stanfordnlp/CoreNLP · error · RuntimeException

Bad arguments: " + x + " and " + lambda

Error message

Bad arguments: " + x + " and " + lambda

What it means

SloppyMath.poisson(int x, double lambda) evaluates the Poisson probability mass function; it requires x >= 0 and lambda > 0. Violations make exp(-lambda)*lambda^x/factorial(x) meaningless, so a RuntimeException("Bad arguments: ...") is thrown with both values.

Solutions

  1. Validate inputs before the call: if (x < 0 || lambda <= 0) skip or handle
  2. Fix the upstream estimate so lambda > 0 (e.g. add a small epsilon floor)
  3. Replace -1 missing-value sentinels with proper filtering before the call

Example fix

// before
double p = SloppyMath.poisson(x, lambda); // lambda = 0.0
// after
if (x >= 0 && lambda > 0.0) {
  double p = SloppyMath.poisson(x, lambda);
} else {
  // handle degenerate case
}
Defensive patterns

Strategy: validation

Validate before calling

if (x < 0 || lambda <= 0.0) { /* skip or floor lambda */ lambda = Math.max(lambda, Double.MIN_VALUE); }

Type guard

static boolean validPoissonArgs(int x, double lambda) { return x >= 0 && lambda > 0.0; }

Try / catch

try {
  double p = SloppyMath.poisson(x, lambda);
} catch (RuntimeException e) {
  log.warn("poisson args invalid: " + e.getMessage());
  // handle degenerate case (p undefined)
}

Prevention

When it happens

Trigger: Calling poisson with a negative observed count x, or lambda <= 0 — e.g. an estimated mean rate of 0 from an empty sample, or x computed as -1 by a sentinel/missing-value convention.

Common situations: Fitting a rate from no data, using -1 as a 'missing' marker for counts, or parameter search over an unbounded range touching lambda = 0 or negative values.

Understand the failure class

Background: "Must be a positive integer", "Invalid value", "Unsupported": the invalid-argument-value error family, when a library rejects the value you pass — this error's family across 35 libraries.

Related errors

AI-assisted analysis of stanfordnlp/CoreNLP@1b7edd19c4 (2026-09-10). Data as JSON: /api/errors/df55b04d3efb63e8. Report an issue: GitHub.

Appendix: source

Thrown at src/edu/stanford/nlp/math/SloppyMath.java:661

    if (cosValue < -1.0 || cosValue > 1.0) {
      throw new IllegalArgumentException("Cosine is not between -1 and 1: " + cosValue);
    }
    int numSamples = 10000;
    if (acosCache == null) {
      acosCache = new float[numSamples + 1];
      for (int i = 0; i <= numSamples; ++i) {
        double x = 2.0 / ((double) numSamples) * ((double) i) - 1.0;
        acosCache[i] = (float) Math.acos(x);
      }
    }

    int i = ((int) (((cosValue + 1.0) / 2.0) * ((double) numSamples)));
    return acosCache[i];
  }


  public static double poisson(int x, double lambda) {
    if (x<0 || lambda<=0.0) throw new RuntimeException("Bad arguments: " + x + " and " + lambda);
    double p = (Math.exp(-lambda) * Math.pow(lambda, x)) / factorial(x);
    if (Double.isInfinite(p) || p<=0.0) throw new RuntimeException(Math.exp(-lambda) +" "+ Math.pow(lambda, x) + ' ' + factorial(x));
    return p;
  }

  /**
   * Uses floating point so that it can represent the really big numbers that come up.
   * @param x Argument to take factorial of
   * @return Factorial of argument
   */
  public static double factorial(int x) {
    double result = 1.0;
    for (int i=x; i>1; i--) {
      result *= i;
    }
    return result;
  }

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