);
    }

    return new TableFactor(
        initialWeights.getVars(), initialWeights.getWeights().replaceValues(probs));
  }