d7df3585734f7d0326d5f854d8f16fe3b3d06373,mir_eval/boundary.py,,detection,#Any#Any#Any#Any#Any#,49

Before Change

    n_ref, n_est = len(reference_boundaries), len(estimated_boundaries)
    
    window_match    = np.abs(np.subtract.outer(reference_boundaries, estimated_boundaries)) <= window
    window_match    = window_match.astype(int)
    
    // L. Lovasz On determinants, matchings and random algorithms. 
    // In L. Budach, editor, Fundamentals of Computation Theory, pages 565-574. Akademie-Verlag, 1979.
    //
    // If we build the skew-symmetric adjacency matrix 
    // D[i, n_ref+j] = 1 <=> ref[i] within window of est[j]
    // D[n_ref + j, i] = -1 <=> same
    //
    // then rank(D) = 2 * maximum matching
    //
    // This way, we find the optimal assignment of reference and annotation boundaries.
    //
    skew_adjacency  = np.zeros((n_ref + n_est, n_ref + n_est), dtype=np.int32)
    skew_adjacency[:n_ref, n_ref:] = window_match
    skew_adjacency[n_ref:, :n_ref] = -window_match.T
    
    matching_size = np.linalg.matrix_rank(skew_adjacency) / 2.0
    
    precision   = matching_size / len(estimated_boundaries)

After Change

                                    window)
    
    precision   = float(len(matching)) / len(estimated_boundaries)
    recall      = float(len(matching)) / len(reference_boundaries)
    
    f_measure   = util.f_measure(precision, recall, beta=beta)
    

Instances