adding missing validations

lib/numerics/gammadist/Gammadist.cs

@@ -0,0 +1,440 @@
+// GAMMADIST: Gamma Distribution CDF
+// Applies the regularized incomplete gamma function P(α, x/β) to a min-max
+// normalized price series over a rolling lookback window.
+// Pipeline: MinMax normalization → [0,10] scaling → Lanczos log-gamma → series/CF evaluation.
+
+using System.Runtime.CompilerServices;
+using System.Runtime.InteropServices;
+
+namespace QuanTAlib;
+
+/// <summary>
+/// GAMMADIST: Gamma Distribution CDF
+/// Computes F(x; α, β) = P(α, x/β) — the regularized lower incomplete gamma
+/// function — applied to a min-max normalized price series over a rolling window.
+/// </summary>
+/// <remarks>
+/// Key properties:
+/// - Output always in [0, 1]
+/// - Rolling window tracks min/max for normalization; flat range returns F(5; α, β)
+/// - α (shape) controls CDF form: α=1 → exponential decay, α&gt;1 → S-curve
+/// - β (scale) controls rise speed: smaller β → faster saturation
+/// - Series expansion for x &lt; α+1; Lentz continued fraction for x ≥ α+1
+/// - Lanczos log-gamma (g=7, 9 coefficients) for numerical accuracy to 1e-15
+/// - NaN/Infinity inputs use last-valid-value substitution
+/// </remarks>
+[SkipLocalsInit]
+public sealed class Gammadist : AbstractBase
+{
+    private readonly int _period;
+    private readonly double _alpha;
+    private readonly double _beta;
+    private readonly double _lnGammaAlpha;
+    private readonly RingBuffer _buffer;
+
+    // Lanczos g=7, 9 coefficients (Numerical Recipes 3rd Ed., Table 6.1)
+    private static ReadOnlySpan<double> LanczosCoeff =>
+    [
+        0.99999999999980993,
+        676.5203681218851,
+        -1259.1392167224028,
+        771.32342877765313,
+        -176.61502916214059,
+        12.507343278686905,
+        -0.13857109526572012,
+        9.9843695780195716e-6,
+        1.5056327351493116e-7
+    ];
+
+    [StructLayout(LayoutKind.Auto)]
+    private record struct State(double LastValid);
+    private State _state, _p_state;
+
+    public override bool IsHot => _buffer.Count >= _period;
+
+    /// <summary>
+    /// Initializes a new Gammadist indicator.
+    /// </summary>
+    /// <param name="alpha">Shape parameter α &gt; 0 (default 2.0)</param>
+    /// <param name="beta">Scale parameter β &gt; 0 (default 1.0)</param>
+    /// <param name="period">Lookback window for min-max normalization (default 14)</param>
+    public Gammadist(double alpha = 2.0, double beta = 1.0, int period = 14)
+    {
+        if (alpha <= 0.0)
+        {
+            throw new ArgumentException("Alpha must be > 0", nameof(alpha));
+        }
+
+        if (beta <= 0.0)
+        {
+            throw new ArgumentException("Beta must be > 0", nameof(beta));
+        }
+
+        if (period < 2)
+        {
+            throw new ArgumentException("Period must be >= 2", nameof(period));
+        }
+
+        _alpha = alpha;
+        _beta = beta;
+        _period = period;
+        _lnGammaAlpha = LnGamma(alpha);
+        _buffer = new RingBuffer(period);
+        Name = $"Gammadist({alpha:F2},{beta:F2},{period})";
+        WarmupPeriod = period;
+        _state = new State(0.5);
+        _p_state = _state;
+    }
+
+    /// <summary>
+    /// Initializes a new Gammadist indicator with source for event-based chaining.
+    /// </summary>
+    /// <param name="source">Source indicator for chaining</param>
+    /// <param name="alpha">Shape parameter α &gt; 0 (default 2.0)</param>
+    /// <param name="beta">Scale parameter β &gt; 0 (default 1.0)</param>
+    /// <param name="period">Lookback window (default 14)</param>
+    public Gammadist(ITValuePublisher source, double alpha = 2.0, double beta = 1.0, int period = 14)
+        : this(alpha, beta, period)
+    {
+        source.Pub += HandleUpdate;
+    }
+
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    private void HandleUpdate(object? sender, in TValueEventArgs e) => Update(e.Value, e.IsNew);
+
+    /// <summary>
+    /// Lanczos log-gamma approximation (g=7, 9 coefficients).
+    /// Accurate to ~15 digits for z > 0.5; uses reflection formula for z &lt; 0.5.
+    /// </summary>
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    internal static double LnGamma(double z)
+    {
+        if (z < 0.5)
+        {
+            return Math.Log(Math.PI / Math.Sin(Math.PI * z)) - LnGamma(1.0 - z);
+        }
+
+        z -= 1.0;
+        ReadOnlySpan<double> c = LanczosCoeff;
+        double x = c[0];
+        for (int i = 1; i < 9; i++)
+        {
+            x += c[i] / (z + i);
+        }
+
+        double t = z + 7.5;
+        return Math.FusedMultiplyAdd(z + 0.5, Math.Log(t), 0.5 * Math.Log(2.0 * Math.PI) - t + Math.Log(x));
+    }
+
+    /// <summary>
+    /// Series expansion for regularized lower incomplete gamma P(a, x).
+    /// Converges for x &lt; a + 1.
+    /// </summary>
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    private static double GammaSeries(double a, double x, double lnGammaA)
+    {
+        const int MaxIter = 200;
+        const double Eps = 1e-12;
+
+        double ap = a;
+        double sum = 1.0 / a;
+        double del = 1.0 / a;
+
+        for (int n = 0; n < MaxIter; n++)
+        {
+            ap += 1.0;
+            del *= x / ap;
+            sum += del;
+            if (Math.Abs(del) < Math.Abs(sum) * Eps)
+            {
+                break;
+            }
+        }
+
+        return sum * Math.Exp(-x + a * Math.Log(x) - lnGammaA);
+    }
+
+    /// <summary>
+    /// Lentz continued fraction for regularized upper incomplete gamma Q(a, x) = 1 - P(a, x).
+    /// Converges for x ≥ a + 1.
+    /// </summary>
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    private static double GammaCF(double a, double x, double lnGammaA)
+    {
+        const int MaxIter = 200;
+        const double Eps = 1e-12;
+        const double FpMin = 1e-300;
+
+        double b = x + 1.0 - a;
+        double c = 1.0 / FpMin;
+        double d = 1.0 / b;
+        double h = d;
+
+        for (int i = 1; i <= MaxIter; i++)
+        {
+            double an = -(double)i * (i - a);
+            b += 2.0;
+            d = Math.FusedMultiplyAdd(an, d, b);
+            if (Math.Abs(d) < FpMin)
+            {
+                d = FpMin;
+            }
+
+            c = b + an / c;
+            if (Math.Abs(c) < FpMin)
+            {
+                c = FpMin;
+            }
+
+            d = 1.0 / d;
+            double del = d * c;
+            h *= del;
+            if (Math.Abs(del - 1.0) < Eps)
+            {
+                break;
+            }
+        }
+
+        return Math.Exp(-x + a * Math.Log(x) - lnGammaA) * h;
+    }
+
+    /// <summary>
+    /// Regularized lower incomplete gamma function P(a, x) = γ(a,x)/Γ(a).
+    /// Uses series for x &lt; a+1; complement of CF for x ≥ a+1.
+    /// </summary>
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    internal static double RegularizedIncompleteGamma(double a, double x, double lnGammaA)
+    {
+        if (x <= 0.0)
+        {
+            return 0.0;
+        }
+
+        if (x < a + 1.0)
+        {
+            return GammaSeries(a, x, lnGammaA);
+        }
+
+        return 1.0 - GammaCF(a, x, lnGammaA);
+    }
+
+    /// <summary>
+    /// Gamma Distribution CDF: F(x; α, β) = P(α, x/β).
+    /// Returns 0 for x ≤ 0.
+    /// </summary>
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    public static double GammaCdf(double x, double alpha, double beta)
+    {
+        if (x <= 0.0)
+        {
+            return 0.0;
+        }
+
+        double lnGammaA = LnGamma(alpha);
+        return RegularizedIncompleteGamma(alpha, x / beta, lnGammaA);
+    }
+
+    /// <summary>
+    /// Pure static CDF helper — identical to <see cref="GammaCdf"/> with an explicit name
+    /// for downstream consumers and validation tests.
+    /// </summary>
+    public static double StaticCdf(double x, double alpha, double beta) => GammaCdf(x, alpha, beta);
+
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    private static (double min, double max) FindMinMax(ReadOnlySpan<double> values)
+    {
+        if (values.Length == 0)
+        {
+            return (double.MaxValue, double.MinValue);
+        }
+
+        double min = values[0];
+        double max = values[0];
+        for (int i = 1; i < values.Length; i++)
+        {
+            double v = values[i];
+            if (v < min)
+            {
+                min = v;
+            }
+
+            if (v > max)
+            {
+                max = v;
+            }
+        }
+
+        return (min, max);
+    }
+
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    public override TValue Update(TValue input, bool isNew = true)
+    {
+        if (isNew)
+        {
+            _p_state = _state;
+        }
+        else
+        {
+            _state = _p_state;
+        }
+
+        double value = input.Value;
+        double result;
+
+        if (double.IsFinite(value))
+        {
+            _buffer.Add(value, isNew);
+
+            var (min, max) = FindMinMax(_buffer.GetSpan());
+            double range = max - min;
+
+            // Flat range → use midpoint 0.5; map [0,1] → [0,10] for useful CDF spread
+            double xNorm = range > 0.0 ? (value - min) / range : 0.5;
+            double xGamma = xNorm * 10.0;
+
+            result = RegularizedIncompleteGamma(_alpha, xGamma / _beta, _lnGammaAlpha);
+            _state = new State(result);
+        }
+        else
+        {
+            result = _state.LastValid;
+        }
+
+        Last = new TValue(input.Time, result);
+        PubEvent(Last, isNew);
+        return Last;
+    }
+
+    public override TSeries Update(TSeries source)
+    {
+        var result = new TSeries(source.Count);
+        ReadOnlySpan<double> values = source.Values;
+        ReadOnlySpan<long> times = source.Times;
+
+        for (int i = 0; i < source.Count; i++)
+        {
+            var tv = Update(new TValue(new DateTime(times[i], DateTimeKind.Utc), values[i]), true);
+            result.Add(tv, true);
+        }
+
+        return result;
+    }
+
+    public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
+    {
+        TimeSpan interval = step ?? TimeSpan.FromSeconds(1);
+        DateTime time = DateTime.UtcNow - (interval * source.Length);
+
+        for (int i = 0; i < source.Length; i++)
+        {
+            Update(new TValue(time, source[i]), true);
+            time += interval;
+        }
+    }
+
+    public static TSeries Batch(TSeries source, double alpha = 2.0, double beta = 1.0, int period = 14)
+    {
+        var indicator = new Gammadist(alpha, beta, period);
+        return indicator.Update(source);
+    }
+
+    /// <summary>
+    /// Calculates Gamma Distribution CDF over a span of values.
+    /// Uses a sliding window min-max normalization identical to the streaming path.
+    /// </summary>
+    public static void Batch(
+        ReadOnlySpan<double> source, Span<double> output,
+        double alpha = 2.0, double beta = 1.0, int period = 14)
+    {
+        if (source.Length == 0)
+        {
+            throw new ArgumentException("Source cannot be empty", nameof(source));
+        }
+
+        if (output.Length < source.Length)
+        {
+            throw new ArgumentException("Output length must be >= source length", nameof(output));
+        }
+
+        if (alpha <= 0.0)
+        {
+            throw new ArgumentException("Alpha must be > 0", nameof(alpha));
+        }
+
+        if (beta <= 0.0)
+        {
+            throw new ArgumentException("Beta must be > 0", nameof(beta));
+        }
+
+        if (period < 2)
+        {
+            throw new ArgumentException("Period must be >= 2", nameof(period));
+        }
+
+        double lnGammaA = LnGamma(alpha);
+        double lastValid = 0.5;
+
+        for (int i = 0; i < source.Length; i++)
+        {
+            double val = source[i];
+            if (!double.IsFinite(val))
+            {
+                output[i] = lastValid;
+                continue;
+            }
+
+            int start = Math.Max(0, i - period + 1);
+
+            double min = double.PositiveInfinity;
+            double max = double.NegativeInfinity;
+
+            for (int j = start; j <= i; j++)
+            {
+                double v = source[j];
+                if (double.IsFinite(v))
+                {
+                    if (v < min)
+                    {
+                        min = v;
+                    }
+
+                    if (v > max)
+                    {
+                        max = v;
+                    }
+                }
+            }
+
+            if (!double.IsFinite(min) || !double.IsFinite(max))
+            {
+                output[i] = lastValid;
+                continue;
+            }
+
+            double range = max - min;
+            double xNorm = range > 0.0 ? (val - min) / range : 0.5;
+            double xGamma = xNorm * 10.0;
+
+            double result = RegularizedIncompleteGamma(alpha, xGamma / beta, lnGammaA);
+            lastValid = result;
+            output[i] = result;
+        }
+    }
+
+    public static (TSeries Results, Gammadist Indicator) Calculate(
+        TSeries source, double alpha = 2.0, double beta = 1.0, int period = 14)
+    {
+        var indicator = new Gammadist(alpha, beta, period);
+        TSeries results = indicator.Update(source);
+        return (results, indicator);
+    }
+
+    public override void Reset()
+    {
+        _buffer.Clear();
+        _state = new State(0.5);
+        _p_state = _state;
+        Last = default;
+    }
+}