(y_true, y_pred)
return sum([w[i, j] for i, j in ind]) * 1.0 / y_pred.size
Calculate clustering accuracy. Require scikit-learn installed

    # Arguments
        y: true labels, numpy.array with shape `(n_samples,)`
        y_pred: predicted labels, numpy.array with shape `(n_samples,)`

    # Return
        accuracy, in [0,1]
Calculate clustering accuracy. Require scikit-learn installed
Calculate clustering accuracy . Require scikit - learn installed
def acc(y_true, y_pred):
    """
    Calculate clustering accuracy. Require scikit-learn installed

    # Arguments
        y: true labels, numpy.array with shape `(n_samples,)`
        y_pred: predicted labels, numpy.array with shape `(n_samples,)`

    # Return
        accuracy, in [0,1]
    """
    y_true = y_true.astype(np.int64)
    assert y_pred.size == y_true.size
    D = max(y_pred.max(), y_true.max()) + 1
    w = np.zeros((D, D), dtype=np.int64)
    for i in range(y_pred.size):
        w[y_pred[i], y_true[i]] += 1
    from sklearn.utils.linear_assignment_ import linear_assignment
    ind = linear_assignment(w.max() - w)
    return sum([w[i, j] for i, j in ind]) * 1.0 / y_pred.size
def acc ( y_true , y_pred ) : y_true = y_true . astype ( np . int64 ) assert y_pred . size == y_true . size D = max ( y_pred . max ( ) , y_true . max ( ) ) + 1 w = np . zeros ( ( D , D ) , dtype = np . int64 ) for i in range ( y_pred . size ) : w [ y_pred [ i ] , y_true [ i ] ] += 1 from sklearn . utils . linear_assignment_ import linear_assignment ind = linear_assignment ( w . max ( ) - w ) return sum ( [ w [ i , j ] for i , j in ind ] ) * 1.0 / y_pred . size