Java 8十個lambda表達式案例

1. 實現Runnable線程案例

使用() -> {} 替代匿名類：

//Before Java 8:
new Thread(new Runnable() {
    @Override
    public void run() {
        System.out.println("Before Java8 ");
    }
}).start();

//Java 8 way:
new Thread( () -> System.out.println("In Java8!") ).start();

你可使用 下面語法實現Lambda:

(params) -> expression
(params) -> statement
(params) -> { statements }

若是你的方法並不改變任何方法參數，好比只是輸出，那麼能夠簡寫以下：

() -> System.out.println("Hello Lambda Expressions");

若是你的方法接受兩個方法參數，以下：

(int even, int odd) -> even + odd

2.實現事件處理

若是你曾經作過Swing 編程，你將永遠不會忘記編寫事件偵聽器代碼。使用lambda表達式以下所示寫出更好的事件偵聽器的代碼

在java 8中你可使用Lambda表達式替代醜陋的匿名類

// Before Java 8:
JButton show =  new JButton("Show");
show.addActionListener(new ActionListener() {
     @Override
     public void actionPerformed(ActionEvent e) {
           System.out.println("without lambda expression is boring");
        }
     });


// Java 8 way:
show.addActionListener((e) -> {
    System.out.println("Action !! Lambda expressions Rocks");
});

3.使用Lambda表達式遍歷List集合

//Prior Java 8 :
List features = Arrays.asList("Lambdas", "Default Method", 
"Stream API", "Date and Time API");
for (String feature : features) {
   System.out.println(feature);
}

//In Java 8:
List features = Arrays.asList("Lambdas", "Default Method", "Stream API",
 "Date and Time API");
features.forEach(n -> System.out.println(n));
//方法引用是使用兩個冒號::這個操做符號
features.forEach(System.out::println);

Output:
Lambdas
Default Method
Stream API
Date and Time API

4.使用Lambda表達式和函數接口

爲了支持函數編程，Java 8加入了一個新的包java.util.function，其中有一個接口java.util.function.Predicate是支持Lambda函數編程：

public static void main(args[]){
  List languages = Arrays.asList("Java", "Scala", "C++", "Haskell", "Lisp");

  System.out.println("Languages which starts with J :");
  filter(languages, (str)->str.startsWith("J"));

  System.out.println("Languages which ends with a ");
  filter(languages, (str)->str.endsWith("a"));

  System.out.println("Print all languages :");
  filter(languages, (str)->true);

   System.out.println("Print no language : ");
   filter(languages, (str)->false);

   System.out.println("Print language whose length greater than 4:");
   filter(languages, (str)->str.length() > 4);
}

 public static void filter(List names, Predicate condition) {
    for(String name: names)  {
       if(condition.test(name)) {
          System.out.println(name + " ");
       }
    }
  }
}

Output:
Languages which starts with J :
Java
Languages which ends with a
Java
Scala
Print all languages :
Java
Scala
C++
Haskell
Lisp
Print no language :
Print language whose length greater than 4:
Scala
Haskell

//Even better
 public static void filter(List names, Predicate condition) {
    names.stream().filter((name) -> (condition.test(name)))
        .forEach((name) -> {System.out.println(name + " ");
    });
 }

你能看到來自Stream API 的filter方法可以接受 Predicate參數, 可以容許測試多個條件

5.複雜的結合Predicate 使用

java.util.function.Predicate提供and(), or() 和 xor()能夠進行邏輯操做，好比爲了獲得一串字符串中以"J"開頭的4個長度：

Predicate<String> startsWithJ = (n) -> n.startsWith("J");
 Predicate<String> fourLetterLong = (n) -> n.length() == 4;
   
 names.stream()
      .filter(startsWithJ.and(fourLetterLong))
      .forEach((n) -> System.out.print("\nName, which starts with
            'J' and four letter long is : " + n));

其中startsWithJ.and(fourLetterLong)是使用了AND邏輯操做

6.使用Lambda實現Map 和 Reduce

最流行的函數編程概念是map，它容許你改變你的對象，在這個案例中，咱們將costBeforeTeax集合中每一個元素改變了增長必定的數值，咱們將Lambda表達式 x -> x*x傳送map()方法，這將應用到stream中全部元素。而後咱們使用 forEach() 打印出這個集合的元素.

// Without lambda expressions:
List costBeforeTax = Arrays.asList(100, 200, 300, 400, 500);
for (Integer cost : costBeforeTax) {
      double price = cost + .12*cost;
      System.out.println(price);
}

// With Lambda expression:
List costBeforeTax = Arrays.asList(100, 200, 300, 400, 500);
costBeforeTax.stream().map((cost) -> cost + .12*cost)
                      .forEach(System.out::println);

Output
112.0
224.0
336.0
448.0
560.0
112.0
224.0
336.0
448.0
560.0

reduce() 是將集合中全部值結合進一個，Reduce相似SQL語句中的sum(), avg() 或count()

// Old way:
List costBeforeTax = Arrays.asList(100, 200, 300, 400, 500);
double total = 0;
for (Integer cost : costBeforeTax) {
 double price = cost + .12*cost;
 total = total + price;
 
}
System.out.println("Total : " + total);

// New way:
List costBeforeTax = Arrays.asList(100, 200, 300, 400, 500);
double bill = costBeforeTax.stream().map((cost) -> cost + .12*cost)
                                    .reduce((sum, cost) -> sum + cost)
                                    .get();
System.out.println("Total : " + bill);

Output
Total : 1680.0
Total : 1680.0

7.經過filtering 建立一個字符串String的集合

Filtering是對大型Collection操做的一個通用操做，Stream提供filter()方法，接受一個Predicate對象，意味着你能傳送lambda表達式做爲一個過濾邏輯進入這個方法：

List<String> filtered = strList.stream().filter(x -> x.length()> 2)
                                        .collect(Collectors.toList());
System.out.printf("Original List : %s, filtered list : %s %n", 
                  strList, filtered);

Output :
Original List : [abc, , bcd, , defg, jk], filtered list : [abc, bcd, defg]

8.對集合中每一個元素應用函數

咱們常常須要對集合中元素運用必定的功能，如表中的每一個元素乘以或除以一個值等等

下面是將字符串轉換爲大寫，而後使用逗號串起來

List<String> G7 = Arrays.asList("USA", "Japan", "France", "Germany", 
                                "Italy", "U.K.","Canada");
String G7Countries = G7.stream().map(x -> x.toUpperCase())
                                .collect(Collectors.joining(", "));
System.out.println(G7Countries);

Output : 
USA, JAPAN, FRANCE, GERMANY, ITALY, U.K., CANADA

9.經過複製不一樣的值建立一個子列表

使用Stream的distinct()方法過濾集合中重複元素。

List<Integer> numbers = Arrays.asList(9, 10, 3, 4, 7, 3, 4);
List<Integer> distinct = numbers.stream().map( i -> i*i).distinct()
                                         .collect(Collectors.toList());
System.out.printf("Original List : %s,  Square Without duplicates :
                   %s %n", numbers, distinct);

Output :
Original List : [9, 10, 3, 4, 7, 3, 4],  Square Without 
                                         duplicates : [81, 100, 9, 16, 49]

10.計算List中的元素的最大值，最小值，總和及平均值

List<Integer> primes = Arrays.asList(2, 3, 5, 7, 11, 13, 17, 19, 23, 29);
IntSummaryStatistics stats = primes.stream().mapToInt((x) -> x)
                                            .summaryStatistics();
System.out.println("Highest prime number in List : " + stats.getMax());
System.out.println("Lowest prime number in List : " + stats.getMin());
System.out.println("Sum of all prime numbers : " + stats.getSum());
System.out.println("Average of all prime numbers : " + stats.getAverage());

Output : 
Highest prime number in List : 29
Lowest prime number in List : 2
Sum of all prime numbers : 129
Average of all prime numbers : 12.9