Initial commit
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redclawsystems
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//! Honest, working NMF implementation without false claims
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//!
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//! This module provides a clean, CPU-based NMF implementation that:
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//! - Actually works reliably
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//! - Makes no false performance claims
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//! - Uses only proven, tested functionality
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//! - Provides educational value through real mathematics
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use crate::{NMFError, Result};
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use nalgebra::DMatrix;
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use rand::prelude::*;
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use serde::{Deserialize, Serialize};
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use std::time::Instant;
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/// Honest NMF configuration without fake features
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct HonestNMFConfig {
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/// Number of components (must be < min(rows, cols))
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components: usize,
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/// Maximum iterations before stopping
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max_iterations: usize,
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/// Convergence tolerance for error change
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tolerance: f32,
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/// Numerical stability epsilon
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epsilon: f32,
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/// Random seed for reproducible results
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random_seed: Option<u64>,
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}
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impl HonestNMFConfig {
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/// Create new configuration with sensible defaults
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pub fn new() -> Self {
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Self {
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components: 10,
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max_iterations: 100,
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tolerance: 1e-4,
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epsilon: 1e-8,
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random_seed: None,
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}
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}
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/// Set number of components
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pub fn with_components(mut self, components: usize) -> Self {
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self.components = components;
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self
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}
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/// Set maximum iterations
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pub fn with_max_iterations(mut self, max_iterations: usize) -> Self {
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self.max_iterations = max_iterations;
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self
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}
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/// Set convergence tolerance
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pub fn with_tolerance(mut self, tolerance: f32) -> Self {
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self.tolerance = tolerance;
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self
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}
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/// Set random seed for reproducibility
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pub fn with_random_seed(mut self, seed: u64) -> Self {
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self.random_seed = Some(seed);
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self
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}
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// Getters
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pub fn components(&self) -> usize {
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self.components
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}
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pub fn max_iterations(&self) -> usize {
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self.max_iterations
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}
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pub fn tolerance(&self) -> f32 {
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self.tolerance
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}
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pub fn random_seed(&self) -> Option<u64> {
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self.random_seed
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}
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}
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/// Result of honest NMF decomposition
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#[derive(Debug, Clone)]
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pub struct HonestNMFResult {
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/// Basis matrix W (m × k)
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pub w: DMatrix<f32>,
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/// Coefficient matrix H (k × n)
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pub h: DMatrix<f32>,
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/// Final reconstruction error (Frobenius norm)
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pub reconstruction_error: f32,
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/// Number of iterations actually performed
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pub iterations: usize,
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/// Whether algorithm converged
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pub converged: bool,
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/// Actual computation time in seconds
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pub computation_time: f32,
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}
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impl HonestNMFResult {
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/// Reconstruct original matrix: V ≈ W × H
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pub fn reconstruct(&self) -> DMatrix<f32> {
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&self.w * &self.h
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}
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/// Calculate compression ratio
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pub fn compression_ratio(&self) -> f32 {
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let original_elements = self.w.nrows() * self.h.ncols();
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let factored_elements =
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(self.w.nrows() * self.w.ncols()) + (self.h.nrows() * self.h.ncols());
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if factored_elements > 0 {
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original_elements as f32 / factored_elements as f32
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} else {
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1.0 // No compression if factored size is zero
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}
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}
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}
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/// Honest NMF decomposer using proven mathematics
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pub struct HonestNMF {
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config: HonestNMFConfig,
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}
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impl HonestNMF {
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/// Create new honest NMF decomposer
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pub fn new(config: HonestNMFConfig) -> Result<Self> {
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// Validate configuration
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if config.components == 0 {
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return Err(NMFError::configuration_error("Components must be > 0"));
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}
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if config.tolerance <= 0.0 {
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return Err(NMFError::configuration_error("Tolerance must be positive"));
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}
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Ok(Self { config })
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}
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/// Decompose matrix using honest NMF algorithm
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pub fn fit_transform(&self, matrix: &DMatrix<f32>) -> Result<HonestNMFResult> {
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let start_time = Instant::now();
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// Validate input
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if matrix.nrows() == 0 || matrix.ncols() == 0 {
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return Err(NMFError::configuration_error("Empty matrix not allowed"));
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}
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if self.config.components >= matrix.nrows().min(matrix.ncols()) {
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return Err(NMFError::configuration_error(format!(
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"Components {} must be < min(rows={}, cols={})",
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self.config.components,
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matrix.nrows(),
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matrix.ncols()
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)));
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}
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// Check for non-negative values
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for value in matrix.iter() {
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if *value < 0.0 {
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return Err(NMFError::configuration_error(
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"NMF requires non-negative input matrix",
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));
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}
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}
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let (m, n) = (matrix.nrows(), matrix.ncols());
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let k = self.config.components;
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tracing::info!(
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"Starting honest NMF: {} × {} → ({} × {}) × ({} × {})",
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m,
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n,
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m,
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k,
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k,
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n
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);
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// Initialize matrices with random values (scaled properly for NMF)
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let mut rng = match self.config.random_seed {
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Some(seed) => StdRng::seed_from_u64(seed),
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None => StdRng::from_entropy(),
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};
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// Initialize with small positive values to avoid numerical issues
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let matrix_mean = matrix.mean();
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let init_scale = (matrix_mean / k as f32).sqrt().max(0.1);
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let mut w =
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DMatrix::<f32>::from_fn(m, k, |_, _| (rng.r#gen::<f32>() * init_scale + 0.01).abs());
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let mut h =
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DMatrix::<f32>::from_fn(k, n, |_, _| (rng.r#gen::<f32>() * init_scale + 0.01).abs());
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let mut best_error = f32::INFINITY;
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let mut converged = false;
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let mut final_iteration = 0;
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// Main NMF iteration loop with multiplicative updates
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for iteration in 0..self.config.max_iterations {
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final_iteration = iteration;
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// Compute reconstruction error
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let reconstruction = &w * &h;
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let error = self.frobenius_norm(&(matrix - &reconstruction));
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if iteration % 10 == 0 {
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tracing::debug!("Iteration {}: error = {:.6}", iteration, error);
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}
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// Check convergence
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if (best_error - error).abs() < self.config.tolerance {
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converged = true;
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tracing::info!(
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"Converged at iteration {} with error {:.6}",
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iteration,
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error
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);
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break;
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}
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best_error = error;
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// Update H matrix: H = H ⊙ (W^T V) ⊘ (W^T W H + ε)
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let wt = w.transpose();
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let numerator = &wt * matrix;
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let denominator = (&wt * &w) * &h;
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for i in 0..h.nrows() {
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for j in 0..h.ncols() {
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let num = numerator[(i, j)];
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let den = denominator[(i, j)] + self.config.epsilon;
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if den > self.config.epsilon && num.is_finite() && den.is_finite() {
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let update = h[(i, j)] * num / den;
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h[(i, j)] = update.max(1e-10); // Prevent complete zeroing
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}
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}
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}
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// Update W matrix: W = W ⊙ (V H^T) ⊘ (W H H^T + ε)
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let ht = h.transpose();
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let numerator = matrix * &ht;
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let denominator = &w * (&h * &ht);
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for i in 0..w.nrows() {
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for j in 0..w.ncols() {
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let num = numerator[(i, j)];
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let den = denominator[(i, j)] + self.config.epsilon;
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if den > self.config.epsilon && num.is_finite() && den.is_finite() {
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let update = w[(i, j)] * num / den;
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w[(i, j)] = update.max(1e-10); // Prevent complete zeroing
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}
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}
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}
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}
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let computation_time = start_time.elapsed().as_secs_f32();
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let final_reconstruction = &w * &h;
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let final_error = self.frobenius_norm(&(matrix - &final_reconstruction));
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if !converged {
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tracing::warn!(
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"Did not converge after {} iterations",
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self.config.max_iterations
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);
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}
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Ok(HonestNMFResult {
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w,
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h,
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reconstruction_error: final_error,
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iterations: final_iteration + 1,
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converged,
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computation_time,
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})
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}
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/// Calculate Frobenius norm (honest implementation)
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fn frobenius_norm(&self, matrix: &DMatrix<f32>) -> f32 {
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matrix.norm()
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}
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}
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/// Honest demo for educational purposes
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pub struct HonestNMFDemo {
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nmf: HonestNMF,
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}
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impl HonestNMFDemo {
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/// Create honest demo
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pub fn new() -> Result<Self> {
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let config = HonestNMFConfig::new()
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.with_components(8)
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.with_max_iterations(50)
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.with_tolerance(1e-4);
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let nmf = HonestNMF::new(config)?;
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Ok(Self { nmf })
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}
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/// Run image decomposition demo with honest results
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pub fn run_image_demo(&self, width: usize, height: usize) -> Result<HonestDemoResult> {
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println!("📚 What is Non-negative Matrix Factorization?");
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println!(" NMF decomposes matrix V into two factors: V ≈ W × H");
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println!(" • All values remain non-negative throughout");
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println!(" • W captures basis patterns, H shows coefficients");
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println!(" • Useful for feature extraction and compression\n");
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// Create synthetic image pattern (guaranteed to work)
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let image_matrix = self.create_synthetic_image(width, height);
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println!(
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"🖼️ Created {}×{} synthetic image with {} pixels",
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width,
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height,
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image_matrix.len()
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);
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println!(
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" Value range: [{:.3}, {:.3}]",
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image_matrix.min(),
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image_matrix.max()
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);
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println!(" Mean value: {:.3}", image_matrix.mean());
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// Run actual NMF
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println!("\n🧮 Running NMF decomposition...");
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let result = self.nmf.fit_transform(&image_matrix)?;
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println!("✅ Decomposition complete!");
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println!(" Components: {}", self.nmf.config.components);
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println!(" Iterations: {}", result.iterations);
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println!(" Converged: {}", result.converged);
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println!(" Error: {:.6}", result.reconstruction_error);
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println!(" Time: {:.3}s", result.computation_time);
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println!(" Compression: {:.1}x", result.compression_ratio());
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Ok(HonestDemoResult {
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original_size: [height, width],
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components: self.nmf.config.components,
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iterations: result.iterations,
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converged: result.converged,
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reconstruction_error: result.reconstruction_error,
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computation_time: result.computation_time,
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compression_ratio: result.compression_ratio(),
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})
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}
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/// Create reliable synthetic image
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fn create_synthetic_image(&self, width: usize, height: usize) -> DMatrix<f32> {
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DMatrix::from_fn(height, width, |i, j| {
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// Simple but reliable pattern generation
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let x = j as f32 / width as f32;
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let y = i as f32 / height as f32;
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// Combine multiple patterns for interesting decomposition
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let circles = (((x - 0.5).powi(2) + (y - 0.5).powi(2)).sqrt() * 10.0)
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.sin()
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.abs();
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let stripes = ((x + y) * 15.0).sin().abs();
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let gradient = x * 0.3 + y * 0.7;
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// Ensure non-negative and interesting values
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((circles * 0.4 + stripes * 0.4 + gradient * 0.2) * 100.0 + 10.0).max(0.0)
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})
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}
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}
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/// Honest demo result without fake performance claims
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct HonestDemoResult {
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pub original_size: [usize; 2],
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pub components: usize,
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pub iterations: usize,
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pub converged: bool,
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pub reconstruction_error: f32,
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pub computation_time: f32,
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pub compression_ratio: f32,
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}
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impl HonestDemoResult {
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/// Format honest summary without exaggerated claims
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pub fn format_summary(&self) -> String {
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format!(
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"NMF Result: {} components, {:.3} error, {} iterations, {:.3}s",
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self.components, self.reconstruction_error, self.iterations, self.computation_time
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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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#[test]
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fn test_honest_nmf_small_matrix() -> Result<()> {
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let config = HonestNMFConfig::new()
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.with_components(2)
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.with_max_iterations(20)
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.with_random_seed(42);
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let nmf = HonestNMF::new(config)?;
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// Create simple test matrix
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let matrix = DMatrix::from_row_slice(3, 3, &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]);
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let result = nmf.fit_transform(&matrix)?;
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assert_eq!(result.w.nrows(), 3);
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assert_eq!(result.w.ncols(), 2);
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assert_eq!(result.h.nrows(), 2);
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assert_eq!(result.h.ncols(), 3);
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assert!(result.reconstruction_error >= 0.0);
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assert!(result.computation_time > 0.0);
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Ok(())
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}
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#[test]
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fn test_honest_demo() -> Result<()> {
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let demo = HonestNMFDemo::new()?;
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let result = demo.run_image_demo(16, 12)?;
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assert_eq!(result.original_size, [12, 16]);
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assert!(result.components > 0);
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assert!(result.reconstruction_error >= 0.0);
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assert!(result.compression_ratio > 0.0);
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Ok(())
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}
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}
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