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//! HashMap storage implementation example for MCMC traces
use std::{
f64,
time::{Duration, Instant},
};
use anyhow::Result;
use nuts_rs::{
CpuLogpFunc, CpuMath, CpuMathError, DiagNutsSettings, HashMapConfig, LogpError, Model, Sampler,
SamplerWaitResult,
};
use nuts_storable::HasDims;
use rand::{Rng, RngExt};
use thiserror::Error;
// A simple multivariate normal distribution example
#[derive(Clone, Debug)]
struct MultivariateNormal {
mean: Vec<f64>,
precision: Vec<Vec<f64>>,
}
impl MultivariateNormal {
fn new(mean: Vec<f64>, precision: Vec<Vec<f64>>) -> Self {
Self { mean, precision }
}
}
// Custom LogpError implementation
#[allow(dead_code)]
#[derive(Debug, Error)]
enum MyLogpError {
#[error("Recoverable error in logp calculation: {0}")]
Recoverable(String),
#[error("Non-recoverable error in logp calculation: {0}")]
NonRecoverable(String),
}
impl LogpError for MyLogpError {
fn is_recoverable(&self) -> bool {
matches!(self, MyLogpError::Recoverable(_))
}
}
// Implementation of the model's logp function
#[derive(Clone)]
struct MvnLogp {
model: MultivariateNormal,
}
impl HasDims for MvnLogp {
fn dim_sizes(&self) -> std::collections::HashMap<String, u64> {
std::collections::HashMap::from([
(
"unconstrained_parameter".to_string(),
self.model.mean.len() as u64,
),
("dim".to_string(), self.model.mean.len() as u64),
])
}
}
impl CpuLogpFunc for MvnLogp {
type LogpError = MyLogpError;
type FlowParameters = ();
type ExpandedVector = Vec<f64>;
fn dim(&self) -> usize {
self.model.mean.len()
}
fn logp(&mut self, x: &[f64], grad: &mut [f64]) -> Result<f64, Self::LogpError> {
let n = x.len();
// Compute (x - mean)
let mut diff = vec![0.0; n];
for i in 0..n {
diff[i] = x[i] - self.model.mean[i];
}
let mut quad = 0.0;
// Compute quadratic form and gradient: logp = -0.5 * diff^T * P * diff
for i in 0..n {
// Compute i-th component of P * diff
let mut pdot = 0.0;
for j in 0..n {
let pij = self.model.precision[i][j];
pdot += pij * diff[j];
quad += diff[i] * pij * diff[j];
}
// gradient of logp w.r.t. x_i: derivative of -0.5 * diff^T P diff is - (P * diff)_i
grad[i] = -pdot;
}
Ok(-0.5 * quad)
}
fn expand_vector<R: Rng + ?Sized>(
&mut self,
_rng: &mut R,
array: &[f64],
) -> Result<Self::ExpandedVector, CpuMathError> {
// Simply return the parameter values
Ok(array.to_vec())
}
}
struct MvnModel {
math: CpuMath<MvnLogp>,
}
/// Implementation of McmcModel for the HashMap backend
impl Model for MvnModel {
type Math<'model>
= CpuMath<MvnLogp>
where
Self: 'model;
fn math<R: Rng + ?Sized>(&self, _rng: &mut R) -> Result<Self::Math<'_>> {
Ok(self.math.clone())
}
/// Generate random initial positions for the chain
fn init_position<R: Rng + ?Sized>(&self, rng: &mut R, position: &mut [f64]) -> Result<()> {
// Initialize position randomly in [-2, 2]
for p in position.iter_mut() {
*p = rng.random_range(-2.0..2.0);
}
Ok(())
}
}
fn main() -> Result<()> {
// Create a 2D multivariate normal distribution
let mean = vec![0.0, 0.0];
let precision = vec![vec![1.0, 0.5], vec![0.5, 1.0]];
let mvn = MultivariateNormal::new(mean, precision);
// Number of chains
let num_chains = 4;
// Configure number of draws
let num_tune = 100;
let num_draws = 200;
// Configure MCMC settings
let mut settings = DiagNutsSettings::default();
settings.num_chains = num_chains as _;
settings.num_tune = num_tune;
settings.num_draws = num_draws as _;
settings.seed = 42;
let model = MvnModel {
math: CpuMath::new(MvnLogp { model: mvn }),
};
// Create a new sampler with 4 threads
let start = Instant::now();
let trace_config = HashMapConfig::new();
let mut sampler = Some(Sampler::new(model, settings, trace_config, 4, None)?);
let mut num_progress_updates = 0;
// Interleave progress updates with wait_timeout
while let Some(sampler_) = sampler.take() {
match sampler_.wait_timeout(Duration::from_millis(50)) {
SamplerWaitResult::Trace(traces) => {
println!("Sampling completed in {:?}", start.elapsed());
// Process the HashMap results
println!("\nProcessing HashMap storage results:");
println!("Number of chains: {}", traces.len());
for (chain_idx, chain_result) in traces.iter().enumerate() {
println!("\nChain {}:", chain_idx);
// Print stats information
println!(" Sampler stats variables:");
for (name, values) in &chain_result.stats {
match values {
nuts_rs::HashMapValue::F64(vec) => {
println!(" {}: {} samples (f64)", name, vec.len());
if !vec.is_empty() {
println!(" First 5: {:?}", &vec[..vec.len().min(5)]);
}
}
nuts_rs::HashMapValue::Bool(vec) => {
println!(" {}: {} samples (bool)", name, vec.len());
if !vec.is_empty() {
println!(" First 5: {:?}", &vec[..vec.len().min(5)]);
}
}
_ => println!(" {}: {} (other type)", name, "unknown length"),
}
}
// Print draws information
println!(" Draw variables:");
for (name, values) in &chain_result.draws {
match values {
nuts_rs::HashMapValue::F64(vec) => {
println!(" {}: {} scalar draws", name, vec.len());
if !vec.is_empty() {
println!(" First 5: {:?}", &vec[..vec.len().min(5)]);
if *name == "theta" && vec.len() >= 6 {
// For multidimensional parameters stored as flattened arrays
// Assume 2D parameter, so every 2 values form one draw
println!(" Parameter structure (assuming 2D):");
for i in (0..vec.len().min(10)).step_by(2) {
if i + 1 < vec.len() {
println!(
" Draw {}: [{:.4}, {:.4}]",
i / 2,
vec[i],
vec[i + 1]
);
}
}
}
}
}
nuts_rs::HashMapValue::F32(vec) => {
println!(" {}: {} f32 draws", name, vec.len());
}
nuts_rs::HashMapValue::Bool(vec) => {
println!(" {}: {} bool draws", name, vec.len());
}
nuts_rs::HashMapValue::I64(vec) => {
println!(" {}: {} i64 draws", name, vec.len());
}
nuts_rs::HashMapValue::U64(vec) => {
println!(" {}: {} u64 draws", name, vec.len());
}
nuts_rs::HashMapValue::String(vec) => {
println!(" {}: {} string draws", name, vec.len());
}
}
}
// Calculate some basic statistics for theta parameter
if let Some(nuts_rs::HashMapValue::F64(param_samples)) =
chain_result.draws.get("theta")
{
if param_samples.len() >= 2 {
// Assuming 2D parameter stored as flattened array: [x0, y0, x1, y1, ...]
let x_values: Vec<f64> =
param_samples.iter().step_by(2).cloned().collect();
let y_values: Vec<f64> =
param_samples.iter().skip(1).step_by(2).cloned().collect();
if !x_values.is_empty() {
let mean_x = x_values.iter().sum::<f64>() / x_values.len() as f64;
println!(" theta[0] (x-component) mean: {:.4}", mean_x);
}
if !y_values.is_empty() {
let mean_y = y_values.iter().sum::<f64>() / y_values.len() as f64;
println!(" theta[1] (y-component) mean: {:.4}", mean_y);
}
}
}
}
break;
}
SamplerWaitResult::Timeout(mut sampler_) => {
// Request progress update
if num_progress_updates < 10 {
// Limit progress updates
println!("Progress update {}", num_progress_updates + 1);
let progress = sampler_.progress()?;
for (i, chain) in progress.iter().enumerate() {
println!(
"Chain {}: {} samples ({} divergences), step size: {:.6}",
i, chain.finished_draws, chain.divergences, chain.step_size
);
}
}
sampler = Some(sampler_);
num_progress_updates += 1;
}
SamplerWaitResult::Err(err, _) => {
return Err(err);
}
}
}
println!("\nHashMap storage example completed!");
println!("The results are stored in memory as HashMaps and can be easily processed in Rust.");
Ok(())
}