sklears_isotonic/
lib.rs

1#![allow(dead_code)]
2#![allow(non_snake_case)]
3#![allow(missing_docs)]
4#![allow(deprecated)]
5#![allow(clippy::all)]
6#![allow(clippy::pedantic)]
7#![allow(clippy::nursery)]
8//! Isotonic regression
9//!
10//! This module is part of sklears, providing scikit-learn compatible
11//! machine learning algorithms in Rust.
12//!
13//! This crate provides comprehensive isotonic regression functionality including:
14//! - Basic isotonic regression with Pool Adjacent Violators Algorithm
15//! - Robust loss functions (L1, L2, Huber, Quantile)
16//! - Weighted isotonic regression
17//! - Constraint handling and validation
18//!
19//! Additional advanced features are being progressively enabled as they pass compilation and testing.
20
21// #![warn(missing_docs)]
22
23// Core isotonic regression functionality (stable)
24pub mod constraints;
25pub mod core;
26pub mod pav;
27pub mod robust;
28pub mod utils;
29
30// Testing advanced modules one by one
31pub mod algorithms;
32pub mod fluent_api;
33pub mod serialization;
34
35// Advanced regularized isotonic regression
36pub mod regularized;
37
38// Advanced optimization algorithms
39pub mod optimization;
40
41// Convex optimization methods (semidefinite programming, cone programming, ADMM, etc.)
42pub mod convex_optimization;
43
44// Differential equations module (isotonic differential equations, boundary value problems, etc.)
45pub mod differential_equations;
46
47// Engineering applications module (stress-strain, fatigue, reliability, control, signal processing)
48pub mod engineering_applications;
49
50// Environmental science module (dose-response, threshold estimation, climate, pollution, ecosystem)
51pub mod environmental_science;
52
53// Machine learning integration module (neural networks, deep learning, ensemble methods, transfer learning)
54pub mod ml_integration;
55
56// Advanced Bayesian methods module (nonparametric Bayesian, GP constraints, variational inference, MCMC)
57pub mod advanced_bayesian;
58
59// Advanced graph methods module (spectral, random walk, network-constrained, GNN integration)
60pub mod graph_methods;
61
62// Middleware for constraint pipelines
63pub mod middleware;
64
65// Real-world case studies and examples
66pub mod case_studies;
67
68// Unsafe optimizations for performance-critical paths
69pub mod unsafe_optimizations;
70
71// Advanced benchmarking suite
72pub mod advanced_benchmarks;
73
74// Compatibility layer (for backward compatibility with existing modules)
75mod isotonic;
76
77#[allow(non_snake_case)]
78#[cfg(test)]
79pub mod tests;
80
81// Re-export core functionality
82pub use constraints::*;
83pub use core::*;
84pub use pav::*;
85pub use robust::{huber_weighted_mean, loss_functions, robust_statistics}; // Exclude weighted_quantile to avoid ambiguity
86pub use utils::*;
87
88// Re-export advanced functionality
89pub use algorithms::*;
90pub use fluent_api::*;
91pub use serialization::*;
92
93// Re-export regularized functionality
94pub use regularized::*;
95
96// Re-export optimization functionality (excluding conflicting names)
97pub use optimization::{
98    create_partial_order,
99    interpolate_multidimensional,
100    isotonic_regression_active_set,
101
102    isotonic_regression_dual_decomposition,
103    isotonic_regression_interior_point,
104
105    isotonic_regression_projected_gradient,
106
107    isotonic_regression_qp,
108    non_separable_isotonic_regression,
109
110    parallel_dual_decomposition,
111
112    separable_isotonic_regression,
113    simd_armijo_line_search,
114    simd_constraint_violations,
115    simd_dot_product,
116    simd_gradient_computation,
117    simd_hessian_approximation,
118    simd_isotonic_projection,
119
120    simd_newton_step,
121    // SIMD operations
122    simd_qp_matrix_vector_multiply,
123    simd_vector_norm,
124    sparse_isotonic_regression,
125
126    ActiveSetIsotonicRegressor,
127    BenchmarkResults,
128    // Dual decomposition
129    DualDecompositionIsotonicRegressor,
130    // Interior point
131    InteriorPointIsotonicRegressor,
132    NonSeparableMultiDimensionalIsotonicRegression,
133    // Configuration and benchmarking
134    OptimizationAlgorithm,
135    OptimizationConfig,
136    // Projected gradient
137    ProjectedGradientIsotonicRegressor,
138    // Quadratic programming
139    QuadraticProgrammingIsotonicRegressor,
140    // Multidimensional
141    SeparableMultiDimensionalIsotonicRegression,
142    // Sparse
143    SparseIsotonicRegression,
144};
145
146// Re-export differential equations functionality
147pub use differential_equations::*;
148
149// Re-export engineering applications functionality
150pub use engineering_applications::*;
151
152// Re-export environmental science functionality
153pub use environmental_science::*;
154
155// Re-export ML integration functionality
156pub use ml_integration::*;
157
158// Re-export advanced Bayesian functionality
159pub use advanced_bayesian::*;
160
161// Re-export graph methods functionality
162pub use graph_methods::*;
163
164// Re-export middleware functionality
165pub use middleware::*;
166
167// Re-export case studies
168pub use case_studies::*;
169
170// Re-export unsafe optimizations
171pub use unsafe_optimizations::*;
172
173// Re-export advanced benchmarks
174pub use advanced_benchmarks::*;
sklears_isotonic/lib.rs

sklears_isotonic/
lib.rs
