TheAlgorithms/C-Sharp · error · ArgumentException

Input data cannot be null.

Error message

Input data cannot be null.

What it means

LinearRegression.Fit requires two non-null lists of observations, x (independent) and y (dependent); nulls cannot be measured for count/variance. The library throws ArgumentException('Input data cannot be null.') when either list is null.

Solutions

1. Pass non-null lists for both x and y.
2. Check the data-loading step: if the source file/parse failed, the lists may be null — handle that before calling Fit.
3. Initialize lists to empty collections rather than leaving them null, then rely on the empty check.

Example fix

// before
regression.Fit(xData, yData); // yData null when file load failed
// after
if (xData == null || yData == null)
    throw new InvalidOperationException("Data not loaded.");
regression.Fit(xData, yData);

if (xs == null || ys == null)
    throw new InvalidOperationException("Regression data not loaded.");
regression.Fit(xs, ys);

static bool CanFit(IList<double>? x, IList<double>? y) => x != null && y != null;

try
{
    regression.Fit(xs, ys);
}
catch (ArgumentException ex)
{
    // null / empty / length-mismatch inputs — check ex.Message to distinguish
    logger.LogError(ex, "Fit rejected input data");
}

When it happens

Trigger: Calling Fit(null, y), Fit(x, null), or Fit(a, b) where either argument is null from a failed data load or uninitialized variable.

Common situations: CSV/JSON loading producing null lists on file-not-found or parse failure; optional dataset fields left null; refactoring where data assignment was removed.

Related errors

• Input data cannot be empty.
• ArgumentNullException: features
• Input lists must have the same length.
• Variance of X must not be zero.
• Model must be fitted before prediction.

AI-assisted analysis of TheAlgorithms/C-Sharp@96e2905cab (2026-09-13). Data as JSON: /api/errors/c0e49ed12a2bd69f. Report an issue: GitHub.

Appendix: source

Thrown at Algorithms/MachineLearning/LinearRegression.cs:32

{
    // Intercept (a) and slope (b) of the fitted line
    public double Intercept { get; private set; }

    public double Slope { get; private set; }

    public bool IsFitted { get; private set; }

    /// <summary>
    /// Fits the linear regression model to the provided data.
    /// </summary>
    /// <param name="x">List of independent variable values.</param>
    /// <param name="y">List of dependent variable values.</param>
    /// <exception cref="ArgumentException">Thrown if input lists are null, empty, or of different lengths.</exception>
    public void Fit(IList<double> x, IList<double> y)
    {
        if (x == null || y == null)
        {
            throw new ArgumentException("Input data cannot be null.");
        }

        if (x.Count == 0 || y.Count == 0)
        {
            throw new ArgumentException("Input data cannot be empty.");
        }

        if (x.Count != y.Count)
        {
            throw new ArgumentException("Input lists must have the same length.");
        }

        // Calculate means
        double xMean = x.Average();
        double yMean = y.Average();

        // Calculate slope (b) and intercept (a)
        double numerator = 0.0;

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