neural/linear/stats.py [215:222]:
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        cov = (Y_true * Y_pred).mean(dim)
        na, nb = [(i**2).mean(dim)**0.5 for i in [Y_true, Y_pred]]
        norms = na * nb
        R_matrix = cov / norms

    if score == "relativemse":
        Y_err = Y_pred - Y_true
        R_matrix = (Y_err**2).mean(dim) / (Y_true**2).mean(dim)  # rename this score matrix!!!
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neural/visuals.py [42:49]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        cov = (Y_true * Y_pred).mean(dim)
        na, nb = [(i**2).mean(dim)**0.5 for i in [Y_true, Y_pred]]
        norms = na * nb
        R_matrix = cov / norms

    if score == "relativemse":
        Y_err = Y_pred - Y_true
        R_matrix = (Y_err**2).mean(dim) / (Y_true**2).mean(dim)  # rename this score matrix!!!
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