scirs2_transform/
lib.rs

1//! Data transformation module for SciRS2
2//!
3//! This module provides utilities for transforming data in ways that are useful
4//! for machine learning and data analysis. The main functionalities include:
5//!
6//! - Data normalization and standardization
7//! - Feature engineering
8//! - Dimensionality reduction
9
10#![warn(missing_docs)]
11
12/// Error handling for the transformation module
13pub mod error;
14
15/// Basic normalization methods for data
16pub mod normalize;
17
18/// Feature engineering techniques
19pub mod features;
20
21/// Dimensionality reduction algorithms
22pub mod reduction;
23
24/// Advanced scaling and transformation methods
25pub mod scaling;
26
27/// Missing value imputation utilities
28pub mod impute;
29
30/// Categorical data encoding utilities
31pub mod encoding;
32
33/// Feature selection utilities
34pub mod selection;
35
36// Re-export important types and functions
37pub use encoding::{BinaryEncoder, OneHotEncoder, OrdinalEncoder, TargetEncoder};
38pub use error::{Result, TransformError};
39pub use features::{
40    binarize, discretize_equal_frequency, discretize_equal_width, log_transform, power_transform,
41    PolynomialFeatures, PowerTransformer,
42};
43pub use impute::{
44    DistanceMetric, ImputeStrategy, IterativeImputer, KNNImputer, MissingIndicator, SimpleImputer,
45    WeightingScheme,
46};
47pub use normalize::{normalize_array, normalize_vector, NormalizationMethod, Normalizer};
48pub use reduction::{trustworthiness, TruncatedSVD, LDA, PCA, TSNE};
49pub use scaling::{MaxAbsScaler, QuantileTransformer};
50pub use selection::VarianceThreshold;
scirs2_transform/lib.rs

scirs2_transform/
lib.rs
