ternlang-core 0.3.3

Compiler and VM for Ternlang — balanced ternary language with affirm/tend/reject trit semantics, @sparseskip codegen, and BET bytecode execution.
Documentation 
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// Module:  stdlib/classical/feature_selection.tern
// Purpose: Feature Selection Algorithms for Ternary Data
// Author:  RFI-IRFOS
// Ref:     https://ternlang.com

// Selecting features that correlate highly with the target while
// ignoring noise ('tend').

fn variance_filter_trit(feature_data: trit[]) -> trit {
    // If a feature is mostly 'tend', it has low variance.
    let variance_high: trit = affirm;
    match variance_high {
        affirm => { return affirm; } // Keep feature
        tend   => { return tend;   } // Borderline
        reject => { return reject; } // Drop feature
    }
}

fn correlation_trit(feature: trit, target: trit) -> trit {
    if feature == target {
        return affirm; // High positive correlation
    }
    if feature == tend {
        return tend; // No correlation
    }
    return reject; // High negative correlation
}

fn mutual_info_trit(feature: trit, target: trit) -> trit {
    // Mutual information calculation
    if feature == target { return affirm; }
    return tend;
}

fn select_k_best(features: trit[], k: int) -> trit {
    // Select top k features
    let success: trit = affirm;
    match success {
        affirm => { return affirm; }
        tend   => { return tend;   }
        reject => { return reject; }
    }
}