# accuracy, fmeasure, precision,recall

def mcor(y_true, y_pred):

    y_pred_pos = K.round(K.clip(y_pred, 0, 1))

    y_pred_neg = 1-y_pred_pos

    y_pos = K.round(K.clip(y_true, 0, 1))

    y_neg = 1-y_pos

    tp = K.sum(y_pos*y_pred_pos)

    tn = K.sum(y_neg*y_pred_neg)

    fp = K.sum(y_neg*y_pred_pos)

    fn = K.sum(y_pos*y_pred_neg)

    numerator = (tp*tn - fp*fn)

    denominator = K.sqrt((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn))

    return numerator/(denominator+K.epsilon())

def precision(y_true, y_pred):

    true_positives = K.sum(K.round(K.clip(y_true*y_pred, 0, 1)))

    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))

    precision = true_positives / (predicted_positives + K.epsilon())

    return precision

def recall(y_true, y_pred):

    true_positives = K.sum(K.round(K.clip(y_true*y_pred, 0, 1)))

    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))

    recall = true_positives/(possible_positives+K.epsilon())

    return recall

def f1(y_true, y_pred):

    def recall(y_true, y_pred):

        true_positives = K.sum(K.round(K.clip(y_true*y_pred, 0, 1)))

        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))

        recall = true_positives/(possible_positives+K.epsilon())

        return recall

    def precision(y_true, y_pred):

        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))

        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))

        precision = true_positives / (predicted_positives + K.epsilon())

        return precision

    precision = precision(y_true, y_pred)

    recall = recall(y_true, y_pred)

    return 2*((precision*recall)/(precision+recall+K.epsilon()))