sklears-feature-selection 0.1.1

Feature selection algorithms for sklears - scikit-learn compatible ML in Rust
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35

//! Non-parametric statistical tests for feature selection

use scirs2_core::error::CoreError;
use scirs2_core::ndarray::ArrayView1;
type CoreResult<T> = std::result::Result<T, CoreError>;

/// mann_whitney_u
pub fn mann_whitney_u(_x: &ArrayView1<f64>, _y: &ArrayView1<f64>) -> CoreResult<(f64, f64)> {
    // Stub implementation
    Ok((0.0, 0.5))
}

/// kruskal_wallis
pub fn kruskal_wallis(_samples: &[ArrayView1<f64>]) -> CoreResult<(f64, f64)> {
    // Stub implementation
    Ok((0.0, 0.5))
}

/// wilcoxon_signed_rank
pub fn wilcoxon_signed_rank(_x: &ArrayView1<f64>, _y: &ArrayView1<f64>) -> CoreResult<(f64, f64)> {
    // Stub implementation
    Ok((0.0, 0.5))
}

/// friedman_test
pub fn friedman_test(_samples: &[ArrayView1<f64>]) -> CoreResult<(f64, f64)> {
    // Stub implementation
    Ok((0.0, 0.5))
}

/// kolmogorov_smirnov
pub fn kolmogorov_smirnov(_x: &ArrayView1<f64>, _y: &ArrayView1<f64>) -> CoreResult<(f64, f64)> {
    // Stub implementation
    Ok((0.0, 0.5))
}