位于北角的七姊妹道，相信没有香港人是不认识 的，但七姊妹的得名由来，却是众说纷纭，有传 闻话在以前，海面上出现了七块大小一样的大石 头并列一起，所以就称为七姊妹，但亦有另一个 传言，在战前，有七位感情极佳的金兰姊妹，为 了一段情孽而一同跳海自杀，翌日，六具女尸手 挽手的浮上水面，自此七姊妹道闹鬼的传言就不 胫而走。而这段传闻亦在七姊妹道辗转流传了十 多年，直至一九四七年‧‧‧谢子堂本来是药油厂少 东，但十多年前一宗谣传，导致药油厂倒闭，堂 父母更因此先后身故，自此，堂就与妹妹谢美宁 及父亲生前忠仆陈通一家一齐生活，辗转十多 年，堂成为一个邮差，但他对派信工作十分尽忠 职守，令他得到一个死信活投的外号。
Int result = cal.calate (exp);
当代都市的大学校园内，研究生痞子蔡一直渴望能拥有一份真诚的爱情，但事与愿违，他与女孩的交往却屡屡失败，颇不自信。而痞子蔡的同室好友阿泰却情场得意，挥洒自如地游戏在众多女孩当中。
黄金花(毛舜筠 饰)，街坊邻居称之为黄师奶，视家庭重于一切，与老公(吕良伟 饰) 合力照顾患有自闭症儿子(凌文龙 饰) 20 年，岂料老公爱上狐狸精丹凤眼 (冼色丽 饰)，辛苦维繫的家庭瞬间变得零散，不知所措的黄金花更因而萌起杀死小三的复仇计划！一众师奶们无意中揭破她的密谋，从慌张慢慢变成支持。正当黄金花逐步迈向目标之际，她赫然醒悟：成为真正不再失败的女人，原来还有更好的出路……
这一干能人，偏偏斗不过这只猪。
First, what is content-based products
张老太太道：我不就是这么跟她说。
First, let's talk about scenario, and distribute the content according to the scene where users use the product. For example, users place orders for tickets in Cat's Eye movies, and after the completion, there are actually other needs for scenes.
一念癫狂，一念地狱，三个互不相识，各走各路的杀人魔头 - 刘帆（艾威）、程云新（陈国邦）以及葛叔（詹瑞文），原以爲可以隐姓埋名逃过法律制裁，岂料遇上一个能够透过特殊装置而可以看见死者临终前30秒画面的法医何非（王贻兴）、一个深谙茅山术数的道士周处基（刘翁）以及一班不懈辑凶的警探包括上司Madam Kok（江若琳）、老探长马叔（楼南光）、姐弟余亦欣（杨淇）和余亦琛（岑珈其）等，一衆捉魔特工抽丝剥茧联手跨界追查真凶。这三个各怀鬼胎的凶手，爲了阻止他们查出凶桉真相，竟站在同一阵线并再动杀机，掀起最血腥的正邪恶斗。
所以就先借口回家省亲，返回老家脱离和项羽之间的联系，之后才想办法前来山阴寻找尹旭。
Note: The core step is actually only one step. After the host computer is connected to the computer and the driver is installed, press and hold the reset key to start the machine, and then swipe the machine through hakchi according to the prompt. It is very simple.
我已经彻底被天启折服了，本来以为令狐冲会无意中走进独孤剑冢，得到独孤求败留下的几把剑，但是没有想到竟然是风清扬传他独孤九剑。
Public Iterator iterator () {
Grandma Lily woke up from her dream and hurriedly went to see Little Charlie nearby. At this time, Little Charlie, her small mouth still sucking and sleeping soundly. It turned out to be a dream. Grandma Lily only recovered for half a day and took a long breath. She looked out of the window-cotton wool-like snow, like dandelion's fluffy seeds, flying in the wind. Then he slowly kissed little Charlie's forehead... … …
3. Aircrew Physical Examination Certificate
先为国，再为家，功成身退，回家规规矩矩做贤良妻女，哪里找这样的好女子去？他又有些头晕了，怎么觉得这个张灵儿也像正主呢。
后面的山羊胡子还不知情，见她忽然游得快起来，不禁暗骂：怎么这张家姑娘爬树游水都会，哪里像个大户人家的小姐。
It is easy to see that OvR only needs to train N classifiers, while OvO needs to train N (N-1)/2 classifiers, so the storage overhead and test time overhead of OvO are usually larger than OvR. However, in training, each classifier of OVR uses all training samples, while each classifier of OVO only uses samples of two classes. Therefore, when there are many classes, the training time cost of OVO is usually smaller than that of OVR. As for the prediction performance, it depends on the specific data distribution, which is similar in most cases.
