(prob_matrix, label_num)
return ret
Args:
        prob_matrix: probability matrix have the shape of (data_num, class_num), 
            type of float. Generated from softmax activation
        label_num: specify how much positive class to pick, have the shape of (data_num), type of int

    Returns:
        ret: for each case, set all positive class to 1, shape of(data_num, class_num)
def pred_from_prob_multi(prob_matrix, label_num):
    """

    Args:
        prob_matrix: probability matrix have the shape of (data_num, class_num), 
            type of float. Generated from softmax activation
        label_num: specify how much positive class to pick, have the shape of (data_num), type of int

    Returns:
        ret: for each case, set all positive class to 1, shape of(data_num, class_num)
    """
    order = np.argsort(prob_matrix,axis=1)
    ret = np.zeros_like(prob_matrix, np.int32)
    
    for i in range(len(label_num)):
        ret[i][order[i][-label_num[i]:]]=1
    return ret
def pred_from_prob_multi ( prob_matrix , label_num ) : order = np . argsort ( prob_matrix , axis = 1 ) ret = np . zeros_like ( prob_matrix , np . int32 ) for i in range ( len ( label_num ) ) : ret [ i ] [ order [ i ] [ - label_num [ i ] : ] ] = 1 return ret