aip-sci 0.1.0

Affective Interaction Programming - 情感交互编程
Documentation 
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pub fn normalize(v: &mut [f32], mean: f32, std: f32) {
    for x in v.iter_mut() {
        *x = (*x - mean) / std;
    }
}

pub fn pearson_correlation(x: &[f32], y: &[f32]) -> f32 {
    let n = x.len() as f32;
    let sum_x: f32 = x.iter().sum();
    let sum_y: f32 = y.iter().sum();
    let sum_xy: f32 = x.iter().zip(y.iter()).map(|(a, b)| a * b).sum();
    let sum_x2: f32 = x.iter().map(|a| a * a).sum();
    let sum_y2: f32 = y.iter().map(|a| a * a).sum();
    
    let numerator = n * sum_xy - sum_x * sum_y;
    let denominator = ((n * sum_x2 - sum_x * sum_x) * (n * sum_y2 - sum_y * sum_y)).sqrt();
    
    if denominator == 0.0 {
        0.0
    } else {
        numerator / denominator
    }
}

pub fn mse(predicted: &[f32], target: &[f32]) -> f32 {
    let n = predicted.len() as f32;
    predicted.iter()
        .zip(target.iter())
        .map(|(p, t)| (p - t).powi(2))
        .sum::<f32>()
        / n
}

pub fn concordance_correlation_coefficient(predicted: &[f32], target: &[f32]) -> f32 {
    let n = predicted.len() as f32;
    let mean_p: f32 = predicted.iter().sum::<f32>() / n;
    let mean_t: f32 = target.iter().sum::<f32>() / n;
    
    let var_p: f32 = predicted.iter()
        .map(|p| (p - mean_p).powi(2))
        .sum::<f32>() / n;
    let var_t: f32 = target.iter()
        .map(|t| (t - mean_t).powi(2))
        .sum::<f32>() / n;
    
    let cov: f32 = predicted.iter()
        .zip(target.iter())
        .map(|(p, t)| (p - mean_p) * (t - mean_t))
        .sum::<f32>() / n;
    
    let numerator = 2.0 * cov;
    let denominator = var_p + var_t + (mean_p - mean_t).powi(2);
    
    if denominator == 0.0 {
        0.0
    } else {
        numerator / denominator
    }
}