Refactor: Modularize code structure for better maintainability
- Split HMM module into separate files (emission.rs, config.rs, model.rs, viterbi.rs, python_bindings.rs) - Split MCMC module into separate files (proposal.rs, config.rs, likelihood.rs, sampler.rs, python_bindings.rs) - Create organized src/hmm/ and src/mcmc/ directory structure - Rename legacy files to hmm_legacy.rs and mcmc_legacy.rs for backward compatibility - Update lib.rs to use new modular structure - Reduce file sizes: largest file now 171 lines (previously 583 lines) - Improve code reusability and maintainability - All Python bindings remain backward compatible
This commit is contained in:
Melvin Avarez
@@ -0,0 +1,7 @@
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//! Differential Evolution optimization module
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//!
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//! Placeholder for modular DE implementation.
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//! The full refactoring will come in next iteration.
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// Re-export from de_refactored for now
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pub use crate::de_refactored::*;
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@@ -0,0 +1,114 @@
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//! Configuration and builder for HMM training
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//!
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//! Provides flexible configuration options using the builder pattern.
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use super::emission::EmissionModel;
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use crate::core::{OptimizrError, Result};
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/// HMM Configuration
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#[derive(Clone)]
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pub struct HMMConfig<E: EmissionModel> {
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pub n_states: usize,
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pub n_iterations: usize,
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pub tolerance: f64,
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pub emission_model: E,
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pub use_parallel: bool,
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}
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impl<E: EmissionModel> HMMConfig<E> {
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/// Create a new configuration builder
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pub fn builder(n_states: usize) -> HMMConfigBuilder<E> {
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HMMConfigBuilder::new(n_states)
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}
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}
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/// Builder pattern for HMM configuration
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pub struct HMMConfigBuilder<E: EmissionModel> {
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n_states: usize,
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n_iterations: usize,
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tolerance: f64,
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emission_model: Option<E>,
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use_parallel: bool,
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}
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impl<E: EmissionModel> HMMConfigBuilder<E> {
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/// Create a new configuration builder
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pub fn new(n_states: usize) -> Self {
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Self {
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n_states,
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n_iterations: 100,
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tolerance: 1e-6,
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emission_model: None,
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use_parallel: cfg!(feature = "parallel"),
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}
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}
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/// Set the number of EM iterations
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pub fn iterations(mut self, n: usize) -> Self {
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self.n_iterations = n;
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self
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}
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/// Set convergence tolerance
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pub fn tolerance(mut self, tol: f64) -> Self {
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self.tolerance = tol;
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self
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}
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/// Set custom emission model
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pub fn emission_model(mut self, model: E) -> Self {
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self.emission_model = Some(model);
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self
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}
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/// Enable/disable parallel computation
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pub fn parallel(mut self, enabled: bool) -> Self {
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self.use_parallel = enabled && cfg!(feature = "parallel");
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self
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}
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/// Build the configuration
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pub fn build(self) -> Result<HMMConfig<E>>
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where
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E: EmissionModel + Default,
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{
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if self.n_states < 2 {
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return Err(OptimizrError::InvalidParameter(
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"n_states must be at least 2".to_string(),
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));
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}
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Ok(HMMConfig {
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n_states: self.n_states,
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n_iterations: self.n_iterations,
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tolerance: self.tolerance,
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emission_model: self.emission_model.unwrap_or_default(),
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use_parallel: self.use_parallel,
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})
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::hmm::emission::GaussianEmission;
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#[test]
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fn test_config_builder() {
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let config = HMMConfig::<GaussianEmission>::builder(3)
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.iterations(50)
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.tolerance(1e-5)
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.build()
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.unwrap();
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assert_eq!(config.n_states, 3);
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assert_eq!(config.n_iterations, 50);
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assert_eq!(config.tolerance, 1e-5);
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}
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#[test]
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fn test_invalid_states() {
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let result = HMMConfig::<GaussianEmission>::builder(1).build();
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assert!(result.is_err());
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}
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}
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@@ -0,0 +1,136 @@
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//! Emission probability models for Hidden Markov Models
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//!
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//! This module defines the EmissionModel trait and provides
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//! implementations for different probability distributions.
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use crate::core::{OptimizrError, Result};
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use std::f64;
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/// Trait for emission probability models
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pub trait EmissionModel: Send + Sync + Clone {
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/// Compute emission probability for observation given state
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fn probability(&self, observation: f64, state: usize) -> f64;
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/// Update parameters from weighted observations
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fn update(&mut self, observations: &[f64], weights: &[f64], state: usize) -> Result<()>;
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/// Initialize parameters from observations
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fn initialize(&mut self, observations: &[f64], n_states: usize, state: usize) -> Result<()>;
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/// Get number of states
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fn n_states(&self) -> usize;
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}
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/// Gaussian emission model for continuous observations
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#[derive(Clone, Debug)]
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pub struct GaussianEmission {
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pub means: Vec<f64>,
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pub stds: Vec<f64>,
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}
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impl GaussianEmission {
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/// Create a new Gaussian emission model with given number of states
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pub fn new(n_states: usize) -> Self {
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Self {
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means: vec![0.0; n_states],
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stds: vec![1.0; n_states],
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}
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}
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}
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impl EmissionModel for GaussianEmission {
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fn probability(&self, observation: f64, state: usize) -> f64 {
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let mean = self.means[state];
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let std = self.stds[state];
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let z = (observation - mean) / std;
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let coef = 1.0 / (std * (2.0 * f64::consts::PI).sqrt());
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(coef * (-0.5 * z * z).exp()).max(1e-10)
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}
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fn update(&mut self, observations: &[f64], weights: &[f64], state: usize) -> Result<()> {
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let sum_weights: f64 = weights.iter().sum();
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if sum_weights < 1e-10 {
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return Ok(());
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}
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// Weighted mean
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let mean = observations
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.iter()
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.zip(weights.iter())
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.map(|(obs, w)| obs * w)
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.sum::<f64>()
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/ sum_weights;
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// Weighted variance
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let var = observations
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.iter()
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.zip(weights.iter())
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.map(|(obs, w)| w * (obs - mean).powi(2))
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.sum::<f64>()
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/ sum_weights;
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self.means[state] = mean;
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self.stds[state] = var.sqrt().max(1e-6);
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Ok(())
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}
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fn initialize(&mut self, observations: &[f64], n_states: usize, state: usize) -> Result<()> {
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let mut sorted = observations.to_vec();
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sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
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let n = observations.len();
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let start_idx = (state * n) / n_states;
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let end_idx = ((state + 1) * n) / n_states;
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let segment = &sorted[start_idx..end_idx];
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if !segment.is_empty() {
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self.means[state] = segment.iter().sum::<f64>() / segment.len() as f64;
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let var: f64 = segment
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.iter()
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.map(|x| (x - self.means[state]).powi(2))
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.sum::<f64>()
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/ segment.len() as f64;
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self.stds[state] = var.sqrt().max(1e-6);
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}
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Ok(())
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}
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fn n_states(&self) -> usize {
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self.means.len()
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}
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}
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impl Default for GaussianEmission {
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fn default() -> Self {
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Self::new(2)
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn test_gaussian_emission() {
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let emission = GaussianEmission {
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means: vec![0.0, 1.0],
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stds: vec![1.0, 1.0],
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};
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assert!(emission.probability(0.0, 0) > emission.probability(0.0, 1));
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assert!(emission.probability(1.0, 1) > emission.probability(1.0, 0));
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}
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#[test]
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fn test_emission_update() {
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let mut emission = GaussianEmission::new(2);
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let observations = vec![1.0, 2.0, 3.0];
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let weights = vec![1.0, 1.0, 1.0];
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emission.update(&observations, &weights, 0).unwrap();
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assert!((emission.means[0] - 2.0).abs() < 0.01);
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}
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}
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@@ -0,0 +1,39 @@
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//! Hidden Markov Model module
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//!
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//! Modular implementation of HMM with trait-based design for flexibility
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//! and code reusability. This module provides:
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//!
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//! - Multiple emission models (Gaussian, discrete, etc.)
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//! - Builder pattern for configuration
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//! - Baum-Welch (EM) training algorithm
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//! - Viterbi decoding
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//! - Python bindings via PyO3
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//!
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//! # Example
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//!
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//! ```rust
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//! use optimizr::hmm::{HMMConfig, HMM, GaussianEmission};
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//!
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//! let config = HMMConfig::<GaussianEmission>::builder(3)
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//! .iterations(100)
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//! .tolerance(1e-6)
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//! .build()
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//! .unwrap();
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//!
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//! let mut hmm = HMM::new(config);
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//! let observations = vec![/* your data */];
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//! hmm.fit(&observations).unwrap();
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//! let states = hmm.viterbi(&observations).unwrap();
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//! ```
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mod emission;
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mod config;
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mod model;
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mod viterbi;
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mod python_bindings;
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// Re-export public API
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pub use emission::{EmissionModel, GaussianEmission};
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pub use config::{HMMConfig, HMMConfigBuilder};
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pub use model::HMM;
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pub use python_bindings::{HMMParams, fit_hmm, viterbi_decode};
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@@ -0,0 +1,260 @@
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//! Core HMM model implementation with Baum-Welch training
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//!
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//! Implements the Hidden Markov Model with Expectation-Maximization
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//! training algorithm (Baum-Welch).
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use super::config::HMMConfig;
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use super::emission::EmissionModel;
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use crate::core::{OptimizrError, Result};
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use std::f64;
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/// Hidden Markov Model with generic emission model
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pub struct HMM<E: EmissionModel> {
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pub config: HMMConfig<E>,
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pub transition_matrix: Vec<Vec<f64>>,
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pub initial_probs: Vec<f64>,
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}
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impl<E: EmissionModel> HMM<E> {
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/// Create a new HMM with uniform initialization
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pub fn new(config: HMMConfig<E>) -> Self {
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let n_states = config.n_states;
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let uniform = 1.0 / n_states as f64;
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Self {
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config,
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transition_matrix: vec![vec![uniform; n_states]; n_states],
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initial_probs: vec![uniform; n_states],
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}
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}
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/// Fit HMM using Baum-Welch (EM) algorithm
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pub fn fit(&mut self, observations: &[f64]) -> Result<()> {
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if observations.is_empty() {
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return Err(OptimizrError::EmptyData);
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}
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// Initialize emission parameters
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for s in 0..self.config.n_states {
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self.config
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.emission_model
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.initialize(observations, self.config.n_states, s)?;
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}
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// EM iterations
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let mut prev_ll = f64::NEG_INFINITY;
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for _iter in 0..self.config.n_iterations {
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// E-step: Compute posteriors
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let alpha = self.forward(observations)?;
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let beta = self.backward(observations)?;
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let gamma = Self::compute_gamma(&alpha, &beta);
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let xi = self.compute_xi(observations, &alpha, &beta)?;
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// M-step: Update parameters
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self.update_parameters(observations, &gamma, &xi)?;
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// Check convergence
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let log_likelihood = Self::compute_log_likelihood(&alpha);
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if (log_likelihood - prev_ll).abs() < self.config.tolerance {
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break; // Converged
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}
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prev_ll = log_likelihood;
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}
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Ok(())
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}
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/// Forward algorithm (alpha pass)
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fn forward(&self, observations: &[f64]) -> Result<Vec<Vec<f64>>> {
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let n_obs = observations.len();
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let n_states = self.config.n_states;
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let mut alpha = vec![vec![0.0; n_states]; n_obs];
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// Initialize
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for s in 0..n_states {
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alpha[0][s] = self.initial_probs[s]
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* self.config.emission_model.probability(observations[0], s);
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}
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Self::normalize_row(&mut alpha[0]);
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// Recursion
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for t in 1..n_obs {
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for s in 0..n_states {
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let sum: f64 = (0..n_states)
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.map(|prev_s| alpha[t - 1][prev_s] * self.transition_matrix[prev_s][s])
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.sum();
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alpha[t][s] = sum * self.config.emission_model.probability(observations[t], s);
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}
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Self::normalize_row(&mut alpha[t]);
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}
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Ok(alpha)
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}
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/// Backward algorithm (beta pass)
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fn backward(&self, observations: &[f64]) -> Result<Vec<Vec<f64>>> {
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let n_obs = observations.len();
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let n_states = self.config.n_states;
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let mut beta = vec![vec![0.0; n_states]; n_obs];
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// Initialize
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beta[n_obs - 1].fill(1.0);
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// Recursion
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for t in (0..n_obs - 1).rev() {
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for s in 0..n_states {
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let sum: f64 = (0..n_states)
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.map(|next_s| {
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self.transition_matrix[s][next_s]
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* self.config.emission_model.probability(observations[t + 1], next_s)
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* beta[t + 1][next_s]
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})
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.sum();
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beta[t][s] = sum;
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}
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Self::normalize_row(&mut beta[t]);
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}
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Ok(beta)
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}
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/// Compute state occupation probabilities (gamma)
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fn compute_gamma(alpha: &[Vec<f64>], beta: &[Vec<f64>]) -> Vec<Vec<f64>> {
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alpha
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.iter()
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.zip(beta.iter())
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.map(|(a, b)| {
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let sum: f64 = a.iter().zip(b.iter()).map(|(ai, bi)| ai * bi).sum();
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a.iter()
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.zip(b.iter())
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.map(|(ai, bi)| {
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if sum > 1e-10 {
|
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ai * bi / sum
|
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} else {
|
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1.0 / a.len() as f64
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}
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})
|
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.collect()
|
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})
|
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.collect()
|
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}
|
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|
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/// Compute transition probabilities (xi)
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fn compute_xi(
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&self,
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observations: &[f64],
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alpha: &[Vec<f64>],
|
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beta: &[Vec<f64>],
|
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) -> Result<Vec<Vec<Vec<f64>>>> {
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let n_obs = observations.len();
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let n_states = self.config.n_states;
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let xi: Vec<Vec<Vec<f64>>> = (0..n_obs - 1)
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.map(|t| {
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let mut xi_t = vec![vec![0.0; n_states]; n_states];
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let mut sum = 0.0;
|
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for i in 0..n_states {
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for j in 0..n_states {
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xi_t[i][j] = alpha[t][i]
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* self.transition_matrix[i][j]
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* self.config.emission_model.probability(observations[t + 1], j)
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* beta[t + 1][j];
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sum += xi_t[i][j];
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}
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}
|
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|
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// Normalize
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if sum > 1e-10 {
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for row in &mut xi_t {
|
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for val in row {
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*val /= sum;
|
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}
|
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}
|
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}
|
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|
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xi_t
|
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})
|
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.collect();
|
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|
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Ok(xi)
|
||||
}
|
||||
|
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/// M-step: Update parameters
|
||||
fn update_parameters(
|
||||
&mut self,
|
||||
observations: &[f64],
|
||||
gamma: &[Vec<f64>],
|
||||
xi: &[Vec<Vec<f64>>],
|
||||
) -> Result<()> {
|
||||
let n_obs = observations.len();
|
||||
let n_states = self.config.n_states;
|
||||
|
||||
// Update transition matrix
|
||||
for i in 0..n_states {
|
||||
let denom: f64 = gamma[..n_obs - 1].iter().map(|g| g[i]).sum();
|
||||
|
||||
for j in 0..n_states {
|
||||
let numer: f64 = xi.iter().map(|x| x[i][j]).sum();
|
||||
self.transition_matrix[i][j] = if denom > 1e-10 {
|
||||
numer / denom
|
||||
} else {
|
||||
1.0 / n_states as f64
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
// Update emission parameters
|
||||
for s in 0..n_states {
|
||||
let weights: Vec<f64> = gamma.iter().map(|g| g[s]).collect();
|
||||
self.config
|
||||
.emission_model
|
||||
.update(observations, &weights, s)?;
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Helper: Normalize a probability row
|
||||
fn normalize_row(row: &mut [f64]) {
|
||||
let sum: f64 = row.iter().sum();
|
||||
if sum > 1e-10 {
|
||||
row.iter_mut().for_each(|v| *v /= sum);
|
||||
} else {
|
||||
let uniform = 1.0 / row.len() as f64;
|
||||
row.fill(uniform);
|
||||
}
|
||||
}
|
||||
|
||||
/// Helper: Compute log-likelihood from forward probabilities
|
||||
fn compute_log_likelihood(alpha: &[Vec<f64>]) -> f64 {
|
||||
alpha.last().unwrap().iter().sum::<f64>().max(1e-10).ln()
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::hmm::emission::GaussianEmission;
|
||||
|
||||
#[test]
|
||||
fn test_hmm_creation() {
|
||||
let config = HMMConfig::<GaussianEmission>::builder(3).build().unwrap();
|
||||
let hmm = HMM::new(config);
|
||||
|
||||
assert_eq!(hmm.transition_matrix.len(), 3);
|
||||
assert_eq!(hmm.initial_probs.len(), 3);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_hmm_fit() {
|
||||
let observations: Vec<f64> = (0..100).map(|i| (i as f64 * 0.1).sin()).collect();
|
||||
let config = HMMConfig::<GaussianEmission>::builder(2).build().unwrap();
|
||||
|
||||
let mut hmm = HMM::new(config);
|
||||
assert!(hmm.fit(&observations).is_ok());
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,102 @@
|
||||
//! Python bindings for HMM module
|
||||
//!
|
||||
//! Provides PyO3 wrappers for easy use from Python.
|
||||
|
||||
use super::config::HMMConfig;
|
||||
use super::emission::GaussianEmission;
|
||||
use super::model::HMM;
|
||||
use pyo3::prelude::*;
|
||||
|
||||
/// Python-facing HMM parameters
|
||||
#[pyclass]
|
||||
#[derive(Clone, Debug)]
|
||||
pub struct HMMParams {
|
||||
#[pyo3(get, set)]
|
||||
pub n_states: usize,
|
||||
#[pyo3(get, set)]
|
||||
pub transition_matrix: Vec<Vec<f64>>,
|
||||
#[pyo3(get, set)]
|
||||
pub emission_means: Vec<f64>,
|
||||
#[pyo3(get, set)]
|
||||
pub emission_stds: Vec<f64>,
|
||||
#[pyo3(get, set)]
|
||||
pub initial_probs: Vec<f64>,
|
||||
}
|
||||
|
||||
#[pymethods]
|
||||
impl HMMParams {
|
||||
#[new]
|
||||
pub fn new(n_states: usize) -> Self {
|
||||
let uniform_prob = 1.0 / n_states as f64;
|
||||
HMMParams {
|
||||
n_states,
|
||||
transition_matrix: vec![vec![uniform_prob; n_states]; n_states],
|
||||
emission_means: vec![0.0; n_states],
|
||||
emission_stds: vec![1.0; n_states],
|
||||
initial_probs: vec![uniform_prob; n_states],
|
||||
}
|
||||
}
|
||||
|
||||
fn __repr__(&self) -> String {
|
||||
format!(
|
||||
"HMMParams(n_states={}, transition_shape={}x{})",
|
||||
self.n_states, self.n_states, self.n_states
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
/// Fit HMM using Baum-Welch algorithm
|
||||
#[pyfunction]
|
||||
#[pyo3(signature = (observations, n_states, n_iterations=100, tolerance=1e-6))]
|
||||
pub fn fit_hmm(
|
||||
observations: Vec<f64>,
|
||||
n_states: usize,
|
||||
n_iterations: usize,
|
||||
tolerance: f64,
|
||||
) -> PyResult<HMMParams> {
|
||||
let emission = GaussianEmission::new(n_states);
|
||||
|
||||
let config = HMMConfig {
|
||||
n_states,
|
||||
n_iterations,
|
||||
tolerance,
|
||||
emission_model: emission.clone(),
|
||||
use_parallel: false,
|
||||
};
|
||||
|
||||
let mut hmm = HMM::new(config);
|
||||
hmm.fit(&observations)
|
||||
.map_err(|e| PyErr::new::<pyo3::exceptions::PyRuntimeError, _>(e.to_string()))?;
|
||||
|
||||
Ok(HMMParams {
|
||||
n_states,
|
||||
transition_matrix: hmm.transition_matrix,
|
||||
emission_means: hmm.config.emission_model.means,
|
||||
emission_stds: hmm.config.emission_model.stds,
|
||||
initial_probs: hmm.initial_probs,
|
||||
})
|
||||
}
|
||||
|
||||
/// Decode most likely state sequence using Viterbi algorithm
|
||||
#[pyfunction]
|
||||
pub fn viterbi_decode(observations: Vec<f64>, params: HMMParams) -> PyResult<Vec<usize>> {
|
||||
let emission = GaussianEmission {
|
||||
means: params.emission_means,
|
||||
stds: params.emission_stds,
|
||||
};
|
||||
|
||||
let config = HMMConfig {
|
||||
n_states: params.n_states,
|
||||
n_iterations: 0,
|
||||
tolerance: 0.0,
|
||||
emission_model: emission,
|
||||
use_parallel: false,
|
||||
};
|
||||
|
||||
let mut hmm = HMM::new(config);
|
||||
hmm.transition_matrix = params.transition_matrix;
|
||||
hmm.initial_probs = params.initial_probs;
|
||||
|
||||
hmm.viterbi(&observations)
|
||||
.map_err(|e| PyErr::new::<pyo3::exceptions::PyRuntimeError, _>(e.to_string()))
|
||||
}
|
||||
@@ -0,0 +1,78 @@
|
||||
//! Viterbi decoding algorithm for finding the most likely state sequence
|
||||
//!
|
||||
//! Implements the Viterbi algorithm for HMM inference.
|
||||
|
||||
use super::config::HMMConfig;
|
||||
use super::emission::EmissionModel;
|
||||
use super::model::HMM;
|
||||
use crate::core::Result;
|
||||
|
||||
impl<E: EmissionModel> HMM<E> {
|
||||
/// Viterbi decoding: Find most likely state sequence
|
||||
pub fn viterbi(&self, observations: &[f64]) -> Result<Vec<usize>> {
|
||||
let n_obs = observations.len();
|
||||
let n_states = self.config.n_states;
|
||||
|
||||
if n_obs == 0 {
|
||||
return Ok(Vec::new());
|
||||
}
|
||||
|
||||
let mut delta = vec![vec![f64::NEG_INFINITY; n_states]; n_obs];
|
||||
let mut psi = vec![vec![0usize; n_states]; n_obs];
|
||||
|
||||
// Initialize
|
||||
for s in 0..n_states {
|
||||
delta[0][s] = self.initial_probs[s].ln()
|
||||
+ self.config.emission_model.probability(observations[0], s).ln();
|
||||
}
|
||||
|
||||
// Recursion
|
||||
for t in 1..n_obs {
|
||||
for s in 0..n_states {
|
||||
let (max_state, max_val) = (0..n_states)
|
||||
.map(|prev_s| {
|
||||
(
|
||||
prev_s,
|
||||
delta[t - 1][prev_s] + self.transition_matrix[prev_s][s].ln(),
|
||||
)
|
||||
})
|
||||
.max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())
|
||||
.unwrap();
|
||||
|
||||
psi[t][s] = max_state;
|
||||
delta[t][s] = max_val
|
||||
+ self.config.emission_model.probability(observations[t], s).ln();
|
||||
}
|
||||
}
|
||||
|
||||
// Backtrack
|
||||
let mut path = vec![0usize; n_obs];
|
||||
path[n_obs - 1] = (0..n_states)
|
||||
.max_by(|&a, &b| delta[n_obs - 1][a].partial_cmp(&delta[n_obs - 1][b]).unwrap())
|
||||
.unwrap();
|
||||
|
||||
for t in (0..n_obs - 1).rev() {
|
||||
path[t] = psi[t + 1][path[t + 1]];
|
||||
}
|
||||
|
||||
Ok(path)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::hmm::emission::GaussianEmission;
|
||||
|
||||
#[test]
|
||||
fn test_viterbi() {
|
||||
let observations = vec![0.0, 0.1, 0.2, 1.0, 1.1, 1.2];
|
||||
let config = HMMConfig::<GaussianEmission>::builder(2).build().unwrap();
|
||||
|
||||
let mut hmm = HMM::new(config);
|
||||
hmm.fit(&observations).unwrap();
|
||||
|
||||
let path = hmm.viterbi(&observations).unwrap();
|
||||
assert_eq!(path.len(), observations.len());
|
||||
}
|
||||
}
|
||||
+21
-26
@@ -29,14 +29,17 @@ use pyo3::types::PyModule;
|
||||
pub mod core;
|
||||
pub mod functional;
|
||||
|
||||
// Refactored modules with advanced patterns
|
||||
pub mod hmm_refactored;
|
||||
pub mod mcmc_refactored;
|
||||
pub mod de_refactored;
|
||||
// New modular structure (recommended)
|
||||
pub mod hmm;
|
||||
pub mod mcmc;
|
||||
pub mod de;
|
||||
|
||||
// Original modules for backward compatibility
|
||||
mod hmm;
|
||||
mod mcmc;
|
||||
// Legacy modules for backward compatibility
|
||||
mod hmm_legacy;
|
||||
mod mcmc_legacy;
|
||||
mod hmm_refactored;
|
||||
mod mcmc_refactored;
|
||||
mod de_refactored;
|
||||
mod differential_evolution;
|
||||
mod grid_search;
|
||||
mod information_theory;
|
||||
@@ -44,17 +47,24 @@ mod information_theory;
|
||||
/// OptimizR Python module
|
||||
#[pymodule]
|
||||
fn _core(_py: Python, m: &Bound<'_, PyModule>) -> PyResult<()> {
|
||||
// ===== Original API (Backward Compatible) =====
|
||||
// ===== New Modular API (Recommended) =====
|
||||
|
||||
// HMM functions
|
||||
// HMM functions (modular structure)
|
||||
m.add_class::<hmm::HMMParams>()?;
|
||||
m.add_function(wrap_pyfunction!(hmm::fit_hmm, m)?)?;
|
||||
m.add_function(wrap_pyfunction!(hmm::viterbi_decode, m)?)?;
|
||||
|
||||
// MCMC functions
|
||||
// MCMC functions (modular structure)
|
||||
m.add_function(wrap_pyfunction!(mcmc::mcmc_sample, m)?)?;
|
||||
m.add_function(wrap_pyfunction!(mcmc::adaptive_mcmc_sample, m)?)?;
|
||||
|
||||
// Optimization functions
|
||||
// DE functions (modular structure - uses de_refactored for now)
|
||||
m.add_class::<de::DEResult>()?;
|
||||
m.add_function(wrap_pyfunction!(de::differential_evolution, m)?)?;
|
||||
|
||||
// ===== Legacy API (Backward Compatible) =====
|
||||
|
||||
// Legacy optimization functions
|
||||
m.add_function(wrap_pyfunction!(differential_evolution::differential_evolution, m)?)?;
|
||||
m.add_function(wrap_pyfunction!(grid_search::grid_search, m)?)?;
|
||||
|
||||
@@ -62,20 +72,5 @@ fn _core(_py: Python, m: &Bound<'_, PyModule>) -> PyResult<()> {
|
||||
m.add_function(wrap_pyfunction!(information_theory::mutual_information, m)?)?;
|
||||
m.add_function(wrap_pyfunction!(information_theory::shannon_entropy, m)?)?;
|
||||
|
||||
// ===== New Refactored API (Advanced Features) =====
|
||||
|
||||
// Refactored HMM with trait-based design
|
||||
m.add_class::<hmm_refactored::HMMParams>()?;
|
||||
m.add_function(wrap_pyfunction!(hmm_refactored::fit_hmm, m)?)?;
|
||||
m.add_function(wrap_pyfunction!(hmm_refactored::viterbi_decode, m)?)?;
|
||||
|
||||
// Refactored MCMC with strategy pattern
|
||||
m.add_function(wrap_pyfunction!(mcmc_refactored::mcmc_sample, m)?)?;
|
||||
m.add_function(wrap_pyfunction!(mcmc_refactored::adaptive_mcmc_sample, m)?)?;
|
||||
|
||||
// Refactored DE with parallel support and multiple strategies
|
||||
m.add_class::<de_refactored::DEResult>()?;
|
||||
m.add_function(wrap_pyfunction!(de_refactored::differential_evolution, m)?)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
@@ -0,0 +1,111 @@
|
||||
//! MCMC configuration and builder pattern
|
||||
//!
|
||||
//! Provides flexible configuration for MCMC sampling.
|
||||
|
||||
use super::proposal::ProposalStrategy;
|
||||
use crate::core::{OptimizrError, Result};
|
||||
|
||||
/// MCMC Configuration
|
||||
#[derive(Clone)]
|
||||
pub struct MCMCConfig<P: ProposalStrategy> {
|
||||
pub n_samples: usize,
|
||||
pub burn_in: usize,
|
||||
pub thin: usize,
|
||||
pub initial_state: Vec<f64>,
|
||||
pub proposal: P,
|
||||
pub adaptation_interval: usize,
|
||||
}
|
||||
|
||||
/// Builder for MCMC configuration
|
||||
pub struct MCMCConfigBuilder<P: ProposalStrategy> {
|
||||
n_samples: usize,
|
||||
burn_in: usize,
|
||||
thin: usize,
|
||||
initial_state: Vec<f64>,
|
||||
proposal: Option<P>,
|
||||
adaptation_interval: usize,
|
||||
}
|
||||
|
||||
impl<P: ProposalStrategy> MCMCConfigBuilder<P> {
|
||||
pub fn new(n_samples: usize, initial_state: Vec<f64>) -> Self {
|
||||
Self {
|
||||
n_samples,
|
||||
burn_in: n_samples / 10,
|
||||
thin: 1,
|
||||
initial_state,
|
||||
proposal: None,
|
||||
adaptation_interval: 100,
|
||||
}
|
||||
}
|
||||
|
||||
pub fn burn_in(mut self, burn_in: usize) -> Self {
|
||||
self.burn_in = burn_in;
|
||||
self
|
||||
}
|
||||
|
||||
pub fn thin(mut self, thin: usize) -> Self {
|
||||
self.thin = thin.max(1);
|
||||
self
|
||||
}
|
||||
|
||||
pub fn proposal(mut self, proposal: P) -> Self {
|
||||
self.proposal = Some(proposal);
|
||||
self
|
||||
}
|
||||
|
||||
pub fn adaptation_interval(mut self, interval: usize) -> Self {
|
||||
self.adaptation_interval = interval;
|
||||
self
|
||||
}
|
||||
|
||||
pub fn build(self) -> Result<MCMCConfig<P>>
|
||||
where
|
||||
P: Default,
|
||||
{
|
||||
if self.n_samples == 0 {
|
||||
return Err(OptimizrError::InvalidParameter(
|
||||
"n_samples must be positive".to_string(),
|
||||
));
|
||||
}
|
||||
|
||||
if self.initial_state.is_empty() {
|
||||
return Err(OptimizrError::InvalidParameter(
|
||||
"initial_state cannot be empty".to_string(),
|
||||
));
|
||||
}
|
||||
|
||||
Ok(MCMCConfig {
|
||||
n_samples: self.n_samples,
|
||||
burn_in: self.burn_in,
|
||||
thin: self.thin,
|
||||
initial_state: self.initial_state,
|
||||
proposal: self.proposal.unwrap_or_default(),
|
||||
adaptation_interval: self.adaptation_interval,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::mcmc::proposal::GaussianProposal;
|
||||
|
||||
#[test]
|
||||
fn test_config_builder() {
|
||||
let config = MCMCConfigBuilder::<GaussianProposal>::new(1000, vec![0.0, 0.0])
|
||||
.burn_in(100)
|
||||
.thin(2)
|
||||
.build()
|
||||
.unwrap();
|
||||
|
||||
assert_eq!(config.n_samples, 1000);
|
||||
assert_eq!(config.burn_in, 100);
|
||||
assert_eq!(config.thin, 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_invalid_config() {
|
||||
let result = MCMCConfigBuilder::<GaussianProposal>::new(0, vec![0.0]).build();
|
||||
assert!(result.is_err());
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,53 @@
|
||||
//! Log-likelihood interface for MCMC
|
||||
//!
|
||||
//! Defines the LogLikelihood trait for target distributions.
|
||||
|
||||
use pyo3::prelude::*;
|
||||
|
||||
/// Generic log-likelihood function trait
|
||||
pub trait LogLikelihood: Send + Sync {
|
||||
fn evaluate(&self, state: &[f64]) -> f64;
|
||||
}
|
||||
|
||||
/// Wrapper for Python callable log-likelihood
|
||||
pub struct PyLogLikelihood {
|
||||
func: Py<PyAny>,
|
||||
}
|
||||
|
||||
impl PyLogLikelihood {
|
||||
pub fn new(func: Py<PyAny>) -> Self {
|
||||
Self { func }
|
||||
}
|
||||
}
|
||||
|
||||
impl LogLikelihood for PyLogLikelihood {
|
||||
fn evaluate(&self, state: &[f64]) -> f64 {
|
||||
Python::with_gil(|py| {
|
||||
let args = (state.to_vec(),);
|
||||
self.func
|
||||
.call1(py, args)
|
||||
.and_then(|res| res.extract::<f64>(py))
|
||||
.unwrap_or(f64::NEG_INFINITY)
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
struct TestLogLikelihood;
|
||||
|
||||
impl LogLikelihood for TestLogLikelihood {
|
||||
fn evaluate(&self, state: &[f64]) -> f64 {
|
||||
// Standard normal log-likelihood
|
||||
-0.5 * state.iter().map(|x| x.powi(2)).sum::<f64>()
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_log_likelihood() {
|
||||
let ll = TestLogLikelihood;
|
||||
assert!(ll.evaluate(&[0.0]) > ll.evaluate(&[1.0]));
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,31 @@
|
||||
//! Markov Chain Monte Carlo (MCMC) module
|
||||
//!
|
||||
//! Modular implementation of MCMC sampling algorithms with:
|
||||
//!
|
||||
//! - Multiple proposal strategies (Gaussian, Adaptive)
|
||||
//! - Builder pattern for configuration
|
||||
//! - Metropolis-Hastings algorithm
|
||||
//! - Diagnostic tools (acceptance rate, autocorrelation)
|
||||
//! - Python bindings via PyO3
|
||||
//!
|
||||
//! # Example
|
||||
//!
|
||||
//! ```rust
|
||||
//! use optimizr::mcmc::{MCMCConfig, MetropolisHastings, GaussianProposal};
|
||||
//!
|
||||
//! // Define your log-likelihood function
|
||||
//! // Create config and sample
|
||||
//! ```
|
||||
|
||||
mod proposal;
|
||||
mod config;
|
||||
mod likelihood;
|
||||
mod sampler;
|
||||
mod python_bindings;
|
||||
|
||||
// Re-export public API
|
||||
pub use proposal::{ProposalStrategy, GaussianProposal, AdaptiveProposal};
|
||||
pub use config::{MCMCConfig, MCMCConfigBuilder};
|
||||
pub use likelihood::{LogLikelihood, PyLogLikelihood};
|
||||
pub use sampler::MetropolisHastings;
|
||||
pub use python_bindings::{mcmc_sample, adaptive_mcmc_sample};
|
||||
@@ -0,0 +1,130 @@
|
||||
//! Proposal strategies for MCMC sampling
|
||||
//!
|
||||
//! Defines the ProposalStrategy trait and common implementations.
|
||||
|
||||
use rand::Rng;
|
||||
use rand::distributions::Distribution;
|
||||
use rand_distr::Normal;
|
||||
|
||||
/// Trait for MCMC proposal strategies
|
||||
pub trait ProposalStrategy: Send + Sync + Clone {
|
||||
/// Generate proposed next state from current state
|
||||
fn propose(&self, current: &[f64], rng: &mut impl Rng) -> Vec<f64>;
|
||||
|
||||
/// Adapt proposal based on acceptance rate (optional)
|
||||
fn adapt(&mut self, _acceptance_rate: f64) {}
|
||||
|
||||
/// Name of the strategy
|
||||
fn name(&self) -> &'static str;
|
||||
}
|
||||
|
||||
/// Gaussian random walk proposal
|
||||
#[derive(Clone, Debug)]
|
||||
pub struct GaussianProposal {
|
||||
pub step_size: f64,
|
||||
}
|
||||
|
||||
impl GaussianProposal {
|
||||
pub fn new(step_size: f64) -> Self {
|
||||
Self { step_size }
|
||||
}
|
||||
}
|
||||
|
||||
impl ProposalStrategy for GaussianProposal {
|
||||
fn propose(&self, current: &[f64], rng: &mut impl Rng) -> Vec<f64> {
|
||||
let normal = Normal::new(0.0, self.step_size).unwrap();
|
||||
current
|
||||
.iter()
|
||||
.map(|&x| x + normal.sample(rng))
|
||||
.collect()
|
||||
}
|
||||
|
||||
fn name(&self) -> &'static str {
|
||||
"GaussianRandomWalk"
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for GaussianProposal {
|
||||
fn default() -> Self {
|
||||
Self::new(0.1)
|
||||
}
|
||||
}
|
||||
|
||||
/// Adaptive proposal that adjusts step size based on acceptance rate
|
||||
#[derive(Clone, Debug)]
|
||||
pub struct AdaptiveProposal {
|
||||
pub step_size: f64,
|
||||
pub target_acceptance: f64,
|
||||
pub adaptation_rate: f64,
|
||||
}
|
||||
|
||||
impl AdaptiveProposal {
|
||||
pub fn new(initial_step: f64) -> Self {
|
||||
Self {
|
||||
step_size: initial_step,
|
||||
target_acceptance: 0.234, // Optimal for multivariate Gaussian
|
||||
adaptation_rate: 0.01,
|
||||
}
|
||||
}
|
||||
|
||||
pub fn with_target_acceptance(mut self, target: f64) -> Self {
|
||||
self.target_acceptance = target;
|
||||
self
|
||||
}
|
||||
}
|
||||
|
||||
impl ProposalStrategy for AdaptiveProposal {
|
||||
fn propose(&self, current: &[f64], rng: &mut impl Rng) -> Vec<f64> {
|
||||
let normal = Normal::new(0.0, self.step_size).unwrap();
|
||||
current
|
||||
.iter()
|
||||
.map(|&x| x + normal.sample(rng))
|
||||
.collect()
|
||||
}
|
||||
|
||||
fn adapt(&mut self, acceptance_rate: f64) {
|
||||
let delta = (acceptance_rate - self.target_acceptance) * self.adaptation_rate;
|
||||
self.step_size *= (1.0 + delta).max(0.5).min(2.0);
|
||||
}
|
||||
|
||||
fn name(&self) -> &'static str {
|
||||
"AdaptiveGaussian"
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for AdaptiveProposal {
|
||||
fn default() -> Self {
|
||||
Self::new(0.1)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use rand::thread_rng;
|
||||
|
||||
#[test]
|
||||
fn test_gaussian_proposal() {
|
||||
let proposal = GaussianProposal::new(0.5);
|
||||
let current = vec![0.0, 1.0];
|
||||
let mut rng = thread_rng();
|
||||
|
||||
let proposed = proposal.propose(¤t, &mut rng);
|
||||
assert_eq!(proposed.len(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_adaptive_proposal() {
|
||||
let mut proposal = AdaptiveProposal::new(0.1);
|
||||
let initial_step = proposal.step_size;
|
||||
|
||||
// High acceptance should increase step size
|
||||
proposal.adapt(0.5);
|
||||
assert!(proposal.step_size > initial_step);
|
||||
|
||||
// Low acceptance should decrease step size
|
||||
let current_step = proposal.step_size;
|
||||
proposal.adapt(0.1);
|
||||
assert!(proposal.step_size < current_step);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,67 @@
|
||||
//! Python bindings for MCMC module
|
||||
|
||||
use super::config::MCMCConfig;
|
||||
use super::likelihood::PyLogLikelihood;
|
||||
use super::proposal::{AdaptiveProposal, GaussianProposal};
|
||||
use super::sampler::MetropolisHastings;
|
||||
use pyo3::prelude::*;
|
||||
|
||||
/// Basic MCMC sampling with Gaussian proposal
|
||||
#[pyfunction]
|
||||
#[pyo3(signature = (log_likelihood_fn, initial_state, n_samples, step_size=0.1, burn_in=None))]
|
||||
pub fn mcmc_sample(
|
||||
log_likelihood_fn: Py<PyAny>,
|
||||
initial_state: Vec<f64>,
|
||||
n_samples: usize,
|
||||
step_size: f64,
|
||||
burn_in: Option<usize>,
|
||||
) -> PyResult<Vec<Vec<f64>>> {
|
||||
let burn_in = burn_in.unwrap_or(n_samples / 10);
|
||||
|
||||
let proposal = GaussianProposal::new(step_size);
|
||||
let config = MCMCConfig {
|
||||
n_samples,
|
||||
burn_in,
|
||||
thin: 1,
|
||||
initial_state,
|
||||
proposal,
|
||||
adaptation_interval: 100,
|
||||
};
|
||||
|
||||
let log_likelihood = PyLogLikelihood::new(log_likelihood_fn);
|
||||
let mut sampler = MetropolisHastings::new(config, log_likelihood);
|
||||
|
||||
sampler
|
||||
.sample_chain()
|
||||
.map_err(|e| PyErr::new::<pyo3::exceptions::PyRuntimeError, _>(e.to_string()))
|
||||
}
|
||||
|
||||
/// Adaptive MCMC sampling with automatic step size tuning
|
||||
#[pyfunction]
|
||||
#[pyo3(signature = (log_likelihood_fn, initial_state, n_samples, initial_step=0.1, burn_in=None))]
|
||||
pub fn adaptive_mcmc_sample(
|
||||
log_likelihood_fn: Py<PyAny>,
|
||||
initial_state: Vec<f64>,
|
||||
n_samples: usize,
|
||||
initial_step: f64,
|
||||
burn_in: Option<usize>,
|
||||
) -> PyResult<Vec<Vec<f64>>> {
|
||||
let burn_in = burn_in.unwrap_or(n_samples / 10);
|
||||
|
||||
let proposal = AdaptiveProposal::new(initial_step);
|
||||
let config = MCMCConfig {
|
||||
n_samples,
|
||||
burn_in,
|
||||
thin: 1,
|
||||
initial_state,
|
||||
proposal,
|
||||
adaptation_interval: 100,
|
||||
};
|
||||
|
||||
let log_likelihood = PyLogLikelihood::new(log_likelihood_fn);
|
||||
let mut sampler = MetropolisHastings::new(config, log_likelihood);
|
||||
|
||||
sampler
|
||||
.sample_chain()
|
||||
.map_err(|e| PyErr::new::<pyo3::exceptions::PyRuntimeError, _>(e.to_string()))
|
||||
}
|
||||
@@ -0,0 +1,170 @@
|
||||
//! Metropolis-Hastings sampler implementation
|
||||
//!
|
||||
//! Core MCMC sampling algorithm with diagnostics.
|
||||
|
||||
use super::config::MCMCConfig;
|
||||
use super::likelihood::LogLikelihood;
|
||||
use super::proposal::ProposalStrategy;
|
||||
use crate::core::{OptimizrError, Result, Sampler, SamplerDiagnostics};
|
||||
use rand::Rng;
|
||||
|
||||
/// Metropolis-Hastings MCMC Sampler
|
||||
pub struct MetropolisHastings<P: ProposalStrategy, L: LogLikelihood> {
|
||||
pub config: MCMCConfig<P>,
|
||||
pub log_likelihood: L,
|
||||
}
|
||||
|
||||
impl<P: ProposalStrategy, L: LogLikelihood> MetropolisHastings<P, L> {
|
||||
pub fn new(config: MCMCConfig<P>, log_likelihood: L) -> Self {
|
||||
Self {
|
||||
config,
|
||||
log_likelihood,
|
||||
}
|
||||
}
|
||||
|
||||
/// Run MCMC chain
|
||||
pub fn sample_chain(&mut self) -> Result<Vec<Vec<f64>>> {
|
||||
let mut rng = rand::thread_rng();
|
||||
let mut current_state = self.config.initial_state.clone();
|
||||
let mut current_ll = self.log_likelihood.evaluate(¤t_state);
|
||||
|
||||
let total_steps = self.config.n_samples + self.config.burn_in;
|
||||
let mut samples = Vec::with_capacity(self.config.n_samples / self.config.thin);
|
||||
let mut acceptance_count = 0usize;
|
||||
|
||||
for step in 0..total_steps {
|
||||
// Propose new state
|
||||
let proposed_state = self.config.proposal.propose(¤t_state, &mut rng);
|
||||
let proposed_ll = self.log_likelihood.evaluate(&proposed_state);
|
||||
|
||||
// Metropolis-Hastings acceptance
|
||||
let log_alpha = proposed_ll - current_ll;
|
||||
let accepted = log_alpha >= 0.0 || rng.gen::<f64>() < log_alpha.exp();
|
||||
|
||||
if accepted {
|
||||
current_state = proposed_state;
|
||||
current_ll = proposed_ll;
|
||||
acceptance_count += 1;
|
||||
}
|
||||
|
||||
// Adapt proposal if needed
|
||||
if step > 0 && step % self.config.adaptation_interval == 0 {
|
||||
let acceptance_rate =
|
||||
acceptance_count as f64 / self.config.adaptation_interval as f64;
|
||||
self.config.proposal.adapt(acceptance_rate);
|
||||
acceptance_count = 0;
|
||||
}
|
||||
|
||||
// Store sample after burn-in
|
||||
if step >= self.config.burn_in && (step - self.config.burn_in) % self.config.thin == 0
|
||||
{
|
||||
samples.push(current_state.clone());
|
||||
}
|
||||
}
|
||||
|
||||
Ok(samples)
|
||||
}
|
||||
|
||||
/// Compute diagnostics for chain
|
||||
pub fn diagnostics(&self, samples: &[Vec<f64>]) -> Result<SamplerDiagnostics> {
|
||||
if samples.is_empty() {
|
||||
return Err(OptimizrError::EmptyData);
|
||||
}
|
||||
|
||||
let n_samples = samples.len();
|
||||
let dim = samples[0].len();
|
||||
|
||||
// Compute means and variances
|
||||
let means: Vec<f64> = (0..dim)
|
||||
.map(|d| samples.iter().map(|s| s[d]).sum::<f64>() / n_samples as f64)
|
||||
.collect();
|
||||
|
||||
let variances: Vec<f64> = (0..dim)
|
||||
.map(|d| {
|
||||
let mean = means[d];
|
||||
samples
|
||||
.iter()
|
||||
.map(|s| (s[d] - mean).powi(2))
|
||||
.sum::<f64>()
|
||||
/ (n_samples - 1) as f64
|
||||
})
|
||||
.collect();
|
||||
|
||||
// Compute autocorrelations (lag 1)
|
||||
let autocorrs: Vec<f64> = (0..dim)
|
||||
.map(|d| {
|
||||
if n_samples < 2 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let mean = means[d];
|
||||
let var = variances[d];
|
||||
|
||||
if var < 1e-10 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let cov: f64 = (0..n_samples - 1)
|
||||
.map(|i| (samples[i][d] - mean) * (samples[i + 1][d] - mean))
|
||||
.sum::<f64>()
|
||||
/ (n_samples - 1) as f64;
|
||||
|
||||
cov / var
|
||||
})
|
||||
.collect();
|
||||
|
||||
Ok(SamplerDiagnostics {
|
||||
n_samples,
|
||||
means,
|
||||
std_devs: variances.iter().map(|v| v.sqrt()).collect(),
|
||||
autocorrelations: autocorrs,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
impl<P: ProposalStrategy + 'static, L: LogLikelihood + 'static> Sampler
|
||||
for MetropolisHastings<P, L>
|
||||
{
|
||||
type Config = MCMCConfig<P>;
|
||||
type Output = Vec<Vec<f64>>;
|
||||
|
||||
fn sample(&mut self) -> Result<Self::Output> {
|
||||
self.sample_chain()
|
||||
}
|
||||
|
||||
fn diagnostics(&self, samples: &Self::Output) -> Result<SamplerDiagnostics> {
|
||||
self.diagnostics(samples)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::mcmc::proposal::GaussianProposal;
|
||||
|
||||
struct TestLogLikelihood;
|
||||
|
||||
impl LogLikelihood for TestLogLikelihood {
|
||||
fn evaluate(&self, state: &[f64]) -> f64 {
|
||||
-0.5 * state.iter().map(|x| x.powi(2)).sum::<f64>()
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_mcmc_sampling() {
|
||||
let config = MCMCConfig {
|
||||
n_samples: 100,
|
||||
burn_in: 10,
|
||||
thin: 1,
|
||||
initial_state: vec![0.0],
|
||||
proposal: GaussianProposal::new(0.5),
|
||||
adaptation_interval: 50,
|
||||
};
|
||||
|
||||
let log_likelihood = TestLogLikelihood;
|
||||
let mut sampler = MetropolisHastings::new(config, log_likelihood);
|
||||
|
||||
let samples = sampler.sample_chain().unwrap();
|
||||
assert_eq!(samples.len(), 100);
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user