a490a1e41a8d3a4547fa0a46e12cf0584431e520,mir_eval/structure.py,,pairwise,#Any#Any#Any#Any#Any#Any#,69

Before Change

    agree_ref   = np.equal.outer(y_ref, y_ref)

    // Index just the triangle above the main diagonal
    idx         = np.triu_indices_from(agree_ref, k=1)

    // Slice down to just the trignale
    agree_ref   = agree_ref[idx]

    // Construct the frame label-agreement matrix (estimated)
    agree_est   = np.equal.outer(y_est, y_est)
    agree_est   = agree_est[idx]

    // Find the pairs in agreement
    matches     = float((agree_ref & agree_est).sum())
    precision   = matches / agree_est.sum()
    recall      = matches / agree_ref.sum()
    f_measure   = util.f_measure(precision, recall, beta=beta)

After Change

    matches     = 0.0
    n_agree_ref = 0.0
    n_agree_est = 0.0
    for i in xrange(len(y_ref)):
        for j in xrange(i + 1, len(y_ref)):
            // Do i and j match in reference?
            ref_match = (y_ref[i] == y_ref[j])
            n_agree_ref += ref_match

            // Or in estimate?
            est_match = (y_est[i] == y_est[j])
            n_agree_est += est_match

            // If both, we have agreement
            matches += (ref_match & est_match)
            
    precision   = matches / n_agree_est
    recall      = matches / n_agree_ref
    f_measure   = util.f_measure(precision, recall, beta=beta)

Instances