rs-stats 2.0.3

//! # Binomial Distribution
//!
//! This module implements the Binomial distribution, a discrete probability distribution
//! that models the number of successes in a sequence of independent experiments.
//!
//! ## Key Characteristics
//! - Models the number of successes in `n` independent trials
//! - Each trial has success probability `p`
//! - Discrete probability distribution
//!
//! ## Common Applications
//! - Quality control testing
//! - A/B testing
//! - Risk analysis
//! - Genetics (Mendelian inheritance)
//!
//! ## Mathematical Formulation
//! The probability mass function (PMF) is given by:
//!
//! P(X = k) = C(n,k) * p^k * (1-p)^(n-k)
//!
//! where:
//! - n is the number of trials
//! - k is the number of successes
//! - p is the probability of success
//! - C(n,k) is the binomial coefficient (n choose k)

use crate::error::{StatsError, StatsResult};
use crate::utils::special_functions::ln_gamma;
use num_traits::ToPrimitive;
use serde::{Deserialize, Serialize};

/// Configuration for the Binomial distribution.
///
/// # Fields
/// * `n` - The number of trials (must be positive)
/// * `p` - The probability of success (must be between 0 and 1)
///
/// # Examples
/// ```
/// use rs_stats::distributions::binomial_distribution::BinomialConfig;
///
/// let config = BinomialConfig { n: 10, p: 0.5 };
/// assert!(config.n > 0);
/// assert!(config.p >= 0.0 && config.p <= 1.0);
/// ```
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub struct BinomialConfig<T>
where
    T: ToPrimitive,
{
    /// The number of trials.
    pub n: u64,
    /// The probability of success in a single trial.
    pub p: T,
}

impl<T> BinomialConfig<T>
where
    T: ToPrimitive,
{
    /// Creates a new BinomialConfig with validation
    ///
    /// # Arguments
    /// * `n` - The number of trials
    /// * `p` - The probability of success
    ///
    /// # Returns
    /// `Some(BinomialConfig)` if parameters are valid, `None` otherwise
    pub fn new(n: u64, p: T) -> StatsResult<Self> {
        let p_64 = p.to_f64().ok_or_else(|| StatsError::ConversionError {
            message: "BinomialConfig::new: Failed to convert p to f64".to_string(),
        })?;

        if n == 0 {
            return Err(StatsError::InvalidInput {
                message: "BinomialConfig::new: n must be positive".to_string(),
            });
        }
        if !((0.0..=1.0).contains(&p_64)) {
            return Err(StatsError::InvalidInput {
                message: "BinomialConfig::new: p must be between 0 and 1".to_string(),
            });
        }
        Ok(Self { n, p })
    }
}

/// Probability mass function (PMF) for the Binomial distribution.
///
/// Calculates the probability of observing exactly `k` successes in `n` trials
/// with success probability `p`.
///
/// # Arguments
/// * `k` - The number of successes (must be ≤ n)
/// * `n` - The total number of trials (must be positive)
/// * `p` - The probability of success in a single trial (must be between 0 and 1)
///
/// # Returns
/// The probability of exactly `k` successes occurring.
///
/// # Errors
/// Returns an error if:
/// - n is zero
/// - p is not between 0 and 1
/// - k > n
/// - Type conversion to f64 fails
///
/// # Examples
/// ```
/// use rs_stats::distributions::binomial_distribution::pmf;
///
/// // Calculate probability of 3 successes in 10 trials with p=0.5
/// let prob = pmf(3, 10, 0.5).unwrap();
/// assert!((prob - 0.1171875).abs() < 1e-10);
/// ```
#[inline]
pub fn pmf<T>(k: u64, n: u64, p: T) -> StatsResult<f64>
where
    T: ToPrimitive,
{
    let p_64 = p.to_f64().ok_or_else(|| StatsError::ConversionError {
        message: "binomial_distribution::pmf: Failed to convert p to f64".to_string(),
    })?;
    if n == 0 {
        return Err(StatsError::InvalidInput {
            message: "binomial_distribution::pmf: n must be positive".to_string(),
        });
    }
    if !((0.0..=1.0).contains(&p_64)) {
        return Err(StatsError::InvalidInput {
            message: "binomial_distribution::pmf: p must be between 0 and 1".to_string(),
        });
    }
    let combinations = combination(n, k)?;

    // Use log-space calculation to avoid:
    // 1. Casting u64 to i32 (information loss)
    // 2. Numerical underflow/overflow with large exponents
    // 3. Better numerical stability
    // Formula: p^k * (1-p)^(n-k) = exp(k * ln(p) + (n-k) * ln(1-p))

    // Handle edge cases explicitly for correctness
    if p_64 == 0.0 {
        // If p = 0, then p^k = 0 for k > 0, and 1 for k = 0
        return Ok(if k == 0 { combinations } else { 0.0 });
    }
    if p_64 == 1.0 {
        // If p = 1, then (1-p)^(n-k) = 0 for k < n, and 1 for k = n
        return Ok(if k == n { combinations } else { 0.0 });
    }

    // Convert to f64 (no information loss for reasonable values)
    let k_f64 = k as f64;
    let n_minus_k_f64 = (n - k) as f64;

    // Calculate in log space: k * ln(p) + (n-k) * ln(1-p)
    // Both p and (1-p) are guaranteed to be in (0, 1) here
    let log_prob = k_f64 * p_64.ln() + n_minus_k_f64 * (1.0 - p_64).ln();

    // Convert back from log space
    let prob = log_prob.exp();

    Ok(combinations * prob)
}

/// Cumulative distribution function (CDF) for the Binomial distribution.
///
/// Calculates the probability of observing `k` or fewer successes in `n` trials
/// with success probability `p`.
///
/// # Arguments
/// * `k` - The maximum number of successes (must be ≤ n)
/// * `n` - The total number of trials (must be positive)
/// * `p` - The probability of success in a single trial (must be between 0 and 1)
///
/// # Returns
/// The cumulative probability of `k` or fewer successes occurring.
///
/// # Errors
/// Returns an error if:
/// - n is zero
/// - p is not between 0 and 1
/// - k > n
/// - Type conversion to f64 fails
///
/// # Examples
/// ```
/// use rs_stats::distributions::binomial_distribution::cdf;
///
/// // Calculate probability of 3 or fewer successes in 10 trials with p=0.5
/// let prob = cdf(3, 10, 0.5).unwrap();
/// assert!((prob - 0.171875).abs() < 1e-10);
/// ```
#[inline]
pub fn cdf(k: u64, n: u64, p: f64) -> StatsResult<f64> {
    if n == 0 {
        return Err(StatsError::InvalidInput {
            message: "binomial_distribution::cdf: n must be positive".to_string(),
        });
    }
    if !((0.0..=1.0).contains(&p)) {
        return Err(StatsError::InvalidInput {
            message: "binomial_distribution::cdf: p must be between 0 and 1".to_string(),
        });
    }
    if k > n {
        return Err(StatsError::InvalidInput {
            message: "binomial_distribution::cdf: k must be less than or equal to n".to_string(),
        });
    }
    // Use PMF recurrence relation: P(X=i+1) = P(X=i) * (n-i)/(i+1) * p/(1-p)
    // This avoids recomputing factorials at each step: O(k) total, O(1) per step
    if p == 0.0 {
        return Ok(1.0); // P(X <= k) = 1 when p = 0 (all mass at k=0)
    }
    if p == 1.0 {
        return Ok(if k >= n { 1.0 } else { 0.0 });
    }

    // Start with pmf(0) = (1-p)^n
    let q = 1.0 - p;
    let mut pmf_i = q.powi(n as i32);
    // For very large n where powi overflows, fall back to log-space
    if pmf_i == 0.0 && n > 0 {
        let log_pmf_0 = (n as f64) * q.ln();
        pmf_i = log_pmf_0.exp();
    }
    let mut cdf_sum = pmf_i;
    let ratio = p / q;

    for i in 0..k {
        pmf_i *= ((n - i) as f64 / (i + 1) as f64) * ratio;
        cdf_sum += pmf_i;
    }

    Ok(cdf_sum.clamp(0.0, 1.0))
}

/// Calculate the binomial coefficient (n choose k).
#[inline]
fn combination(n: u64, k: u64) -> StatsResult<f64> {
    if k > n {
        return Err(StatsError::InvalidInput {
            message: "binomial_distribution::combination: k must be less than or equal to n"
                .to_string(),
        });
    }

    // Use a more numerically stable algorithm
    if k > n / 2 {
        return combination(n, n - k);
    }

    Ok((1..=k).fold(1.0_f64, |acc, i| acc * (n - i + 1) as f64 / i as f64))
}

// ── Typed struct + DiscreteDistribution impl ───────────────────────────────────

/// Binomial distribution Binomial(n, p) as a typed struct.
///
/// # Examples
/// ```
/// use rs_stats::distributions::binomial_distribution::Binomial;
/// use rs_stats::distributions::traits::DiscreteDistribution;
///
/// let b = Binomial::new(10, 0.5).unwrap();
/// assert!((b.mean() - 5.0).abs() < 1e-10);
/// ```
#[derive(Debug, Clone, Copy)]
pub struct Binomial {
    /// Number of trials n (must be ≥ 1)
    pub n: u64,
    /// Success probability p ∈ [0, 1]
    pub p: f64,
}

impl Binomial {
    /// Creates a `Binomial` distribution with validation.
    pub fn new(n: u64, p: f64) -> StatsResult<Self> {
        if n == 0 {
            return Err(StatsError::InvalidInput {
                message: "Binomial::new: n must be at least 1".to_string(),
            });
        }
        if !(0.0..=1.0).contains(&p) {
            return Err(StatsError::InvalidInput {
                message: "Binomial::new: p must be in [0, 1]".to_string(),
            });
        }
        Ok(Self { n, p })
    }

    /// MLE: assume n = max(data), p = mean(data) / n.
    pub fn fit(data: &[f64]) -> StatsResult<Self> {
        if data.is_empty() {
            return Err(StatsError::InvalidInput {
                message: "Binomial::fit: data must not be empty".to_string(),
            });
        }
        let n = data
            .iter()
            .cloned()
            .fold(f64::NEG_INFINITY, f64::max)
            .round() as u64;
        let mean = data.iter().sum::<f64>() / data.len() as f64;
        let p = if n == 0 { 0.5 } else { mean / n as f64 };
        Self::new(n.max(1), p.clamp(0.0, 1.0))
    }
}

impl crate::distributions::traits::DiscreteDistribution for Binomial {
    fn name(&self) -> &str {
        "Binomial"
    }
    fn num_params(&self) -> usize {
        2
    }
    fn pmf(&self, k: u64) -> StatsResult<f64> {
        pmf(k, self.n, self.p)
    }
    /// Log-space PMF for numerical stability with large n or k.
    ///
    /// ln P(X=k) = ln Γ(n+1) − ln Γ(k+1) − ln Γ(n−k+1) + k·ln(p) + (n−k)·ln(1−p)
    fn logpmf(&self, k: u64) -> StatsResult<f64> {
        let n = self.n;
        if k > n {
            return Ok(f64::NEG_INFINITY);
        }
        // ln C(n,k) via ln_gamma — exact and stable for any n, k.
        let log_binom =
            ln_gamma((n + 1) as f64) - ln_gamma((k + 1) as f64) - ln_gamma((n - k + 1) as f64);
        let log_p = match (self.p, k) {
            (0.0, 0) => 0.0,
            (0.0, _) => return Ok(f64::NEG_INFINITY),
            (_, _) => k as f64 * self.p.ln(),
        };
        let log_q = match (self.p, n - k) {
            (1.0, 0) => 0.0,
            (1.0, _) => return Ok(f64::NEG_INFINITY),
            (_, nk) => nk as f64 * (1.0 - self.p).ln(),
        };
        Ok(log_binom + log_p + log_q)
    }
    fn cdf(&self, k: u64) -> StatsResult<f64> {
        cdf(k, self.n, self.p)
    }
    fn mean(&self) -> f64 {
        self.n as f64 * self.p
    }
    fn variance(&self) -> f64 {
        self.n as f64 * self.p * (1.0 - self.p)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_binomial_pmf() {
        let n = 10;
        let p = 0.5;
        let k = 5;
        let result = pmf(k, n, p).unwrap();
        assert!(
            !result.is_nan(),
            "PMF returned NaN for k={}, n={}, p={}",
            k,
            n,
            p
        );
    }

    #[test]
    fn test_binomial_cdf() {
        let n = 10;
        let p = 0.5;
        let k = 5;
        let result = cdf(k, n, p).unwrap();
        assert!(
            !result.is_nan(),
            "CDF returned NaN for k={}, n={}, p={}",
            k,
            n,
            p
        );
    }

    #[test]
    fn test_binomial_pmf_large_values_n() {
        // Test with values that exceed i32::MAX to verify overflow protection
        // Using values just above i32::MAX (2,147,483,647)
        let n = 2_200_000_000u64;
        let k = 5u64;
        let p = 0.5;

        // This should not panic or truncate - should use powf() path
        let result = pmf(k, n, p);

        // Result might be very small or NaN due to numerical precision, but shouldn't panic
        match result {
            Ok(val) => {
                // Value should be valid (might be very small due to large n)
                assert!(
                    !val.is_infinite(),
                    "PMF should not be infinite for large values"
                );
            }
            Err(_) => {
                // Error is acceptable for very large values (numerical precision limits)
            }
        }
    }

    #[test]
    fn test_binomial_pmf_large_values_k() {
        // Test with values that exceed i32::MAX to verify overflow protection
        // Using values just above i32::MAX (2,147,483,647)
        let n = 2u64;
        let k = 2_200_000_000_000u64;
        let p = 0.5;

        // This should not panic or truncate - should use powf() path
        let result = pmf(k, n, p);

        // Result might be very small or NaN due to numerical precision, but shouldn't panic
        match result {
            Ok(val) => {
                // Value should be valid (might be very small due to large n)
                assert!(
                    !val.is_infinite(),
                    "PMF should not be infinite for large values"
                );
            }
            Err(_) => {
                // Error is acceptable for very large values (numerical precision limits)
            }
        }
    }

    #[test]
    fn test_binomial_config_new_valid() {
        let config = BinomialConfig::new(10, 0.5);
        assert!(config.is_ok());
        let config = config.unwrap();
        assert_eq!(config.n, 10);
    }

    #[test]
    fn test_binomial_config_new_n_zero() {
        let result = BinomialConfig::new(0, 0.5);
        assert!(result.is_err());
        assert!(matches!(
            result.unwrap_err(),
            StatsError::InvalidInput { .. }
        ));
    }

    #[test]
    fn test_binomial_config_new_p_out_of_range_negative() {
        let result = BinomialConfig::new(10, -0.1);
        assert!(result.is_err());
        assert!(matches!(
            result.unwrap_err(),
            StatsError::InvalidInput { .. }
        ));
    }

    #[test]
    fn test_binomial_config_new_p_out_of_range_above_one() {
        let result = BinomialConfig::new(10, 1.1);
        assert!(result.is_err());
        assert!(matches!(
            result.unwrap_err(),
            StatsError::InvalidInput { .. }
        ));
    }

    #[test]
    fn test_binomial_config_new_p_zero() {
        let config = BinomialConfig::new(10, 0.0);
        assert!(config.is_ok());
    }

    #[test]
    fn test_binomial_config_new_p_one() {
        let config = BinomialConfig::new(10, 1.0);
        assert!(config.is_ok());
    }

    #[test]
    fn test_binomial_pmf_p_zero_k_zero() {
        // When p=0.0 and k=0, PMF should return combinations (which is 1 for k=0)
        let result = pmf(0, 10, 0.0).unwrap();
        assert_eq!(result, 1.0);
    }

    #[test]
    fn test_binomial_pmf_p_zero_k_greater_than_zero() {
        // When p=0.0 and k>0, PMF should return 0.0
        let result = pmf(5, 10, 0.0).unwrap();
        assert_eq!(result, 0.0);
    }

    #[test]
    fn test_binomial_pmf_p_one_k_equals_n() {
        // When p=1.0 and k=n, PMF should return combinations (which is 1 for k=n)
        let result = pmf(10, 10, 1.0).unwrap();
        assert_eq!(result, 1.0);
    }

    #[test]
    fn test_binomial_pmf_p_one_k_less_than_n() {
        // When p=1.0 and k<n, PMF should return 0.0
        let result = pmf(5, 10, 1.0).unwrap();
        assert_eq!(result, 0.0);
    }

    #[test]
    fn test_binomial_pmf_n_zero() {
        let result = pmf(0, 0, 0.5);
        assert!(result.is_err());
        assert!(matches!(
            result.unwrap_err(),
            StatsError::InvalidInput { .. }
        ));
    }

    #[test]
    fn test_binomial_pmf_p_out_of_range() {
        let result = pmf(5, 10, 1.5);
        assert!(result.is_err());
        assert!(matches!(
            result.unwrap_err(),
            StatsError::InvalidInput { .. }
        ));
    }

    #[test]
    fn test_binomial_cdf_k_greater_than_n() {
        let result = cdf(15, 10, 0.5);
        assert!(result.is_err());
        assert!(matches!(
            result.unwrap_err(),
            StatsError::InvalidInput { .. }
        ));
    }

    #[test]
    fn test_binomial_combination_symmetry() {
        // Test that combination(n, k) == combination(n, n-k) when k > n/2
        // This tests the symmetry optimization path
        let n = 10u64;
        let k = 8u64; // k > n/2, so should use symmetry

        // Direct call should use symmetry path
        let result1 = combination(n, k).unwrap();
        // Should be same as combination(n, n-k)
        let result2 = combination(n, n - k).unwrap();
        assert_eq!(result1, result2);

        // Verify it's correct: C(10, 8) = C(10, 2) = 45
        assert_eq!(result1, 45.0);
    }

    #[test]
    fn test_binomial_combination_k_greater_than_n() {
        let result = combination(10, 15);
        assert!(result.is_err());
        assert!(matches!(
            result.unwrap_err(),
            StatsError::InvalidInput { .. }
        ));
    }

    #[test]
    fn test_binomial_combination_k_equals_n() {
        // C(n, n) = 1
        let result = combination(10, 10).unwrap();
        assert_eq!(result, 1.0);
    }

    #[test]
    fn test_binomial_combination_k_zero() {
        // C(n, 0) = 1
        let result = combination(10, 0).unwrap();
        assert_eq!(result, 1.0);
    }

    #[test]
    fn test_binomial_config_new_n_one() {
        // Test edge case: n = 1 (minimum valid value)
        let config = BinomialConfig::new(1, 0.5);
        assert!(config.is_ok());
        let config = config.unwrap();
        assert_eq!(config.n, 1);
    }

    #[test]
    fn test_binomial_pmf_k_greater_than_n() {
        // When k > n, combination() should return an error
        let result = pmf(15, 10, 0.5);
        assert!(result.is_err());
        assert!(matches!(
            result.unwrap_err(),
            StatsError::InvalidInput { .. }
        ));
    }

    #[test]
    fn test_binomial_cdf_n_zero() {
        let result = cdf(5, 0, 0.5);
        assert!(result.is_err());
        assert!(matches!(
            result.unwrap_err(),
            StatsError::InvalidInput { .. }
        ));
    }

    #[test]
    fn test_binomial_cdf_p_out_of_range() {
        let result = cdf(5, 10, 1.5);
        assert!(result.is_err());
        assert!(matches!(
            result.unwrap_err(),
            StatsError::InvalidInput { .. }
        ));
    }

    #[test]
    fn test_binomial_combination_k_exactly_n_over_2() {
        // Test boundary case: k = n/2 (should not use symmetry)
        let n = 10u64;
        let k = 5u64; // k = n/2, should not use symmetry
        let result = combination(n, k).unwrap();
        // C(10, 5) = 252
        assert_eq!(result, 252.0);
    }

    #[test]
    fn test_binomial_combination_k_just_over_n_over_2() {
        // Test k = n/2 + 1 (should use symmetry)
        let n = 10u64;
        let k = 6u64; // k > n/2, should use symmetry
        let result1 = combination(n, k).unwrap();
        let result2 = combination(n, n - k).unwrap();
        assert_eq!(result1, result2);
        // C(10, 6) = C(10, 4) = 210
        assert_eq!(result1, 210.0);
    }
}