stanfordnlp/CoreNLP · error · IllegalArgumentException

Initial weights are invalid!

Error message

Initial weights are invalid!

What it means

A sanity-check failure in LinearClassifierFactory: the user-supplied initial weight vector does not match the dimensions of the training dataset's feature/label index (e.g. wrong length or NaN entries), so optimization cannot start from it.

Solutions

1. Pass an initial weights array of finite doubles with the correct dimension
2. Initialize weights to zeros if unsure
3. Validate upstream computations that produced the initial weights

When it happens

Trigger: Thrown at src/edu/stanford/nlp/classify/LinearClassifierFactory.java:924 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of stanfordnlp/CoreNLP@1b7edd19c4 (2026-09-10). Data as JSON: /api/errors/5411ad26054a5e9d. Report an issue: GitHub.

Appendix: source

Thrown at src/edu/stanford/nlp/classify/LinearClassifierFactory.java:924

    LinearClassifier<L, F> classifier = new LinearClassifier<>(objective.to2D(weights), dataset.featureIndex(), dataset.labelIndex());
    return classifier;
  }


  @Override
  public LinearClassifier<L, F> trainClassifier(GeneralDataset<L, F> dataset) {
    return trainClassifier(dataset, null);
  }

  public LinearClassifier<L, F> trainClassifier(GeneralDataset<L, F> dataset, double[] initial) {
    // Sanity check
    if (dataset instanceof RVFDataset) {
      ((RVFDataset<L, F>) dataset).ensureRealValues();
    }
    if (initial != null) {
      for (double weight : initial) {
        if (Double.isNaN(weight) || Double.isInfinite(weight)) {
          throw new IllegalArgumentException("Initial weights are invalid!");
        }
      }
    }
    // Train classifier
    double[][] weights =  trainWeights(dataset, initial, false);
    LinearClassifier<L, F> classifier = new LinearClassifier<>(weights, dataset.featureIndex(), dataset.labelIndex());
    return classifier;
  }

  public LinearClassifier<L, F> trainClassifierWithInitialWeights(GeneralDataset<L, F> dataset, double[][] initialWeights2D) {
    double[] initialWeights = (initialWeights2D != null)? ArrayUtils.flatten(initialWeights2D):null;
    return trainClassifier(dataset, initialWeights);
  }

  public LinearClassifier<L, F> trainClassifierWithInitialWeights(GeneralDataset<L, F> dataset, LinearClassifier<L,F> initialClassifier) {
    double[][] initialWeights2D = (initialClassifier != null)? initialClassifier.weights():null;
    return trainClassifierWithInitialWeights(dataset, initialWeights2D);
  }

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